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Data Pipeline- Definition, Architecture, Examples, and Use Cases

ProjectPro

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. As data is expanding exponentially, organizations struggle to harness digital information's power for different business use cases.

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Forge Your Career Path with Best Data Engineering Certifications

ProjectPro

With so many data engineering certifications available , choosing the right one can be a daunting task. There are over 133K data engineer job openings in the US, but how will you stand out in such a crowded job market? The answer is- by earning professional data engineering certifications! Don’t worry!

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15 ETL Project Ideas for Practice in 2023

ProjectPro

The big data analytics market is expected to grow at a CAGR of 13.2 This indicates that more businesses will adopt the tools and methodologies useful in big data analytics, including implementing the ETL pipeline. Let us now understand why the ETL pipelines hold such great value in Data Science and Analytics.

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20+ Data Engineering Projects for Beginners with Source Code

ProjectPro

Most of us have observed that data scientist is usually labeled the hottest job of the 21st century, but is it the only most desirable job? For beginners or peeps who are utterly new to the data industry, Data Scientist is likely to be the first job title they come across, and the perks of being one usually make them go crazy.

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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. Table of Contents What is a Big Data Project?

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The Rise of Streaming Data and the Modern Real-Time Data Stack

Rockset

Not Just Modern, But Real Time The modern data stack emerged a decade ago, a direct response to the shortcomings of big data. Companies that undertook big data projects ran head-long into the high cost, rigidity and complexity of managing complex on-premises data stacks. The inevitable next stage?