Introduction to Big Data Analytics Software
Big data is the buzzword. It is the most preferred and highly in-demand job. Today, in this Big Data Analytics Software article, we shall be talking about what big data is, why it is essential, how it is done, and most importantly, we shall focus on what tools and software are available in the market to do big data analytics.
Big data refers to data that is exceptionally large. Generally, data of several terabytes or more is classified as big data. You can understand big data as the POS machine-generated data by Walmart’s various stores globally in a day or over a week. There are four characteristic features of big data: – High Volume, High Velocity, High Variety, and High Veracity. It means that data of huge size is generated at high speed and contains many internal variations in terms of data type, data format, etc. can be classified as big data.
Big data is also called distributed computing.
The growing scope and demand for big data arise from generating vast amounts of data daily and the significant potential for extracting valuable business insights from it.
Important Concepts of Big Data Analytics Software
How to handle and process big data is a common question. This occurs in the minds of young professionals who want to start learning big data technologies and senior VP and directors of engineering of large corporations who want to analyze big data’s potential and implement it in their organization.
Data injection, storage, processing, and insights generation are the usual workflow in the big data space. First, data is injected from the source system into the extensive data ecosystem (Hadoop, for example), and the same can be done through a data injection system such as AVRO or Scoop. After that, the injected data must be stored somewhere; HDFS is used for that most commonly. Processing can be done via Pig or Hive, and Spark can carry out analysis and insights generation. But other than that, several different components of the Hadoop ecosystem provide one or another essential functionality.
An entire Hadoop framework is provided by many distributors such as Cloudera, Horton Works, IBM, Amazon, etc.
Apache Hadoop is the most common platform for Hadoop. Hadoop is a collection of open-source software utilities. It solves problems that involve handling and processing a massive amount of data through a network of computers called clusters.
Hadoop applications are run using the MapReduce paradigm. In MapReduce, the data is processed on different CPU nodes in parallel. The Hadoop framework can develop applications that run on clusters of computers and are highly fault-tolerant.
Hadoop Architecture
Hadoop architecture has four modules:
1. Hadoop Common
- Java libraries and utilities required by other Hadoop modules
- Provide file system and OS-level abstractions
- Containing the essential Java files and scripts 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.
Big Data Analytics Software
Following are a 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 costs, 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 various operating systems, programming languages, 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 a state of an art system that provides a versatile range of software.
- Cloudera Enterprise: It is powered by Apache Hadoop. It can do everything in a secure and scalable environment, from analytics to data science, providing limitless possibilities.
- MongoDB: It is the next-generation database based on the NoSQL format. It uses a document data model, which is similar to JSON.
Examples of Big Data Analytics Software
This section provides a wide range of Big data Analytics software.
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List of Big Data Analytics Software |
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| 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 |
| Blue Talon | Wavefront | Qubole | MongoDB |
| 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
From the above, we can understand that big data analytics has many tools and technology. Some of the technologies mentioned above are proprietary, which implies they require a subscription for access. In contrast, some others are open source and hence wholly free. For example, a subscription must be taken where payment is charged hourly for AWS. Cloudera and Horton’s work, on the other hand, is free. Hence one needs to choose wisely which tools or technology to opt for. Paid and licensed software is often preferable for developing enterprise-level software due to the included support and maintenance warranty. Hence there are no last-time surprises, while open source is good for learning and initial development. However, it does not mean open-source technologies are not for production-level software development; many software are built using open-source technologies.
Recommended Articles
This has been a guide to Big Data Analytics Software. Here we have discussed the important concept with different Big Data Analytics Software like Amazon Web services, Microsoft Azure, Cloudera Enterprise, etc. You may also look at the following articles to learn more –
