On-Demand Webinar · 60 min

DataOps & the Cloud: A Match Made in Heaven

Capgemini's Deepak Juneja, VP of Global Data Management and Data Fabric Practice Leader, joins DataKitchen's Chris Bergh on cloud DataOps: why a cloud data platform still needs it, why a single seamless system is hard to assemble out of disparate cloud tools, and what companies that got there did. Recorded March 2021; updated August 2026.

Presented by Chris Bergh

What you'll learn 6 points
  • Capgemini's argument for why cloud environments need DataOps: cross-platform orchestration and platform independence, collaboration between data consumers and data providers, data democratization and self-service, and simplifying data operations through automation and lean process.
  • Capgemini puts the business value of DataOps in concrete terms: real-time visibility of data and data processing, trustable data with fewer quality issues, hybrid environments provisioned in hours, data and insights delivered in days rather than weeks, and new products and services launched 30 percent faster or more.
  • AWS, Azure and GCP ship a powerful collection of data tools with no defined process for using them as a system. The result is data integration without process integration, and it is overwhelming to design a solution from DataOps principles on top of it. What is missing is a DataOps superstructure over the tools.
  • DevOps and workflow tools fall short at DataOps for six reasons: no end-to-end meta-orchestrated production pipeline, no environment pipeline, the complex team and data center coordination that data analytics needs, no common system or vocabulary, no process measurement to drive behavior change, and the fact that the work is not DevOps CI/CD but continuous self-service sandboxes, meta-orchestration, integration and deployment, and testing and monitoring.
  • A top 10 global health company ran two separate analytic groups: a New Jersey team on a large Spark cluster with a best-of-breed toolchain and high-value drug development data, and an Azure cloud team on ADLS and Databricks with research data sets. Drift between the two schemas caused unexpected delays, errors, mistrust of the data on both sides, and conflict between the teams. Schema and meta-orchestration, system-wide testing and monitoring, and versioning resolved it.
  • Production monitoring in this model runs five kinds of test: traditional data quality, statistical process control, location balance, historic balance, and business-based tests. They run automatically, in production, on top of the entire toolchain, sending alerts and keeping a history.

Slides

42 slides

Questions from this session

Why do cloud data platforms need DataOps?

A cloud platform gives you a powerful collection of data tools but no defined process for running them as a single system, so you get data integration without process integration. DataOps supplies the missing layer: cross-platform orchestration and platform independence, collaboration between data providers and consumers, self-service with governance, and automation that keeps operations lean.

What capabilities does a DataOps platform need?

Capgemini names six. CI/CD enablement through data and analytics pipeline automation, test automation with self-provisioning of test data, ML-assisted data mapping quality assessment, on-demand environment provisioning across hybrid environments, automated data lifecycle management and governance, and automated monitoring of data drift, data processing, and data quality.

Why is DevOps CI/CD not enough for data analytics?

Data analytics needs continuous self-service sandboxes, continuous meta-orchestration across many tools, continuous integration and deployment, and continuous testing and monitoring. It also needs an environment pipeline, coordination across teams and data centers, a common vocabulary, and process measurement, none of which a software CI/CD pipeline supplies.

What kinds of tests catch data errors in production?

Five kinds: traditional data quality tests, statistical process control, location balance tests, historic balance tests, and business-based tests. Run them automatically in production, on top of the whole toolchain rather than inside one tool, and keep a history of results so trends are visible. The payoff is fewer errors, more customer trust in the data, and less downtime.

What is a self-service data sandbox?

A sandbox is a prepared data analytic environment that a central IT or data group gives to a business team for short-term use, then monitors, governs, and takes back. A top 5 US bank built them for more than 1,000 non-IT users so that legal rules on data usage and lifetime were followed and usage was tracked. Ideas that prove out earn the right to be reimplemented centrally with recipes and tests.

How does an enterprise start a DataOps program?

Capgemini describes five stages: strategize by identifying business goals and defining the target state, engage by picking a use case that can demonstrate value, implement by running a proof of concept or pilot with a partner and platform, expand by converting the pilot to full-scale delivery and standing up a Center of Excellence, and scale up by consolidating the platform and establishing an enterprise adoption framework.

Where to go next