The "Building Data Pipelines with Spark and StreamSets" document explores the challenges of data drift in modern data engineering and the solutions provided by StreamSets Data Collector running pipelines on Spark. Data drift refers to the unpredictable, unannounced, and unending mutation of data characteristics caused by system operations, maintenance, and modernization. This poses a significant challenge to data engineers who need to ensure consistency and accuracy in their data pipelines.
StreamSets Data Collector offers a solution to this problem by providing a platform for ingesting, analyzing, and storing data from various sources. It allows for the creation of robust data pipelines that can adapt to changes in data characteristics over time. Running these pipelines on Spark enables faster processing and analysis of large datasets, making it an effective tool for handling data drift.
The document outlines the evolution of data-in-motion, from traditional ETL processes to emerging data ingestion and analysis techniques. It emphasizes the importance of building flexible and scalable data pipelines that can accommodate changes in data sources, stores, and consumers.
Overall, "Building Data Pipelines with Spark and StreamSets" provides valuable insights into the challenges of data drift and the solutions offered by StreamSets Data Collector running on Spark. It serves as a comprehensive guide for data engineers looking to build robust and adaptable data pipelines in today's rapidly changing data landscape.
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