Remove Data Governance Remove Data Pipeline Remove Demo
article thumbnail

Low Friction Data Governance With Immuta

Data Engineering Podcast

Summary Data governance is a term that encompasses a wide range of responsibilities, both technical and process oriented. One of the more complex aspects is that of access control to the data assets that an organization is responsible for managing. What is data governance? How is the Immuta platform architected?

article thumbnail

A Guide to Data Pipelines (And How to Design One From Scratch)

Striim

Data pipelines are the backbone of your business’s data architecture. Implementing a robust and scalable pipeline ensures you can effectively manage, analyze, and organize your growing data. We’ll answer the question, “What are data pipelines?” Table of Contents What are Data Pipelines?

Insiders

Sign Up for our Newsletter

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

article thumbnail

Elevating Productivity: Cloudera Data Engineering Brings External IDE Connectivity to Apache Spark

Cloudera

As advanced analytics and AI continue to drive enterprise strategy, leaders are tasked with building flexible, resilient data pipelines that accelerate trusted insights. A New Level of Productivity with Remote Access The new Cloudera Data Engineering 1.23 Jupyter, PyCharm, and VS Code).

article thumbnail

Self Service is Simply Efficient – Cloudera DataFlow Designer GA announcement

Cloudera

Data leaders will be able to simplify and accelerate the development and deployment of data pipelines, saving time and money by enabling true self service. It is no secret that data leaders are under immense pressure. For more information or to see a demo, go to the DataFlow Product page.

article thumbnail

Snowflake and Databricks Summit Recap: Why Shovels Were Hotter Than Gold

Monte Carlo

Of course, there were plenty of flashy generative demos (like Shutterstock AI)—not to mention a couple of live snafus—but these were merely a light palette cleanser between keynotes as these events hurdled toward the real star of the shows—data enablement. Look, we’ve all been told there’s data in the LLM hills.

article thumbnail

Revisit The Fundamental Principles Of Working With Data To Avoid Getting Caught In The Hype Cycle

Data Engineering Podcast

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.

Data Lake 100
article thumbnail

Data Engineering Weekly #192

Data Engineering Weekly

The learning goes back to the fundamentals of pipeline design principles. Regularly review if pipelines are still required. Minimize the data used in pipelines, aka do incremental data pipeline design. Optimize pipeline schedules. Filter data effectively to make sure the query uses partition pruning.