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Sqoop vs. Flume Battle of the Hadoop ETL tools

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Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment.

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Top 10 Data Engineering Tools You Must Learn in 2025

ProjectPro

Features of Apache Spark Allows Real-Time Stream Processing- Spark can handle and analyze data stored in Hadoop clusters and change data in real time using Spark Streaming. Faster and Mor Efficient processing- Spark apps can run up to 100 times faster in memory and ten times faster in Hadoop clusters.

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30+ Data Engineering Projects for Beginners in 2025

ProjectPro

In 2024, the data engineering job market is flourishing, with roles like database administrators and architects projected to grow by 8% and salaries averaging $153,000 annually in the US (as per Glassdoor ). These trends underscore the growing demand and significance of data engineering in driving innovation across industries.

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SQL for Data Engineering: Success Blueprint for Data Engineers

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According to the 8,786 data professionals participating in Stack Overflow's survey, SQL is the most commonly-used language in data science. Despite the buzz surrounding NoSQL , Hadoop , and other big data technologies, SQL remains the most dominant language for data operations among all tech companies.

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Data Lake vs Data Warehouse - Working Together in the Cloud

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Is Snowflake a data lake or data warehouse? Is Hadoop a data lake or data warehouse? This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data from data warehouses is queried using SQL.

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How to Learn Spark: A Comprehensive Guide

ProjectPro

Before diving into the how, let's briefly discuss why learning Apache Spark is worthwhile: High Performance: Spark offers in-memory processing, which makes it significantly faster than traditional disk-based data processing systems like Hadoop MapReduce. Master concepts like shuffling, data partitioning, and lineage.

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Spark vs Hive - What's the Difference

ProjectPro

The datasets are usually present in Hadoop Distributed File Systems and other databases integrated with the platform. Hive is built on top of Hadoop and provides the measures to read, write, and manage the data. Spark SQL, for instance, enables structured data processing with SQL.

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