Hadoop ecosystem is a platform used to solve big data problems. The Java-based platform is used to handle and analyze large volumes of data. It involves a lot of services including data ingestion, storage, analysis, and maintaining organizational data.
The ecosystem involves two key services; MapReduce which acts as a framework for handling large amounts of data, and the Hadoop distributed file system (HDFS) for handling complex files. Other Hadoop core sets used include YARN and the Hadoop resource manager.
- Hadoop distributed file system
This is a java based file system that offers primary storage space for the Hadoop application. The distributed system provides a reliable, fault tolerance and cost-effective storage for big data applications.
Components of Hadoop distributed file system
NameNode or master node is used to maintain and manage the Data node by recording metadata of files stored in the cluster. It does not store actual data sets and records each change of metadata files taking. The metadata includes the location of the block, the size of the file and the file hierarchy or permission.
Tasks performed by NameNode
- It manages the file system namespace
- Regulates access to the files by the clients
- Carries out file execution processes e.g. opening files, closing files, and naming
It is also known as a slave node and runs in every slave machine. It is used to store actual data and it receives read and write requests operations from the client. Two files are used in this node; one file for data and the other file for recording a block of metadata. It can create blocks, delete or even replicate the blocks based on name node decisions.
Tasks performed by DataNode
- Performs data operations like block creation, deletion or replication based on instructions received from name node.
- It manages all data storage activities in the system.
The MapReduce framework acts as a high-performance engine for data processing on large clusters of a commodity hardware. Hadoop system executes queries and performs other batch read operations against vast amounts of data.
The MapReduce systems are parallel and used for large-scale data analysis involving multiple cluster machines. It increases the speed and reliability of the operation running on the cluster.
The MapReduce ecosystem works in two phases;
The map phase converts one set of data into another set of data and the individual elements of the data are further broken down into tuples.
The reduce phase uses an output from the map as its input and combines the data tuples according to a certain key and later modifies the value of that key.
Features of MapReduce component
- Scalability: the MapReduce component can process petabytes data sets.
- Speed: It uses parallel processing of data reducing the processing tasks which took hours to just a few minutes.
- Simplicity: Makes it easy to run the applications
- Fault tolerance: reduces system failure in that if one copy of data is unavailable another machine provides a copy of the same data to be processed.
- YARN (Yet Another Resource Negotiator)
This is another Hadoop ecosystem component which manages resources. It is mostly referred to as the operating system of Hadoop since it manages and monitors all operations. It stores multiple data processing engines in a single framework. E.g., real-time data processing and batch processing can be stored in a single server.
The accessories and tools used for Hadoop ecosystem development
The Hadoop ecosystem is an open source framework which consists of Apache Hadoop software library component, various accessories, and tools.
- Apache Hive: this acts as a data analysis tool. It addresses how data is accessed, queried, and structured in the Hadoop distributed file system.
- Apache Spark: It used as an engine to process big data. It provides an in-memory gateway for computing purpose. Data is executed in the memory instead of being executed on the disk. Apache spark bypass the MapReduce framework and access data from the HDFS making file execution 10 times faster than when carrying disk operation.
It is mostly used as an alternative for MapReduce data processing tasks. It also supports SQL operations and works interactively with programs like Python, Scala and R Shells.
- Apache Pig: This is used as a data flow language. It is a procedural language and automatically generates MapReduce functions. The pig language is used in complex data environments which require various data operations.
Users can customize the application with their own functions to manipulate and sort data. Data manipulation operations like filter, join, and grouping are used.
- HBase: It acts as a database tool for storing structured data in table format. HBase is used for retrieving historical data from large datasets.
- Ambarl: It acts as the Hadoop ecosystem manager which helps in the administration of various apache resources.
Hadoop Big data Architecture
Hadoop big data architect has become an important asset to businesses. It helps in planning for the next-generation big data developments system and also how to manage large-scale Hadoop applications.
A company that deals with big data environment, it requires an architect who can provide a complete Hadoop solution in the big data deployment lifecycle. This involves carrying out requirement analysis, selection of the platform, designing the platform architecture, development, testing, and deployment of the final product.
Over the last few years, Hadoop ecosystem has grown tremendously because it helps in meeting the needs of the organization. It offers flexible data analysis techniques with the unmatched price-performance curve. Data analysis involves unstructured data formats such as raw text, structured data formats, and semi-structured formats.
Hadoop is used in environments where data is collected from multiple sources. The data is processed as a batch job on the same server machine. This saves users the cost of acquiring additional hardware for processing data. It also reduces the time and the effort needed to load data in a new system since data can be processed within the Hadoop system.