Big Data training
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The document provides an overview of big data concepts and frameworks. It discusses the dimensions of big data including volume, velocity, variety, veracity, value and variability. It then describes the traditional approach to data processing and its limitations in dealing with large, complex data. Hadoop and its core components HDFS and YARN are introduced as the solution. Spark is presented as a faster alternative to Hadoop for processing large datasets in memory. Other frameworks like Hive, Pig and Presto are also briefly mentioned.
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Big Data training
- 2. About me • I’m Vishal Periyasamy Rajendran • Senior Data Engineer • Focused on architecting and developing big data solutions at AWS cloud. • 8x AWS certifications + other certifications on Azure, Snowflake etc. • You can find me on • LinkedIn: https://www.linkedin.com/in/vishal-p- 2703a9131/ • Medium: https://medium.com/@vishalrv1904 2
- 3. Agenda • Big data Overview • Dimensions of Big data • Traditional approach and limitations • Hadoop Overview • Spark Overview • Hive Overview • Other Big data frameworks 3
- 5. What is Big data? • Each user with a smartphone generates approximately 40 Exabytes of data every month. • According to Forbes, 2.5 Quintillion bytes of data are created every day. 5
- 6. What is Big data? • Collection of data that is so huge & complex like none of the traditional data management tool can store or process it. 6
- 7. Dimensions of Big Data
- 8. 6v’s Of Big data • Volume • The scale of data. • Velocity • Speed of data. • Variety • Diversity of data. • Veracity • Accuracy of data. • Value • Insights gained from data. • Variability • How often data can change. 8
- 10. Big Data Phases • Data collection • Data Cleansing / Validation • Data Transformation • Data Storage • Data Visualization Different Pipelines: • ETL (Extract, Transform, Load) • ELT (Extract, Load, Transform) 10
- 12. Traditional Approach • An enterprise will have a computer to store and process big data. • Limitations: • Processor that is processing the data. • Dealing with huge amounts amounts of scalable data 12
- 13. Traditional Approach • Google’s Solution: • Solved the processor problem using an algorithm called MapReduce. • Divides the task into small parts and assigns them to many computers. 13
- 14. Hadoop Overview
- 15. Hadoop Overview • Using the solution provided by Google, Doug Cutting and his team developed an Open-Source Project called HADOOP. 15
- 16. Hadoop Overview • Framework for distributed data processing Maps data to key/value pairs Reduces intermediate results to final output Largely supplanted by Spark these days • Yet Another Resource Negotiator Manages cluster resources for multiple data processing frameworks • Hadoop Distributed File System Distributes data blocks across clusters in a redundant manner 16
- 17. Spark Overview
- 18. Spark Overview • Hadoop MapReduce must persist data back to the disk after every Map or Reduce action. • This brings processing slowness. • Spark - Distributed processing framework for big data. • Apache Spark is very much popular for its speed. It runs 100 times faster in memory and ten times faster on disk than Hadoop MapReduce since it processes data in memory (RAM). • Supports Java, Scala, Python, and R. 18
- 20. How Spark Works • Spark apps are run as independent processes on a cluster. • Executors run computations and store data. • Spark context sends application code and tasks to executors • Cluster manager – Yarn 20
- 21. Spark Context vs SQL Context vs Hive Context vs Spark Session • Spark 1.x three entry points were introduced, • Spark Context: • The entry point of all spark application • Spark Context is the first step to use RDD and connect to Spark Cluster • SQL Context: • Used for the spark SQL executions & Structured data processing. • Hive Context: • Used for the application to communicate with the hive. 21
- 22. Spark Context vs SQL Context vs Hive Context vs Spark Session • Spark 2.x introduced spark session, • Spark Session: • Combination of spark context, SQL context and hive context. 22
- 23. Resilient Distributed Dataset (RDD) & Dataframe • RDD (Resilient Distributed Dataset) is a fundamental data structure of Spark. • The data frame is organized into named columns. • Data frame supports APIs such as select, agg, sum, avg etc. • Support Spark SQL • Catalyst Optimizer is available. • Both are fault-tolerant, immutable distributed collections of objects, which means you cannot change once you create. 23
- 24. Different types of Evaluation • Eager Evaluation: • Is the evaluation strategy you’ll most probably be familiar with and is used in most programming languages • Lazy Evaluation: • Is an evaluation strategy that delays the evaluation of an expression until its value is needed. • Lazy Evaluation means that you can apply as many TRANSFORMATIONs as you want, but Spark will not start the execution of the process until an ACTION is called. 24
- 25. Transformation & Actions • Transformations are the instructions you use to modify the Data Frame in the way you want and are lazily executed. • Narrow transformations: • Select • Filter • with column • Wide transformations: • Group by • Repartition • Actions are statements that will ask for a value to be computed immediately and are eager statements. • Show, collect, save, count. 25
- 26. Spark’s Catalyst Optimizer • When performing different transformations, Spark will store them in a Directed Acyclic Graph (or DAG). • Once the DAG is constructed, Spark’s catalyst optimizer will perform a set of rule-based and cost-based optimizations to determine a logical and then physical plan of execution. • Spark’s Catalyst optimizer will group operations together, reducing the number of passes on data and improving performance. 26
- 27. © Presidio, Inc. All rights reserved. Proprietary and Confidential. Spark Hands-on 27
- 28. Spark Assignment • Input: • Covid data CSV file • Expected outputs: • Convert all state names to lowercase. • The day had a greater number of covid cases. • The state has the second-largest number of covid cases. • Which Union Territory has the least number of death. • The state has the lowest Death to Total Confirmed cases ratio. • Find which month the more Newer recovered cases. • If the month is 02 it should display as February. 28
- 29. Hive Overview
- 30. Apache Hive • Uses familiar SQL syntax (HiveQL) • Scalable – works with “big data” on a cluster • Really most appropriate for data warehouse applications • Easy OLAP queries – WAY easier than writing MapReduce in Java. • Interactive & Highly optimized. 30
- 32. Other Big Data Frameworks 32 • Pig introduces Pig Latin, a scripting language that lets you use SQL-like syntax to define your map and reduce steps. Apache Pig: • Non-relational, petabyte-scale database. • In-memory, Based on Google’s Bigtable, on top of HDFS Apache HBase: • It can connect to many different “big data” databases and data stores at once, and query across them. • Interactive queries at the petabyte scale. Presto: • Interactively run scripts/code against your data. Apache Zeppelin:
- 33. © Presidio, Inc. All rights reserved. Proprietary and Confidential. Questions 33