Remove tags dbt-cloud
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Why Upgrade to dbt Cloud over dbt Core?

phData: Data Engineering

So you’ve heard all the talk around dbt , but now you’re working to determine if you should go with dbt Core or dbt Cloud and you’re wanting to know what advantages dbt Cloud has over the free dbt Core offering. However, dbt Cloud offers you much more than that.

Cloud 52
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Operational data lineage with dbt

Datakin

dbt is an amazing way to transform data within a data warehouse. If you use Datakin to observe your dbt models as they run, you can always know exactly where your datasets came from and how they were created. This is a quick tutorial that can show you how to observe dbt pipelines with Datakin. dbt/profiles.yml.

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Snowpark Offers Expanded Capabilities Including Fully Managed Containers, Native ML APIs, New Python Versions, External Access, Enhanced DevOps and More

Snowflake

Snowpark — Set of libraries and runtimes for secure deployment and processing of non-SQL code on the Snowflake Data Cloud. In this blog we’ll dive into the latest announcements on Snowpark client libraries and server side enhancements on warehouses.

Python 52
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DataOps: What Is It, Core Principles, and Tools For Implementation

phData: Data Engineering

You may need to build another data center or work with a cloud provider to have another set of infrastructure to failover to. The biggest gain with using Git over Subversion is that your developer’s branching and tagging can be separate from the central repository. This could be on-premises servers or cloud providers.

IT 52
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61 Data Observability Use Cases From Real Data Teams

Monte Carlo

In less than three years it has gone from an idea sketched out in a Barr Moses blog post to climbing the Gartner Hype Cycle for Emerging Technology. Fintech Checkout.com monitors 4,600+ data sets, supports 300+ active data platform users every day, and supports 1,200+ dbt models that run various transformations on a daily basis.

Data 52
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61 Data Observability Use Cases That Aren’t Totally Made Up

Monte Carlo

In less than three years it has gone from an idea sketched out in a Barr Moses blog post to climbing the Gartner Hype Cycle for Emerging Technology. Fintech Checkout.com monitors 4,600+ data sets, supports 300+ active data platform users every day, and supports 1,200+ dbt models that run various transformations on a daily basis.