Remove Blog Remove Data Pipeline Remove Pipeline-centric
article thumbnail

Introducing CDP Data Engineering: Purpose Built Tooling For Accelerating Data Pipelines

Cloudera

To tackle these challenges, we’re thrilled to announce CDP Data Engineering (DE) , the only cloud-native service purpose-built for enterprise data engineering teams. Native Apache Airflow and robust APIs for orchestrating and automating job scheduling and delivering complex data pipelines anywhere.

article thumbnail

Data Engineering Weekly #203

Data Engineering Weekly

With Astro, you can build, run, and observe your data pipelines in one place, ensuring your mission critical data is delivered on time. This blog captures the current state of Agent adoption, emerging software engineering roles, and the use case category.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Data Engineering Weekly #196

Data Engineering Weekly

The blog emphasizes the importance of starting with a clear client focus to avoid over-engineering and ensure user-centric development. link] Gunnar Morling: Revisiting the Outbox Pattern The blog is an excellent summary of the path we crossed with the outbox pattern and the challenges ahead.

article thumbnail

Delivering Modern Enterprise Data Engineering with Cloudera Data Engineering on Azure

Cloudera

After the launch of CDP Data Engineering (CDE) on AWS a few months ago, we are thrilled to announce that CDE, the only cloud-native service purpose built for enterprise data engineers, is now available on Microsoft Azure. . CDP data lifecycle integration and SDX security and governance. Key features of CDP Data Engineering.

article thumbnail

The Race For Data Quality in a Medallion Architecture

DataKitchen

When data reaches the Gold layer, it is highly curated and structured, offering a single version of the truth for decision-makers across the organization. We have also seen a fourth layer, the Platinum layer , in companies’ proposals that extend the Data pipeline to OneLake and Microsoft Fabric.

article thumbnail

Microsoft Fabric vs. Snowflake: Key Differences You Need to Know

Edureka

Snowflake is completely managed, but its main focus is on the data warehouse layer, and users need to integrate with other tools for BI, ML, or ETL. Ideal for: Business-centric workflows involving fabric Snowflake = environments with a lot of developers and data engineers 2.

BI 52
article thumbnail

Use Consistent And Up To Date Customer Profiles To Power Your Business With Segment Unify

Data Engineering Podcast

However, that's also something we're re-thinking with our warehouse-centric strategy. How does reverse ETL factor into the enrichment process for profile data? Contact Info Kevin LinkedIn Blog Hanhan LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?