Remove Data Warehouse Remove Events Remove Metadata
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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. Start trusting your data with Monte Carlo today! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads?

Metadata 100
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Keeping Your Data Warehouse In Order With DataForm

Data Engineering Podcast

Summary Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council.

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Optimizing data warehouse storage

Netflix Tech

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. Some of the optimizations are prerequisites for a high-performance data warehouse.

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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.

Metadata 100
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Databricks, Snowflake and the future

Christophe Blefari

Snowflake was founded in 2012 around its data warehouse product, which is still its core offering, and Databricks was founded in 2013 from academia with Spark co-creator researchers, becoming Apache Spark in 2014. It adds metadata, read, write and transactions that allow you to treat a Parquet file as a table.

Metadata 147
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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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Functional Data Engineering — a modern paradigm for batch data processing

Maxime Beauchemin

When functions are “pure” — meaning they do not have side-effects — they can be written, tested, reasoned-about and debugged in isolation, without the need to understand external context or history of events surrounding its execution. But how do we model this in a functional data warehouse without mutating data?