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Every year March 14 (3.14) emphasizes AWS PI Day innovations to help you manage and work with your data. What began in 2021 as a way to commemorate the 15th anniversary of Amazon Simple Storage Service (Amazon S3) has now grown to an element that emphasizes how cloud technologies transform data, analytics and artificial intelligence.
This year, AWS PI Day returns with a focus on accelerating analytics and AI innovation with Unified Data Foundation to AWS. The data landscape is undergoing deep transformation such as AI ÉMERGES in most business strategies, while AI analysts and workload are increasingly converging around many of the same data and working OW. You need an easy way to access all your data and use all your preferred analytical and AA tools in one integrated experience. This day AWS PI has introduced with new abilities to help you build united and integrated data experience.
Another generation Amazon Sagemaker: Center of All Your Data, Analysts and AI
In Re: Invent 2024, we introduced the next generation Amazon Sagemaker, centers of all your data, analysts and AI. Sagemaker included virtually all the components you need to explore, prepare and integrate data, process large data, SQL rapid analytics, model development and machine learning (ML) and generative development AI. With this new generation Amazon Sagemaker, Sagemaker Lake offers you unified access to your data and Sagemaker catalog will help you meet you manage and safety requirements. You can read the blog post to start my colleague Antja and learn more details.
The core of the next generation Amazon Sagemaker is the Sagemaker Unified Studio, the only AI data and development environment, where you can use all your data and tools for analysis and AI. Sagemaker Unified Studio is now generally available.
Sagemaker Unified Studio makes it easier to cooperate between scientists, analysts, engineers and developers working on data, analysts, workflows AI and applications. It provides well -known ASP Analytics and Artificial Intelligence and Machine Learning (AI/ml), including data processing, SQL analysts, ML model and generative AI to develop AI into a single user interface.
Sagemaker Unified Studio also brings a selected ability Amazon Bedrock to Sagemaker. Now you can quickly prototype, customize and share generative AI applications using FMS and advanced features such as Amazon knowledge bases, Amazon Bedrock railing, Amazon bedrock and Amazon flows of the subsoil and create a custom -adapted solution with your requirements and responsible AI guidelines.
Last but not least, Amazon Q Developer is now generally available in Sagemaker Unified Studio. Amazon Q Developer provides generative assistance driven AI for data development and AI. It helps you with tasks such as writing SQL questions, building extract, transformation and loading (ETL) and problem solving, and available at a free level and for existing subscribers.
You can learn more about Sagemaker Unified Studio in this recent writer of the blog post from my colleague Donnie.
During Re: Invent 2024, we also launched Amazon Sagemaker Lake as part of the next generation Sagemaker. Sagemaker Lake unifies all your data across Amazon S3 data lakes, Amazon Redshift data warehouses and third -party data, and federated data sources. It helps you create a powerful analysis and AI/ml application on one copy of your data. SageMaker Lake gives you the flexibility to access and ask your data on the spot with the Apache Iceberg-Compavible tools and engines. In addition, Zero-Andl integration automates the data introduction into SageMaker Lakemous from AWS data sources such as Amazon Aurora Dynamodb AZ applications such as Salesforce, Facebook ads, Instagram ads, Servicenow, SAP, Zendes and Zho CRM. The full integration list is available in the FAQ Sagemaker Lake.
Building on Data Foundation with Amazon S3
Building a data foundation is the cornerstone of the AII analysis and workload, which allows organizations to smoothly manage, discover and use their data assets on any scale. The Amazon S3 is the best place in the world to build a data lake with a virtually unlimited scale and provides a basic basis for this transformation.
I am always amazed to learn about the scale on which we run Amazon S3: currently holds over 400 trillion objects, data exits and processes the mind by 150 million per second of 150 million. Just ten years ago, or 100 customers stored more than petabytes (PB) data on S3. Today, thousands of customers overcome 1 PB Milestone.
The Amazon S3 stores exabyta data data and average more than 15 million requirements for spreadsheets per second. To help you reduce undifferentiated heavy lifting when managing tabular data in S3 buckets, we announced the Amazon S3 in AWS Re: Invent 2024. S3 tables are specifically optimized for analytical workload, resulting in up to three times faster query throughput and up to ten times high transactions per second compared to self -tanned tables.
We are announcing today General availability of Amazon S3 integration tables with Amazon Sagemaker Lakehouse Amazon S3 tables are now integrated with Amazon Sagemaker Lake, which makes it easier for you to access the S3 from AWS Analytics serchs such as Amazon Redshift, Amazon Athena, Amazon EMR, AWS Glue and Apache Iceberg – compatible engines such as Apache Spark or Pyiceberg. Sagemaker Lakehouse allows centralized management of fine -grained permissions to access data for S3 tables and other sources and lands them on all engines.
For those of you who use third -party catalog, they have their own execution of the catalog or need only basic access to reading and writing to the table data in a bucket with one table, we added New APIs that are compatible with the Iceberg Rest catalog standard. This allows any application compatible with a smoothly for smoothly creation, update, list, and delete tables in the S3 table. You can also use the S3 with Lakemous Sagemaker tables for unified data management across all of your spreadsheets, data and fine -grained access controls.
To help you access S3 tables, we launched updates in the AWS Management Console console. Now you can create a table, fill it with data and ask it directly from the S3 console using Amazon Athena, making it easier to start and analyze data in the S3 buckets.
The following screenshot shows how to approach Athena directly from the S3 console.
When I select Questioning tables with athena gold Create a table with AthenaOpens Athena on the correct data source, catalog and database.
Sale Re: Invent 2024, we continued to add new skills to S3 tables at a quick pace. For example, we have added support to the definition of scheme to CreateTable API and now you can create up to 10,000 tables in the S3 bucket table. We also launched the S3 tables to eight other AWS regions, with Asia the latest Pacific (Seoul, Singapore, Sydney) on March 4, with another one. On the S3 Tabs AWS Regions page, you can get an AWS S3 documentation to get a list of eleven regions where S3 tables are available today.
Amazon S3 Metadata is announced during Re: Invent 2024-Has was generally left on January 27. It is the fastest and easst way to help you discover and understand your S3 data with an automated, flawlessly querized metado that is updated almost in real time. S3 metadata works with S3 objects. Brands help you Logally Group data for various reasons, for example to use the IAM principles to provide fine -grained access, specific filters based on brands management rules and selectively replicate data to another area. In regions where S3 metadata is available, you can capture and ask your own metadata, which are stored as objects. To reduce the costs associated with the object marks in the use of metadata S3, Amazon S3 reduced prices for marking S3 objects by 35 molo In all regions it is cheaper to use your own metadata.
AWS PI DAY 2025
Over the years AWS PI Day has introduced the main milestones in the cloud storage and data analysis. This year, the AWS PI Day virtual event will introduce panels designed for developers and technical decision staff, data engineers, AI/ml experts and IT leaders. Among the key most important are deep dives, live demonstrations and experts on all services and skills in this post Discusd.
Waiting for this event you will learn how to speed up your analytics and innovation AI. You will learn how you can use the S3 tables with the native support of Apache Iceberg Support and S3 metadata to create scalable data, which serves traditional analysts and emerging workload AI/ml. You will also discover the next generation Amazon Sagemaker, centers for all your data, analytics and AI to help your teams work together and build faster from the united AWS tools with access to all your third -party data data data data.
For those who want to stay in front of the latest cloud trends, AWS PI DAY 2025 is an event you can’t miss. When creating lake houses, AI training, creating generative AI applications, or optimizing analytical workloads, shared information will help you maximize the value of your data.
Tune in today and explore the latest cloud data innovation. Do not miss the opportunity to participate in AWS experts, partners and customers who shape the future of data, analysts and artificial intelligence.
If you missed a virtual event on March 14, you can visit the event page whenever you have all the content available on request!
– seb
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