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Data Engineering Weekly #161

Data Engineering Weekly

Editor’s Note: Chennai, India Meetup - March-08 Update We are thankful to Ideas2IT to host our first Data Hero’s meetup. There will be food, networking, and real-world talks around data engineering.

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Data Engineering Weekly #170

Data Engineering Weekly

link] LinkedIn: LakeChime - A Data Trigger Service for Modern Data Lakes LinkedIn points out two critical flaws in a partitioned approach to data management. The granularity of partition creation constrained data consumption.

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Cloud Native Data Orchestration For Machine Learning And Data Engineering With Flyte

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.

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Maintain Your Data Engineers' Sanity By Embracing Automation

Data Engineering Podcast

Summary Building and maintaining reliable data assets is the prime directive for data engineers. While it is easy to say, it is endlessly complex to implement, requiring data professionals to be experts in a wide range of disparate topics while designing and implementing complex topologies of information workflows.

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Top 10 Azure Data Engineer Job Opportunities in 2024 [Career Options]

Knowledge Hut

Data engineering is one of them. According to AnalytixLabs , the data science market is expected to be worth USD 230.80 All these numbers point to one thing–increased job roles and careers, especially when we talk about data engineering jobs in Azure, which are on the rise every year. Let’s get started.

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Data Engineering Trends With Aswin & Ananth

Data Engineering Weekly

Welcome to another insightful edition of Data Engineering Weekly. As we approach the end of 2023, it's an opportune time to reflect on the key trends and developments that have shaped the field of data engineering this year. In conclusion, 2023 has been a year of significant developments and shifts in data engineering.

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Charting A Path For Streaming Data To Fill Your Data Lake With Hudi

Data Engineering Podcast

Summary Data lake architectures have largely been biased toward batch processing workflows due to the volume of data that they are designed for. With more real-time requirements and the increasing use of streaming data there has been a struggle to merge fast, incremental updates with large, historical analysis.

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