Getting started big data
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Apache Hadoop is a popular open-source framework for storing and processing large datasets across clusters of computers. It includes Apache HDFS for distributed storage, YARN for job scheduling and resource management, and MapReduce for parallel processing. The Hortonworks Data Platform is an enterprise-grade distribution of Apache Hadoop that is fully open source.
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Getting started big data
- 1. Getting Started With Big Data Apache Hadoop
- 2. Apache Hadoop Apache Hadoop • is a popular open-source framework for storing and processing large data sets across clusters of computers. • HDP 2.2 on Sandbox system Requirements: – Now runs on 32-bit and 64-bit OS (Windows XP, Windows 7, Windows 8 and Mac OSX) – Minimum 4GB RAM; 8Gb required to run Ambari and Hbase – Virtualization enabled on BIOS – Browser: Chrome 25+, IE 9+, Safari 6+ recommended. (Sandbox will not run on IE 10) • An ideal way to get started Enterprise Hadoop. Sandbox is a self-contained virtual machine with Apache Hadoop pre-configured alongside a set of hands-on, step-by-step Hadoop tutorials. • Sandbox is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials. • It includes many of the most exciting developments from the latest HDP distribution, packaged up in a virtual environment that you can get up and running in 15 minutes!
- 3. Hadoop… Getting Started Terminologies • Hadoop • YARN – the Hadoop Operating system – enables a user to interact with all data in multiple ways simultaneously, making Hadoop a true multi-use data platform and allowing it to take its place in a modern data architecture. – A framework for job scheduling and cluster resource management. – This means that many different processing engines can operate simultaneously across a Hadoop cluster, on the same data, at the same time. • the Hadoop Distributed File System (HDFS) – A distributed file system that provides high- throughput access to application data. • MapReduce – A YARN-based system for parallel processing of large data sets. • Sqoop • theHiveODBC Driver Hortonworks Data Platform(HDP) • is a 100% open source distribution of Apache Hadoop that is truly enterprise grade having been built, tested and hardened with enterprise rigor.
- 4. Introducing Apache Hadoop to Developers • Apache Hadoop is a community driven open-source project governed by the Apache Software Foundation. • originally implemented at Yahoo based on papers published by Google in 2003 and 2004. • Since then Apache Hadoop has matured and developed to become a data platform for not just processing humongous amount of data in batch but with the advent of YARN it now supports many diverse workloads such as Interactive queries over large data with Hive on Tez, Realtime data processing with Apache Storm, super scalable NoSQL datastore like HBase, in-memory datastore like Spark and the list goes on.
- 6. Core of Hadoop • A set of machines running HDFS and MapReduce is known as a Hadoop Cluster. Individual machines are known as nodes. A cluster can have as few as one node to as many as several thousands. For most application scenarios Hadoop is linearly scalable, which means you can expect better performance by simply adding more nodes. • The Hadoop Distributed File System (HDFS) • MapReduce
- 7. MapReduce • a method for distributing a task across multiple nodes. Each node processes data stored on that node to the extent possible. • A running Map Reduce job consists of various phases such as Map - > Sort -> Shuffle -> Reduce • Advantages: – Automatic parallelization and distribution of data in blocks across a distributed, scale-out infrastructure. – Fault-tolerance against failure of storage, compute and network infrastructure – Deployment, monitoring and security capability – A clean abstraction for programmers • Most MapReduce programs are written in Java. It can also be written in any scripting language using the Streaming API of Hadoop.
- 8. The MapReduce Concepts and Terminology • MapReduce jobs are controlled by a software daemon known as the JobTracker. The JobTracker resides on a 'master node'. Clients submit MapReduce jobs to the JobTracker. The JobTracker assigns Map and Reduce tasks to other nodes on the cluster. • These nodes each run a software daemon known as the TaskTracker. The TaskTracker is responsible for actually instantiating the Map or Reduce task, and reporting progress back to the JobTracker • A job is a program with the ability of complete execution of Mappers and Reducers over a dataset. A task is the execution of a single Mapper or Reducer over a slice of data.
- 9. Hadoop Distributed File System • the foundation of the Hadoop cluster. • manages how the datasets are stored in the Hadoop cluster. • responsible for distributing the data across the data nodes, managing replication for redundancy and administrative tasks like adding, removing and recovery of data nodes.
- 10. Apache Hive • provides a data warehouse view of the data in HDFS. • Using a SQL-like language Hive lets you create summarizations of your data, perform ad-hoc queries, and analysis of large datasets in the Hadoop cluster. • The overall approach with Hive is to project a table structure on the dataset and then manipulate it with HiveQL. • Since you are using data in HDFS your operations can be scaled across all the datanodes and you can manipulate huge datasets.
- 11. Apache HCatalog • Used to hold location and metadata about the data in a Hadoop cluster. This allows scripts and MapReduce jobs to be decoupled from data location and metadata like the schema. • since it supports many tools, like Hive and Pig, the location and metadata can be shared between tools. Using the open APIs of HCatalog other tools like Teradata Aster can also use the location and metadata in HCatalog. • how can we reference data by name and inherit the location and metadata???
- 12. Apache Pig • a language for expressing data analysis and infrastructure processes. • is translated into a series of MapReduce jobs that are run by the Hadoop cluster. • is extensible through user-defined functions that can be written in Java and other languages. • Pig scripts provide a high level language to create the MapReduce jobs needed to process data in a Hadoop cluster.