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Today, big data technology is no longer an attempt and experience for enterprises, it has become an important part of the business. According to a survey by research firm IDC , global market revenue and business analysis (BDA) revenues will reach $150.8 billion in 2017, an increase of 12.4% compared to 2016. By 2020, its revenue will exceed $210 billion.

Most of this comes from hardware and services. For big data software, in some cases, each company’s needs are based on the unique needs of the vertical industry. Even in the same industry, such as the retail industry or the manufacturing industry, the needs of each company will be different, so developing a packaged software is difficult to serve potential customers in all industries.

For big data software, the key to success is to provide the underlying applications and tools for the enterprise to build custom applications. People can understand what a real big data application is. Many of these companies that provide applications are well-known in the industry, however, there are also products of interesting startups included.

Below are the applications from 20 companies specializing in big data building or related businesses. There is no special order for this list.

DFor big data software, the key to success is to provide the underlying applications and tools for the enterprise to build custom applications. People can understand what a real big data application is. Many of these companies that provide applications are well-known in the industry, however, there are also products of interesting startups included.

Below are the applications from 20 companies specializing in big data building or related businesses. There is no special order for this list.

Domo

Josh James, former CEO of Omniture, founded Domo in 2010 to provide a way for companies to view data from different sources and islands. It automatically extracts data from spreadsheets, social media, internal storage, databases, cloud-based applications, and data warehouses, and displays information on customizable dashboards. It is known for its ease of use and the ability of almost anyone to build and use it, not just data scientists. It comes with a number of preloaded charts and data source designs that can be moved quickly.

2. Teradata Database

Beginning with Teradata Database 15, the company added new big data capabilities such as Teradata’s unified data architecture, enabling organizations to access and process analytic queries across multiple systems, including importing and exporting bidirectional data from Hadoop. It also adds 3D display and processing of geospatial data, as well as enhanced workload management and system availability. The cloud-based version that supports AWS and Azure is called Teradata Everywhere, which provides massive parallel processing analysis between public cloud-based data and locally deployed data.

3. Hitachi Vantara

Hitachi Vantara’s big data products are based on some popular open source tools. Founded in 2017, Hitachi Vantara is the storage and data center infrastructure business unit of Hitachi Data Systems, a joint venture between the Hitachi Insight Group IoT business and Hitachi Pentaho Big Data. Pentaho is based on the Apache Spark in-memory computing framework and the Apache Kafka messaging system. Pentaho 8.0 also adds support for Apache Knox Gateway to authenticate users and enforce access rules for big data repositories. It also adds support for building analytics applications through Docker containers.

4. TIBCO’s Statistica

TIBCO’s Statistica is a predictive analytics software for companies of all sizes, using Hadoop technology to perform data mining on structured and unstructured data, solving IoT data, deploying analysis and support on devices and gateways anywhere in the world. In-database analysis of functions from platforms such as Apache Hive, MySQL, Oracle, and Teradata. It uses templates to design complete analysis, so fewer technical users can perform their own analysis and export models from a computer to another device.

5. Panoply

Panoply sells so-called intelligent cloud data warehouses using artificial intelligence to eliminate the development and coding required to transform, integrate, and manage data. The company claims that its intelligent cloud data warehouse essentially provides data management as a service that can consume and process up to 1 petabyte of data without any intervention. Its machine learning algorithm can examine data from any data source and perform queries and visualizations on it.

6. IBM Watson Analytics

Watson Analytics is IBM’s cloud-based analytics service. When users upload data to Watson, it provides users with questions that can be answered based on data analysis and provides critical data visualization immediately . It also enables simple analysis, predictive analytics, intelligent data discovery, and a variety of self-service dashboards. IBM also has another analytics product, SPSS, that can be used to discover patterns from data and find correlations between data points.

7. SAS Visual Analytics

The Statistical Analysis System (SAS) was created in 1976, earlier than Big Data, to process large amounts of data. It can mine, change, manage, and retrieve data from a variety of sources, perform statistical analysis on the data, and present it in a series of methods, such as statistics, charts, etc., or write data to other files. It supports all types of data prediction and analysis points, along with predictive tools to analyze and predict the process.

8. Sisense Business Intelligence Software

Sisense claims to provide the only business intelligence software that allows users to prepare, analyze and visualize complex data from multiple sources on commodity server hardware. Sisense’s on-chip high-performance data engine can query terabytes of data in less than a second and provide a set of templates for different industries.

9. Talend’s Big Data Studio

Talend has been focused on generating clean native code for Hadoop without having to write all the code by hand. It provides interfaces to a variety of big data repositories such as Cloudera, MapR, Hortonworks, and Amazon EMR . It recently added a data preparation application that allows customers to create a common dictionary and use machine learning to automate the data cleanup process to prepare data for data processing in less time.

10. Cloudera

Apache Hadoop is the most popular provider and supporter, and it has partnerships with companies such as Dell, Intel, Oracle, SAS, Deloitte and Capgemini. It consists of five main applications: Cloudera Essentials, the core data management platform, Cloudera Enterprise Data Hub, the data management platform, Cloudera Analytic DB for business intelligence and SQL-based analysis, Cloudera Operational DB, the highly scalable NoSQL database , and Cloudera Data Science and Engineering, data processing, data science, and machine learning running on the Core Essentials platform.

11. MongoDB

MongoDB has become the database of choice for big data projects across industries. Its NoSQL support is suitable for unstructured data that is often used by big data. Its flexibility, support for JSON and JavaScript, a flexible framework, a rich query language, and extensive industry support make it the standard for databases.

12. Vertica Analytics Platform

Big data databases are traditionally unstructured, meaning that any type of data can be stored in them. Micro Focus’s Vertica analytics platform uses a traditional column-oriented relational database format, but is specifically designed to handle modern analytics workloads from Hadoop clusters. The platform uses clustering to store data and fully supports SQL, JDBC, and ODBC. It uses columnar storage instead of row storage because accessing columns makes it easier to group data.

13. SAP Vora

SAP HANA itself is not suitable for big data. This is an in-memory RDBMS system. But when users add the big data interface HANA Vora, it becomes more feasible. Vora allows HANA to connect to Hadoop repositories and extend the Apache Spark execution framework for interactive analysis of enterprise and Hadoop data. So data scientists can gain the power of HANA by supporting big data storage.

14. Oracle Big Data suite

Oracle’s database giant has a full suite of big data integration products, such as data integration platform cloud, stream analysis, IoT support, and support for Oracle Event Hub cloud services, supporting real-time data streaming, bulk data processing, enterprise data quality and data governance capabilities. Apache Kafka.

15. Apache Cassandra

Although MongoDB is the leading database, Cassandra has an advantage in scalability. This was written by former Facebook employees who spanned a large number of commodity servers to ensure no fault points and advanced fault tolerance.

16. Plotly

Plotly or Plot.ly focus on data visualization without the need for programming or data science skills. Its GUI is designed to import and analyze data and use the D3.js JavaScript library for all of its graphics. Its dashboards can be generated in real time, generated from existing data pools, and exported to a variety of visualization tools, including Excel, SQL Database, Python, R, and MATLAB.

17. Wolfram Alpha

Want to calculate or learn about new things about things? Wolfram Alpha is a great tool for finding information about everything. Doug Smith of Proessaywriting said his company uses the platform for advanced research in finance, history, social and other professional fields. For example, if you enter “Microsoft,” you will receive input explanations, fundamentals and financial information, latest transactions, price history, performance comparisons, data return analysis, correlation matrices, and many other information.

18. Tibco Spotfire

Spotfire is an in-memory analytics platform that includes support for big data repositories and performs predictive analytics. It provides a connector for Apache Hadoop that allows users to perform data mashups, data discovery and analysis tasks on big data, just as they do for Oracle, SAP and other traditional data sources. It also supports real-time data-driven event visualization and an artificial intelligence-driven recommendation engine that reduces data discovery time.

19. AnswerRocket

AnswerRocket focuses on natural language search data discovery, making it a tool for business users, not a mysterious tool for data scientists. It can provide answers in minutes instead of waiting a few days to form a query.

AnswerRocket users can ask questions in everyday language and get visual effects in seconds, and then they can drill down on specific charts or charts for further insight.

20. Tableau

Tableau specializes in drawing from multiple data silos and integrating them into a single dashboard to create interactive, flexible dashboards with custom filters and drags and connections with just a few mouse clicks. Tableau also uses natural language queries, so users can ask for business questions, not technical issues.

Josh James, former CEO of Omniture, founded Domo in 2010 to provide a way for companies to view data from different sources and islands. It automatically extracts data from spreadsheets, social media, internal storage, databases, cloud-based applications, and data warehouses, and displays information on customizable dashboards. It is known for its ease of use and the ability of almost anyone to build and use it, not just data scientists. It comes with a number of preloaded charts and data source designs that can be moved quickly.

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