In this article, we will delve into the process of effectively managing historical record lookups and implementing Slowly Changing Dimensions (SCD) Type-2 utilizing Apache Iceberg. This strategy involves creating new records for each modification while retaining previous entries, thereby ensuring a comprehensive historical record. By the conclusion of this discussion, you will grasp how to leverage Apache Iceberg to adeptly handle historical records within a standard Change Data Capture (CDC) framework.
Additionally, we’ll explore how the REA Group approaches capacity planning for their Amazon MSK clusters. This digital real estate company employs Amazon Managed Streaming for Apache Kafka along with a data streaming platform known as Hydro to streamline data sharing and access across various domains. Their methodology not only boosts performance but also maintains cost-effectiveness, thus meeting the rising demands from users.
Furthermore, Amazon SageMaker Lakehouse is transforming enterprise data accessibility by merging data from various sources, including Amazon S3 and Amazon Redshift. This integration allows secure access across the organization, enabling teams to utilize their preferred tools for customer churn predictions and analyses. For more insights into this topic, check out another blog post here.
Moreover, AWS Glue 5.0 introduces fine-grained access control, allowing for a precise management of access to data lake resources at various levels—table, column, and even row. This functionality, integrated with AWS Lake Formation, ensures a robust governance structure over your data assets.
On the horizon, open table format libraries like Iceberg, Hudi, and Delta Lake are reshaping data management techniques in AWS Glue 5.0. The recent release of AWS Glue 5.0 accelerates data integration workloads by upgrading to Apache Spark 3.5.2 and Python 3.11, providing a more efficient development environment for users.
A key focus of our discussion will also be on configuring a third-party engine to work with AWS Glue Iceberg REST Catalog for reading and writing data to Amazon S3 tables. This integration, managed through AWS Lake Formation for metadata and access control, streamlines data operations significantly.
Lastly, Amazon SageMaker Unified Studio is set to enhance ETL processes by offering a low-code and no-code environment for building visual data flows. This innovative approach simplifies the ingestion and transformation of data across multiple sources.
For those interested in further exploring these advancements, you can refer to this excellent resource.
Amazon IXD – VGT2 is located at 6401 E Howdy Wells Ave, Las Vegas, NV 89115.
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