Deploying efficient DataOps in Snowpark
DataOps helps data teams reduce development times, increase data quality, and maximize the business value of data by bringing more rigor to the development and management of data pipelines. It also ensures that the data is clean, accurate, and up-to-date in a streamlined environment with data governance. Data engineering introduces the processes and capabilities required to effectively develop, manage, and deploy data engineering pipelines. The following diagram highlights the DataOps approach:
Figure 4.6 – DataOps process
The DataOps process focuses on bringing agile development to data engineering pipelines using an iterative development, testing, and deployment process in loops. It also includes continuous integration and continuous deployment (CI/CD) for data, schema changes, and the data versioning and automation of data models and artifacts. This section will show an example of a data engineering pipeline...
