Alation

AUGUST 3, 2021

DataOps and DevOps are two distinctly different pursuits. But where DevOps focuses on product development, DataOps aims to reduce the time from data need to data success. At its best, DataOps shortens the cycle time for analytics and aligns with business goals. What is DataOps? What is DevOps?

DataOps 59

DataOps Data Pipeline Data Analyst Analytics 59

IBM Journey to AI blog

SEPTEMBER 5, 2024

Everything is data—digital messages, emails, customer information, contracts, presentations, sensor data—virtually anything humans interact with can be converted into data, analyzed for insights or transformed into a product. Managing this level of oversight requires adept handling of large volumes of data.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

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.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

IBM Journey to AI blog

AUGUST 12, 2024

It helps companies streamline and automate the end-to-end ML lifecycle, which includes data collection, model creation (built on data sources from the software development lifecycle), model deployment, model orchestration, health monitoring and data governance processes.

Big Data 106

Big Data Big Data ML ML 106

Alation

DECEMBER 13, 2022

People come to the data catalog to find trusted data, understand it, and use it wisely. Today a modern catalog hosts a wide range of users (like business leaders, data scientists and engineers) and supports an even wider set of use cases (like data governance , self-service , and cloud migration ).

DataOps 52

DataOps Data Governance Data Silos Data Scientist 52

Precisely

JULY 15, 2024

Data domain teams have a better understanding of the data and their unique use cases, making them better positioned to enhance the value of their data and make it available for data teams. With this approach, demands on each team are more manageable, and analysts can quickly get the data they need.

Data Governance 72

Data Governance DataOps Data Silos Data Pipeline 72