Category: Data Engineering
-
Scaling Apache Iceberg Tables with AWS Lake Formation Hybrid Access Mode
Apache Iceberg tables combined with AWS Lake Formation’s hybrid access mode provide a powerful solution for enterprises managing large datasets. This approach allows organizations to use Lake Formation for read access while maintaining IAM policy-based permissions for write operations, offering fine-grained access control without disrupting existing workflows.
-
Streamlining Cross-Account Orchestration with Amazon MWAA
Learn how to orchestrate data workflows across multiple AWS accounts and regions using Amazon Managed Workflows for Apache Airflow (MWAA). This article covers implementing secure cross-account access, creating custom Airflow operators, and following best practices for distributed data processing and machine learning pipelines.
-
Building an Operational Data Store with AWS Purpose-Built Databases for Finance Applications
Discover how Amazon Finance Automation built a high-performance operational data store using AWS purpose-built databases. Learn how they combined DynamoDB, OpenSearch, and Neptune to handle hundreds of millions of daily financial transactions with millisecond latency while maintaining flexibility and data accuracy.
-
Unify Data Analytics: Integrating Amazon S3 Tables with SageMaker Lakehouse
Amazon SageMaker Lakehouse now integrates with Amazon S3 Tables, offering unified access to data across S3, Redshift warehouses, and other sources. This integration enables seamless analytics using preferred tools while maintaining security through fine-grained permissions, helping organizations derive insights from distributed data without duplication or complex connectors.
-
Accelerate Data to AI Innovation with Amazon SageMaker Unified Studio
AWS announces the general availability of Amazon SageMaker Unified Studio, bringing together analytics and AI capabilities in a single development environment. This integrated platform enables teams to discover data, collaborate on projects, and build advanced applications with built-in governance, dramatically reducing time-to-value for data-driven initiatives.
-
Cross-Account Data Collaboration with Amazon DataZone and AWS Analytics Tools
Amazon DataZone enables secure cross-account data collaboration for AWS services. This solution streamlines data sharing between producer and consumer accounts while maintaining governance. Learn how to set up, publish, and consume shared data assets across accounts using AWS Glue and Amazon Redshift.
-
Implementing Streaming Data Governance with Amazon DataZone and DSF on AWS
Discover how to implement comprehensive streaming data governance using Amazon DataZone and the Data Solutions Framework on AWS. Learn about custom asset types, authorization flows, and best practices for managing real-time data streams securely and efficiently.
-
Revolutionizing Data Engineering: How Gemini in BigQuery Transforms Data Management
Discover how Gemini in BigQuery is revolutionizing data engineering through automated schema management, enhanced data quality control, and sophisticated data generation capabilities. Learn practical implementations and best practices for modern data solutions.
-
Building Netflix’s Impression System: Powering Personalized Content Discovery at Scale
Explore Netflix’s innovative impression system that processes billions of user interactions daily to power personalized content discovery. Learn how this sophisticated architecture combines real-time processing with historical data analysis for enhanced user experiences.