arried out by Spark. But other than that, there are several other components of the Hadoop ecosystem which provide one or other important functionality.
An entire Hadoop framework is provided by many distributors such as Cloudera, Horton work, IBM, Amazon etc.
Apache Hadoop is the most common platform for Hadoop. Hadoop is the collection of open source software utilities. It solves problems that involve handling and processing a massive amount of data through a network of computer called clusters.
Hadoop applications are run using the MapReduce paradigm. In MapReduce, the data is processed on different CPU nodes in parallel. Hadoop framework can develop applications that run on clusters of computers and are highly fault tolerant.
Hadoop architecture has four modules: –
1. Hadoop common: –
- Java libraries and utilities required by other Hadoop modules
- provide file system and OS level abstractions
- contains the essential Java files and scripts that are required to start and run Hadoop.
2. Hadoop YARN:
- framework for job scheduling
- cluster resource management.
3. Hadoop Distributed File System (HDFS):
- provides high-throughput access to application data.
4. Hadoop MapReduce:
- YARN-based system for parallel processing of large data sets.
Following are few Big Data Analytics Software: –
- Amazon Web Services: – Probably the most popular Big data platform, AWS is super cool. It is cloud-based and provides data storage, computing power, databases, analytics, networking etc. These services reduce operational cost, faster execution, and greater scalability.
- Microsoft Azure: – Azure is great for improving productivity. Integrated tools and pre-built templates make everything simple and fast. It supports a spectrum of operating systems, programming language, frameworks, and tools.
- Horton works data platform: – Based on open source Apache Hadoop, it is trusted by all and provides a centralized YARN. It is state of art system which provides a versatile range of software.
- Cloudera Enterprise: – It is powered by Apache Hadoop. From analytics to data science, it can do everything under a secure and scalable environment and provides limitless possibilities.
- MongoDB: – It is the next generation database based on NoSQL format. It uses a document data model which is similar to JSON.
Examples of Big Data Analytics Software
In this section, we provide a wide range of Big data Analytics software.
List of Big Data Analytics Software
|Arcadia Data||Actian Analytics platform||FICO big data analyzer||Syncsort|
|Amazon Web services||Google Bigdata||Palantir BigData||Splunk Big data analytics|
|Google Big Query||Datameer||Oracle Bigdata Analytics||VMWare|
|Microsoft Azure||IBM Big Data||DataTorrent||Pentaho Bigdata Analytics|
|Informatica power center bigdata edition||Cloudera Enterprise Big data||MapR converged data platform||BigObject|
|GoodData||Opera solutions signal hub||HortonWork data platform||SAP Big Data Analytics|
|Next Pathway||CSC big data platform||Kognito Analytical platform||1010data|
|GE Industrial internet||DataStax Bigdata||SGI Bigdata||Teradata Bigdata analytics|
|Intel Bigdata||Guavas||HP Big Data||Dell Big data Analytics|
|Pivotal Bigdata||Mu Sigma Big Data||Cisco Bigdata||MicroStrategy Bigdata|
Conclusion – Big Data Analytics Software
From above, we can understand that there is a wide range of available tools and technology in the field of big data analytics. One point that needs to be kept in mind that some of the technologies mentioned above is propriety and hence available only after a subscription while some others are open source and hence completely free. For AWS, for example, a subscription needs to be taken where payment is charged at an hourly rate. Cloudera and Horton work, on the other hand, are free. Hence one needs to choose wisely which tools or technology to opt for. Usually, a paid, licensed software is good for developing enterprise level software as it comes with a support and maintenance warranty hence there are no last time surprises, while open source is good for learning and initial devel