Best Practices in DataOps: How to Create Robust, Automated Data Pipelines

DataOps is an emerging set of practices, processes, and technologies for building and automating data pipelines to meet business needs quickly. It speeds cycle times while reducing defects. 

Diving into DataOps: The Underbelly of Modern Data Pipelines

This article presents a framework for understanding DataOps processes, technologies, and data flows.

DataOps: Industrializing Data and Analytics

This report will examine the role of DataOps in BI, analytics, and data management.

The Ultimate Guide to DataOps: Product Evaluation and Selection Criteria

This report drills into the technology and tools required to build robust, automated data pipelines with DataOps. 

DataOps: More Than DevOps for Data Pipelines

Converging development and operations for data pipelines and analytic modeling is the essence of DataOps.


Shop Talk Webinar - DataOps: How to Get Started

DataOps uses automated testing, orchestration, collaborative development, containerization, and monitoring to continuously accelerate output and improve data quality. It builds upon the software engineering principals espoused in DevOps, but in a manner more specifically tailored for data. How do...

DataOps for Machine Learning: A Guide for Data Engineers and Data Scientists

DataOps helps data engineers and scientists get machine learning right. Improve results with modular development, flexible execution and rigorous testing.

Self-Driving Data: A Critical Enabler for DataOps

Self-driving data has become a reality. Just like autonomous vehicles automate driving, self-driving data automates data using DataOps techniques.

Shakeeb Akhter: DataOps in Action - Implementing Agile and Automation

In this episode, Wayne Eckerson and Shakeeb Akhter dive into DataOps. They discuss what DataOps is, the goals and principles of DataOps, and reasons to...

What is a DataOps Engineer?

This article examines the emerging role of the DataOps engineer, a dedicated technical staff member responsible for the data development lifecycle.


DataOps Deep Dive: Different Approaches to the DataOps Platform

DataOps is a new practice that is gaining adoption as organizations recognize the importance of transitioning from artisanal to industrial-scale processes for developing and running data pipelines. DataOps enables organizations to evolve from slow, one-off development efforts to a team-based...

Data Pipeline Design Patterns

Design patterns have proven valuable in many endeavors. Can data pipeline design patterns help to break the data engineering logjam?

Data Engineering Coming of Age

Data engineering is one of the hottest and most difficult jobs to fill in the field of analytics. Breadth and depth of required skills limits the number...

Trends in DataOps: Bringing Scale and Rigor to Data and Analytics

This report surveys data and analytics professionals and provides an overview of the trends in DataOps, including adoption rates, benefits, challenges,...

Why and How Data Engineers Realize DataOps Benefits with Apache Kafka Streaming

Learn how to achieve the DataOps objectives of improved efficiency and data quality by migrating to a streaming architecture based on Apache Kafka.