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Top 12 Data Engineering Project Ideas [With Source Code]

Knowledge Hut

Best Data Science certifications online or offline are available to assist you in establishing a solid foundation for every end-to-end data engineering project. What are Data Engineering Projects? You should be able to identify potential weak spots in data pipelines and construct robust solutions to withstand them.

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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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Top Big Data Hadoop Projects for Practice with Source Code

ProjectPro

But when you browse through hadoop developer job postings, you become a little worried as most of the big data hadoop job descriptions require some kind of experience working on projects related to Hadoop. Hadoop projects for beginners are simply the best thing to do to learn the implementation of big data technologies like Hadoop.

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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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50 PySpark Interview Questions and Answers For 2023

ProjectPro

Here’s an example showing how to utilize the distinct() and dropDuplicates() methods- First, we need to create a sample dataframe. Cluster mode should be utilized for deployment if the client computers are not near the cluster. Client mode can be utilized for deployment if the client computer is located within the cluster.

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100+ Big Data Interview Questions and Answers 2023

ProjectPro

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Steps for Data preparation.