Tag: ETL
-
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.
-
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.
-
How Open Universities Australia Reduced ETL Costs Using AWS Cloud Services
Discover how Open Universities Australia revolutionized their data infrastructure by transitioning from costly third-party ETL tools to AWS services, achieving significant cost savings and improved efficiency in just 5 months.
-
Amazon Q Data Integration: Enhanced DataFrame Support and Context-Aware ETL Development
Discover how Amazon Q data integration has evolved with DataFrame support and context-aware development, revolutionizing ETL workflows. Learn about its enhanced capabilities, multiple data source support, and seamless integration with AWS services.
-
Implementing Write-Audit-Publish Pattern with Apache Iceberg and AWS Glue Data Quality
Explore how to implement the Write-Audit-Publish pattern using Apache Iceberg and AWS Glue Data Quality for robust data validation. Learn about efficient data quality management strategies and their practical applications in modern data architectures.
-
Streamlining Spark Debugging: AWS Glue Introduces Generative AI Troubleshooting Feature
AWS Glue introduces a game-changing generative AI troubleshooting feature for Apache Spark applications. This innovative solution automates root cause analysis and provides actionable recommendations, transforming hours of debugging into minutes.
-
Optimize ETL Workflows with Amazon Redshift Data API’s Persistent Sessions Feature
Discover how Amazon Redshift’s Data API session reuse capability revolutionizes ETL workflows by enabling persistent database sessions, optimizing connection management, and streamlining multi-step data transformation processes.
-
Amazon MWAA Introduces Micro Environments: Cost-Effective Apache Airflow Solution
Discover Amazon MWAA’s new mw1.micro environment class, a cost-effective solution for Apache Airflow that offers essential features with optimized resources. Perfect for development, testing, and small production workloads while maintaining core functionalities.