Amazon S3 Tables now support all data types in the Apache Iceberg V3 specification. You can create V3 tables or upgrade existing V2 tables to take advantage of V3 features like deletion vectors, row lineage, and new data types such as variant, nanosecond timestamps, unknown, geometry, and geography.

Apache Iceberg has become the open standard for managing large analytics datasets. It lets you manage petabyte-scale tables with features like schema evolution, hidden partitioning, and time travel queries, while keeping your data in open Parquet files in data lakes on object storage like Amazon S3. Amazon S3 Tables offer storage purpose-built to keep Iceberg tables performant and cost-effective as they grow, with fully managed features like automatic compaction, maintenance, replication, and Intelligent-Tiering.

Teams running analytics on Apache Iceberg V2 tables often hit the same limits as their data grows. A compliance request to delete 50,000 user records from a 2-billion-row table leaves behind positional delete files that slow queries until compaction runs. Semi-structured events land as JSON strings that every query has to parse. Geospatial coordinates and nanosecond-precision timestamps get encoded as strings or integers. Each workaround adds storage cost, query latency, and pipeline code. With V3, Iceberg solves these challenges by offering native support for semi-structured and geospatial data, faster row-level operations, and built-in row lineage for data governance.

Starting today, Amazon S3 Tables support all V3 data types, including variant, nanosecond timestamps, geometry, geography, and unknown, along with deletion vectors and row lineage. You can create new V3 tables or upgrade existing V2 tables in place, and S3 Tables continue to run compaction and maintenance for you.

Apache Iceberg V3
V3 is the latest version of the Iceberg specification. Among its many improvements, V3 introduces capabilities that address the most common pain points in V2. This includes:

Deletion vectors replace V2’s positional delete files with a compact binary format. That 50,000-row compliance delete now writes a single deletion vector file instead of thousands of small deletes, significantly reducing compaction time and delete file overhead.

Row lineage adds _row_id and _last_updated_sequence_number to each record automatically. Your downstream pipelines can query these fields to find changed rows without scanning the full table.

New data types let you store semi-structured, geospatial, and nanosecond-precision data natively instead of encoding it as strings or integers:

• Nanosecond timestamp(tz) for nanosecond-precision timestamps
• Geometry and geography for geospatial data
• Unknown for columns with no known type

Variant data type stores semi-structured data in columnar format. During writes, the engine shreds variant data into hidden columns and collects statistics. At query time, those statistics enable file pruning that significantly reduces I/O compared to parsing JSON strings.

The following sections walk through how to use these V3 capabilities in practice, with examples that show how to create tables, work with the new data types, and manage data at scale.

Getting started
A retail analytics team tracks user behavior across web and mobile apps. Each event has a different structure: page views include URLs and duration, purchases include items and amounts, and searches include query terms and result counts. With V3’s variant type, you store all event shapes in one table without predefined schemas:

CREATE TABLE my_catalog.namespace.clickstream (
  event_id bigint,
  event_time timestamp,
  user_id string,
  payload variant
)
USING iceberg
TBLPROPERTIES ('format-version' = '3')

Insert events with different payload shapes without worrying about schema evolution:

INSERT INTO my_catalog.namespace.clickstream VALUES
  (1, current_timestamp(), 'user-42',
   PARSE_JSON('{"action": "purchase", "amount": 99.99, "items": ["laptop_stand"]}')),
  (2, current_timestamp(), 'user-17',
   PARSE_JSON('{"action": "page_view", "url": "/products/webcam", "duration_ms": 4200}'));

Now query the variant column directly, without PARSE_JSON at read time. With Amazon EMR Spark, use variant_get:

SELECT
  event_id,
  user_id,
  variant_get(payload, '$.action', 'string') AS action,
  variant_get(payload, '$.amount', 'double') AS amount
FROM my_catalog.namespace.clickstream
WHERE variant_get(payload, '$.action', 'string') = 'purchase'
  AND variant_get(payload, '$.amount', 'double') > 50.00

To enable deletion vectors for write operations, configure merge-on-read mode:

ALTER TABLE my_catalog.namespace.clickstream
SET TBLPROPERTIES (
  'write.delete.mode' = 'merge-on-read',
  'write.update.mode' = 'merge-on-read',
  'write.merge.mode' = 'merge-on-read'
)

Now when you run a compliance delete, V3 writes a small deletion vector instead of rewriting data files:

DELETE FROM my_catalog.namespace.clickstream
WHERE user_id = 'user-42'

S3 Tables compaction handles these deletion vector files automatically on the next maintenance cycle.

Upgrading from V2
AWS provides backwards compatibility for both versions to minimize disruption during migration to V3. Existing V2 readers continue to work on upgraded tables until you’re ready to fully adopt V3 features. For more details, see the S3 Tables Iceberg V3 documentation.
Upgrade an existing table atomically without rewriting data:

ALTER TABLE my_catalog.namespace.existing_table
SET TBLPROPERTIES ('format-version' = '3')

On the next compaction cycle, S3 Tables remove old V2 delete files. New modifications use deletion vectors automatically. Row lineage fields initialize on the first data modification after the upgrade.

This is a one-way operation. The Apache Iceberg specification does not support downgrading from V3 to V2. Verify that all engines accessing the table support V3 before upgrading.

Using row lineage for incremental pipelines
After your table has V3 data, use row lineage to build efficient incremental pipelines:

SELECT *, _row_id, _last_updated_sequence_number
FROM my_catalog.namespace.clickstream
WHERE _last_updated_sequence_number > 42

This returns only rows modified after sequence number 42. Your downstream jobs can checkpoint this value and process only new changes on each run, instead of scanning the full table.

Compatibility across AWS analytics services
AWS offers the broadest native Apache Iceberg support of any major cloud provider, with Iceberg-compatible services at every layer of the data stack: ingestion, storage, catalog, and analytics. You can store and automatically optimize V3 tables in Amazon S3 Tables, write data with Amazon EMR Spark, integrate and manage data with AWS Glue, and run analytics with Amazon Redshift. To learn more about AWS analytics support for V3, see the Apache Iceberg on AWS prescriptive guidance.

Both S3 Tables and AWS Glue Data Catalog support the Iceberg REST Catalog (IRC) API, enabling interoperability across engines regardless of the catalog endpoint.

Things to know

• S3 Tables compaction fully supports V3 deletion vector files and preserves row lineage metadata.
• The new V3 data types (variant, nanosecond timestamps, geometry, geography, and unknown) require an engine built on Apache Spark 4.0 or later, such as AWS Glue 6.0 or later, or Amazon EMR release 8.1 or later.
• You can create V3 tables from the Amazon S3 console, AWS CLI, or any engine that supports the Iceberg REST Catalog API.
• The new V3 data types are supported only for tables that use the Parquet file format (not ORC or Avro).
• Columns of type variant, geometry, geography, or nanosecond timestamp can’t be included in a table’s sort order for compaction. Tables containing these columns still compact under the sort and Z-order strategies when the sort order uses columns of other types.

Now available
Amazon S3 Tables support for all Apache Iceberg V3 data types is now available in all AWS Regions where S3 Tables are supported. Apache Iceberg V3 support is available at no additional charge; standard S3 Tables pricing applies.

To get started, visit the Amazon S3 Tables documentation or create a table bucket from the Amazon S3 console. If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Send feedback to AWS re:Post or through your usual AWS Support contacts.

– Daniel Abib

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Today, we’re announcing metadata pre-filtering for Amazon S3 Vectors, which delivers higher recall on filtered queries by evaluating your metadata filter before the similarity search. You can filter on attributes such as tenant, category, status, or time, and pre-filtering adds prefix matching with $startsWith for paths, URLs, and hierarchical keys. Each vector carries up to 2 KB of filterable metadata, and a single query supports up to 100 filter constraints. There is no additional cost, no re-ingestion, and no change to your queries.

Most applications never search a whole index. They search the part of it that belongs to a particular user, account, or category, and they express that scope as a metadata filter. Semantic search, retrieval-augmented generation (RAG), and agentic applications all need the same thing from a filtered query: a similarity search that covers the vectors matching the filter, and returns the closest of them. With pre-filtering, a filtered query returns more of the relevant matches your index contains, giving you higher recall on filtered searches.

Common use cases
Pre-filtering applies wherever results have to be both relevant and correctly scoped:

• Legal and professional services: A law firm or e-discovery platform searches documents scoped to a single client, and with $startsWith narrows further by matter number, folder path, or document ID prefix. A single client is a small share of a firm-wide archive, and filters this narrow are where pre-filtering improves recall most.
• Financial services: An investment research platform searches analyst notes, filings, and call transcripts scoped by issuer, document type, and publication date.
• Media and entertainment: A streaming service filters by content rating and regional licensing before the semantic search, finding similar titles restricted to G and PG content licensed in one territory.
• Agentic applications: An agent working within a user’s session filters on fields such as owner, document set, and timestamp so its searches cover the material relevant to the task at hand. Higher recall means more of that material reaches the agent, which improves task reliability

How pre-filtering works
Each vector in an S3 Vectors index can carry application-defined metadata, and a query can filter on those fields.

Every vector index has an index mode. On an index whose index mode is ENHANCED, S3 Vectors resolves your filter first, then searches only the vectors that match. On an index whose index mode is CLASSIC, S3 Vectors performs the vector search and filter evaluation in tandem, validating each candidate vector against your filter as it searches. Existing indexes use CLASSIC until you update them.

Consider a support knowledge base of 8 million tickets, where an agent searches one customer’s history for a recurring error. If that customer accounts for 400 of those tickets, resolving customer_id first means the similarity search runs across all 400 of them, so the agent sees that customer’s prior occurrences. Before the index was updated, the same query drew its candidates from the full 8 million, and the result set contained fewer of that customer’s matching tickets.

On highly selective filters, pre-filtering returns up to 5x more of the matching vectors than the same query returned before on CLASSIC indexes.

Getting started
Before you start, make sure your IAM policy grants permissions for the new actions.

You can get started in three steps. The walkthrough below builds a small product-catalog index and runs a selective filter against it, the same pattern you would use for a multi-tenant RAG store or a document search scoped to one client.

First, create a vector index:

aws s3vectors create-index  
  --index-name product-catalog  
  --vector-bucket-name my-vector-bucket  
  --dimension 1536  
  --distance-metric cosine

The dimension must match the output size of your embedding model, and distance-metric should match how that model was trained (cosine is common for text embeddings). Second, write vectors with the PutVectors API, attaching up to 2 KB of filterable metadata to each vector:

aws s3vectors put-vectors 
  --index-name product-catalog 
  --vector-bucket-name my-vector-bucket 
  --vectors '[{
    "key": "doc-001",
    "data": {"float32": [0.1, 0.2, 0.3, ...]},
    "metadata": {
      "tenant_id": "t-10428",
      "category": "legal",
      "created_date": "2026-03-15",
      "active": true
    }
  }]'

Each vector carries the attributes your application filters on. In this example, tenant_id scopes results to a single customer, category narrows by document type, created_date records when the document was created, and active is a boolean flag. By default every metadata field is filterable, so you can query on any of them without declaring a schema up front.

Third, run a filtered similarity query with the QueryVectors API. The filter uses a compact JSON syntax where a bare key-value pair is an equality match, and operators such as $and, $or, and $gt combine or refine conditions. Pass --return-metadata so the query returns each vector’s metadata:

aws s3vectors query-vectors 
  --index-name product-catalog 
  --vector-bucket-name my-vector-bucket 
  --query-vector '{"float32": [0.1, 0.2, 0.3, ...]}' 
  --top-k 50 
  --return-metadata 
  --filter '{"$and": [
    {"tenant_id": "t-10428"},
    {"category": "legal"},
    {"active": true}
  ]}'

The expected result is a single vector, doc-001, the only one matching all three filter conditions (tenant_id, category, and active):

{
  "vectors": [
    {
      "distance": 0.9717477560043335,
      "key": "doc-001",
      "metadata": {
        "tenant_id": "t-10428",
        "category": "legal",
        "created_date": "2026-03-15",
        "active": true
      }
    }
  ],
  "distanceMetric": "cosine"
}

S3 Vectors first narrows the search space to vectors matching all three filter conditions, then returns the 50 most similar vectors from that subset. Because the filter is applied before the search, those results are drawn from across all the vectors that match it.

Prefix matching with $startsWith
Pre-filtering adds a prefix match operator for filtering on paths, URLs, and hierarchical keys. A document store that encodes case and folder structure into a document ID can scope a search to a subtree in one condition:

--filter '{"$startsWith": {"document_id": "matter-4417/exhibits/"}}'

$startsWith joins the existing operators: equality, numeric range, set membership, existence checks, and boolean logic with $and and $or.

Turning on pre-filtering for existing indexes
Call UpdateIndexMode on an existing index to turn on pre-filtering:

aws s3vectors update-index-mode 
  --vector-bucket-name my-vector-bucket 
  --index-name product-catalog 
  --index-mode ENHANCED

Pre-filtering takes effect in place. Your existing vectors are not re-ingested, your queries do not change, and the new filter operators are available immediately.

Here is the difference on the same index and the same query. Before the update, a query scoped to one tenant returns two of the ten results requested:

aws s3vectors query-vectors 
  --vector-bucket-name my-vector-bucket 
  --index-name product-catalog 
  --query-vector '{"float32": [0.1, 0.2, 0.3, ...]}' 
  --top-k 10 
  --return-metadata 
  --filter '{"tenant_id": "t-10428"}'

{
  "vectors": [
    { "key": "doc-114", "distance": 0.41 },
    { "key": "doc-322", "distance": 0.55 }
  ],
  "distanceMetric": "cosine"
}

After the update, the same query returns a full result set drawn from across that tenant’s documents:

{
  "vectors": [
    { "key": "doc-018", "distance": 0.09 },
    { "key": "doc-207", "distance": 0.13 },
    { "key": "doc-114", "distance": 0.41 },
    ... 7 more
  ],
  "distanceMetric": "cosine"
}

Rolling out across your indexes
Once you have validated pre-filtering on an index, set the default index mode on the vector bucket so that new indexes use ENHANCED without a follow-up call:

aws s3vectors put-vector-bucket-default-index-mode 
  --vector-bucket-name my-vector-bucket 
  --default-index-mode ENHANCED

To bring the rest of your existing indexes across, list them and check the index mode on each one, then call UpdateIndexMode on the ones still using CLASSIC:

aws s3vectors list-indexes 
  --vector-bucket-name my-vector-bucket

aws s3vectors get-index 
  --vector-bucket-name my-vector-bucket 
  --index-name product-catalog

Things to know 

• Indexes created in vector buckets created on or after September 30, 2026 use index mode ENHANCED. Indexes in buckets that existed before that date use CLASSIC until you set the bucket default, including indexes created in those buckets afterward.
• A single query supports up to 100 filter constraints, counted per value the filter evaluates. If a query exceeds that, you can usually consolidate the filter, replacing a 300-value $in over legal cases with a single caseId field, for example, or split it into smaller queries, run them in parallel, and merge the results by distance.

Now available
Metadata pre-filtering is available at no additional cost in all commercial AWS Regions where Amazon S3 Vectors is available, and in the AWS China Regions. You pay standard S3 Vectors pricing for storage, PUT requests, and queries. For full pricing details, visit the Amazon S3 pricing page. For regional availability, visit Amazon S3 Vectors Regions and quotas.

Whether you’re scoping a RAG application to one tenant, scoping an agent’s searches to one user’s documents, or narrowing a catalog search to a licensing window, pre-filtering lets you apply those filters without trading away recall. To learn more and get started, visit the Amazon S3 Vectors documentation. Send feedback to AWS re:Post for S3 or through your usual AWS Support contacts.

— Daniel Abib

The post Amazon S3 Vectors now supports metadata pre-filtering for higher recall on filtered searches appeared first on The NOKIA Blog.

Today, we’re announcing a new capability for Amazon Aurora PostgreSQL that you can use to directly query operational data together with data stored in your data lake in Apache Iceberg and Apache Parquet formats, using your existing PostgreSQL applications and tools. By eliminating the need to extract, transform, and load (ETL) structured data from data lakes into your operational database, you can reduce operational complexity and simplify application development. You can also use Aurora PostgreSQL to query data from data lakes managed in Iceberg REST Catalog (IRC)-compatible catalogs, giving you access to data across a breadth of analytics systems without moving or duplicating it. Whether you’re powering real-time dashboards, enriching transactions with historical context, or building AI agents that reason over both live and archived data, you can now do it all through a single, familiar interface.

Previously, if your application needed to combine recent transactional data in Aurora with historical records stored in Amazon S3, a common approach was to build reverse ETL pipelines that duplicated data, increased infrastructure costs, and required ongoing engineering effort to keep everything synchronized. This challenge only grows as you increasingly embed AI agents into your applications, where it is impractical to predict and pre-replicate every dataset an agent might need.

DuckLabs, the team that maintains the DuckDB project, recently joined Amazon, and this capability is an example of how the efficiency of DuckDB is being integrated into our services. DuckDB is now embedded directly within Aurora PostgreSQL, so you can query live operational data (including uncommitted writes) alongside your data lake in a single query. Query processing stays within Aurora, with no additional network hops and no ETL pipelines that duplicate data. You can query Apache Iceberg tables managed through the AWS Glue Data Catalog, as well as Parquet and Iceberg data stored in Amazon S3 and S3 Tables. You do all of this using familiar PostgreSQL syntax and your existing applications and tools.

We’re excited to bring the speed and simplicity of DuckDB directly into Aurora PostgreSQL, so you and your agents can query and combine operational and Iceberg data using the familiar PostgreSQL applications, tools, and endpoints already in use. By building this capability around DuckDB, future improvements to the open source engine can continue to bring performance and functionality gains to Aurora and other AWS services.

What is new
This capability is supported on two Aurora PostgreSQL major versions: 17 (starting with 17.11) and 18 (starting with 18.6). To use it, you create an Aurora PostgreSQL cluster, attach an IAM role with the AuroraAnalytics feature, and enable the aurora_analytics extension. The IAM role is what gives Aurora access to your data in Amazon S3 and the AWS Glue Data Catalog. You then create foreign tables that point to your Iceberg or Parquet data in the data lake, and query them using familiar PostgreSQL syntax. You can complete this setup through the Amazon RDS console, or with any PostgreSQL client such as psql. The process is well documented in the Aurora PostgreSQL documentation.

You can query data across external IRC-compatible catalogs through AWS Glue Data Catalog federation. You register the external catalog once with Glue, and then create foreign tables for the tables you want to query, the same way you would for any Glue-native table. A single query can then join data stored in Aurora with Iceberg tables registered across multiple catalogs, so applications get a unified view without moving data or replacing your existing catalog investments.

Aurora also applies optimizations such as predicate pushdown and column pruning so that only the relevant data is read. This keeps queries efficient even as the underlying data grows. Frequently accessed data is also cached in your Aurora instance, so subsequent queries against the same data return faster. You can inspect this behavior per query using aurora_analytics_stat_statements(), which reports metrics such as rows scanned, bytes read from Amazon S3, and cache hits.

To see how direct querying works, I connected to my Aurora PostgreSQL database using psql and created the extension:

CREATE EXTENSION aurora_analytics;

For my walkthrough, I set up a simple financial scenario. I have a recent_transactions table in Aurora with the last 7 days of customer transactions, and a Parquet file in Amazon S3 containing 5 years of historical transaction data. To make Aurora aware of the historical data, I created a foreign table pointing at the Parquet file in S3:

CREATE FOREIGN TABLE transaction_history ()
SERVER aurora_analytics_server
OPTIONS (
    location 's3://<my-bucket>/finance/transaction_history.parquet',
    format 'parquet'
);

Notice the empty parentheses in the CREATE FOREIGN TABLE statement. Aurora automatically reads the schema from the Parquet file metadata, so you do not need to define columns manually. For workloads with many tables, you can skip creating them one at a time: a single IMPORT FOREIGN SCHEMA statement bulk-creates foreign tables for every Iceberg or Parquet table in an AWS Glue Data Catalog database, inferring schemas automatically.

With both tables in place, I ran a single query that combines the recent operational data in Aurora with the historical data in S3:

SELECT merchant, category, amount, transaction_date, 'recent' AS source
FROM recent_transactions
WHERE customer_id = 'C-1001'
UNION ALL
SELECT merchant, category, amount, transaction_date, 'historical' AS source
FROM transaction_history
WHERE customer_id = 'C-1001'
  AND transaction_date >= CURRENT_DATE - INTERVAL '5 years'
ORDER BY transaction_date DESC
LIMIT 15;

The result shows both recent and historical transactions in a single result set. The 7 most recent rows come from Aurora, and the rest come directly from the Parquet file in S3. DuckDB handles the analytical scan of the Parquet data under the hood, while Aurora handles the operational data. That single query would have previously required a pipeline to move the historical data into the database first.

If a query pattern needs single-digit-millisecond latency, you can materialize data from the data lake into a native Aurora PostgreSQL table using familiar commands such as CREATE TABLE AS SELECT, INSERT INTO ... SELECT, or MERGE INTO. The materialized table lives in Aurora and is queried like any other PostgreSQL table, giving you a low-latency path for hot data without operating a separate ingestion pipeline. The read queries can run on any Aurora PostgreSQL instance in your cluster, whether the writer or a read replica, so you can offload analytical scans from your operational workload. The materialization commands write data into Aurora, so they run on the writer instance.

Get started today
Direct querying of Apache Iceberg and Parquet data from Amazon Aurora PostgreSQL is available today in all commercial AWS Regions and AWS GovCloud (US) Regions, at no additional charge. You pay only for the incremental Aurora compute the queries consume and Amazon S3 request costs for reading data lake files.

To learn more, visit the Amazon Aurora features page, read the Aurora PostgreSQL documentation, or try it in the Amazon RDS console. We welcome your feedback through AWS re:Post or through your usual AWS Support contacts.

— Esra

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Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders. AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul.

My team regularly visits countries across the Asia-Pacific region, listens to feedback from user group leaders, and works to support their communities. At this meeting, leaders honestly shared what they did well in the first half of the year, what needs improvement, and what they asked of AWS Developer Experience team. We also enjoyed a pleasant conversation during our Chimaek time together.

Now, let’s take a closer look at key launches of last week.

A one-click Lambda setup prompt for coding agents caught my eye most last week. This prompt configures your agent with AWS Serverless skills and the Serverless Model Context Protocol (MCP) server, embedding serverless best practices from the start. This prompt references the Lambda agent setup guide, which includes installation commands for Claude Code, Kiro, Cursor, GitHub Copilot, Codex, Devin Desktop, and OpenCode.

To get started, choose the Copy agent prompt button on the Lambda console screen or copy fetch https://docs.aws.amazon.com/lambda/latest/dg/samples/aws-lambda-agent-setup.md directly, and paste this URL in your preferred AI agent.

You can also use Agent Toolkit for AWS to give your coding agent current AWS knowledge and safe resource access. Use fetch https://raw.githubusercontent.com/aws/agent-toolkit-for-aws/refs/heads/main/setup-instructions/setup.md for installing AWS MCP Server.

Last week’s launches

Here are last week’s launches that caught my attention:

• OpenAI GPT-5.6 Sol, Terra, and Luna on Amazon Bedrock: You can use the smartest family of models from OpenAI yet on Bedrock’s next-generation inference engine built for high performance, security, and reliability. The three models span capability tiers from flagship reasoning (Sol) to balanced performance (Terra) to fast, cost-efficient inference (Luna), all accessible through the Responses API on Amazon Bedrock.
• Same-day transitions to Amazon S3 Standard-IA and S3 One Zone-IA: You can now transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) and S3 One Zone-Infrequent Access (S3 One Zone-IA) as soon as the day they are created, without the previous 30-day minimum retention period in S3 Standard. These storage classes offer up to 40% lower storage costs than S3 Standard while still providing millisecond access when needed, making them ideal for backups, log analytics, and compliance workloads where data becomes cold within hours or days.
• Self-managed code storage on AWS Lambda: With self-managed Amazon S3 buckets for code storage, you can reference source code directly from your own S3 buckets without Lambda creating intermediate copies. This eliminates code storage limits and reduces function activation time after function creates and updates by removing the copy step.
• Importing users with password hashes on Amazon Cognito: You can now import users with password hashes in CSV user imports. Previously, imported users had to reset their passwords on first sign-in. Now, you can include password hashes in the CSV import, enabling users to sign in immediately with their existing credentials. When creating a CSV import, you specify the password hashing algorithm used by your source system.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Additional updates

Here are some additional news items that you might find interesting:

• Amazon SQS turns 20: Two decades of reliable messaging at scale: When Amazon SQS launched publicly in July 2006, it made this pattern available to every AWS customer. Twenty years later, that core function, decoupling producers from consumers, remains the reason customers use SQS. Let’s look back important milestones after Jeff’s 15th anniversary post.
• Open Protocols with the Strands Agents SDK: Learn how open AI protocols such as MCP, A2A, UTCP, AG-UI, and x402 work together using Strands Agents SDK for building AI agents as an example implementation, though the patterns apply to any agent framework.
• Open source Bulk Executor for Amazon DynamoDB: Performing bulk operations against all items in a DynamoDB table has historically required custom coding. The Bulk Executor for DynamoDB simplifies bulk tasks like these. You can use this feature to invoke commands like count, find, delete, or update. No coding is required, even when running at large scale.
• Transform AWS Support Case Workflows with Kiro CLI: Explore how Kiro CLI’s MCP integration accelerates support case workflows by combining investigation, documentation lookup, and case creation into a single conversational interface across three real-world scenarios: AWS Glue job failures, AWS Lambda cold start investigation, and AWS WAF false positive analysis.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events including AWS Summits. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

Finally, some customers experienced an issue with Cost Explorer displaying inaccurate estimated billing data in last weekend. They may have received erroneous budget and cost anomaly detection alerts, and observed inflated estimated cost and usage data. The issue has been resolved, and all AWS services are operating normally. We apologize for the concern this incident caused our customers and are conducting a thorough retrospective to prevent events like this from reoccurring, as well as improving our response when billing incidents occur. For more information, visit the AWS Health Dashboard.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy

The post AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026) appeared first on The NOKIA Blog.

Last week AWS Summit New York City brought together thousands of customers, partners, and builders for a free, one-day event showcasing the latest in cloud and AI innovation. Dr. Swami Sivasubramanian, VP of Agentic AI at AWS unveiled a stack of AI launches in his keynote, all built around one thesis: agents that compound value over time.

• Agents for working – You can launch autonomous agents and access a smarter activity feed with new Amazon Quick features, which now let you create and run multi-step agents directly in the desktop app and consolidates email, Slack, calendar, and tasks into a single prioritized view with personalized rules.
• Agents for securing – You can shift from reactive to proactive security with AWS Continuum, a new AI-native security service that reasons, validates, and acts at machine speed across the full code vulnerability lifecycle. AWS Security Agent (now part of AWS Continuum) adds new features: threat modeling; pull request code scanning with remediation across major Git platforms; and IDE integrations via Kiro power, Claude Code plugin, and MCP.
• Agents for building – You can write, ship, and modernize code in one continuous loop with Kiro, AWS DevOps Agent, and AWS Transform. Kiro introduces a native iOS app; AWS DevOps Agent adds release management capabilities to assess code changes before production; and AWS Transform continuous modernization reduces tech debt autonomously.
• Agents customers create – You can go from agent idea to production in minutes with Amazon Bedrock AgentCore, which now includes a GA harness for infrastructure and orchestration, Web Search, Managed Knowledge Base, policy integrations with Guardrails, and the new AWS Context service for mapping organizational data relationships.

To learn more, visit the Summit recap from our top announcements blog post and Amazon News post.

Last week’s launches

Here are last week’s launches that caught my attention:

• AWS Local Zone in Hanoi, Vietnam  —This new Local Zone is one of the first AWS Local Zones in the Asia Pacific with support for Amazon S3 and Amazon EBS Local Snapshots, enabling customers to meet data residency requirements by storing and backing up data locally. To get started, enable the Hanoi Local Zone (ap-southeast-1-han-1a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API.
• AWS Blocks, an open-source TypeScript framework for application developers (preview) — AWS Blocks runs a fully functional local environment with Postgres, authentication, and real-time messaging, no AWS account required. When you’re ready to deploy, the same application code runs on production AWS services with zero changes, and you can drop into AWS CDK at any point for direct resource configuration.
• Grok 4.3 from xAI in Amazon Bedrock —You can use the Grok 4.3 model on Amazon Bedrock, giving you even more choice as you build generative AI applications across reasoning, agentic, and enterprise workflows. Grok 4.3 runs on a new inference engine in Bedrock designed for price performance, with support for tool calling, structured output, and response streaming.
• Amazon S3 annotations: attach rich, queryable context directly to your objects — Amazon S3 now lets you attach up to 1 GB of rich, mutable, and queryable context directly to your objects using annotations, purpose-built for AI agents and autonomous workflows that need to discover, understand, and act on data at scale without maintaining separate metadata systems.
• Amazon ECS announces faster service auto scaling — Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster).
• Amazon EC2 G7 instances accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs — AWS is the first major cloud provider to support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances are accelerated by these GPUs with custom sixth-generation Intel Xeon Scalable processors, delivering up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6 instances.
• Strands Agents introduces new capabilities — Strands is an open source toolkit for building production agents. You can now use better context management in Harness SDK, a new isolated execution environment with Strands Shell, and chaos testing and red teaming in Strands Evals.
• AWS Management Console Private Access – You can access the AWS Console from VPCs without internet connectivity, allowing enterprises to manage their AWS infrastructure through the console while maintaining strict network security controls in air-gapped environments.
• AWS Marketplace Storefront is now generally available – AWS Partners can create and deploy their own branded catalog of solutions and services on their website or application in hours. Channel Partners and Independent Software Vendors can now simplify how they manage their cloud marketplace business and make it easier for customers to discover and purchase their solutions from AWS Marketplace.
• Palo Alto Networks (PANW) Advanced DNS Security on Amazon Route 53 Resolver DNS Firewall (preview) – You can now enforce DNS threat protections from Palo Alto Networks directly on Route 53 DNS Firewall rules, without deploying separate firewalls or modifying VPC configurations — by subscribing to PANW from the DNS Firewall console through the embedded AWS Marketplace widget.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Price reductions 

AWS continues to look for ways to increase performance and lower prices for our customers. I noticed a few such efforts last week, so I’d like to share them:

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events as well as AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy

The post AWS Weekly Roundup: NY Summit recap, Local Zone in Hanoi, Grok 4.3 in Bedrock, price reductions, and more (June 22, 2026) appeared first on The NOKIA Blog.

Today at the AWS Summit in New York City, Swami Sivasubramanian, AWS VP of Agentic AI, provided the day’s keynote.

Here’s our roundup of the biggest announcements from the event:

New in agents customers create

We’re introducing new capabilities on Amazon Bedrock AgentCore: connecting AI agents to organizational, web, and paid knowledge, helping teams find and fix what’s going wrong in production, and enforcing controls that scale as agents grow more capable.

Together, these capabilities help you build more capable agents faster, govern those agents with controls that scale, and improve them continuously. To learn more, read our blog post covering all the new features.

• Introducing Amazon Bedrock Managed Knowledge Base for faster, more accurate enterprise AI applications — You can build enterprise RAG pipelines with the managed Knowledge Base on Bedrock. It provides native data connectors, Smart Parsing for automatic multi-format data preparation, and an Agentic Retriever for complex multi-step queries—all integrated with AgentCore Gateway so developers can focus on business outcomes rather than infrastructure management.
• Announcing Web Search on Amazon Bedrock AgentCore: Ground your AI agents in current, accurate web knowledge — You can use a fully managed web search tool that enables agents to ground responses in current, cited web knowledge with zero data egress from customer’ secured AWS environment. You can focus on building agents instead of manually adding web search to agents on Bedrock AgentCore and managing its infrastructure.
• AWS WAF adds AI traffic monetization capability to help content owners charge AI bots for content access — You can use a new Bot Control capability that enables content providers and publishers price, meter, and collect payment from AI bots and agents accessing their content and APIs. AWS WAF now lets you set a price for that access, accept payment through third-party providers, and grant scoped access directly at the edge.
• Amazon Bedrock AgentCore harness in now generally available — You can do building and running production-grade AI agents in minutes—without coding orchestration loops—by defining your agent’s model, tools, skills, and instructions in configuration, with Bedrock AgentCore harness.
• Coming soon: AWS Context — This is a new service that automatically maps the relationships across your existing data into a knowledge graph and provides agentic search so AI agents in the organization can access governed data relationships, business rules, and domain knowledge at runtime.

New in agents for securing

• Introducing AWS Continuum: Security at machine speed — AWS Continuum for code vulnerabilities, available in a gated preview, takes findings from across your environment, prioritizes by business impact, proves which are exploitable, and drives a fix through your own process.
• AWS Security Agent (now part of AWS Continuum) adds threat modeling, Kiro power and Claude Code plugin, and more — You can generate the new threat modeling (preview) to understand the full context of your application and identify threats with recommended mitigations using the STRIDE framework. You can also use pull request code scanning with remediation across major Git platforms, and IDE integrations via Kiro power, Claude Code plugin, and MCP — letting developers run security reviews and fix issues without context switching.

New in agents for building

• Introducing Kiro for iOS — Kiro introduces a native iOS app, available in a gated preview, built for real engineering work that gives developers a new surface to kick off, monitor, steer, and interact with their Kiro sessions directly from their phone. That means you can now start sessions, check back when they’re done, review diffs, and approve changes all while staying connected to your work with no laptop running.
• AWS DevOps Agent adds release management capabilities to assess code changes before production — You can use a new release readiness review of code changes and autonomous release testing. These new features verify every change against the natural language standards you give to the DevOps Agent and run change-specific tests in production-like environments.
• Proactively reduce tech debt autonomously with AWS Transform – continuous modernization — You can use continuous analysis (preview) to automatically scan your code repositories against configurable baselines and generates findings in hours, not weeks. Once you’ve identified and prioritized findings, you can configure autonomous remediations that generate pull requests for affected repositories automatically.

In addition to the keynote announcements, we have other important launches this week:

The post Top announcements of the AWS Summit in New York, 2026 appeared first on The NOKIA Blog.

Today, we’re announcing a new metadata capability for Amazon Simple Storage Service (Amazon S3) called annotations, enabling you to attach rich, large-scale business context directly to your objects. You can store up to 1,000 named annotations per object, each up to 1 MB in size, totaling up to 1 GB per object, in flexible formats like JSON, XML, YAML, or plain text. You can modify or delete an annotation at any time, without re-writing your objects, making it easy to keep your object context current.

Organizations are building AI agents and autonomous workflows that need to find, understand, and act on data without human intervention. To support these agentic workflows, you need metadata that can evolve alongside the data, scale to petabytes of objects, and remain queryable without expensive retrieval.

With S3 annotations, you can store context such as AI-generated transcripts, content ratings, or technical specifications directly alongside your objects. Your context moves automatically with the object during copy, replication, and cross-region transfers, and S3 removes it when you delete the object. When you enable S3 Metadata, annotations automatically flow into fully managed annotation tables that you can query with Amazon Athena and other analytics engines.

Common use cases

Annotations solve complex metadata challenges across industries:

• Media & Entertainment: Track transcripts, content moderation results, subtitle files, and licensing metadata as separate annotations on video assets, eliminating the need to synchronize metadata across multiple media asset management systems.
• Financial Services: Attach AI-generated investment summaries and sentiment analysis to research documents, enabling autonomous research agents to discover relevant datasets through natural-language queries without maintaining separate metadata databases.
• Life Sciences: Annotate clinical trial data with regulatory status, patient cohort details, and approval chains, making compliance audits faster while keeping full context accessible for archived data in Amazon S3 Glacier storage classes without retrieval charges.

How annotations address metadata challenges

Amazon S3 already supports several ways to describe your objects. System-defined metadata captures properties like size and storage class. Object tags support operational tasks like access control and lifecycle management. User-defined metadata lets you add small amounts of custom information at upload time.

While these capabilities work well for their intended purposes, they have limitations when you need to attach much richer context without building and maintaining separate metadata systems. Annotations address these needs by providing metadata capabilities at a fundamentally different scale and flexibility, offering mutable, queryable context per object compared to 10 immutable tags or 2 KB of headers.

Today, metadata describing S3 objects often lives in separate databases or sidecar files, requiring complex synchronization workflows that can exceed data storage costs. When you enable S3 Metadata annotation tables, this context becomes queryable at scale through Amazon Athena. AI agents can discover your data through natural language with the S3 Tables MCP server, which provides a standardized interface for AI models to query your annotations. You can query annotations for objects in any storage class, without restoring the objects or paying retrieval charges.

Getting started with annotations

To start using annotations, make sure your AWS Identity and Access Management (IAM) policy or bucket policy grants permissions for the s3:PutObjectAnnotation and s3:GetObjectAnnotation actions. You can then add annotations to any existing or new S3 object using the PutObjectAnnotation API.

For example, a media company can attach technical specifications and AI-produced summaries to a video asset using the AWS Command Line Interface (AWS CLI):

# Create a JSON file with technical metadata
cat > mediainfo.json << 'EOF'
{"codec":"H.265","resolution":"3840x2160","audio_tracks":8,"frame_rate":29.97}
EOF

# Attach it as an annotation
aws s3api put-object-annotation 
  --bucket my-media-bucket 
  --key videos/documentary-2026.mp4 
  --annotation-name mediainfo 
  --annotation-payload ./mediainfo.json

# Attach a plain-text AI-generated summary as a separate annotation
echo "A 90-minute nature documentary covering wildlife migration patterns across three continents, featuring aerial footage and underwater sequences. Languages: English, Spanish, Portuguese." > ai_summary.txt

aws s3api put-object-annotation 
  --bucket my-media-bucket 
  --key videos/documentary-2026.mp4 
  --annotation-name ai_summary 
  --annotation-payload ./ai_summary.txt

These commands attach two separate annotations to the same video object. The mediainfo annotation stores structured technical specifications as JSON, while the ai_summary annotation stores a text description. Each annotation is identified by a unique name, and you can read and modify each one independently. With unique names for each annotation, you can use different annotations to support multiple concurrent enrichment workflows, for example, one team adding technical metadata while another team adds content classifications, without interfering with each other.

Retrieve a specific annotation using the GetObjectAnnotation API:

aws s3api get-object-annotation 
  --bucket my-media-bucket 
  --key videos/documentary-2026.mp4 
  --annotation-name mediainfo 
  ./mediainfo-output.json

To see all annotations attached to an object, use the ListObjectAnnotations API:

aws s3api list-object-annotations 
  --bucket my-media-bucket 
  --key videos/documentary-2026.mp4

When you no longer need a specific annotation, remove it using the DeleteObjectAnnotation API:

aws s3api delete-object-annotation 
  --bucket my-media-bucket 
  --key videos/documentary-2026.mp4 
  --annotation-name mediainfo

You can update an existing annotation at any time by calling PutObjectAnnotation again with the same annotation name. For large objects uploaded using multipart upload, attach annotations after completing the multipart upload using the PutObjectAnnotation API.

Querying annotations at scale with S3 Metadata tables

Attaching annotations to individual objects is useful, but the real power comes when you query across all your annotations at scale. When you enable S3 Metadata annotation tables on your bucket, S3 automatically indexes your annotations into a fully managed Apache Iceberg table, called an annotation table. You can query annotation tables with Amazon Athena or any Iceberg-compatible engine.

To enable annotation tables, use the S3 console or the CreateBucketMetadataConfiguration API. The following example creates a new metadata configuration with annotation tables enabled while keeping journal tables for change tracking and disabling the live inventory table:

{
  "JournalTableConfiguration": {
    "RecordExpiration": { "Expiration": "DISABLED" }
  },
  "InventoryTableConfiguration": { "ConfigurationState": "DISABLED" },
  "AnnotationTableConfiguration": {
    "ConfigurationState": "ENABLED",
    "Role": "arn:aws:iam::123456789012:role/S3MetadataAnnotationRole"
  }
}

This configuration tells S3 to automatically capture all your annotations in a queryable table. Once applied, any annotation you attach to objects in this bucket will appear in the table within approximately one hour.

If the bucket already has a metadata configuration, use the UpdateBucketMetadataAnnotationTableConfiguration API:

aws s3api update-bucket-metadata-annotation-table-configuration 
  --bucket my-media-bucket 
  --annotation-table-configuration '{"ConfigurationState":"ENABLED","Role":"arn:aws:iam::123456789012:role/S3MetadataAnnotationRole"}'

Once enabled, your annotations automatically flow into the annotation table. Journal tables update in near real time, while annotation tables refresh within an hour. Unlike traditional metadata tables that require predefined schemas, annotation tables automatically adapt to any JSON, XML, or YAML structure you write. Each annotation becomes a row in the table with its content stored in a text_value column, letting you query across all annotations without schema migrations.

If you enable annotation tables on a bucket that already has annotated objects, S3 automatically backfills existing annotations into the table. The backfill process runs in the background and can take several hours to days depending on the number of objects.

For example, to find all video assets with more than 8 audio tracks across your entire bucket using Amazon Athena:

SELECT DISTINCT bucket, object_key
FROM "s3tablescatalog/aws-s3"."b_my_media_bucket"."annotation"
WHERE name = 'mediainfo'
AND CAST(json_extract_scalar(text_value, '$.audio_tracks') AS INTEGER) > 8

This query scans the annotation table for all annotations named mediainfo, extracts the audio_tracks field from the JSON content, and returns objects where the count exceeds 8.

Or to find all objects that received new annotations in the last 24 hours through the journal table:

SELECT bucket, key, version_id, record_timestamp, annotation.name
FROM "s3tablescatalog/aws-s3"."b_my_media_bucket"."journal"
WHERE record_timestamp >= (current_date - interval '1' day)
AND annotation.name IS NOT NULL
AND record_type IN ('CREATE_ANNOTATION', 'DELETE_ANNOTATION')

This query uses the journal table to track annotation changes in near real time, which is ideal for building event-driven workflows that respond to new or deleted annotations.

You can also use natural language to search objects by their annotations using agents in Amazon SageMaker Unified Studio or any IDE with the S3 Tables MCP server. For example, asking “find all PG-rated movies with Spanish subtitles from 2023” returns results in seconds instead of the hours it would take querying multiple disconnected systems.

Get started today

You can start using Amazon S3 annotations today in all AWS Regions, including the AWS China Regions. Annotation tables are available in all AWS Regions where S3 Metadata is available.

Whether you’re building AI agents that need to discover data autonomously, managing petabytes of media assets with complex metadata, or tracking compliance context for archived datasets, annotations give you the scale and flexibility to attach rich metadata directly to your objects without managing separate systems.

Annotation storage is always billed at S3 Standard rates, even if the parent object is in S3 Glacier or another storage class. For full pricing details, visit the Amazon S3 pricing page.

To learn more and get started, visit the Amazon S3 Metadata overview page and the Amazon S3 documentation. Send feedback to AWS re:Post for S3 or through your usual AWS Support contacts.

Daniel Abib

The post Amazon S3 annotations: attach rich, queryable context directly to your objects appeared first on The NOKIA Blog.

In my last Week in Review post, I mentioned how much time I’ve been spending on AI-Driven Development Lifecycle (AI-DLC) workshops with customers this year. A common theme in those sessions is the need for better cost visibility. Teams are moving fast with AI, but as they go from experimenting to full production, finance and leadership really need to know who is using which resources and at what cost. That’s why I was so excited to see the launch of Amazon Bedrock new support for cost allocation by IAM user and role this week. This lets you tag IAM principals with attributes like team or cost center and then activate those tags in your Billing and Cost Management console. The resulting cost data flows into AWS Cost Explorer and the detailed Cost and Usage Report, giving you a clear line of sight into model inference spending. Whether you’re scaling agents across teams, tracking foundation model use by department, or running tools like Claude Code on Amazon Bedrock, this new feature is a game changer for tracking and managing your AI investments. You can get all the details on setting this up in the IAM principal cost allocation documentation.

Now, let’s get into this week’s AWS news…

Headlines

Amazon Bedrock now offers Claude Mythos Preview Anthropic’s most sophisticated AI model to date is now available on Amazon Bedrock as a gated research preview through Project Glasswing. Claude Mythos introduces a new model class focused on cybersecurity, capable of identifying sophisticated security vulnerabilities in software, analyzing large codebases, and delivering state of the art performance across cybersecurity, coding, and complex reasoning tasks. Security teams can use it to discover and address vulnerabilities in critical software before threats emerge. Access is currently limited to allowlisted organizations, with Anthropic and AWS prioritizing internet critical companies and open source maintainers.

AWS Agent Registry for centralized agent discovery and governance now in preview AWS launched Agent Registry through Amazon Bedrock AgentCore, providing organizations with a private catalog for discovering and managing AI agents, tools, skills, MCP servers, and custom resources. The registry helps teams locate existing capabilities rather than duplicating them, with semantic and keyword search, approval workflows, and CloudTrail audit trails. It is accessible via the AgentCore Console, AWS CLI, SDK, and as an MCP server queryable from IDEs.

Last week’s launches

Here are some launches and updates from this past week that caught my attention:

• Announcing Amazon S3 Files, making S3 buckets accessible as file systems — Amazon S3 Files transforms S3 buckets into shared file systems that connect any AWS compute resource directly with your S3 data. Built on Amazon EFS technology, it delivers full file system semantics with low latency performance, caching actively used data and providing multiple terabytes per second of aggregate read throughput. Applications can access S3 data through both file system and S3 APIs simultaneously without code modifications or data migration.
• Amazon OpenSearch Service supports Managed Prometheus and agent tracing —Amazon OpenSearch Service now provides a unified observability platform that consolidates metrics, logs, traces, and AI agent tracing into a single interface. The update includes native Prometheus integration with direct PromQL query support, RED metrics monitoring, and OpenTelemetry GenAI semantic convention support for LLM execution visibility. Operations teams can correlate slow traces to logs and overlay Prometheus metrics on dashboards without switching between tools.
• Amazon WorkSpaces Advisor now available for AI powered troubleshooting— AWS launched Amazon WorkSpaces Advisor, an AI powered administrative tool that uses generative AI to help IT administrators troubleshoot Amazon WorkSpaces Personal deployments. It analyzes WorkSpace configurations, detects problems automatically, and provides actionable recommendations to restore service and optimize performance.
• Amazon Braket adds support for Rigetti’s 108 qubit Cepheus QPU — Amazon Braket now offers access to Rigetti’s Cepheus-1-108Q device, the first 100+ qubit superconducting quantum processor on the platform. The modular design features twelve 9 qubit chiplets with CZ gates that offer enhanced resilience to phase errors. It supports multiple frameworks including Braket SDK, Qiskit, CUDA-Q, and Pennylane, with pulse level control for researchers.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news

Here are some additional posts and resources that you might find interesting:

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• What’s Next with AWS (April 28, Virtual) Join this livestream at 9am PT for a candid discussion about how agentic AI is transforming how businesses operate. Featuring AWS CEO Matt Garman, SVP Colleen Aubrey, and OpenAI leaders discussing emerging agent capabilities, Amazon’s internal experiences, and new agentic solutions and platform capabilities.

Browse here for upcoming AWS led in person and virtual events, startup events, and developer focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

~ micah

The post AWS Weekly Roundup: Claude Mythos Preview in Amazon Bedrock, AWS Agent Registry, and more (April 13, 2026) appeared first on The NOKIA Blog.

I’m excited to announce Amazon S3 Files, a new file system that seamlessly connects any AWS compute resource with Amazon Simple Storage Service (Amazon S3).

More than a decade ago, as an AWS trainer, I spent countless hours explaining the fundamental differences between object storage and file systems. My favorite analogy was comparing S3 objects to books in a library (you can’t edit a page, you need to replace the whole book) versus files on your computer that you can modify page by page. I drew diagrams, created metaphors, and helped customers understand why they needed different storage types for different workloads. Well, today that distinction becomes a bit more flexible.

With S3 Files, Amazon S3 is the first and only cloud object store that offers fully-featured, high-performance file system access to your data. It makes your buckets accessible as file systems. This means changes to data on the file system are automatically reflected in the S3 bucket and you have fine-grained control over synchronization. S3 Files can be attached to multiple compute resources enabling data sharing across clusters without duplication.

Until now, you had to choose between Amazon S3 cost, durability, and the services that can natively consume data from it or a file system’s interactive capabilities. S3 Files eliminates that tradeoff. S3 becomes the central hub for all your organization’s data. It’s accessible directly from any AWS compute instance, container, or function, whether you’re running production applications, training ML models, or building agentic AI systems.

You can access any general purpose bucket as a native file system on your Amazon Elastic Compute Cloud (Amazon EC2) instances, containers running on Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS), or AWS Lambda functions. The file system presents S3 objects as files and directories, supporting all Network File System (NFS) v4.1+ operations like creating, reading, updating, and deleting files.

As you work with specific files and directories through the file system, associated file metadata and contents are placed onto the file system’s high-performance storage. By default, files that benefit from low-latency access are stored and served from the high performance storage. For files not stored on high performance storage such as those needing large sequential reads, S3 Files automatically serves those files directly from Amazon S3 to maximize throughput. For byte-range reads, only the requested bytes are transferred, minimizing data movement and costs.

The system also supports intelligent pre-fetching to anticipate your data access needs. You have fine-grained control over what gets stored on the file system’s high performance storage. You can decide whether to load full file data or metadata only, which means you can optimize for your specific access patterns.

Under the hood, S3 Files uses Amazon Elastic File System (Amazon EFS) and delivers ~1ms latencies for active data. The file system supports concurrent access from multiple compute resources with NFS close-to-open consistency, making it ideal for interactive, shared workloads that mutate data, from agentic AI agents collaborating through file-based tools to ML training pipelines processing datasets.

Let me show you how to get started.

Creating my first Amazon S3 file system, mounting, and using it from an EC2 instance is straightforward.

I have an EC2 instance and a general purpose bucket. In this demo, I configure an S3 file system and access the bucket from an EC2 instance, using regular file system commands.

For this demo, I use the AWS Management Console. You can also use the AWS Command Line Interface (AWS CLI) or infrastructure as code (IaC).

Here is the architecture diagram for this demo.

Step 1: Create an S3 file system.

On the Amazon S3 section of the console, I choose File systems and then Create file system.

I enter the name of the bucket I want to expose as a file system and choose Create file system.

Step 2: Discover the mount target.

A mount target is a network endpoint that will live in my virtual private cloud (VPC). It allows my EC2 instance to access the S3 file system.

The console creates the mount targets automatically. I take notes of the Mount target IDs on the Mount targets tab.

When using the CLI, two separate commands are necessary to create the file system and its mount targets. First, I create the S3 file system with create-file-system. Then, I create the mount target with create-mount-target.

Step 3: Mount the file system on my EC2 instance.

After it’s connected to an EC2 instance, I type:

sudo mkdir /home/ec2-user/s3files sudo mount -t s3files fs-0aa860d05df9afdfe:/ /home/ec2-user/s3files

I can now work with my S3 data directly through the mounted file system in ~/s3files, using standard file operations.

When I make updates to my files in the file system, S3 automatically manages and exports all updates as a new object or a new version on an existing object back in my S3 bucket within minutes.

Changes made to objects on the S3 bucket are visible in the file system within a few seconds but can sometimes take a minute or longer.

# Create a file on the EC2 file system 
echo "Hello S3 Files" > s3files/hello.txt 

# and verify it's here 
ls -al s3files/hello.txt
 -rw-r--r--. 1 ec2-user ec2-user 15 Oct 22 13:03 s3files/hello.txt 

# See? the file is also on S3 
aws s3 ls s3://s3files-aws-news-blog/hello.txt 
2025-10-22 13:04:04 15 hello.txt 

# And the content is identical! 
aws s3 cp s3://s3files-aws-news-blog/hello.txt . && cat hello.txt
Hello S3 Files

Things to know

Let me share some important technical details that I think you’ll find useful.

Another question I frequently hear in customer conversations is about choosing the right file service for your workloads. Yes, I know what you’re thinking: AWS and its seemingly overlapping services, keeping cloud architects entertained during their architecture review meetings. Let me help demystify this one.

S3 Files works best when you need interactive, shared access to data that lives in Amazon S3 through a high performance file system interface. It’s ideal for workloads where multiple compute resources—whether production applications, agentic AI agents using Python libraries and CLI tools, or machine learning (ML) training pipelines—need to read, write, and mutate data collaboratively. You get shared access across compute clusters without data duplication, sub-millisecond latency, and automatic synchronization with your S3 bucket.

For workloads migrating from on-premises NAS environments, Amazon FSx provides the familiar features and compatibility you need. Amazon FSx is also ideal for high-performance computing (HPC) and GPU cluster storage with Amazon FSx for Lustre. It’s particularly valuable when your applications require specific file system capabilities from Amazon FSx for NetApp ONTAP, Amazon FSx for OpenZFS, or Amazon FSx for Windows File Server.

Pricing and availability

S3 Files is available today in all commercial AWS Regions.

You pay for the portion of data stored in your S3 file system, for small file read and all write operations to the file system, and for S3 requests during data synchronization between the file system and the S3 bucket. The Amazon S3 pricing page has all the details.

From discussions with customers, I believe S3 Files helps simplify cloud architectures by eliminating data silos, synchronization complexity, and manual data movement between objects and files. Whether you’re running production tools that already work with file systems, building agentic AI systems that rely on file-based Python libraries and shell scripts, or preparing datasets for ML training, S3 Files lets these interactive, shared, hierarchical workloads access S3 data directly without choosing between the durability of Amazon S3 and cost benefits and a file system’s interactive capabilities. You can now use Amazon S3 as the place for all your organizations’ data, knowing the data is accessible directly from any AWS compute instance, container, and function.

To learn more and get started, visit the S3 Files documentation.

I’d love to hear how you use this new capability. Feel free to share your feedback in the comments below.

— seb

The post Launching S3 Files, making S3 buckets accessible as file systems appeared first on The NOKIA Blog.

Twenty years ago this past week, Amazon S3 launched publicly on March 14, 2006. While Amazon Simple Storage Service is often considered the foundational storage service that defined cloud infrastructure, what began as a simple object storage service has grown into something far larger in scope and scale.

As of March 2026, S3 stores more than 500 trillion objects, serves more than 200 million requests per second globally across hundreds of exabytes of data, and the price has dropped to just over 2 cents per gigabyte — an approximately 85% reduction since launch. My colleague Sébastien Stormacq wrote a detailed look at the engineering and the road ahead in Twenty years of Amazon S3 and building what’s next, and if you want to read about those earliest customers and how they shaped what AWS became, I recommend How three startups helped Amazon invent cloud computing and paved the way for AI. Twenty years is worth pausing to celebrate.

Alongside the 20th anniversary of S3, Channy Yun also wrote about a new S3 feature this week: Account regional namespaces for Amazon S3 general purpose buckets. With this feature, you can create general purpose buckets in your own account regional namespace by appending your account’s unique suffix to your requested bucket name, ensuring your desired names are always reserved exclusively for your account. You can enforce adoption across your organization using AWS IAM policies and AWS Organizations service control policies with the new s3:x-amz-bucket-namespace condition key. Read Channy’s post to learn more about account regional namespaces for Amazon S3 general purpose buckets.

This week’s featured launch is one I have a personal connection to: the general availability of Amazon Route 53 Global Resolver. I wrote about the preview of this capability back in December at re:Invent 2025, and I had a great time putting that post together, so I am happy to hear that it’s generally available now.

Amazon Route 53 Global Resolver is an internet-reachable anycast DNS resolver that provides DNS resolution for authorized clients from any location. It is now generally available across 30 AWS Regions, with support for both IPv4 and IPv6 DNS query traffic. Route 53 Global Resolver gives authorized clients in your organization anycast DNS resolution of public internet domains and private domains associated with Route 53 private hosted zones — from any location, not just from within a specific VPC or Region. It also provides DNS query filtering to block potentially malicious domains, domains that are not safe for work, and domains associated with advanced DNS threats such as DNS tunneling and Domain Generation Algorithms (DGA). Centralized query logging is included as well. With general availability, Global Resolver adds protection against Dictionary DGA threats.

Last week’s launches

Here are some of the other announcements from last week:

• Amazon Bedrock AgentCore Runtime now supports stateful MCP server features — Amazon Bedrock AgentCore Runtime now supports stateful Model Context Protocol (MCP) server features, enabling developers to build MCP servers that use elicitation, sampling, and progress notifications alongside existing support for resources, prompts, and tools. With stateful MCP sessions, each user session runs in a dedicated microVM with isolated resources, and the server maintains session context across multiple interactions using an Mcp-Session-Id header. Elicitation enables server-initiated, multi-turn conversations to gather structured input from users during tool execution. Sampling allows servers to request LLM-generated content from the client for tasks such as personalized recommendations. Progress notifications keep clients informed during long-running operations. To learn more, see the Amazon Bedrock AgentCore documentation.
• Amazon WorkSpaces now supports Microsoft Windows Server 2025 — New bundles powered by Microsoft Windows Server 2025 are now available for Amazon WorkSpaces Personal and Amazon WorkSpaces Core. These bundles include security capabilities such as Trusted Platform Module 2.0 (TPM 2.0), Unified Extensible Firmware Interface (UEFI) Secure Boot, Secured-core server, Credential Guard, Hypervisor-protected Code Integrity (HVCI), and DNS-over-HTTPS. Existing Windows Server 2016, 2019, and 2022 bundles remain available. You can use the managed Windows Server 2025 bundles or create a custom bundle and image. This support is available in all AWS Regions where Amazon WorkSpaces is available. For more information, visit the Amazon WorkSpaces FAQs.
• AWS Builder ID now supports Sign in with GitHub and Amazon — AWS Builder ID now supports two additional social login options: GitHub and Amazon. These options join the existing Google and Apple sign-in capabilities. With this update, developers can access their AWS Builder ID profile — and services including AWS Builder Center, AWS Training and Certification, and Kiro — using their existing GitHub or Amazon account credentials, without managing a separate set of credentials. To learn more and get started, visit the AWS Builder ID documentation.
• Amazon Redshift introduces reusable templates for COPY operations — Amazon Redshift now supports templates for the COPY command, allowing you to store and reuse frequently used COPY parameters. Templates help maintain consistency across data ingestion operations, reduce the effort required to execute COPY commands, and simplify maintenance by applying template updates automatically to all future uses. Support for COPY templates is available in all AWS Regions where Amazon Redshift is available, including the AWS GovCloud (US) Regions. To get started, see the documentation or read the Standardize Amazon Redshift operations using Templates blog.

For a full list of AWS announcements, be sure to keep an eye on our News Blog channel the What’s New with AWS page.

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

AWS Summits – Join AWS Summits in 2026, free in-person events where you can explore emerging cloud and AI technologies, learn best practices, and network with industry peers and experts. Upcoming Summits include Paris (April 1), London (April 22), and Bengaluru (April 23–24).

AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders, featuring technical discussions, workshops, and hands-on labs. Upcoming events include Pune (March 21), San Francisco (April 10), and Romania (April 23-24).

AWS at NVIDIA GTC 2026 — Join us at our AWS sessions, booths, demos, and ancillary events in NVIDIA GTC 2026 on March 16 – 19, 2026 in San Jose. You can receive 20% off event passes through AWS and request a 1:1 meeting at GTC.

AWS Community GameDay Europe — Taking place on March 17, 2026, AWS Community GameDay Europe is a team-based, hands-on AWS challenge event running simultaneously across 50+ cities in Europe. Your team is dropped into a broken AWS environment — misconfigured services, failing architectures, and security gaps — and has two hours to fix as much as possible. Find your nearest city and sign up at awsgameday.eu.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: Amazon S3 turns 20, Amazon Route 53 Global Resolver general availability, and more (March 16, 2026) appeared first on The NOKIA Blog.

Twenty years ago today, on March 14, 2006, Amazon Simple Storage Service (Amazon S3) quietly launched with a modest one-paragraph announcement on the What’s New page:

Amazon S3 is storage for the Internet. It is designed to make web-scale computing easier for developers. Amazon S3 provides a simple web services interface that can be used to store and retrieve any amount of data, at any time, from anywhere on the web. It gives any developer access to the same highly scalable, reliable, fast, inexpensive data storage infrastructure that Amazon uses to run its own global network of web sites.

Even Jeff Barr’s blog post was only a few paragraphs, written before catching a plane to a developer event in California. No code examples. No demo. Very low fanfare. Nobody knew at the time that this launch would shape our entire industry.

The early days: Building blocks that just work

At its core, S3 introduced two straightforward primitives: PUT to store an object and GET to retrieve it later. But the real innovation was the philosophy behind it: create building blocks that handle the undifferentiated heavy lifting, which freed developers to focus on higher-level work.

From day one, S3 was guided by five fundamentals that remain unchanged today.

Security means your data is protected by default. Durability is designed for 11 nines (99.999999999%), and we operate S3 to be lossless. Availability is designed into every layer, with the assumption that failure is always present and must be handled. Performance is optimized to store virtually any amount of data without degradation. Elasticity means the system automatically grows and shrinks as you add and remove data, with no manual intervention required.

When we get these things right, the service becomes so straightforward that most of you never have to think about how complex these concepts are.

S3 today: Scale beyond imagination

Throughout 20 years, S3 has remained committed to its core fundamentals even as it’s grown to a scale that’s hard to comprehend.

When S3 first launched, it offered approximately one petabyte of total storage capacity across about 400 storage nodes in 15 racks spanning three data centers, with 15 Gbps of total bandwidth. We designed the system to store tens of billions of objects, with a maximum object size of 5 GB. The initial price was 15 cents per gigabyte.

Today, S3 stores more than 500 trillion objects and serves more than 200 million requests per second globally across hundreds of exabytes of data in 123 Availability Zones in 39 AWS Regions, for millions of customers. The maximum object size has grown from 5 GB to 50 TB, a 10,000 fold increase. If you stacked all of the tens of millions S3 hard drives on top of each other, they would reach the International Space Station and almost back.

Even as S3 has grown to support this incredible scale, the price you pay has dropped. Today, AWS charges slightly over 2 cents per gigabyte. That’s a price reduction of approximately 85% since launch in 2006. In parallel, we’ve continued to introduce ways to further optimize storage spend with storage tiers. For example, our customers have collectively saved more than $6 billion in storage costs by using Amazon S3 Intelligent-Tiering as compared to Amazon S3 Standard.

Over the past two decades, the S3 API has been adopted and used as a reference point across the storage industry. Multiple vendors now offer S3 compatible storage tools and systems, implementing the same API patterns and conventions. This means skills and tools developed for S3 often transfer to other storage systems, making the broader storage landscape more accessible.

Despite all of this growth and industry adoption, perhaps the most remarkable achievement is this: the code you wrote for S3 in 2006 still works today, unchanged. Your data went through 20 years of innovation and technical advances. We migrated the infrastructure through multiple generations of disks and storage systems. All the code to handle a request has been rewritten. But the data you stored 20 years ago is still available today, and we’ve maintained complete API backward compatibility. That’s our commitment to delivering a service that continually “just works.”

The engineering behind the scale

What makes S3 possible at this scale? Continuous innovation in engineering.

Much of what follows is drawn from a conversation between Mai-Lan Tomsen Bukovec, VP of Data and Analytics at AWS, and Gergely Orosz of The Pragmatic Engineer. The in-depth interview goes further into the technical details for those who want to go deeper. In the following paragraphs, I share some examples:

At the heart of S3 durability is a system of microservices that continuously inspect every single byte across the entire fleet. These auditor services examine data and automatically trigger repair systems the moment they detect signs of degradation. S3 is designed to be lossless: the 11 nines design goal reflects how the replication factor and re-replication fleet are sized, but the system is built so that objects aren’t lost.

S3 engineers use formal methods and automated reasoning in production to mathematically prove correctness. When engineers check in code to the index subsystem, automated proofs verify that consistency hasn’t regressed. This same approach proves correctness in cross-Region replication or for access policies.

Over the past 8 years, AWS has been progressively rewriting performance-critical code in the S3 request path in Rust. Blob movement and disk storage have been rewritten, and work is actively ongoing across other components. Beyond raw performance, Rust’s type system and memory safety guarantees eliminate entire classes of bugs at compile time. This is an essential property when operating at S3 scale and correctness requirements.

S3 is built on a design philosophy: “Scale is to your advantage.” Engineers design systems so that increased scale improves attributes for all users. The larger S3 gets, the more de-correlated workloads become, which improves reliability for everyone.

Looking forward

The vision for S3 extends beyond being a storage service to becoming the universal foundation for all data and AI workloads. Our vision is simple: you store any type of data one time in S3, and you work with it directly, without moving data between specialized systems. This approach reduces costs, eliminates complexity, and removes the need for multiple copies of the same data.

Here are a few standout launches from recent years:

• S3 Tables – Fully managed Apache Iceberg tables with automated maintenance that optimize query efficiency and reduce storage cost over time.
• S3 Vectors – Native vector storage for semantic search and RAG, supporting up to 2 billion vectors per index with sub-100ms query latency. In only 5 months (July–December 2025), you created more than 250,000 indices, ingested more than 40 billion vectors, and performed more than 1 billion queries.
• S3 Metadata – Centralized metadata for instant data discovery, removing the need to recursively list large buckets for cataloging and significantly reducing time-to-insight for large data lakes.

Each of these capabilities operates at S3 cost structure. You can handle multiple data types that traditionally required expensive databases or specialized systems but are now economically feasible at scale.

From 1 petabyte to hundreds of exabytes. From 15 cents to 2 cents per gigabyte. From simple object storage to the foundation for AI and analytics. Through it all, our five fundamentals–security, durability, availability, performance, and elasticity–remain unchanged, and your code from 2006 still works today.

Here’s to the next 20 years of innovation on Amazon S3.

— seb

The post Twenty years of Amazon S3 and building what’s next appeared first on The NOKIA Blog.

Today, we’re announcing a new feature of Amazon Simple Storage Service (Amazon S3) you can use to create general purpose buckets in your own account regional namespace simplifying bucket creation and management as your data storage needs grow in size and scope. You can create general purpose bucket names across multiple AWS Regions with assurance that your desired bucket names will always be available for you to use.

With this feature, you can predictably name and create general purpose buckets in your own account regional namespace by appending your account’s unique suffix in your requested bucket name. For example, I can create the bucket mybucket-123456789012-us-east-1-an in my account regional namespace. mybucket is the bucket name prefix that I specified, then I add my account regional suffix to the requested bucket name: -123456789012-us-east-1-an. If another account tries to create buckets using my account’s suffix, their requests will be automatically rejected.

Your security teams can use AWS Identity and Access Management (AWS IAM) policies and AWS Organizations service control policies to enforce that your employees only create buckets in their account regional namespace using the new s3:x-amz-bucket-namespace condition key, helping teams adopt the account regional namespace across your organization.

Create your S3 bucket with account regional namespace in action

To get started, choose Create bucket in the Amazon S3 console. To create your bucket in your account regional namespace, choose Account regional namespace. If you choose this option, you can create your bucket with any name that is unique to your account and region.

This configuration supports all of the same features as general purpose buckets in the global namespace. The only difference is that only your account can use bucket names with your account’s suffix. The bucket name prefix and the account regional suffix combined must be between 3 and 63 characters long.

Using the AWS Command Line Interface (AWS CLI), you can create a bucket with account regional namespace by specifying the x-amz-bucket-namespace:account-regional request header and providing a compatible bucket name.

$ aws s3api create-bucket --bucket mybucket-123456789012-us-east-1-an 
   --bucket-namespace account-regional 
   --region us-east-1

You can use the AWS SDK for Python (Boto3) to create a bucket with account regional namespace using CreateBucket API request.

import boto3

class AccountRegionalBucketCreator:
    """Creates S3 buckets using account-regional namespace feature."""
    
    ACCOUNT_REGIONAL_SUFFIX = "-an"
    
    def __init__(self, s3_client, sts_client):
        self.s3_client = s3_client
        self.sts_client = sts_client
    
    def create_account_regional_bucket(self, prefix):
        """
        Creates an account-regional S3 bucket with the specified prefix.
        Resolves caller AWS account ID using the STS GetCallerIdentity API.
        Format: ---an
        """
        account_id = self.sts_client.get_caller_identity()['Account']
        region = self.s3_client.meta.region_name
        bucket_name = self._generate_account_regional_bucket_name(
            prefix, account_id, region
        )
        
        params = {
            "Bucket": bucket_name,
            "BucketNamespace": "account-regional"
        }
        if region != "us-east-1":
            params["CreateBucketConfiguration"] = {
                "LocationConstraint": region
            }
        
        return self.s3_client.create_bucket(**params)
    
    def _generate_account_regional_bucket_name(self, prefix, account_id, region):
        return f"{prefix}-{account_id}-{region}{self.ACCOUNT_REGIONAL_SUFFIX}"


if __name__ == '__main__':
    s3_client = boto3.client('s3')
    sts_client = boto3.client('sts')
    
    creator = AccountRegionalBucketCreator(s3_client, sts_client)
    response = creator.create_account_regional_bucket('test-python-sdk')
    
    print(f"Bucket created: {response}")

You can update your infrastructure as code (IaC) tools, such as AWS CloudFormation, to simplify creating buckets in your account regional namespace. AWS CloudFormation offers the pseudo parameters, AWS::AccountId and AWS::Region, making it easy to build CloudFormation templates that create account regional namespace buckets.

The following example demonstrates how you can update your existing CloudFormation templates to start creating buckets in your account regional namespace:

BucketName: !Sub "amzn-s3-demo-bucket-${AWS::AccountId}-${AWS::Region}-an"
BucketNamespace: "account-regional"

Alternatively, you can also use the BucketNamePrefix property to update your CloudFormation template. By using the BucketNamePrefix, you can provide only the customer defined portion of the bucket name and then it automatically adds the account regional namespace suffix based on the requesting AWS account and Region specified.

BucketNamePrefix: 'amzn-s3-demo-bucket'
BucketNamespace: "account-regional"

Using these options, you can build a custom CloudFormation template to easily create general purpose buckets in your account regional namespace.

Things to know

You can’t rename your existing global buckets to bucket names with account regional namespace, but you can create new general purpose buckets in your account regional namespace. Also, the account regional namespace is only supported for general purpose buckets. S3 table buckets and vector buckets already exist in an account-level namespace and S3 directory buckets exist in a zonal namespace.

To learn more, visit Namespaces for general purpose buckets in the Amazon S3 User Guide.

Now available

Creating general purpose buckets in your account regional namespace in Amazon S3 is now available in 37 AWS Regions including the AWS China and AWS GovCloud (US) Regions. You can create general purpose buckets in your account regional namespace at no additional cost.

Give it a try in the Amazon S3 console today and send feedback to AWS re:Post for Amazon S3 or through your usual AWS Support contacts.

— Channy

The post Introducing account regional namespaces for Amazon S3 general purpose buckets appeared first on The NOKIA Blog.