title
Hadoop vs Spark | Which One to Choose? | Hadoop Training | Spark Training | Edureka

description
🔥 Edureka Apache Spark Training: https://www.edureka.co/apache-spark-scala-certification-training 🔥 Edureka Hadoop Training: https://www.edureka.co/big-data-hadoop-training-certification This Edureka Hadoop vs Spark video will help you to understand the differences between Hadoop and Spark. We will be comparing them on various parameters. We will be taking a broader look at: 1. Introduction to Hadoop 2. Introduction to Apache Spark 3. Spark vs Hadoop - Performance Ease of Use Cost Data Processing Fault tolerance Security 4. Hadoop Use-cases 5. Spark Use-cases --------------------Edureka Big Data Training and Certifications------------------------ 🔵 Edureka Hadoop Training: http://bit.ly/2YBlw29 🔵 Edureka Spark Training: http://bit.ly/2PeHvc9 🔵 Edureka Kafka Training: http://bit.ly/34e7Riy 🔵 Edureka Cassandra Training: http://bit.ly/2E9AK54 🔵 Edureka Talend Training: http://bit.ly/2YzYIjg 🔵 Edureka Hadoop Administration Training: http://bit.ly/2YE8Nf9 Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Hadoop playlist here: https://goo.gl/hzUO0m & Spark Playlist here: https://goo.gl/AgXjeC - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 40 hours of assignment and 30 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you: 1. Master the concepts of HDFS and MapReduce framework 2. Understand Hadoop 2.x Architecture 3. Setup Hadoop Cluster and write Complex MapReduce programs 4. Learn data loading techniques using Sqoop and Flume 5. Perform data analytics using Pig, Hive and YARN 6. Implement HBase and MapReduce integration 7. Implement Advanced Usage and Indexing 8. Schedule jobs using Oozie 9. Implement best practices for Hadoop development 10. Work on a real life Project on Big Data Analytics 11. Understand Spark and its Ecosystem 12. Learn how to work in RDD in Spark - - - - - - - - - - - - - - Who should go for this course? If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career: 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building successful career in Big Data - - - - - - - - - - - - - - Why Learn Hadoop? Big Data! A Worldwide Problem? According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. - - - - - - - - - - - - - - Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! For more information, Please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free). Customer Review: Michael Harkins, System Architect, Hortonworks says: “The courses are top rate. The best part is live instruction, with playback. But my favourite feature is viewing a previous class. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! ~ This is the killer education app... I've take two courses, and I'm taking two more.”

detail
{'title': 'Hadoop vs Spark | Which One to Choose? | Hadoop Training | Spark Training | Edureka', 'heatmap': [{'end': 76.906, 'start': 54.282, 'weight': 0.735}, {'end': 179.755, 'start': 89.136, 'weight': 0.717}, {'end': 309.102, 'start': 287.919, 'weight': 0.797}, {'end': 598.463, 'start': 584.322, 'weight': 0.76}], 'summary': "Compares hadoop and apache spark, emphasizing hadoop's data storage and processing across clusters, and spark's fast and general-purpose cluster computing, highlighting their strengths in real-time analytics, stream processing, and batch processing for big data analysis and business benefits.", 'chapters': [{'end': 132.371, 'segs': [{'end': 88.568, 'src': 'heatmap', 'start': 54.282, 'weight': 0, 'content': [{'end': 59.509, 'text': 'But first, it is important to get an overview about what is Hadoop and what is Apache Spark.', 'start': 54.282, 'duration': 5.227}, {'end': 61.953, 'text': 'So let me just tell you a little bit about Hadoop.', 'start': 60.03, 'duration': 1.923}, {'end': 68.744, 'text': 'Hadoop is a framework to store and process large sets of data across computer clusters,', 'start': 62.543, 'duration': 6.201}, {'end': 76.906, 'text': 'and Hadoop can scale from single computer system up to thousands of commodity systems that offer local storage and compute power.', 'start': 68.744, 'duration': 8.162}, {'end': 82.547, 'text': 'And Hadoop is composed of modules that work together to create the entire Hadoop framework.', 'start': 77.566, 'duration': 4.981}, {'end': 88.568, 'text': 'These are some of the components that we have in the entire Hadoop framework or the Hadoop ecosystem.', 'start': 82.967, 'duration': 5.601}], 'summary': 'Hadoop is a framework for processing and storing large data sets across computer clusters, scaling from single systems to thousands of commodity systems.', 'duration': 66.18, 'max_score': 54.282, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C854282.jpg'}], 'start': 0.049, 'title': 'Hadoop vs spark comparison', 'summary': 'Compares hadoop and apache spark, two prominent big data frameworks used for analyzing large datasets, with hadoop storing and processing data across clusters and spark offering fast and general-purpose cluster computing, both aiming to provide competitive advantages and business benefits.', 'chapters': [{'end': 132.371, 'start': 0.049, 'title': 'Hadoop vs spark comparison', 'summary': 'Compares hadoop and apache spark, two prominent big data frameworks used for analyzing large datasets, uncovering hidden patterns, and deriving valuable business insights, with hadoop being a framework to store and process large sets of data across computer clusters and spark being a fast and general-purpose cluster computing system, with both frameworks offering various components and tools for big data processing and analysis, aiming to provide organizations with competitive advantages and business benefits.', 'duration': 132.322, 'highlights': ['Hadoop is a framework to store and process large sets of data across computer clusters, scaling from single computer system up to thousands of commodity systems, and composed of modules like HDFS, YARN, Apache Hive, Pig, NoSQL databases, Apache Spark, Apache Storm, Flume, and Scoop. Highlights the key components and modules of the Hadoop framework, showcasing its scalability and variety of tools for big data processing and analysis.', "Apache Spark is a fast and general-purpose cluster computing system that can run up to 100 times faster than Hadoop's MapReduce for large-scale data processing, and it offers modules like Spark SQL, Spark Streaming, MLlib, and GraphX for various data processing and analysis tasks. Emphasizes the speed and versatility of Apache Spark compared to Hadoop's MapReduce, along with its modules for different data processing and analysis tasks.", 'Both Hadoop and Spark aim to provide organizations with competitive advantages over rival organizations and other business benefits by analyzing large datasets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful business information. Highlights the common objective of both frameworks to empower organizations with valuable business insights and competitive advantages through big data analysis.']}], 'duration': 132.322, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C849.jpg', 'highlights': ["Apache Spark is a fast and general-purpose cluster computing system, running up to 100 times faster than Hadoop's MapReduce for large-scale data processing.", 'Hadoop is a framework to store and process large sets of data across computer clusters, scaling from single computer system up to thousands of commodity systems, and composed of modules like HDFS, YARN, Apache Hive, Pig, NoSQL databases, Apache Spark, Apache Storm, Flume, and Scoop.', 'Both Hadoop and Spark aim to provide organizations with competitive advantages over rival organizations and other business benefits by analyzing large datasets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful business information.']}, {'end': 547.357, 'segs': [{'end': 166.425, 'src': 'embed', 'start': 132.851, 'weight': 0, 'content': [{'end': 138.814, 'text': 'Apache Spark is a lightning-fast cluster computing technology that is designed for fast computation.', 'start': 132.851, 'duration': 5.963}, {'end': 146.959, 'text': 'The main feature of Spark is its in-memory cluster computing that increases the processing of speed of an application.', 'start': 139.395, 'duration': 7.564}, {'end': 155.216, 'text': 'Spark performs similar operations to that of Hadoop modules, but it uses an in-memory processing and optimizes the steps.', 'start': 147.529, 'duration': 7.687}, {'end': 166.425, 'text': 'The primary difference between MapReduce and Hadoop and Spark is that MapReduce uses persistent storage and Spark uses resilient distributed datasets,', 'start': 156.016, 'duration': 10.409}], 'summary': "Apache spark is a fast cluster computing technology with in-memory processing and optimized steps, differing from hadoop's mapreduce by using resilient distributed datasets.", 'duration': 33.574, 'max_score': 132.851, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8132851.jpg'}, {'end': 227.972, 'src': 'embed', 'start': 202.819, 'weight': 2, 'content': [{'end': 207.921, 'text': 'Spark streaming is the component of Spark which is used to process real-time streaming data.', 'start': 202.819, 'duration': 5.102}, {'end': 213.284, 'text': 'It enables high throughput and fault-tolerant stream processing of live data streams.', 'start': 208.322, 'duration': 4.962}, {'end': 215.123, 'text': 'We have Spark SQL.', 'start': 213.982, 'duration': 1.141}, {'end': 222.608, 'text': "Spark SQL is a new module in Spark which integrates relational processing with Spark's functional programming API.", 'start': 215.543, 'duration': 7.065}, {'end': 227.972, 'text': 'It supports querying data either via SQL or via the Hive query language.', 'start': 223.109, 'duration': 4.863}], 'summary': 'Spark streaming handles real-time data, enabling high throughput and fault-tolerant stream processing. spark sql integrates relational processing with functional programming api.', 'duration': 25.153, 'max_score': 202.819, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8202819.jpg'}, {'end': 311.483, 'src': 'heatmap', 'start': 280.815, 'weight': 3, 'content': [{'end': 285.258, 'text': 'We will be comparing these two frameworks based on these parameters.', 'start': 280.815, 'duration': 4.443}, {'end': 287.279, 'text': "Let's start with performance first.", 'start': 285.778, 'duration': 1.501}, {'end': 290.921, 'text': 'Spark is fast because it has in-memory processing.', 'start': 287.919, 'duration': 3.002}, {'end': 294.803, 'text': "It can also use disk for data that doesn't fit into memory.", 'start': 291.421, 'duration': 3.382}, {'end': 304.82, 'text': 'Sparks in-memory processing delivers near real-time analytics and this makes Sparks suitable for credit card processing system, machine learning,', 'start': 295.575, 'duration': 9.245}, {'end': 309.102, 'text': 'security analytics and processing data for IoT sensors.', 'start': 304.82, 'duration': 4.282}, {'end': 311.483, 'text': "Now let's talk about Hadoop's performance.", 'start': 309.682, 'duration': 1.801}], 'summary': 'Comparing spark and hadoop based on performance and use cases.', 'duration': 30.668, 'max_score': 280.815, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8280815.jpg'}, {'end': 356.104, 'src': 'embed', 'start': 327.818, 'weight': 4, 'content': [{'end': 332.119, 'text': 'Main idea behind YARN is parallel processing over distributed dataset.', 'start': 327.818, 'duration': 4.301}, {'end': 341.222, 'text': 'The problem with comparing the two is that they have different way of processing and the idea behind the development is also divergent.', 'start': 332.639, 'duration': 8.583}, {'end': 343.162, 'text': 'Next, ease of use.', 'start': 342.022, 'duration': 1.14}, {'end': 349.324, 'text': 'Spark comes with a user-friendly APIs for Scala, Java, Python, and Spark SQL.', 'start': 343.902, 'duration': 5.422}, {'end': 356.104, 'text': 'Spark SQL is very similar to SQL, so it becomes easier for SQL developers to learn it.', 'start': 350.299, 'duration': 5.805}], 'summary': 'Yarn enables parallel processing over distributed data; spark offers user-friendly apis for scala, java, python, and spark sql, making it easier for sql developers to learn.', 'duration': 28.286, 'max_score': 327.818, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8327818.jpg'}, {'end': 458.273, 'src': 'embed', 'start': 430.146, 'weight': 5, 'content': [{'end': 435.729, 'text': 'So with Hadoop we need a lot of disk space as well as faster transfer speed.', 'start': 430.146, 'duration': 5.583}, {'end': 440.97, 'text': 'Hadoop also requires multiple systems to distribute the disk input output.', 'start': 436.269, 'duration': 4.701}, {'end': 450.312, 'text': 'But in case of Apache Spark, due to its in-memory processing, it requires a lot of memory, but it can deal with a standard speed and amount of disk.', 'start': 441.47, 'duration': 8.842}, {'end': 458.273, 'text': 'As disk space is a relatively inexpensive commodity and since Spark does not use disk input output for processing,', 'start': 450.772, 'duration': 7.501}], 'summary': 'Hadoop requires a lot of disk space and faster transfer speed, while apache spark needs a lot of memory but can work with standard disk speed and amount.', 'duration': 28.127, 'max_score': 430.146, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8430146.jpg'}, {'end': 561.624, 'src': 'embed', 'start': 527.165, 'weight': 6, 'content': [{'end': 530.107, 'text': 'Stream processing is the current trend in the big data world.', 'start': 527.165, 'duration': 2.942}, {'end': 536.45, 'text': 'Need of the hour is speed and real-time information, which is what stream processing does.', 'start': 530.767, 'duration': 5.683}, {'end': 542.934, 'text': 'Batch processing does not allow businesses to quickly react to changing business needs in real time.', 'start': 537.131, 'duration': 5.803}, {'end': 547.357, 'text': 'Stream processing has seen a rapid growth in that demand.', 'start': 543.975, 'duration': 3.382}, {'end': 553.861, 'text': 'Now coming back to Apache Spark versus Hadoop, YARN is basically a batch processing framework.', 'start': 548.058, 'duration': 5.803}, {'end': 561.624, 'text': 'When we submit a job to YARN, it reads data from the cluster, performs operation and write the results back to the cluster.', 'start': 554.461, 'duration': 7.163}], 'summary': 'Stream processing meets real-time needs, surpassing batch processing. apache spark and hadoop differ in processing approach.', 'duration': 34.459, 'max_score': 527.165, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8527165.jpg'}], 'start': 132.851, 'title': 'Apache spark and hadoop comparison', 'summary': 'Introduces apache spark, emphasizing its features such as in-memory cluster computing, spark core engine, spark streaming, spark sql, and graphx. it also compares apache spark and hadoop on performance, ease of use, costs, and data processing, emphasizing their respective strengths in real-time analytics, stream processing, and batch processing.', 'chapters': [{'end': 253.327, 'start': 132.851, 'title': 'Apache spark: lightning-fast cluster computing', 'summary': 'Introduces apache spark, highlighting its features like in-memory cluster computing, spark core engine, spark streaming for real-time data processing, spark sql for relational processing, and graphx for graph parallel computation.', 'duration': 120.476, 'highlights': ['The chapter introduces Apache Spark, highlighting its features like in-memory cluster computing, Spark Core engine, Spark streaming for real-time data processing, Spark SQL for relational processing, and GraphX for graph parallel computation.', "Spark's in-memory cluster computing increases the processing speed, with resilient distributed datasets (RDDs) residing in memory, enabling high throughput and fault-tolerant stream processing of live data streams.", 'The Spark Core engine serves as the base engine for large-scale parallel and distributed data processing, responsible for memory management, fault recovery, scheduling, and monitoring jobs in a cluster, and interacting with storage systems.', "Spark SQL integrates relational processing with Spark's functional programming API, supporting querying data via SQL or the Hive query language, providing an easy transition from traditional relational data processing tools for those familiar with RDBMS.", 'GraphX is the Spark API for graphs and graph parallel computation, extending the Spark resilient distributed datasets with a resilient distributed property, graph.']}, {'end': 547.357, 'start': 254.367, 'title': 'Apache spark vs hadoop: a comparison', 'summary': "Compares apache spark and hadoop on performance, ease of use, costs, and data processing, highlighting spark's in-memory processing for near real-time analytics and stream processing demand, while emphasizing hadoop's batch processing and user-friendly interfaces.", 'duration': 292.99, 'highlights': ["Performance Comparison Apache Spark's in-memory processing delivers near real-time analytics suitable for credit card processing, machine learning, security analytics, and IoT sensors, whereas Hadoop uses batch processing and is not built for real-time processing.", 'Ease of Use Spark offers user-friendly APIs for Scala, Java, and Python, and a SQL-like interface in Spark SQL, with an interactive shell for immediate feedback, while Hadoop provides easy data ingestion and integration with tools like Scoop, Flume, Hive, and Pig for analytics.', "Costs Both Apache Spark and Hadoop are open source with no software cost, but Spark's in-memory processing requires more memory and incurs higher costs, although it reduces the number of required systems, while Hadoop relies on disk-based storage and standard memory.", 'Data Processing Batch processing, crucial for processing large static data sets, is used in Hadoop, while stream processing, essential for real-time information and rapid reactions to changing business needs, is the current trend in the big data world.']}], 'duration': 414.506, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8132851.jpg', 'highlights': ['Apache Spark introduces in-memory cluster computing, Spark Core engine, Spark streaming, Spark SQL, and GraphX.', "Spark's in-memory cluster computing increases processing speed with resilient distributed datasets (RDDs) residing in memory.", "Spark SQL integrates relational processing with Spark's functional programming API, supporting querying data via SQL or the Hive query language.", "Performance: Apache Spark's in-memory processing delivers near real-time analytics suitable for credit card processing, machine learning, security analytics, and IoT sensors.", 'Ease of Use: Spark offers user-friendly APIs for Scala, Java, and Python, and a SQL-like interface in Spark SQL.', "Costs: Spark's in-memory processing requires more memory and incurs higher costs, while Hadoop relies on disk-based storage and standard memory.", 'Data Processing: Batch processing is used in Hadoop, while stream processing is the current trend in the big data world.']}, {'end': 927.567, 'segs': [{'end': 578.396, 'src': 'embed', 'start': 548.058, 'weight': 3, 'content': [{'end': 553.861, 'text': 'Now coming back to Apache Spark versus Hadoop, YARN is basically a batch processing framework.', 'start': 548.058, 'duration': 5.803}, {'end': 561.624, 'text': 'When we submit a job to YARN, it reads data from the cluster, performs operation and write the results back to the cluster.', 'start': 554.461, 'duration': 7.163}, {'end': 569.248, 'text': 'And then it again reads the updated data, performs the next operation and write the results back to the cluster and so on.', 'start': 562.225, 'duration': 7.023}, {'end': 578.396, 'text': 'On the other hand, Spark is designed to cover a wide range of workloads, such as batch application, iterative algorithms,', 'start': 570.248, 'duration': 8.148}], 'summary': 'Apache spark covers a wide range of workloads, including batch applications and iterative algorithms.', 'duration': 30.338, 'max_score': 548.058, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8548058.jpg'}, {'end': 617.787, 'src': 'heatmap', 'start': 584.322, 'weight': 0.76, 'content': [{'end': 588.526, 'text': 'Hadoop and Spark both provides fault tolerance but have different approaches.', 'start': 584.322, 'duration': 4.204}, {'end': 598.463, 'text': 'For HDFS and YARN both master daemons that is the name node in HDFS and resource manager in YARN checks the heartbeat of the slave daemons.', 'start': 589.201, 'duration': 9.262}, {'end': 601.724, 'text': 'The slave daemons are data nodes and node managers.', 'start': 598.843, 'duration': 2.881}, {'end': 609.925, 'text': 'So if any slave daemon fails the master daemons reschedules all pending and in progress operations to another slave.', 'start': 602.464, 'duration': 7.461}, {'end': 617.787, 'text': 'Now this method is effective but it can significantly increase the completion time for operations with single failure also.', 'start': 610.525, 'duration': 7.262}], 'summary': "Hadoop and spark offer fault tolerance, with hdfs and yarn using master daemons to check slave daemons' heartbeat, rescheduling operations on failure, which can increase completion time for operations with single failure.", 'duration': 33.465, 'max_score': 584.322, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8584322.jpg'}, {'end': 793.214, 'src': 'embed', 'start': 767.895, 'weight': 0, 'content': [{'end': 778.057, 'text': 'real-time data analysis means processing data that is getting generated by the real-time event streams coming in at the rate of millions of events per second.', 'start': 767.895, 'duration': 10.162}, {'end': 784.969, 'text': 'The strength of Spark lies in its abilities to support streaming of data along with distributed processing.', 'start': 778.866, 'duration': 6.103}, {'end': 793.214, 'text': 'And Spark claims to process data 100 times faster than MapReduce, while 10 times faster with the disks.', 'start': 785.449, 'duration': 7.765}], 'summary': 'Spark processes data 100x faster than mapreduce, 10x faster with disks, handling millions of events per second.', 'duration': 25.319, 'max_score': 767.895, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8767895.jpg'}, {'end': 889.21, 'src': 'embed', 'start': 862.53, 'weight': 1, 'content': [{'end': 871.674, 'text': 'Hadoop brings huge datasets under control by commodity systems, and Spark provides real-time in-memory processing for those datasets.', 'start': 862.53, 'duration': 9.144}, {'end': 880.242, 'text': "When we combine Apache Spark's ability, that is, the high processing speed and advanced analytics and multiple integration support,", 'start': 872.375, 'duration': 7.867}, {'end': 885.867, 'text': "with Hadoop's low-cost operation on commodity hardware, it gives the best results.", 'start': 880.242, 'duration': 5.625}, {'end': 889.21, 'text': 'Hadoop complements Apache Spark capabilities.', 'start': 886.347, 'duration': 2.863}], 'summary': 'Hadoop and spark combine for efficient big data processing.', 'duration': 26.68, 'max_score': 862.53, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8862530.jpg'}], 'start': 548.058, 'title': 'Comparing apache spark and hadoop', 'summary': 'Compares apache spark and hadoop, highlighting differences in processing frameworks, workloads, and fault tolerance approaches, with a focus on batch processing and fault tolerance mechanisms. spark offers 100 times faster data processing than mapreduce, excels in real-time big data analysis, graph processing, and iterative machine learning algorithms, while hadoop is best suited for analyzing archive data and batch processing.', 'chapters': [{'end': 609.925, 'start': 548.058, 'title': 'Apache spark vs hadoop: comparison', 'summary': 'Compares apache spark and hadoop, highlighting the differences in their processing frameworks, workloads, and fault tolerance approaches, with a focus on batch processing and fault tolerance mechanisms.', 'duration': 61.867, 'highlights': ['The chapter discusses the batch processing framework of YARN in Hadoop, where jobs read, process, and write data back to the cluster, contrasting it with the wider range of workloads covered by Spark, including batch applications, iterative algorithms, interactive queries, and streaming.', "Hadoop and Spark both provide fault tolerance, but with different approaches. Hadoop relies on master daemons like the name node in HDFS and resource manager in YARN to check the heartbeat of slave daemons, while Spark's fault tolerance mechanisms are not explicitly detailed in the provided transcript."]}, {'end': 927.567, 'start': 610.525, 'title': 'Hadoop vs spark: best use cases', 'summary': 'Compares hadoop and spark based on fault tolerance, security, and best use cases. spark provides fault tolerance using rdds and offers 100 times faster data processing than mapreduce. hadoop is best suited for analyzing archive data and batch processing, while spark excels in real-time big data analysis, graph processing, and iterative machine learning algorithms.', 'duration': 317.042, 'highlights': ['Spark provides fault tolerance using RDDs and offers 100 times faster data processing than MapReduce. RDDs can persist a data set in memory across operations, making future actions 10 times faster. Spark claims to process data 100 times faster than MapReduce.', 'Hadoop is best suited for analyzing archive data and batch processing, while Spark excels in real-time big data analysis, graph processing, and iterative machine learning algorithms. Hadoop allows parallel processing over huge amounts of data for batch processing. Spark is best for real-time data analysis, graph processing, and iterative machine learning algorithms.', 'Hadoop complements Apache Spark capabilities, as it brings huge datasets under control by commodity systems, while Spark provides real-time in-memory processing for those datasets. Hadoop brings huge datasets under control by commodity systems, and Spark provides real-time in-memory processing for those datasets.']}], 'duration': 379.509, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xDpvyu0w0C8/pics/xDpvyu0w0C8548058.jpg', 'highlights': ['Spark offers 100 times faster data processing than MapReduce, excels in real-time big data analysis, graph processing, and iterative machine learning algorithms.', 'Hadoop is best suited for analyzing archive data and batch processing, while Spark excels in real-time big data analysis, graph processing, and iterative machine learning algorithms.', 'Hadoop complements Apache Spark capabilities, as it brings huge datasets under control by commodity systems, while Spark provides real-time in-memory processing for those datasets.', 'The chapter discusses the batch processing framework of YARN in Hadoop, where jobs read, process, and write data back to the cluster, contrasting it with the wider range of workloads covered by Spark, including batch applications, iterative algorithms, interactive queries, and streaming.']}], 'highlights': ["Apache Spark is a fast and general-purpose cluster computing system, running up to 100 times faster than Hadoop's MapReduce for large-scale data processing.", 'Both Hadoop and Spark aim to provide organizations with competitive advantages over rival organizations and other business benefits by analyzing large datasets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful business information.', 'Apache Spark introduces in-memory cluster computing, Spark Core engine, Spark streaming, Spark SQL, and GraphX.', "Spark's in-memory cluster computing increases processing speed with resilient distributed datasets (RDDs) residing in memory.", "Spark SQL integrates relational processing with Spark's functional programming API, supporting querying data via SQL or the Hive query language.", "Performance: Apache Spark's in-memory processing delivers near real-time analytics suitable for credit card processing, machine learning, security analytics, and IoT sensors.", 'Ease of Use: Spark offers user-friendly APIs for Scala, Java, and Python, and a SQL-like interface in Spark SQL.', "Costs: Spark's in-memory processing requires more memory and incurs higher costs, while Hadoop relies on disk-based storage and standard memory.", 'Data Processing: Batch processing is used in Hadoop, while stream processing is the current trend in the big data world.', 'Spark offers 100 times faster data processing than MapReduce, excels in real-time big data analysis, graph processing, and iterative machine learning algorithms.', 'Hadoop is best suited for analyzing archive data and batch processing, while Spark excels in real-time big data analysis, graph processing, and iterative machine learning algorithms.', 'Hadoop complements Apache Spark capabilities, as it brings huge datasets under control by commodity systems, while Spark provides real-time in-memory processing for those datasets.', 'The chapter discusses the batch processing framework of YARN in Hadoop, where jobs read, process, and write data back to the cluster, contrasting it with the wider range of workloads covered by Spark, including batch applications, iterative algorithms, interactive queries, and streaming.']}