Remove Data Engineering Remove Engineering Management Remove Serverless
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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning - AI

That’s where the new Amazon EMR Serverless application integration in Amazon SageMaker Studio can help. In this post, we demonstrate how to leverage the new EMR Serverless integration with SageMaker Studio to streamline your data processing and machine learning workflows.

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Ultimate Guide to Citus Con: An Event for Postgres, 2023 edition

The Citus Data

(on-demand talk, Citus open source user) 6 Citus engineering talks Citus & Patroni: The Key to Scalable and Fault-Tolerant PostgreSQL , by Alexander Kukushkin who is a principal engineer at Microsoft and lead engineer for Patroni.

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Introducing CDP Data Engineering: Purpose Built Tooling For Accelerating Data Pipelines

Cloudera

With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estimated rate of 50% year over year.

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The Good and the Bad of Snowflake Data Warehouse

Altexsoft

With Snowflake, multiple data workloads can scale independently from one another, serving well for data warehousing, data lakes , data science, data sharing, and data engineering. BTW, we have an engaging video explaining how data engineering works. Zero management.

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Organise your engineering teams around the work by reteaming

Abhishek Tiwari

This leads to endless meetings where engineering management get involved to discuss what's to be built, how to break up dependencies in manageable chunks and delegate them to various teams. Thirdly, let engineers themselves choose the delivery teams and organise them around the initiative.