Spark vs Hadoop: Which Big Data Framework to Choose?
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The document compares the big data frameworks Apache Spark and Hadoop based on factors such as architecture, ease of use, data processing capabilities, fault tolerance, compatibility, performance, security, cost-effectiveness, and future growth potential. Hadoop features a more robust architecture with better fault tolerance, while Spark offers faster data processing and user-friendly APIs. However, Hadoop has higher security measures and is predicted to grow at a faster rate than Spark in the coming years.
Spark vs Hadoop: Which Big Data Framework to Choose?
- 1. Which Big Data framework to choose? Spark vs Hadoop:
- 2. Choosing between Spark and Hadoop big data frameworks can be a tough task for an Entrepreneur or developer. But not when you consider these factors for comparison.
- 3. Architecture Hadoop, unlike Spark, has two prime elements YARN and HDFS to manage different data storage and resource allocation processes. This makes Hadoop’s architecture capable of delivering better solutions that that of Spark. Hadoop Spark 1 0
- 4. Ease of Use In comparison to Apache Hadoop, Spark offers more user-friendly APIs and interactive mode facilities to mobile app development companies. This eases the process of learning and using Spark. Hadoop Spark 10 https://appinventiv.com/
- 5. Data Processing While Hadoop offer only batch processing, Spark avails the opportunity to perform different types of data processing, including interactive, graph, iterative, and batch processing. Hadoop Spark 10
- 6. Hadoop Spark 1 0 Fault Tolerance In case of Spark, one has to begin from scratch if a process fails in between. But, in the case of Hadoop, one can continue from the point of crash. Something that proves that Hadoop offers higher levels of fault tolerance.
- 7. Hadoop Spark 1 1 Compatibility When it comes to compatibility, both Hadoop and Spark big data frameworks provides higher grades of compatibility. They act as standalone applications, as well as, work together by supporting each others' data sources and file formats.
- 8. Hadoop Spark 10 Performance Spark, when compared to Hadoop, runs 10 times faster on disk and 100 times in-memory. Also, it is able to manage 100TB of data in 3 times faster than Apache Hadoop.
- 9. Hadoop Spark 1 0 Security Unlike Spark, Hadoop encourages Kerberos authentication, third-party authentication, conventional file permissions, and access control lists. Something that makes it easier for Hadoop big data framework to deliver higher security.
- 10. Hadoop Spark 1 0 Cost-Effectiveness When compared to Hadoop, Apache Spark requires more memory on disk and has less developers in the market. This makes mobile application development using Big data framework expensive.
- 11. Hadoop Spark 1 0 Future Possibilities Though both Hadoop and Spark are used by big companies, the former is expected to grow with a CAGR of 65.6% in between 2018 and 2025, when compared to Spark that will flourish with a CAGR of 33.9% only.
- 12. For a detailed information about this topic, please refer to this blog: Spark vs Hadoop: Which Big Data Framework Will Elevate Your Business?https://appinventiv.com/blog/spark-vs-hadoop-big-data-frameworks/
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