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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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Data News — Week 24.11

Christophe Blefari

With yato you give a folder with SQL queries and it guesses the DAG and runs the queries in the right order. BigQuery supports DELETE to delete partitions in a SQL query. Attributing Snowflake cost to whom it belongs — Fernando gives ideas about metadata management to attribute better Snowflake cost. This is Croissant.

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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! Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

Metadata 100
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How to get started with dbt

Christophe Blefari

dbt Core is an open-source framework that helps you organise data warehouse SQL transformation. dbt was born out of the analysis that more and more companies were switching from on-premise Hadoop data infrastructure to cloud data warehouses. This switch has been lead by modern data stack vision.

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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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Data Warehouse vs Data Lake vs Data Lakehouse: Definitions, Similarities, and Differences

Monte Carlo

Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

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Choosing the right Data Warehouse SQL Engine: Apache Hive LLAP vs Apache Impala

Cloudera

Some of the most powerful results come from combining complementary superpowers, and the “dynamic duo” of Apache Hive LLAP and Apache Impala, both included in Cloudera Data Warehouse , is further evidence of this. Both Impala and Hive can operate at an unprecedented and massive scale, with many petabytes of data.