AWS Big Data Blog

This post will explore how to look up the history of records and tables using Apache Iceberg, focusing on Slowly Changing Dimensions (SCD) Type-2. This method creates new records for each data change while preserving old ones, thus maintaining a full history. By the end, you’ll understand how to use Apache Iceberg to manage historical records effectively on a typical CDC architecture.

REA Group, a digital real estate business, uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and a data streaming platform called Hydro to efficiently share and access large amounts of data across multiple domains and services. This approach allows REA Group to maintain optimal performance and cost-efficiency while scaling to meet growing user demands. In this post, they share their approach to MSK cluster capacity planning.

Amazon SageMaker Lakehouse offers a unified solution for enterprise data access, combining data from warehouses and lakes. This post demonstrates how SageMaker Lakehouse integrates scattered data sources, enabling secure enterprise-wide access, and allowing teams to use their preferred tools for predicting and analyzing customer churn. The solution involves multiple data sources, including Amazon S3, Amazon Redshift, and AWS Glue Data Catalog, with AWS Lake Formation managing permissions.

AWS Glue 5.0 supports fine-grained access control (FGAC) based on your policies defined in AWS Lake Formation. FGAC enables you to granularly control access to your data lake resources at the table, column, and row levels. This post demonstrates how to enforce FGAC on AWS Glue 5.0 through Lake Formation permissions.

Open table formats are emerging in the rapidly evolving domain of big data management, fundamentally altering the landscape of data storage and analysis. In earlier posts, we discussed AWS Glue 5.0 for Apache Spark. In this post, we highlight notable updates on Iceberg, Hudi, and Delta Lake in AWS Glue 5.0.

Today, we are launching AWS Glue 5.0, a new version of AWS Glue that accelerates data integration workloads in AWS. AWS Glue 5.0 upgrades the Spark engines to Apache Spark 3.5.2 and Python 3.11, giving you newer Spark and Python releases so you can develop, run, and scale your data integration workloads and get insights faster. This post describes what’s new in AWS Glue 5.0, performance improvements, key highlights on Spark and related libraries, and how to get started on AWS Glue 5.0.

In this post, we will explore how to harness the power of Open source Apache Spark and configure a third-party engine to work with AWS Glue Iceberg REST Catalog. The post will include details on how to perform read/write data operations against Amazon S3 tables with AWS Lake Formation managing metadata and underlying data access using temporary credential vending.

Amazon SageMaker Unified Studio (preview) provides an integrated data and AI development environment within Amazon SageMaker. This post shows how you can build a low-code and no-code (LCNC) visual ETL flow that enables seamless data ingestion and transformation across multiple data sources.

AWS has introduced zero-ETL integration support from external applications to AWS Glue, simplifying data integration for organizations. This new feature allows for seamless replication of data from popular platforms like Salesforce, ServiceNow, and Zendesk into Amazon SageMaker Lakehouse and Amazon Redshift. This blog post demonstrates a use case involving ServiceNow data integration, outlining the process of setting up a connector, creating a zero-ETL integration, and verifying both initial data load and change data capture (CDC). It also highlights the advantages of using Apache Iceberg for data versioning and time travel capabilities within zero-ETL integrations.

In this post, we show how to connect to, govern, and run federated queries on data stored in Redshift, DynamoDB (Preview), and Snowflake (Preview). To query our data, we use Athena, which is seamlessly integrated with SageMaker Unified Studio. We use SageMaker Lakehouse to present data to end-users as federated catalogs, a new type of catalog object. Finally, we demonstrate how to use column-level security permissions in AWS Lake Formation to give analysts access to the data they need while restricting access to sensitive information.