Efficient Change Data Capture (CDC) on Databricks Delta Tables with Spark

In today’s data-driven applications, organizations face a critical challenge: ensuring near-real-time data aggregation and accuracy for display on dashboards. As businesses integrate larger and more complex datasets from various sources, including streaming data from Kafka streams, they encounter intricate challenges that expose the limitations of traditional methods for tracking data updates. One primary issue is the large volume of data organizations deal with today. Traditional methods like periodic batch processing struggle to keep pace with the speed at which data is generated, ingested, and modified. This leads to delays in data availability, hindering the ability to meet SLAs. Furthermore, as datasets expand, pinpointing changes becomes a Herculean task. Traditional approaches that involve comparing entire datasets or running exhaustive queries are not only time-consuming but also resource-intensive. This inefficiency can cause delays in data processing and analysis, affecting an organization’s agility and responsiveness. To address these challenges, organizations are increasingly turning to advanced data management solutions like Databricks Delta, offering features such as ACID transactions, schema enforcement, time-travel capabilities, and Change Data Capture (CDC). These features ensure data integrity, simplify tracking of data changes, and provide historical snapshots for smarter analysis. <a href="https://medium.com/@muqtadahussainm/efficient-change-data-capture-cdc-on-databricks-delta-tables-with-spark-7d665a6ff5d6">Read More</a>