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End-to-End Data Pipelines: Hitting Home Runs in Data Strategy

Ascend.io

A star-studded baseball team is analogous to an optimized “end-to-end data pipeline” — both require strategy, precision, and skill to achieve success. Just as every play and position in baseball is key to a win, each component of a data pipeline is integral to effective data management.

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Serverless Data Pipelines On DataCoral

Data Engineering Podcast

Summary How much time do you spend maintaining your data pipeline? This was a fascinating conversation with someone who has spent his entire career working on simplifying complex data problems. Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams.

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Data Pipeline vs. ETL: Which Delivers More Value?

Ascend.io

In the modern world of data engineering, two concepts often find themselves in a semantic tug-of-war: data pipeline and ETL. Fast forward to the present day, and we now have data pipelines. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

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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. Try For Free → Conference Alert: Data Engineering for AI/ML This is a virtual conference at the intersection of Data and AI.

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Data Pipelines in the Healthcare Industry

DareData

One paper suggests that there is a need for a re-orientation of the healthcare industry to be more "patient-centric". Furthermore, clean and accessible data, along with data driven automations, can assist medical professionals in taking this patient-centric approach by freeing them from some time-consuming processes.

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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 #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. The article details how these building blocks are used to implement the JSON type, which provides support for dynamically changing data, high-performance storage, scalability, and tuning options.