Dataconomy

MAY 26, 2025

Big data engineers are essential in today’s data-driven landscape, transforming vast amounts of information into valuable insights. As businesses increasingly depend on big data to tailor their strategies and enhance decision-making, the role of these engineers becomes more crucial.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

Dataversity

MAY 16, 2025

Data products are proliferating in the enterprise, and the good news is that users are consuming data products at an accelerated rate, whether its an AI model, a BI interface, or an embedded dashboard on a website.

DataOps 52

DataOps Data Engineering Data Engineering Data Engineer 52

Alation

SEPTEMBER 28, 2022

What exactly is DataOps ? The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively.

DataOps 52

DataOps Data Pipeline Data Quality Analytics 52

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 Engineer 52

IBM Journey to AI blog

OCTOBER 22, 2024

This adaptability allows organizations to align their data integration efforts with distinct operational needs, enabling them to maximize the value of their data across diverse applications and workflows. With that, a strategy that empowers less technical users and accelerates time to value for specialized data teams is critical.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

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

APRIL 4, 2023

However, the race to the cloud has also created challenges for data users everywhere, including: Cloud migration is expensive, migrating sensitive data is risky, and navigating between on-prem sources is often confusing for users. To build effective data pipelines, they need context (or metadata) on every source.

DataOps 52

DataOps Data Engineering Data Engineering Data Engineer 52