As enterprises democratize data consumption and invest in advanced analytics, they need ever-higher volumes of complex, fast-moving data. To meet this demand, data teams need to accelerate the development of data pipelines, automate their execution, and continuously validate the quality of the output. Along the way, they need to master the lifecycle of data, from ingestion and transformation to testing, orchestration, and monitoring.

Tools span the following categories, each of which plays a part in the data lifecycle. Vendors address these categories with both pure-play products and suites.

1. Data integration automates the design, development, management, monitoring, and adaptation of pipelines.

2. Transformation manipulates data in creative ways to address new use cases such as data-driven applications.

3. DataOps optimizes pipelines with continuous integration and continuous delivery (CI/CD), testing, and monitoring.

4. Data observability tracks and helps optimize pipeline performance and data quality.

5. Orchestration integrates pipelines with applications to kick off operational tasks.

6. Reverse ETL helps operationalize data by integrating data warehouses with applications.

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