With data warehousing, a common challenge is keeping analytics datasets up-to-date as source systems get changed. Full refreshes from source to destination are inefficient for real-time reporting.
This is where change data capture (CDC) comes in – to identify and process only incremental updates.
Combined with incremental etl vs elt azure (extract, load, transform), CDC enables efficient real-time data orchestration in Azure.
The Need for Change Data Capture
In any business, operational systems like ERPs, CRMs, and online applications continuously generate updates.
However, data warehouses traditionally rely on scheduled full loads, resulting in analytics lag. Periodic full extracts and transforms are expensive and time-consuming.
CDC provides granular visibility into data changes by capturing insert, update, and delete operations at the source.
With the CDC, data engineers only extract changed data records from sources. This incremental approach minimizes processing overhead while keeping reporting datasets current.
Azure offers managed CDC services for many data sources like SQL Server and Oracle databases. For unsupported sources, open-source tools like Debezium can be used to implement custom CDC.
Combining CDC with Incremental ELT
ELT (extract, load, transform) methodologies have gained popularity over traditional ETL. But incremental ELT supercharges CDC by only transforming changed data and appending it to existing warehouse tables.
For instance, CDC captures overnight sales data updates from an e-commerce database. These inserts and updates are extracted and directly loaded into the data warehouse.
Then Azure services like Azure Databricks transform the new rows to match the warehouse schema. The transformed rows are inserted or merged into the sales table.
This upsert-based process focuses transformations on incremental data. BI dashboards querying the sales table reflect near real-time figures.
CDC and incremental ELT together provide fresh analytics while minimizing total data processing.
Streamlining Change Tracking with Azure SQL Data Warehouse
Azure SQL Data Warehouse, Azure Synapse Analytics and Azure SQL Database provide in-built CDC functionality through temporal tables.
These special tables track row version history automatically, eliminating script maintenance.
Temporal tables have linked history and current tables. Inserts and updates get written to the current table. The previous row values get moved to the history table with metadata like the change date. This temporal metadata helps easily identify changed rows.
Azure data services incrementally extract the history table rows using this metadata. The changed data is loaded and transformed downstream. Native temporal support makes change tracking seamless.
Identifying Table Dependencies for Incremental Loads
In practice, upstream source data changes often trigger cascading updates to downstream dependent tables.
For holistic incremental processing, these inter-table dependencies need identification.
Azure Purview’s data lineage diagrams provide this by mapping upstream and downstream data sets. By analyzing lineage data in Purview, key dependency flows are uncovered to augment incremental ELT.
Instead of just changed rows, all linked child rows are also extracted and updated downstream.
Orchestrating Distributed CDC Pipelines
Production data sources tend to be distributed across environments. Coordinating change capture across multiple sources requires robust orchestration. Azure Data Factory provides a serverless CDC orchestration layer with distributed pipeline execution.
For example, Data Factory pipelines can connect to CDC streams from regional SQL Server and SAP HANA instances. Changed data is concurrently extracted in the region it occurs using EVENT HUBS before centralized aggregation. With Azure Logic Apps also, complex event workflows can be constructed.
Securing Sensitive Incremental Data in Motion
With real-time data flows, securing data movement is critical. Azure Data Factory allows encrypting CDC data extracts and setting up private network links for secure ingestion. Audit logs provide compliance tracking of incremental loads.
For stream processing, Azure Event Hubs can directly ingest source CDC events into encrypted event streams. Overall, Azure provides secure mechanisms to transport sensitive updating data.
Achieving Zero Downtime Updates
Mission-critical systems have zero tolerance for maintenance outages. With incremental ELT, new data can be ingested real-time into Azure Synapse tables using temporary staging tables. Queries access these tables initially.
Once data is processed, tables can be swapped atomically with the ALTER TABLE syntax to point queries to updated tables. This swap occurs instantaneously to eliminate downtime. Users access fresh data without interruption.
Handling Data Inconsistencies
CDC can sometimes capture updates in the wrong sequence, leading to data inconsistencies during transform. Azure Databricks has mechanisms like watermarking and transactions to handle out-of-order data.
Watermarking buffers incoming CDC streams before transforming further updates. Transaction atomicity ensures no partial commits occur if failures arise mid-transform. With these capabilities, data integrity is maintained.
The Scalable Foundation for Real-Time Analytics
Together CDC and incremental ELT enable scalable architectures where analytics consumption drives data processing.
Azure’s managed services make these patterns easier to implement compared to self-managed ELTLT. The future is real-time.
CDC and incremental ELT help unlock streaming analytics on data updates enterprise-wide.
