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Media Services
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Amazon Kinesis Video Streams Process and analyze video streams
AWS Elemental MediaConnect Reliable and secure live video transport
AWS Elemental MediaConvert Convert file-based video content
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AWS Elemental MediaTailor Video personalization and monetization
AWS Elemental Appliances & Software On-premises media solutions
AWS Deadline Cloud AWS Deadline Cloud: Cloud Render Management
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AWS Thinkbox Frost AWS Thinkbox Frost: Create particle meshes faster
AWS Thinkbox Krakatoa AWS Thinkbox Krakatoa: Render production-grade volumetrics
AWS Thinkbox Sequoia AWS Thinkbox Sequoia: Point cloud data geometry
AWS Thinkbox Stoke AWS Thinkbox Stoke: Particle simulator for Autodesk
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Migration & Modernization
AWS Migration Hub Track migrations from a single place
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AWS Private 5G Easily deploy, manage, and scale a private cellular network
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Amazon Braket Explore and experiment with quantum computing
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Amazon Cognito Identity management for your apps
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Amazon GuardDuty Managed threat detection service
Amazon Inspector Automate vulnerability management
Amazon Macie Discover and protect your sensitive data at scale
Amazon Security Lake Automatically centralize your security data with a few
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Amazon Verified Permissions Fine-grained permissions and authorization for your
applications
AWS Artifact On-demand access to AWS’ compliance reports
AWS Audit Manager Continuously audit your AWS usage
AWS Certificate Manager Provision, manage, and deploy SSL/TLS certificates
AWS CloudHSM Hardware-based key storage for regulatory compliance
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AWS Firewall Manager Central management of firewall rules
AWS Key Management Service Managed creation and control of encryption keys
AWS Network Firewall Network security to protect your VPCs
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AWS Private Certificate Authority Create private certificates to identify
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AWS Resource Access Manager Simple, secure service to share AWS resources
AWS Secrets Manager Rotate, manage, and retrieve secrets
AWS Security Hub Unified security and compliance center
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AWS Blog Home Blogs Editions
AWS NEWS BLOG
NEW AMAZON S3 TABLES: STORAGE OPTIMIZED FOR ANALYTICS WORKLOADS
by Jeff Barr on 03 DEC 2024 in Amazon Simple Storage Service (S3),
Announcements, AWS re:Invent, Featured, Launch, News Permalink Comments Share
*
*
*
*
*
Amazon S3 Tables give you storage that is optimized for tabular data such as
daily purchase transactions, streaming sensor data, and ad impressions in Apache
Iceberg format, for easy queries using popular query engines like Amazon Athena,
Amazon EMR, and Apache Spark. When compared to self-managed table storage, you
can expect up to 3x faster query performance and up to 10x more transactions per
second, along with the operational efficiency that is part-and-parcel when you
use a fully managed service.
Iceberg has become the most popular way to manage Parquet files, with thousands
of AWS customers using Iceberg to query across often billions of files
containing petabytes or even exabytes of data.
Table Buckets, Tables, and Namespaces
Table buckets are the third type of S3 bucket, taking their place alongside the
existing general purpose and directory buckets. You can think of a table bucket
as an analytics warehouse that can store Iceberg tables with various schemas.
Additionally, S3 Tables deliver the same durability, availability, scalability,
and performance characteristics as S3 itself, and automatically optimize your
storage to maximize query performance and to minimize cost.
Each table bucket resides in a specific AWS Region and has a name that must be
unique within the AWS account with respect to the region. Buckets are referenced
by ARN and also have a resource policy. Finally, each bucket uses namespaces to
logically group the tables in the bucket.
Tables are structured datasets stored in a table bucket. Like table buckets,
they have ARNs and resource policies, and exist within one of the bucket’s
namespaces. Tables are fully managed, with automatic, configurable continuous
maintenance including compaction, management of aged snapshots, and removal of
unreferenced files. Each table has an S3 API endpoint for storage operations.
Namespaces can be referenced from access policies in order to simplify access
management.
Buckets and Tables from the Command Line
Ok, let’s dive right in, create a bucket, and put a table or two inside. I’ll
use the AWS Command Line Interface (AWS CLI), but AWS Management Console and API
support is also available. For conciseness, I will pipe the output of the more
verbose commands through jq and show you only the most relevant values.
The first step is to create a table bucket:
$ aws s3tables create-table-bucket --name jbarr-table-bucket-2 | jq .arn
"arn:aws:s3tables:us-east-2:123456789012:bucket/jbarr-table-bucket-2"
Bash
For convenience, I create an environment variable with the ARN of the table
bucket:
$ export ARN="arn:aws:s3tables:us-east-2:123456789012:bucket/jbarr-table-bucket-2"
Bash
And then I list my table buckets:
$ aws s3tables list-table-buckets | jq .tableBuckets[].arn
"arn:aws:s3tables:us-east-2:123456789012:bucket/jbarr-table-bucket-1"
"arn:aws:s3tables:us-east-2:123456789012:bucket/jbarr-table-bucket-2"
Bash
I can access and populate the table in many different ways. For testing purposes
I installed Apache Spark, then invoked the Spark shell with command-line
arguments to use the Amazon S3 Tables Catalog for Apache Iceberg package and to
set mytablebucket to the ARN of my table.
I create a namespace (mydata) that I will use to group my tables:
scala> spark.sql("""CREATE NAMESPACE IF NOT EXISTS mytablebucket.mydata""")
SQL
Then I create a simple Iceberg table in the namespace:
spark.sql("""CREATE TABLE IF NOT EXISTS mytablebucket.mydata.table1
(id INT,
name STRING,
value INT)
USING iceberg
""")
SQL
I use somes3tables commands to check my work:
$ aws s3tables list-namespaces --table-bucket-arn $ARN | jq .namespaces[].namespace[]
"mydata"
$
$ aws s3tables list-tables --table-bucket-arn $ARN | jq .tables[].name
"table1"
Bash
Then I return to the Spark shell and add a few rows of data to my table:
spark.sql("""INSERT INTO mytablebucket.mydata.table1
VALUES
(1, 'Jeff', 100),
(2, 'Carmen', 200),
(3, 'Stephen', 300),
(4, 'Andy', 400),
(5, 'Tina', 500),
(6, 'Bianca', 600),
(7, 'Grace', 700)
""")
SQL
Buckets and Tables from the Console
I can also create and work on table buckets using the S3 Console. I click Table
buckets to get started:
Before creating my first bucket I click Enable integration so that I can access
my table buckets from Amazon Athena, Amazon Redshift, Amazon EMR, and other AWS
query engines (I can do this later if I don’t do it now):
I read the fine print and click Enable integration to create the specified IAM
role and an entry in the AWS Glue Data Catalog:
After a few seconds the integration is enabled and I click Create table bucket
to move ahead:
I enter a name (jbarr-table-bucket-3) and click Create table bucket:
From here I can create and use tables as I showed you earlier in the CLI
section.
Table Maintenance
Table buckets take care of some important maintenance duties that would be your
responsibility if you were creating and managing your own Iceberg tables. To
relieve you of these duties so that you can spend more time on your table, the
following maintenance operations are performed automatically:
Compaction – This process combines multiple small table objects into a larger
object to improve query performance, in pursuit of a target file size that can
be configured to be between 64 MiB and 512 MiB. The new object is rewritten as a
new snapshot.
Snapshot Management – This process expires and ultimately removes table
snapshots, with configuration options for the minimum number of snapshots to
retain and the maximum age of a snapshot to retain. Expired snapshots are marked
as non-current, then later deleted after a specified number of days.
Unreferenced File Removal – This process removes and deletes objects that are
not referenced by any table snapshots.
Things to Know
Here are a couple of important things that you should know about table buckets
and tables:
AWS Integration – S3 Tables integration with AWS Glue Data Catalog is in
preview, allowing you to query and visualize data using AWS Analytics services
such as Amazon Athena, Amazon Redshift, Amazon EMR, and Amazon QuickSight.
S3 API Support – Table buckets support relevant S3 API functions including
GetObject, HeadObject, PutObject, and the multi-part upload operations.
Security – All objects stored in table buckets are automatically encrypted.
Table buckets are configured to enforce Block Public Access.
Pricing – You pay for storage, requests, an object monitoring fee, and and fees
for compaction. See the S3 Pricing page for more info.
Regions – You can use this new feature in the US East (Ohio, N. Virginia) and US
West (Oregon) AWS Regions.
— Jeff;
JEFF BARR
Jeff Barr is Chief Evangelist for AWS. He started this blog in 2004 and has been
writing posts just about non-stop ever since.
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