May 2022 Summaries

Data integration is a process that combines data from disparate source systems into a single unified view, which can be accomplished through manual integration, data virtualization, application integration, or moving data to a unified destination. The benefits of data integration include providing a single source of truth, leveraging technology designed for analyzing big data, transforming data in a single location, improving security, and reducing operational risks. ETL (extract, transform, load) and ELT (extract, load, transform) are the two most popular data integration techniques. Data integration can be used to combine data from multiple source systems into databases, data warehouses, data lakes, or data lakehouses, each with its own strengths and weaknesses depending on the use case. Selecting the best data integration tool involves considering factors such as ETL versus ELT, open-source versus closed-source, cloud-hosted versus on-premises, and number of source connectors.

Change Data Capture (CDC) is a software architecture that detects and captures changes made to data in a database, sending these changes to downstream processes or systems. It records INSERT, UPDATE, and DELETE transactions applied to a table, allowing for efficient data replication and near real-time analytics. CDC can be more efficient than traditional batch processing methods, as it only considers data that has changed since the last replication. Log-based CDC is a popular implementation method, using database transaction logs to identify changes with minimal impact on source databases. Airbyte is an open-source data integration platform that supports log-based CDC from various sources and can help manage the heavy lifting involved in implementing CDC in a production data stack.

Data ingestion is a crucial step in the ETL/ELT process, as it connects tools and databases to data warehouses. Following best practices from the start ensures high-quality data for transformations and analyses. These best practices include choosing an ingestion tool, documenting sources, orchestration, testing, and monitoring. Documenting best practices forces a set structure, preventing sloppy work and ensuring consistency across the team. Comparing data ingestion tools using a scorecard with must-have's, nice-to-have's, and dealbreakers helps in deciding on the right tool for the team. Keeping a record of data sources and their connectors is essential to avoid confusion about raw data origins. Maintaining a separate database for raw data ensures its protection and serves as a backup for accidental deletions or modifications. Running syncs and models synchronously ensures accurate validation of data and allows for more precise testing. Creating alerting at the data source level helps in identifying issues early on, making them easier to fix. Following these best practices from the beginning stages of a data stack sets the team up for success and prevents future problems.