Announcement
Big Data Upserts, Deletes, and Incremental Processing
Explore how to manage changes efficiently in large datasets using techniques like upserts, deletes, and incremental processing for modern data lakes and data warehouses.
Project Introduction
Summary
This project introduces robust solutions and data formats designed to enable upserts, deletes, and efficient incremental processing on large datasets stored in distributed file systems, transforming static data lakes into dynamic data platforms.
Problem Solved
Traditional big data storage systems like HDFS are append-only, making it difficult and inefficient to perform updates and deletions or consume only changes, which is crucial for GDPR compliance, data correction, and building low-latency data pipelines.
Core Features
Upsert and Delete Capabilities
Provides efficient methods to update existing records or insert new ones directly into large data files, overcoming the immutability challenge.
Incremental Data Processing
Allows consumers to easily pull only the data changes (inserts, updates, deletes) that occurred since their last read, enabling real-time data pipelines.
Tech Stack
使用场景
This technology is applicable in various scenarios where data in a data lake or warehouse needs to be changed or consumed incrementally.
Change Data Capture (CDC) Ingestion
Details
Applying Change Data Capture (CDC) streams from databases to a data lake, ensuring the lake reflects the latest state of source systems, including updates and deletes.
User Value
Maintains a continuously updated, consistent view of operational data in the data lake, facilitating real-time analytics.
Data Warehouse ETL/ELT
Details
Updating dimensions and facts in a data warehouse built on a data lake, such as correcting historical records or processing late-arriving data.
User Value
Ensures data warehouse accuracy and enables flexible data correction and processing workflows without full data reloads.
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