DataOps, short for Data Operations, represents the seamless orchestration of people, processes, and technology to enhance the quality and reduce the cycle time of data analytics. At the heart of this approach is data versioning, a critical practice that ensures data integrity and traceability by keeping a historical record of data changes over time. In the realm of Apache Iceberg Lakehouses, data versioning plays a pivotal role in facilitating reliable and scalable analytics, enabling teams to manage and analyze vast datasets more efficiently.

This blog post aims to be a comprehensive resource, gathering a wealth of content related to DataOps in the context of Apache Iceberg Lakehouses. We will explore various facets of DataOps, emphasizing the transformative impact of data versioning on data management and analytics, and provide a curated selection of resources to guide you through the intricacies of implementing these practices effectively.

Blogs

• What is Nessie, Catalog Versioning and Git-for-Data?

• What is DataOps? Automating Data Management on the Apache Iceberg Lakehouse

• What is Lakehouse Management?: Git-for-Data, Automated Apache Iceberg Table Maintenance and more

• Git for Data with Dremio’s Lakehouse Catalog: Easily Ensure Data Quality in Your Data Lakehouse

• Data Lakehouse Versioning Comparison: (Nessie, Apache Iceberg, LakeFS)

• Dealing with Data Incidents Using the Rollback Feature in Apache Iceberg

• Multi-Table Transactions on the Lakehouse – Enabled by Dremio Arctic

• Managing Data as Code with Dremio Arctic: Support Machine Learning Experimentation in Your Data Lakehouse

Videos

• Video: Catalog Level Versioning Demo
• Video: Using dbt with Dremio Cloud
• Video: Using dbt with Dremio Software
• Video: What is Nessie Catalog Versioning
• Video: Tour of Dremio's Lakehouse Management Features
• Video: Where Data Lakehouse and DataOps Meet

Podcasts

• Podcast: Next Gen Data Pipelines with dbt & Dremio
• Podcast: Simplify Lakehouse Operations with Zero Copy Environments and Multi-Table Transactions
• Podcast: Catalog Versioning and Table Optimization
• Podcast: Versioning in the Data Lakehouse
• Podcast: ML Experimentation and Reproducability
• Podcast: Enabling Data Mesh with Dremio's Lakehouse Management Features
• Podcast: What is DataOps?
• Podcast: The Power of Nessie Catalogs

Hopefully, these articles will give you a new, in-depth appreciation for DataOps for Apache Iceberg Lakehouses. If you haven't tried a data lakehouse hand-on try out this tutorial that will show you the lakehouse workflow from database to dashboard.