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Meet the new notebook in Amazon SageMaker
The new Amazon SageMaker notebook offers data and AI teams a high-performance, serverless programming environment for analytics and machine learning jobs. It combines the simplicity of an interactive, browser-based interface with the scalability of Amazon Athena for Apache Spark, enabling data engineers, analysts, and data scientists to perform SQL queries, execute Python code, process large-scale data jobs, and create visualizations — all within a single workspace.
Benefits
Start working with your data instantly with a fully managed, serverless notebook — no infrastructure setup required.
Accelerate analytics and machine learning model development with a built-in AI agent that understands your data environment and automatically generates execution plans, code, and end-to-end workflows.
Flexibly combine SQL queries, Python code, or natural language in a single, interactive workspace, eliminating the need to switch between different tools.
The integrated Athena Spark engine delivers high-performance results, automatically scaling from simple SQL queries to petabyte-scale data processing, so you can focus on your project work.
Features
Go from exploration to insights in minutes
Amazon SageMaker Data Agent is available in notebooks to help you build analytics and machine learning applications faster. Simply describe your objectives in natural language and the agent breaks down your request into manageable steps then uses context from your business data catalogs and metadata to generate custom code and step-by-step execution plans.
Get started quickly and scale with ease
The new notebook in Amazon SageMaker is serverless and includes the Amazon Athena for Apache Spark to provide a highly immersive and responsive experience. You can get started in seconds, without the need to pre-provision or tune data processing infrastructure. Scaling and provisioning are handled entirely by SageMaker to meet your workload demands.
Combine SQL, Python, and natural language in a single workspace
Go from data exploration and analysis using SQL, to complex data processing using Python, and natural language interaction in a single environment. Share variables and data across cells to execute your end-to-end data workflows without switching tools.
Turn your data into charts, directly in the notebook
Transform SQL query results and Python dataframes into visualizations without writing any code. Easily switch chart types and configure chart options in the notebook cell. Communicate findings in a visual, narrative format to enable quicker, more informed data-driven decisions.
Access data from multiple sources
Use your existing IAM role to work with your data in your Amazon S3 data lakes or Amazon S3 Tables accessible through AWS Glue Data Catalog. Connect to data sources such as Amazon Redshift, Snowflake and other 3rd party connectors.
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