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Building an Optimized Data Pipeline on Azure Using Spark, Data Factory, Databricks, and Synapse Analytics

DZone

Data processing in the cloud has become increasingly popular due to its scalability, flexibility, and cost-effectiveness. This article will explore how these technologies can be used together to create an optimized data pipeline for data processing in the cloud.

Azure 246
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Generative AI in the Enterprise

O'Reilly

In enterprises, we’ve seen everything from wholesale adoption to policies that severely restrict or even forbid the use of generative AI. In enterprises, we’ve seen everything from wholesale adoption to policies that severely restrict or even forbid the use of generative AI. What’s the reality? Only 4% pointed to lower head counts.

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5 key areas for tech leaders to watch in 2020

O'Reilly

It’s also the data source for our annual usage study, which examines the most-used topics and the top search terms. [1]. This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. ML + AI are up, but passions have cooled.

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Egnyte Architecture: Lessons learned in building and scaling a multi petabyte content platform

High Scalability

Your Laptop has a filesystem used by hundreds of processes. There are a couple of downsides though in case you are looking to use it to support tens of thousands of users working on hundreds of millions of files simultaneously containing petabytes of data. Egnyte is a Secure Content Platform built specifically for businesses.