Alation

MARCH 22, 2022

Data people face a challenge. They must put high-quality data into the hands of users as efficiently as possible. DataOps has emerged as an exciting solution. As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. Accenture’s DataOps Leap Ahead.

DataOps 52

DataOps Data Pipeline Data Engineering Data Engineering 52

Dataversity

SEPTEMBER 8, 2023

Data Management has never been more critical than today. As AI grows more prominent, data initiatives are more important than ever. As the digital age propels us forward, the need for robust DataOps strategies becomes evident. Data-focused practitioners have a unique relationship with data.

DataOps 64

DataOps AI AI Cloud Data 64

Alation

JUNE 21, 2022

The audience grew to include data scientists (who were even more scarce and expensive) and their supporting resources (e.g., ML and DataOps teams). After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that. data pipelines) to support.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

Alation

JANUARY 25, 2022

On the policy front, a feature like Policy Center empowers users to enforce and track policies at scale; this ensures that people use data compliantly, and organizations are prepared for compliance audits. Automated Data Orchestration (AKA DataOps). Automated data orchestration interweaves data with connecting processes.

DataOps 52

DataOps SQL ML ML 52

Alation

DECEMBER 13, 2022

Cloud migration. For example, the researching buyer may seek a catalog that scores 6 for governance, 10 for self-service, 4 for cloud data migration, and 2 for DataOps (let’s call this a {6, 10, 4, 2} profile). How Does This Impact the Buyer’s Journey? Yet another user may prefer a {0, 6, 6, 8} profile.

DataOps 52

DataOps Data Governance Data Silos Data Scientist 52

Alation

APRIL 4, 2023

Building data pipelines is challenging, and complex requirements (as well as the separation of many sources) leads to a lack of trust. Troubleshooting data issues , for an exploding number of disjointed systems and tools, breaks self-service for data users and creates gaps in visibility for dataOps.

DataOps 52

DataOps Data Engineering Data Engineering Data Engineer 52

Alation

AUGUST 11, 2022

DataOps sprung up to connect data sources to data consumers. The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. And now with some of these cloud data warehouses becoming such behemoths, everything is getting centralized again.

Data Warehouse 52

Data Warehouse Data Engineering Data Engineering Data Engineer 52