Final Verdict APACHE SPARK VS HADOOP | INFOGRAPHIC

Final Verdict APACHE SPARK VS HADOOP | INFOGRAPHIC

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  MASTER THE BIGDATA STRATEGY WITH THE BEST USDSI® CERTIFICATIONS © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ® © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ® © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ® © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ® © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ® © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ® © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ® A Data Warehouse System is a digital system that stores and analyzes data from multiple sources. Over time, the Big Data landscape has been dominated by two popular frameworks: Apache Spark and Hadoop. Let us explore the final differentiator! HADOOP Big Data experts use Hadoop as a distributed processing framework that utilizes the MapReduce paradigm. It breaks down large datasets into smaller chunks and processes them in parallel across a cluster of machines. APACHE SPARK HADOOP APACHE SPARK Apache Spark is a fast and general-purpose cluster computing system. It extends the MapReduce model by enabling in-memory data processing, which significantly ramps up iterative computations. USE CASES OF HADOOP BENEFITS OF CONSIDERING HADOOP Fast Flexible Scalable Cost- effective High throughput Resilient to failure ADVANTAGES OF GOING WITH SPARK Powerful Reusability Dynamic in nature Advanced analytics Real-time stream processing Multilingual support LIMITATIONS OF PREFERRING HADOOP Support only batch processing Issues with small files Low Security Iterative Processing Higher Vulnerability DISADVANTAGE OF PICKING SPARK Not suitable for multi-user environment No automatic optimization process Few algorithms No file management process Small files issues Source: Appinventiv.com Source: Appinventiv.com Source: Appinventiv.com Batch processing of large datasets Data warehousing ETL (Extract- Transform-Load) © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ® © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ® © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ® USE CASES OF SPARK GLOBAL GIANTS TRUST … CHOOSING THE RIGHT TOOL Real-time data processing Exceptional ML capabilities Interactive data analysis Current Customer(s) Market Share (Est.) Ranking 6,877 12.82% #3 MARKET SHARE APACHE HADOOP APACHE SPARK 10,553 4.70% #4 Source: 6sense.com PARAMETERS APACHE SPARK APACHE HADOOP Speed About 100 times faster than Hadoop Faster than non-distributed systems Data model Use an in-memory model to transfer data between RDD Use HDFS to read, process, and store large amounts of information Scala Java Created in Batch, real-time, iterative, interactive, graph Batch Processing style Store data in memory Caching data is not supported Caching Faster for iterative computations Slower for iterative computations Performance Basic security features Strong security features Security More challenging Easily scalable by adding nodes Scalability More expensive Affordable Cost ® ADOBE AOL FACEBOOK HULU LINKEDIN SPOTIFY ALIBABA AMAZON GROUP ON MY FITNESS PAL SHOPIFY YAHOO Source: Appinventiv.com The choice between Hadoop and Spark depends on the specific requirements of your project. In some cases, it may even make sense to use both frameworks together, leveraging the strengths of each. THE LAST LAP SPARK is great for … • Iterative algorithms, real-time analysis, ML algorithms, large graph processing • Large data analysis, analysis where time factor is not crucial, step-by-step data processing of large datasets, perfect for managing large amounts of stored data HADOOP is great for … © Copyright 2025. United States Data Science Institute (USDSI ). All Rights Reserved. ®