DataOps applies rigor to the development and execution of data pipelines. Borrowing principles from DevOps, agile, lean, and total quality management, DataOps helps data development evolve from an artisanal craft subject to delays and errors into an industrial process that accelerates delivery and improves data quality. Ultimately, DataOps helps data teams satisfy their internal customers: it fosters greater self-service while fulfilling the promise of “faster, better, cheaper.”

Key Takeaways

• Most of the programs that data pipeline developers build fall into four categories: big data, data science, self-service analytics, and data warehousing.
• Tests accelerate development and minimize operational delays. Finding issues before internal customers do is critically important for a development team.
• DataOps tools foster collaboration that is critical to scale development, increase capacity and output, reduce errors, and accelerate time to market.
• Data teams need to create “design patterns” that define the components they will use when creating data pipelines.
• There are five categories of DataOps products on the market today: 1) all-in-one tools, 2) orchestration tools, 3) component tools, 4) case-specific tools, and 5) open-source tools

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