Category: Learning Levels

Amazon Redshift now provides the ability to incrementally refresh your materialized views on data lake tables including open file and table formats such as Apache Iceberg. In this post, we will show you step-by-step what operations are supported on both open file formats and transactional data lake tables to enable incremental refresh of the materialized view.

In this post, we discuss how to implement fine-grained access control in EMR Serverless using Lake Formation. With this integration, organizations can achieve better scalability, flexibility, and cost-efficiency in their data operations, ultimately driving more value from their data assets.

In this post, we discuss how Volkswagen Autoeuropa used Amazon DataZone to build a data marketplace based on data mesh architecture to accelerate their digital transformation. The data mesh, built on Amazon DataZone, simplified data access, improved data quality, and established governance at scale to power analytics, reporting, AI, and machine learning (ML) use cases. As a result, the data solution offers benefits such as faster access to data, expeditious decision making, accelerated time to value for use cases, and enhanced data governance.

Amazon DataZone now launched authentication support through the  Amazon Athena JDBC driver, allowing data users to seamlessly query their subscribed data lake assets via popular business intelligence (BI) and analytics tools like Tableau, Power BI, Excel, SQL Workbench, DBeaver, and more. This integration empowers data users to access and analyze governed data within Amazon DataZone using familiar tools, boosting both productivity and flexibility.

This is the final part of a three-part series where we show how to build a data lake on AWS using a modern data architecture. This post shows how to process data with Amazon Redshift Spectrum and create the gold (consumption) layer.

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze your data using standard SQL and your existing business intelligence (BI) tools. Tens of thousands of customers today rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it […]

The AWS Glue Jobs API is a robust interface that allows data engineers and developers to programmatically manage and run ETL jobs. To improve customer experience with the AWS Glue Jobs API, we added a new property describing the job mode corresponding to script, visual, or notebook. In this post, we explore how the updated AWS Glue Jobs API works in depth and demonstrate the new experience with the updated API.

This post explores how BMW implemented AWS Lake Formation’s fine-grained access control (FGAC) in the Cloud Data Hub and how this saves them up to 25% on compute and storage costs. By using AWS Lake Formation fine-grained access control capabilities, BMW has transparently implemented finer data access management within the Cloud Data Hub. The integration of Lake Formation has enabled data stewards to scope and grant granular access to specific subsets of data, reducing costly data duplication.

Amazon Redshift now offers enhanced query performance with optimizations such as Enhanced Histograms for Selectivity Estimation in the absence of fresh statistics by relying on metadata statistics gathered during ingestion. In this post, we cover new performance optimizations in Redshift data warehouse query processing and how elastic histogram statistics help enhance selectivity estimation and the overall quality of query plans for Amazon Redshift data warehouse queries in the absence of fresh table statistics.

This post is co-written with Matt Vogt from Immuta.  Organizations are looking for products that let them spend less time managing data and more time on core business functions. Data security is one of the key functions in managing a data warehouse. With Immuta integration with Amazon Redshift, user and data security operations are managed […]