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Hadoop vs Spark | Hadoop And Spark Difference | Hadoop And Spark Training | Simplilearn

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Hadoop and Spark are the two most popular big data technologies used for solving significant big data challenges. In this video, you will learn which of them is faster based on performance. You will know how expensive they are and which among them is fault-tolerant. You will get an idea about how Hadoop and Spark process data, and how easy they are for usage. You will look at the different languages they support and what's their scalability. Finally, you will understand their security features, which of them has the edge over machine learning. Now, let's get started with learning Hadoop vs. Spark.

We will differentiate based on the below categories
1. Performance
2. Cost
3. Fault Tolerance
4. Data Processing
5. Ease of Use
6. Language Support
7. Scalability
8. Security
9. Machine Learning
10. Scheduler

What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.

What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames

Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training

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Hadoop vs Spark | Hadoop And Spark Difference | Hadoop And Spark Training | Simplilearn

  1. 1. What’s in it for you? Performance Cost Fault tolerance Data Processing Ease of Use 1 2 3 4 5 Scalability Security Machine Learning Language Support6 7 8 9 We will compare Hadoop, and Spark based on the following categories: VS
  2. 2. Performance Cost Fault tolerance Data Processing Ease of Use Scalability Security Machine Learning Language Support Comparison based on below criteria Scheduler
  3. 3. Hadoop is generally slow as it performs operations on the disk and cannot deliver near real-time analytics from the data No real-time analytics Performance
  4. 4. What’s in it for you? Performance Cost Fault tolerance Data Processing Ease of Use 1 2 3 4 5 Scalability Security Machine Learning Language Support6 7 8 9 We will compare Hadoop, and Spark based on the following categories: VS Click here to watch the video
  5. 5. Hadoop is generally slow as it performs operations on the disk and cannot deliver near real-time analytics from the data Spark runs 100 times faster in-memory, and 10 times faster on disk. If Spark runs on YARN with other resources demanding services, there could be major degradation No real-time analytics Faster in-memory processing Performance
  6. 6. Hadoop is less expensive as it is an open- source software. It requires more memory on disk which is relatively an inexpensive commodity Cost
  7. 7. Hadoop is less expensive as it is an open- source software. It requires more memory on disk which is relatively an inexpensive commodity Spark is open-source but requires a lot of RAM to run in-memory. This increases the cluster size and its cost Cost
  8. 8. Hadoop is highly fault-tolerant because it was designed to replicate data across many nodes. Each file is split into blocks and replicated numerous times across many machines Fault Tolerance
  9. 9. Hadoop is highly fault-tolerant because it was designed to replicate data across many nodes. Each file is split into blocks and replicated numerous times across many machines Spark uses Resilient Distributed Datasets (RDDs), which are fault-tolerant collections of elements that can be operated on in parallel Fault Tolerance
  10. 10. Hadoop processes data in batches. MapReduce operates in sequential steps by reading data from the cluster, performing its operations on the data, writing the results back to the cluster Output data Data Processing Batches of input data
  11. 11. Hadoop processes data in batches. MapReduce operates in sequential steps by reading data from the cluster, performing its operations on the data, writing the results back to the cluster Sparks performs batch, real-time, and graph processing of data. It reads data from the cluster, performs its operation on the data, and then writes it back to the cluster Output data Data Processing Batches of input data Batch Real-time Graph
  12. 12. Hadoop’s MapReduce has no interactive mode and is complex. It needs to handle low-level APIs to process the data, which requires lots of coding Ease of Use
  13. 13. Hadoop’s MapReduce has no interactive mode and is complex. It needs to handle low-level APIs to process the data, which requires lots of coding Spark supports user-friendly APIs for different languages. It has an interactive mode and provides intermediate feedback for queries and actions Ease of Use
  14. 14. Hadoop framework is developed in Java programming language. While, MapReduce applications can be written in Python, R and C++ MapReduce supports programming languages Language Support
  15. 15. Hadoop framework is developed in Java programming language. While, MapReduce applications can be written in Python, R and C++ Apache Spark is developed in Scala language and supports other programming languages like Python, R, and Java MapReduce supports programming languages Spark supports other programming languages Language Support
  16. 16. Hadoop is highly scalable as we can add n number of nodes in the cluster. Yahoo reportedly used a 42,000 node Hadoop cluster Scalability
  17. 17. Hadoop is highly scalable as we can add n number of nodes in the cluster. Yahoo reportedly used a 42,000 node Hadoop cluster The largest known Spark cluster has 8,000 nodes. But as big data grows, it’s expected that cluster sizes will increase to maintain throughput expectations. Scalability
  18. 18. Hadoop supports Kerberos and LDAP for authentication. It also supports access control lists (ACLs) and a traditional file permissions model Security
  19. 19. Hadoop supports Kerberos and LDAP for authentication. It also supports access control lists (ACLs) and a traditional file permissions model Spark’s security is a bit sparse as it supports authentication via passwords. If you run Spark on HDFS, it can use HDFS ACLs and file-level permissions. Additionally, Spark can run on YARN, giving it the capability of using Kerberos authentication. Security
  20. 20. Hadoop uses Mahout for processing data and building models. Also, Samsara, a Scala-backed DSL language can be used for in-memory algebraic operations and allows users to write their own algorithms Machine Learning
  21. 21. Hadoop uses Mahout for processing data and building models. Also, Samsara, a Scala-backed DSL language can be used for in-memory algebraic operations and allows users to write their own algorithms Spark has a built-in machine learning library that can be used for classification, and regression. It can also build machine- learning pipelines with hyperparameter tuning MLlib Machine Learning
  22. 22. Hadoop MapReduce is dependent on an external scheduler Scheduler
  23. 23. Hadoop MapReduce is dependent on an external scheduler Apache Spark has its own scheduler Scheduler

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