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ETL vs ELT in 2022: Do they matter?

Analytics Vidhya

Introduction Data is ubiquitous in our modern life. Obtaining, structuring, and analyzing these data into new, relevant information is crucial in today’s world. Since contextual data exposes popular patterns and trends, we have arrived at the stage where businesses take data-driven decisions to […].

ETL 349
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Data engineer

Dataconomy

Data engineers are the unsung heroes of the data-driven world, laying the essential groundwork that allows organizations to leverage their data for enhanced decision-making and strategic insights. What is a data engineer?

professionals

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Future trends in ETL

Dataconomy

The acronym ETL—Extract, Transform, Load—has long been the linchpin of modern data management, orchestrating the movement and manipulation of data across systems and databases. This methodology has been pivotal in data warehousing, setting the stage for analysis and informed decision-making.

ETL 195
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What Is a Lakebase?

databricks

Deeply integrated with the lakehouse, Lakebase simplifies operational data workflows. It eliminates fragile ETL pipelines and complex infrastructure, enabling teams to move faster and deliver intelligent applications on a unified data platform In this blog, we propose a new architecture for OLTP databases called a lakebase.

Database 213
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Introducing Agent Bricks: Auto-Optimized Agents Using Your Data

databricks

Agent Bricks is optimized for common industry use cases, including structured information extraction, reliable knowledge assistance, custom text transformation, and orchestrated multi-agent systems. We auto-optimize over the knobs, gain confidence that you are on the most optimized settings.

Analytics 331
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Mosaic AI Announcements at Data + AI Summit 2025

databricks

Just provide a high-level description of the agent’s task and connect your enterprise data — Agent Bricks handles the rest. Agent Bricks is optimized for common industry use cases, including structured information extraction, reliable knowledge assistance, custom text transformation, and building multi-agent systems.

AI 277
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Serverless High Volume ETL data processing on Code Engine

IBM Data Science in Practice

By Santhosh Kumar Neerumalla , Niels Korschinsky & Christian Hoeboer Introduction This blogpost describes how to manage and orchestrate high volume Extract-Transform-Load (ETL) loads using a serverless process based on Code Engine. The source data is unstructured JSON, while the target is a structured, relational database.

ETL 100