The SSIS packages are deployed to Azure-with the Azure-SSIS integration runtime (IR) in Azure Data Factory-to apply data transformation as a step in the ETL pipeline, before loading the transformed data into Azure SQL Database. It is serverless and can automatically provision and manage workers as needed. Extract, transform, and load (ETL) is the process of combining, cleaning, and normalizing data from different sources to get it ready for analytics, artificial intelligence (AI) and machine learning (ML) workloads. AWS Glue, one of the many AWS data integration services, consolidates major data integration capabilities into one place including data discovery, extract, transform, and load (ETL), cleansing, transforming, and centralized cataloging. In this example, the web application logs and custom telemetry are captured with Application Insights, sent to Azure Storage blobs, and then the ETL pipeline is created, scheduled, and managed using Azure Data Factory. Zero-ETL is a set of integrations that eliminates or minimizes the need to build ETL data pipelines. The example can help lead you to the ADAG content to make the right technology choices for your business. Like all the previous posts in this series, we'll work from a technology implementation seen directly in our customer engagements. Azure Data Architecture Guide – Blog #8: Data warehousing.Azure Data Architecture Guide – Blog #7: Intelligent applications.Azure Data Architecture Guide – Blog #6: Business intelligence.1 In this Extract, Transform, Load (ETL) process, AWS Glue is heavily involved in the transformation stage. Azure Data Architecture Guide – Blog #5: Clickstream analysis AWS Glue is a serverless data integration service that makes it easy for analytics users to discover, prepare, move, and integrate data from multiple sources.Azure Data Architecture Guide – Blog #4: Hybrid data architecture.ETL uses a set of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML). Azure Data Architecture Guide – Blog #3: Advanced analytics and deep learning Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse.Azure Data Architecture Guide – Blog #2: On-demand big data analytics.Azure Data Architecture Guide – Blog #1: Introduction.You can create and run an ETL job with a few clicks in the AWS Management Console. This is our ninth and final blog entry exploring the Azure Data Architecture Guide. The previous entries for this blog series are: AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. Part 1 of this multi-post series discusses design best practices for building scalable ETL (extract, transform, load) and ELT (extract, load, transform) data processing pipelines using both primary and short-lived Amazon Redshift clusters.
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