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Data Engineering Weekly #182

Data Engineering Weekly

Adopting LLM in SQL-centric workflow is particularly interesting since companies increasingly try text-2-SQL to boost data usage. Pipeline breakpoint feature. The blog highlights the 2024 Sigmod paper Understanding the Performance Implications of the Design Principles in Storage-Disaggregated Databases.

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A Comprehensive Overview of Microsoft Fabric & Its Use Cases

RandomTrees

With One Lake serving as a primary multi-cloud repository, Fabric is designed with an open, lake-centric architecture. Mirroring (a data replication capability) : Access and manage any database or warehouse from Fabric without switching database clients; Mirroring will be available for Azure Cosmos DB, Azure SQL DB, Snowflake, and Mongo DB.

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Data Engineering Weekly #186

Data Engineering Weekly

Take Astro (the fully managed Airflow solution) for a test drive today and unlock a suite of features designed to simplify, optimize, and scale your data pipelines. The author writes an overview of the performance implication of disaggregated systems compared to traditional monolithic databases.

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Data Engineering Weekly #196

Data Engineering Weekly

The blog emphasizes the importance of starting with a clear client focus to avoid over-engineering and ensure user-centric development. impactdatasummit.com Thumbtack: What we learned building an ML infrastructure team at Thumbtack Thumbtack shares valuable insights from building its ML infrastructure team.

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RAG vs Fine Tuning: How to Choose the Right Method

Monte Carlo

Retrieval augmented generation (RAG) is an architecture framework introduced by Meta in 2020 that connects your large language model (LLM) to a curated, dynamic database. Data retrieval: Based on the query, the RAG system searches the database to find relevant data. A RAG flow in Databricks can be visualized like this.

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Data Engineering Weekly #161

Data Engineering Weekly

2) Why High-Quality Data Products Beats Complexity in Building LLM Apps - Ananth Packildurai I will walk through the evolution of model-centric to data-centric AI and how data products and DPLM (Data Product Lifecycle Management) systems are vital for an organization's system.

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Revolutionizing Build Analytics: How to enhance build processes with ThoughtSpot

ThoughtSpot

It aims to explain how we transformed our development practices with a data-centric approach and offers recommendations to help your teams address similar challenges in your software development lifecycle. Step 3: Implementing a data pipeline To automate the data collection and processing, we integrated a Jenkins job that runs hourly.