Big Data & Data Warehousing

Top 10 Applications

Big Data & Data Warehousing Applications



This was designed keeping in mind a means to give businesses to visualise their data from different silos of origin. Data from spreadsheets, social media, on-premise storage, databases, cloud-based apps and data warehouses is automatically pulled out and the information is presented on a customisable dashboard. It has been lauded for the ease with which one can use and how well it can be set up and used by anyone and not just a trained data scientist. In order to get moving quickly, it comes along with a number of pre installed and preloaded designs for charts and data sources.

Tera Database

This enables companies to access and process analytic queries across multiple systems, including dual direction data import and export from Hadoop. 3D representation and processing of geospatial data, along with top notch workload management and system availability was also added. This features a gigantic parallel processing analytics between public cloud-based data and on-premise data

Big Data by Hitachi Vantara

This is built specifically on some unique open source tools. These are a combination of Hitachi Data Systems storage and data centre infrastructure business, the Hotachi Insight Group IoT business and Hitachi’s Pentaho Big Data business into a combined company. Pentaho is based on the Apache Spark in-memory computing framework and the Apache Kafka messaging system. Pentaho 8.0 also added support for the Apache Knox Gateway to authenticate users and enforce access rules for accessing Big Data repositories. It also adds support for building analytics apps via Docker containers.


This is a predictive analytics software used in businesses, both big and small, using Hadoop technology to perform data mining on structured and unstructured data, addresses IoT data, has the ability and capacity to deplot analytics on devices and gateways anywhere in the world, and supports in-database analytics from varied platforms. Templates are used used for designing full and complete analyses, so that less technical users can do their own analysis, and the models can be exported from PCs to other devices


The Artificial Intelligence that is required to remove the development and coding needed for transforming, integrating and managing data, is used for selling the Smart Cloud Data Warehouse. This most importantly provides data management- as – a – service, with the ability to consume and process up to a petabyte of data without any intervention or interruption. Several queries and visualisations can be performed when data is examined by its machine capturing algorithms

IBM Watson Analytics

When data is uploaded on Watson, it presents you with the questions it can help answer based on its analysis of the data and provide key data visualisations immediately. It also does simple analysis, predictive analytics, smart data discovery, and offers a variety of self-service dashboards. IBM has another analytics product, SPSS, which can be used to uncover patterns from data and find associations between data points.

SAS Visual Analytics

This began long ago before the concept of Big Data could take form, for the purpose of handling huge businesses. It can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on said data, then present it in a range of methods, like statistics, graphs, and such, or write the data out to other files. It supports all types of data forecasting and analysis essentials and comes with forecasting tools to analyze and forecast processes.


Sisense claiming to offer the only business intelligence software that makes it easy for users to prepare, analyze and visualize complex data by drawing from multiple sources on commodity server hardware. Sisense’s In-Chip high performance data engine can perform queries on a terabyte of data in less than one second, and it comes with a batch of templates for different industries.

Big Data Studio by Talend

Talend has always focused on generating clean, native code for Hadoop, eliminating the need to manually code everything. It provides interfaces to a variety of Big Data repositories, like Cloudera, MapR, Hortonworks, and Amazon EMR. Customers can now create a common dictionary, and also use machine learning which helps automate the data cleansing process to get it ready for processing in lesser time.


The most popular provider and supporter of Apache Hadoop, it has partnerships with Dell, Intel, Oracle, SAS, Deloitte and Capgemini. It consists of five primary applications: Cloudera Essentials, the core data management platform; Cloudera Enterprise Data Hub, the data management platform; Cloudera Analytic DB for BI and SQL-based analytics; Cloudera Operational DB, its highly scalable NoSQL database, and Cloudera Data Science and Engineering, the data processing, data science, and machine learning that run on top of the Core Essentials platform