Remove Data Engineering Remove Open Source Remove Scalability
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Elevating Productivity: Cloudera Data Engineering Brings External IDE Connectivity to Apache Spark

Cloudera

As advanced analytics and AI continue to drive enterprise strategy, leaders are tasked with building flexible, resilient data pipelines that accelerate trusted insights. A New Level of Productivity with Remote Access The new Cloudera Data Engineering 1.23 Why Cloudera Data Engineering?

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A Recap of the Data Engineering Open Forum at Netflix

Netflix Tech

A summary of sessions at the first Data Engineering Open Forum at Netflix on April 18th, 2024 The Data Engineering Open Forum at Netflix on April 18th, 2024. Netflix is not the only place where data engineers are solving challenging problems with creative solutions.

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Scalable Entity Resolution With Python and ML

John Snow Labs

This makes it hard to combine them together, especially with growing data volumes. Unfortunately, unharmonized data is not fit for use in customer analytics, risk and compliance and data engineers and scientists end up building some sort of rule or heuristic based system to manage it.

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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO

The core of their problem is applying AI technology to the data they already have, whether in the cloud, on their premises, or more likely both. Imagine that you’re a data engineer. You build your model, but the history and context of the data you used are lost, so there is no way to trace your model back to the source.

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Inferencing holds the clues to AI puzzles

CIO

As with many data-hungry workloads, the instinct is to offload LLM applications into a public cloud, whose strengths include speedy time-to-market and scalability. Inferencing funneled through RAG must be efficient, scalable, and optimized to make GenAI applications useful. Inferencing and… Sherlock Holmes???

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What is Data Engineering: Explaining Data Pipeline, Data Warehouse, and Data Engineer Role

Altexsoft

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

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The 10 most in-demand IT jobs in finance

CIO

In the finance industry, software engineers are often tasked with assisting in the technical front-end strategy, writing code, contributing to open-source projects, and helping the company deliver customer-facing services. Data engineer.