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What Is DataOps?

Operationalizing data integration for constant change and continuous delivery

Defining Your Data Universe with DataOps

The audacious, ambitious goal of teasing order out of the expanding chaos of modern data architectures requires a different approach to data integration. For decades, the automation of business analytics relied on predictable, well-defined data sets used in predictable, well-defined ways. But today’s data is messy. It’s unpredictable. Data producers and consumers find it painful to stay in sync. What is DataOps?

DataOps helps you tease order and discipline out of the chaos and solve the big challenges to turning data into business value. It is a set of practices and technologies that operationalize data management and integration to ensure resiliency and agility in the face of constant change.

Gartner Report: Introducing DataOps into Your Data Management Discipline
Operate For Continuous Data And Constant Change
A driver opts for sustainable energy after receiving a fuel optimization alert
A pharmacist catches a medication conflict thanks to shared urgent care data
A scientist cuts new medication research time in half by provisioning data as needed
StreamSets CEO Girish Pancha Speaking At DataOps Summit

Manage Data Drift with DataOps

The challenge to the provisioning of continuous data is the unexpected, unannounced, and unending changes to data that constantly disrupt dataflow. That’s data drift, and it’s the reason why, sometimes when you go fast, things break. But when you take your time, you fall behind.  

To manage data drift, you need to ensure continuous data flows by automatically identifying and handling data drift. DataOps operationalizes data management and integration, turning data chaos into a continuous, reliable flow of data to the people and systems that turn it into value. 

DataOps: A Paradigm Shift for Modern Data Integration

Why DataOps? Why Now?

Power Real-time Applications

You need data now, not later.

Modern analytics, data science, AI, machine learning…your analysts, data scientists and business innovators are ready to change the world. If you can’t deliver the data they need, faster and with confidence, they’ll find a way around you. (They probably already have.)

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Integration To Data Lakes And Data Warehouses

You depend on data that you can’t control.

When data came from carefully controlled systems, you could plan for change. Data fueling your business today comes from a wide range of internal and external sources. You have to detect and respond to change by design to keep up.

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Adopt Cloud Data Platforms

The next new technology just arrived.

Data warehouses held steady for 30 years. Hadoop had a decade-long run. The shift to the cloud and the rise of data science and machine learning has further accelerated new innovations in the data ecosystem. How do you enable your team to explore the possibilities of what’s next? 

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3 Key Principles of DataOps

Design for Change

DataOps technologies are used to build systems that are resilient to change, and enable self-service for those who know the business and data needs best. 

Watch: DataOps–A Paradigm Shift for Data Integration
Design For Change And Handle Data Drift

Bring DevOps to Data

DataOps practices and technologies enable automation and monitoring across the full lifecycle of productivity from design to deployment and operations. 

Eckerson Group: The Ultimate Guide to DataOps Product Evaluation and Selection Criteria
Bring DevOps To Data And Operated For Continuous Data

Embrace Emergent Design

DataOps technologies reveal data infrastructure, data structure, and semantics as they unfold instead of practicing design from the top down. 

Explore Customer Stories
Gain Visibility Into Emergent Design

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