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SQL and Data Integration: ETL and ELT

KDnuggets

In this article, we will discuss use cases and methods for using ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes along with SQL to integrate data from various sources.

SQL 129
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Eliminate The Overhead In Your Data Integration With The Open Source dlt Library

Data Engineering Podcast

Summary Cloud data warehouses and the introduction of the ELT paradigm has led to the creation of multiple options for flexible data integration, with a roughly equal distribution of commercial and open source options. It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products.

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Enabling Seamless Cloud Migration and Real-Time Data Integration for a Nonprofit Educational Healthcare Organization with Striim

Striim

With Striim’s real-time data integration solution, the institution successfully transitioned to a cloud infrastructure, maintaining seamless operations and paving the way for future advancements. However, while the SIS migration was a significant step forward, the institution’s on-premise SQL Server systems remained vital.

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No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically

DataKitchen

No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically As a data engineer, ensuring data quality is both essential and overwhelming. Writing SQL, Python, or YAML-based rules should not be a prerequisite for their involvement.

SQL 73
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Data News — Week 25.02

Christophe Blefari

The future of data querying with Natural Language — What are all the architecture block needed to make natural language query working with data (esp. Hard data integration problems — As always Max describes the best way the reality. when you have a semantic layer).

Data 130
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Streaming Data Integration Without The Code at Equalum

Data Engineering Podcast

Summary The first stage of every good pipeline is to perform data integration. With the increasing pace of change and the need for up to date analytics the need to integrate that data in near real time is growing. There are a number of projects and platforms on the market that target data integration.

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Do Away With Data Integration Through A Dataware Architecture With Cinchy

Data Engineering Podcast

Summary The reason that so much time and energy is spent on data integration is because of how our applications are designed. By making the software be the owner of the data that it generates, we have to go through the trouble of extracting the information to then be used elsewhere. No more scripts, just SQL.