Remove Data Engineer Remove Data Warehouse Remove Metadata
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Reflecting On The Past 6 Years Of Data Engineering

Data Engineering Podcast

In that time there have been a number of generational shifts in how data engineering is done. Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses.

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How to learn data engineering

Christophe Blefari

Learn data engineering, all the references ( credits ) This is a special edition of the Data News. But right now I'm in holidays finishing a hiking week in Corsica 🥾 So I wrote this special edition about: how to learn data engineering in 2024. Who are the data engineers?

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Eliminate Friction In Your Data Platform Through Unified Metadata Using OpenMetadata

Data Engineering Podcast

Summary A significant source of friction and wasted effort in building and integrating data management systems is the fragmentation of metadata across various tools. Missing data? Start trusting your data with Monte Carlo today! Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

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Data Engineering Weekly #186

Data Engineering Weekly

Try For Free → Conference Alert: Data Engineering for AI/ML This is a virtual conference at the intersection of Data and AI. Instead, you will see engineers, product managers, and founders talking about the data engineering practices of AI/ML. It is not a conference for the hype.

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Data Engineering Weekly #192

Data Engineering Weekly

The key benefits are Improved data quality, Enhanced data governance Increased security Cost efficiency [link] HomeToGo: How HomeToGo improved our Superset Monitoring Framework Apache Superset is the most popular open-source BI tool in the industry. “text-to-SQL” and “text-to-insight.”

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Announcing New Innovations for Data Warehouse, Data Lake, and Data Lakehouse in the Data Cloud 

Snowflake

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh.

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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. No more scripts, just SQL.

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