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Data News — Week 23.09

Christophe Blefari

After last week question about your consideration of a paying subscription I got a few feedbacks and it helped me a lot realise how you see the newsletter and what it means for a you. I'll try to think about it in the following weeks to understand where I go for the third year of the newsletter and the blog.

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Putting Apache Spark Into Action with Jean Georges Perrin - Episode 60

Data Engineering Podcast

Summary Apache Spark is a popular and widely used tool for a variety of data oriented projects. He also discusses what you need to know to get it deployed and keep it running in a production environment and how it fits into the overall data ecosystem. For someone building on top of Spark what are the main software design paradigms?

Scala 100
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Building Real Time Applications On Streaming Data With Eventador

Data Engineering Podcast

Eventador is a managed platform designed to let you focus on using the data that you collect, without worrying about how to make it reliable. What are some of the design changes in the different layers that are necessary to take advantage of the real time capabilities? How does it fit into an application architecture?

Building 100
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Making Sense of Real-Time Analytics on Streaming Data, Part 1: The Landscape

Rockset

Kafka or Kinesis ? This blog series will help demystify streaming data, and more specifically, provide engineering leaders a guide for incorporating streaming data into their analytics pipelines. Stream processing or an OLAP database? Open source or fully managed? We’re going to start with a basic question: what is streaming data?

Kafka 52
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A quick tour of data distribution technologies by David Hope

Scott Logic

In this blog I’ll try and illuminate the differences between the various types of solution on the market and show why you shouldn’t be afraid to have more than one solution in use. I’ll discuss this more in an upcoming blog but all I’ll say for now is the lines are often blurred and many of the technologies support multiple use cases.

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DataOps: What Is It, Core Principles, and Tools For Implementation

phData: Data Engineering

Source Control Management Infrastructure as Code Build/Deploy Strategy Continuous Integration and Delivery (CI/CD) Data Quality and Validation Workflow Management Data Modeling Monitoring and Logging Business Continuity So How Do I Build a DataOps Strategy? Want to Save This eBook for Later? No problem! What’s Data Strategy? Why is that?

IT 52
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20 Solved End-to-End Big Data Projects with Source Code

ProjectPro

This blog lists over 20 big data projects you can work on to showcase your big data skills and gain hands-on experience in big data tools and technologies. Ace your big data interview by adding some unique and exciting Big Data projects to your portfolio. None of this would have been possible without the application of big data.