Remove Data Pipeline Remove Data Workflow Remove Engineering Remove Metadata
article thumbnail

Bringing The Power Of The DataHub Real-Time Metadata Graph To Everyone At Acryl Data

Data Engineering Podcast

Summary The binding element of all data work is the metadata graph that is generated by all of the workflows that produce the assets used by teams across the organization. The DataHub project was created as a way to bring order to the scale of LinkedIn’s data needs. How is the governance of DataHub being managed?

Metadata 100
article thumbnail

An Exploration Of What Data Automation Can Provide To Data Engineers And Ascend's Journey To Make It A Reality

Data Engineering Podcast

Summary The dream of every engineer is to automate all of their tasks. For data engineers, this is a monumental undertaking. Orchestration engines are one step in that direction, but they are not a complete solution. Atlan is the metadata hub for your data ecosystem.

Insiders

Sign Up for our Newsletter

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

article thumbnail

Making Sense Of The Technical And Organizational Considerations Of Data Contracts

Data Engineering Podcast

In this episode Abe Gong brings his experiences with the Great Expectations project and community to discuss the technical and organizational considerations involved in implementing these constraints to your data workflows. Atlan is the metadata hub for your data ecosystem. Struggling with broken pipelines?

Metadata 130
article thumbnail

Effective Pandas Patterns For Data Engineering

Data Engineering Podcast

Summary Pandas is a powerful tool for cleaning, transforming, manipulating, or enriching data, among many other potential uses. As a result it has become a standard tool for data engineers for a wide range of applications. You can observe your pipelines with built in metadata search and column level lineage.

article thumbnail

What Is Data Pipeline Automation?

Ascend.io

As a result, stakeholders limit their reliance on data, making decisions based on gut instinct rather than facts. To shift this harmful approach, companies need to make fundamental changes to their data engineering function and start running at speed and with agility. In fact, it’s been a recurring theme in software engineering.

article thumbnail

What Is Data Pipeline Automation?

Ascend.io

As a result, stakeholders limit their reliance on data, making decisions based on gut instinct rather than facts. To shift this harmful approach, companies need to make fundamental changes to their data engineering function and start running at speed and with agility. In fact, it’s been a recurring theme in software engineering.

article thumbnail

Addressing The Challenges Of Component Integration In Data Platform Architectures

Data Engineering Podcast

In this episode Tobias Macey shares his thoughts on the challenges that he is facing as he prepares to build the next set of architectural layers for his data platform to enable a larger audience to start accessing the data being managed by his team. Developing event-driven pipelines is going to be a lot easier - Meet Functions!