Hadoop is an open-source software framework that was originally developed by Yahoo in the early 2000s. Since then, it has become very popular with companies around the world because of how well it can manage large data sets. It’s a great tool for businesses to use and there are many benefits to using Hadoop over other comparable products. But, what happens when your company needs something different? Here are 10 amazing alternatives to Hadoop that you should consider:
1. Apache Spark
2. MapReduce
3. MongoDB
4. Cassandra
5. CouchDB
6. Neo4j graph database
7. GraphLab Create (Graphlab Inc.)
8. Google BigQuery
9. HBase
10. Spark
Features of these Alternatives:
Apache Spark
Apache Spark is an open-source cluster computing framework that makes it easier for developers to build programs that can efficiently process large amounts of data. It’s a general-purpose tool and has been used by many big companies like Yahoo, eBay, Twitter, Facebook, and Spotify.
It was originally created in response to the limitations of Hadoop HDFS because Apache Spark enables you to use existing tools on your computer rather than forcing you into using something very specific (which may not be compatible with what you are trying to do). This allows people who work with different programming languages or frameworks than Java and MapReduce to have all these options available right from within the program they’re working in instead of having to switch back and forth between two completely different programs.
The first reason is the ability to use your preferred programming language or framework without having to switch back and forth between two completely different programs. This means that people who work with a variety of languages, frameworks, or platforms can find an option within Spark for what they are trying to do. The second reason is its capability at processing large amounts of data which hdfs alternative helps in cases where Hadoop HDFS falls short because it has options such as MLLib and GraphX – both of these tools help process datasets that may not be able to fit into memory by splitting them up over multiple nodes (a distributed system).
MapReduce
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. The MapReduce framework splits the input dataset into multiple chunks that are processed independently in parallel; each output chunk may then be converted to another form (e.g., sorted or reduced). All of this is done iteratively until the entire set of data has been processed.
On Jan 15th, H 2014, Apache announced its intent to release Hadoop version two by the end-of-year 2015 under the new name Apache Spark as well as make other changes in order “to reflect different focus” than traditional MapR’s line of business. The similarity between Hadoop and Spark is the capability of processing data in a distributed way by running in clusters.
HDFS alternative:
In order to get rid of MapReduce, there are also other options for Hadoop alternatives that programmers can use to process big data sets. According to Chris Richardson from WSO Logic, an open-source programming model called Cascading has been used with Apache Hadoop since 2008 and now supports multiple computational languages including Java, Scala, Clojure as well as Python. Moreover, it has been adopted by many companies such as Yahoo!, LinkedIn and Twitter. With respect to this article’s topic on “hdfs alternative”
MongoDB
MongoDB is a document-oriented NoSQL database that stores data in the form of JSON objects. It can be run on Windows, OS X, and Linux operating systems. The software was originally designed by developers from Facebook to store social networking information collected by their website at the time. They open-sourced it in 2008 with an Apache License.
The key features are:
-A wide variety of storage engines for different use cases
-JSON documents are written using JavaScript object notation (or you don’t have to write any special code)
-MapReduce capability
-Embedded driver architecture which makes connecting MongoDB to other languages easy.
Cassandra
Cassandra is a distributed NoSQL database that works on principles of the CAP theorem. Max Schireson, one of its original authors, has described Cassandra as “a parallel DBMS designed for low latency across commodity servers.” It can run on Windows and OS X operating systems. The software was originally developed by Facebook in 2008 to support their social networking website at the time. They open-sourced it under an Apache License in 2010 with some code changes from version 0.0-18rc14 released November 2009.
The key features are:
-Relational data model (it supports SQL)
-Open source since 2004; now managed by DataStax Inc., a commercial company based out of California’s Silicon Valley region.
CouchDB
CouchDB is a document-oriented database system with these features:
-Open source since 2004, and now managed by Apache Software Foundation. Couch DB has been used to store data for the Mozilla Web browser as well as NASA’s Swift satellite project.
The key features are:
-Non-relational data model (No SQL)
-Supports JavaScript Object Notation(JSON). This means that it can be queried using JSON object notation or “path/object” syntax in URL without any need of additional programming interface libraries. The entire API consists only of RESTful web requests over Hypertext Transfer Protocol(HTTPS). It also supports typed datasets in a number of formats including XML, YAML, CSV.
Neo4j graph database
B is a document-oriented database system with these features:
-Open source since 2004, and now managed by Apache Software Foundation. Couch DB has been used to store data for the Mozilla Web browser as well as NASA’s Swift satellite project.
The key features are:
-Non-relational data model (No SQL)
-Supports JavaScript Object Notation(JSON). This means that it can be queried using JSON object notation or “path/object” syntax in URL without any need of additional programming interface libraries. The entire API consists only of restful web requests over Hypertext Transfer Protocol(HTTPS). It also supports typed datasets in a number of formats including XML, YAML, CSV.
GraphLab Create (Graphlab Inc.)
GraphLab Create is a distributed graph processing framework for the analysis of large graphs. The API includes a flexible data model based on RDF that supports joining, querying, and transforming nodes and edges in your graph. GraphLab has two major components:
-A server component with an embedded triple store for managing the relations between datasets as well as storing metadata about each dataset (e.g., annotations). The server also interfaces to read/write storage systems such as Hadoop or Cassandra;
-Client libraries that wrap this backend functionality so you can work interactively with the server from programming languages like Java, C++, Python, Scala or Ruby. These clients provide APIs for reading and writing triples using restful requests.
The Ruby client library is used by the GraphLab Create command-line tool. It provides a simple interface, supporting reading and writing RDF data via RESTful requests to an embedded triple store on the server component of your GraphLab installation.
Graphlab can be run as a standalone service or deployed in production systems that are using other storage solutions (e.g., Hadoop) for large datasets where you need flexible querying, joining, and transforming capabilities with efficient handling of graph sizes beyond what HDFS supports well today.
Some alternatives to Map/Reduce include Apache Pig, Spark SQL, MongoDB Stitch Query Language & Dataset API, Dremio DataFlow Framework, Hivemall Project.
Google BigQuery
Google BigQuery is a hosted data warehouse service that allows users to execute SQL queries against large datasets. It runs on Google’s infrastructure and is accessible from the web, or through its RESTful API.
BigQuery supports querying of nested structures with complex joins in relational tables; query types include SELECT, JOINs, aggregate functions GROUP BY ROLLUP(), CUBE(), DISTINCT() – these allow for powerful analysis not possible using other methods.
The result set can be trillions of rows (i.e., petabytes) due to the ability to append new line feed-delimiter JSON formatted records to it while you are retrieving them by running another query without having to reload the entire dataset into memory.
BigQuery also supports the following types of joins: INNER JOIN, LEFT OUTER JOIN, RIGHT OUTER JOIN.
Yahoo! BOSS (formerly known as HadoopDB) is a high-performance distributed data store designed to handle cost-sensitive and complex queries using map/reduce paradigm in conjunction with an SQL query engine on top of datasets stored in HDFS or Cloud Files. It features columnar storage for large tables along with range indexing which speeds up scans significantly; it scales horizontally by adding nodes automatically when needed without any downtime or service interruption – this makes it highly available at all times.
HBase
HBase is a distributed, scalable and fault-tolerant big data store that supports storage for large quantities of semi-structured data. It’s designed to give programmers high write availability while supporting low latency read access (as opposed to something like HDFS which favors high aggregate throughput). HBase handles row updates in the order that they are written; it can’t update or delete individual columns without rewriting the entire table.