The Ecosystem Components of Hadoop

Hadoop is an open-source software program that allows you to store and analyze massive quantities of data on inexpensive hardware clusters.

Another definition is – Apache Hadoop is a collection of open-source software tools for dealing with huge amounts of data and processing on a distributed network of computers. It’s a software framework for storing and analyzing large amounts of data in a distributed manner.

Please visit here for detailed information about HDFS and Map Reduce, as well as its work-flow architecture. Also, you’ll see how the five daemons (services) work.

We’ll talk about all of Hadoop’s other ecosystem components in this post.

Operating System: Hadoop is available for both Unix and Windows. Although Linux is the sole supported production platform, Hadoop can be run on other Unix variants for development. Windows is only supported as a development platform.

JVM: The operating system and Hadoop are linked through the Java Virtual Machine (JVM). All Java programs require the installation of a JVM in order to run.

HDFS: HDFS is a distributed file system that runs on commodity hardware and can handle huge data volumes. It allows you to grow a single Apache Hadoop cluster to thousands of nodes. HDFS can run on commodity hardware for processing unstructured data. It is managed by 3 daemons namely Name Node, Data Node and Secondary Name Node.

Yarn: Apache Hadoop YARN sits between HDFS and the processing engines needed to run applications. Yarn is a Hadoop resource manager and job scheduler that is handled by three daemons: ResourceManager, NodeManager, and WebAppProxy.

Map Reduce: MapReduce is a programming model and software framework for parallel processing of large volumes of data in a distributed environment. The processing job will be divided and distributed among numerous nodes, with each node working on a portion of the workload at the same time. When working with large datasets, it saves a lot of time.

MapReduce is managed by 2 daemons i.e. Job Tracker and Task Tracker.

Apache Pig: Pig is a high-level scripting language used in Apache Hadoop. Pig allows to write dynamic data transformations without the awareness of Java. Pig’s basic SQL-like scripting language is called Pig Latin and appeals to developers already acquainted with SQL and scripting languages. The below is a sample word-count program to let you know how to work with Pig.

Apache Sqoop: (SQL-to-Hadoop) is a Hadoop ecosystem component and an ETL tool that offers the capability to extract data from various structured data stores such as relational databases with the help of map-reduce. This command-line interpreter works efficiently to transfer a huge volume of the data to the Hadoop file system (HDFS, HBase, and Hive). Similarly, it exports the data from Hadoop to SQL.

Apache Hive: Apache Hive is built on top of Apache Hadoop, which is a distributed, fault-tolerant, and open source data warehouse platform for reading, writing, and handling massive datasets stored directly in HDFS or other data management structures such as Apache HBase. Hive is characterized by the ability to query massive datasets using Apache Tez or MapReduce.

Hive was designed to allow non-programmers who are familiar with SQL to work with petabytes of data using a SQL-like interface known as HiveQL.

Apache Impala: Apache Impala is an open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop. It’s easy for all SQL developers to work in impala. If you are acquainted with MySQL or SQL Server or Oracle SQL*Plus or some kind of RDBMS, operating in Impala is not a big deal. Apache Impala invokes the impalad daemon.

Apache Zookeeper: Apache ZooKeeper is an open-source server that provides a highly reliable distributed coordination service for managing distributed frameworks with many hosts, such as Hadoop, Hbase, and others. In short, it is a tool to manage the cluster. It’s fail-safe synchronization approach is used to handle race conditions and deadlocks. And data inconsistency is handled with atomicity.

Kafka: Apache Kafka is a distributed data storage designed for real-time ingest and processing of streaming data. Streaming data is the data that is continuously generated by thousands of data sources, all of which transmit data records in at the same time. A streaming platform must be able to cope with the constant inflow of data and process it sequentially and progressively.

Kafka is used by businesses for a number of tasks, including building ETL pipelines, data synchronization, real-time streaming, and more.

Hue: Hue is an open source Hadoop user interface with the most intelligent auto-completes and Query Editor components, allowing you to interact with data warehouses.

Apache Oozie: Apache Oozie is a Hadoop task management solution that runs on a server. Workflows in Oozie are defined as a directed acyclic graph with a collection of control flow and action nodes. The beginning and conclusion of a workflow, as well as a mechanism to control the workflow execution path, are defined by control flow nodes.

Oozie assists in the building of complex data transformations from numerous component tasks.

Apache Flume: Apache Flume is a distributed, reliable, and flexible open-source system for collecting, aggregating, and transferring huge volumes of log data effectively. Its design is simple and flexible, based on streaming data flows. Flume is a tool that allows you to reliably stream log and event data from web servers into Apache Hadoop.

Apache Solr: Solr is a scalability and fault tolerance distributed search and index replication solution. Solr is a prominent open-source enterprise search and analytics use cases with a thriving development community and regular updates. Full-text search, hit highlighting, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL features, and rich document (Word, PDF) handling are some of its key features.

Apache Spark: Apache Spark is a free and open-source unified analytics engine for processing large amounts of data. Spark provides a programming interface for entire clusters with implicit data parallelism and fault tolerance. It is used for large-scale data workloads. For fast analytic queries against any size of data, it uses in-memory caching and optimized query execution. Java, Python, Scala, and R are among the programming languages supported by Spark. Spark allows application developers and data scientists to query, analyze, and transform data at scale quickly and easily.

Apache Spark SQL: As stated above, SQL is one of the language supported by Apache Spark. It is used to work with structured data, either within Spark programs or through standard JDBC and ODBC connectors.

Machine Learning (R, Mahout): Machine learning is a type of data analysis that automates the creation of analytical models. It’s a field of artificial intelligence based on the premise that computers can learn from data, recognize patterns, and make judgments with little or no human input. It is used in internet search engines, spam filters, banking software that detects suspicious transactions, speech recognition, etc.

NoSQL (HBase, MongoDB, Cassandra, Riak, CouchBase, etc): A NoSQL database allows data to be stored and retrieved using methods other than tabular relations. NoSQL is a non-relational database management system (DBMS) that does not require a fixed schema, eliminates joins, and is scalable. For distributed data repositories with large data storage requirements, NoSQL DB is utilized. Big data and real-time web apps both employ NoSQL.

NoSQL is divided into four categories: Column Based (BigTable, Cassandra, Hbase, etc.), Graph (Neo4J, InfoGrid, Flock DB, etc.), Document (MongoDB, CouchDB, RavenDB, etc.), and Key-Value (Riak, Memcached, Redis Serveamr, etc.), to deal with structured, semi-structured, unstructured, and polymorphic data.

Hope you find this article helpful.

Subscribing to this site will allow you to receive quick updates on future articles that will cover almost all the Hadoop ecosystem components with multiple examples.