Remove Big Data Skills Remove Data Collection Remove Kafka
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Top 12 Data Engineering Project Ideas [With Source Code]

Knowledge Hut

If you are struggling with Data Engineering projects for beginners, then Data Engineer Bootcamp is for you. Some simple beginner Data Engineer projects that might help you go forward professionally are provided below. Source Code: Stock and Twitter Data Extraction Using Python, Kafka, and Spark 2.

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Unlock Answers to the Top Questions- What is Big Data and what is Hadoop?

ProjectPro

Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization Image Credit: twitter.com There are hundreds of companies like Facebook, Twitter, and LinkedIn generating yottabytes of data.

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Top Big Data Hadoop Projects for Practice with Source Code

ProjectPro

Having multiple hadoop projects on your resume will help employers substantiate that you can learn any new big data skills and apply them to real life challenging problems instead of just listing a pile of hadoop certifications. Get started now on your big data journey. Implementing a Big Data project on AWS.

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

ProjectPro

Ace your big data interview by adding some unique and exciting Big Data projects to your portfolio. 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.

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How to Become a Big Data Engineer in 2023

ProjectPro

Big Data Engineers are professionals who handle large volumes of structured and unstructured data effectively. They are responsible for changing the design, development, and management of data pipelines while also managing the data sources for effective data collection.

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50 PySpark Interview Questions and Answers For 2023

ProjectPro

These DStreams allow developers to cache data in memory, which may be particularly handy if the data from a DStream is utilized several times. The cache() function or the persist() method with proper persistence settings can be used to cache data. You can learn a lot by utilizing PySpark for data intake processes.

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