The Big Data List

Big Data has been the hot topic in BI over the past few years. What are the Big Data sources that you should know about? Here's a list of some major names in the Big Data space.

Big Data Infrastructure Vendors

Cloudera

A software company that provides Apache Hadoop-based software, targeting enterprise-class Hadoop deployments.

Cloudera contains the main, core elements of Hadoop, providing reliable, scalable distributed data processing of large data sets (chiefly MapReduce and HDFS), as well as other enterprise-oriented components that provide security, high availability, and integration with hardware and other software.

SAP HANA

HANA is an in-memory, column-oriented, relational database management system developed and marketed by SAP AG. HANA is an acronym for "High-Performance Analytic Appliance" based on in-memory technology, enabling Big Data analysis to be performed at faster speeds.

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Hortonworks

Started by funding from Yahoo! as an independent company, Hortonworks provides an enterprise-ready version of Hadoop. A Microsoft and Yahoo! partner.

MapR

MapR provides a commercial distribution of Hadoop as an alternative to Cloudera and Hortonworks.

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Big Data Terms

MapReduce

Created by Google, MapReduce is an infrastructure or framework for storing massive amounts of data, that orchestrates by marshalling distributed servers, running the various tasks in parallel, and managing all communications and data transfers between the various parts of the system. The model is inspired by the map and reduce functions commonly used in functional programming.

The map procedure performs filtering and sorting (such as sorting students by first name into queues, one queue for each name) and the reduce procedure performs a summary operation (such as counting the number of students in each queue, yielding name frequencies).

Hadoop

An open-source framework for large-scale data processing and storage on clusters of servers. Hadoop was created by Apache in 2005, and is based on the MapReduce paradigm.

Hadoop is considered to be an ideal environment for extracting and transforming huge volumes of data. Also, Hadoop is known for providing a scalable, reliable and distributed processing environment.

#1 Ranking: Read how InetSoft was rated #1 for user adoption in G2's user survey-based index

Big Data Sources

There are many Big Data sources that can be used for analytics. Some of the most popular include:

Twitter
Facebook
Google Analytics
Web server logs
Social media feeds
Sensor data
Machine-generated data

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InetSoft for Big Data Analytics and Dashboarding

What do all of these Big Data sources have in common? They can all be accessed and integrated with other sources using InetSoft's StyleBI.

Holding to the open source standards, InetSoft's solution can access all major Big Data sources, as well as a broad array of more traditional sources. StyleBI's robust data mashup engine extracts data from all sources and allows non-technical users to create their own data mashups using a simple drag-and-drop tool.

Once mashups are created, the data grid cache pulls the data necessary for reports or analysis and stores it in-memory, enabling analytics to be performed on Big Data sources at very high speed.

To unleash the potential of Big Data, consider InetSoft for your dashboarding and reporting needs.

About InetSoft

Since 1996 InetSoft has been delivering easy, agile, and robust business intelligence software that makes it possible for organizations and solution providers of all sizes to deploy or embed full-featured business intelligence solutions.

Application highlights include visually-compelling and interactive dashboards that ensure greater end-user adoption plus pixel-perfect report generation, scheduling, and bursting. InetSoft's patent pending Data Block™ technology enables productive reuse of queries and a unique capability for end-user defined data mashup. This capability combined with efficient information access enabled by InetSoft's visual analysis technologies allows maximum self-service that benefits the average business user, the IT administrator, and the developer.

InetSoft solutions have been deployed at over 5,000 organizations worldwide, including 25% of Fortune 500 companies, spanning all types of industries.

InetSoft Big Data Articles

Big Data Analytics Vendors
Explains how StyleBI enables in‑depth, high‑speed analytics on Big Data sources like Hadoop, Cloudera, MapR and SAP HANA. Highlights hybrid in‑memory and disk‑based grid caching for fast performance. Demonstrates drag‑and‑drop mashup capability to unify disparate data sources.
Big Data Visualization Solution
Describes how InetSoft’s visualization software delivers pixel‑perfect interactive dashboards over massive data sets. Emphasizes self‑service analytics with intuitive visual tools requiring no coding. Shows mashups of big data with enterprise sources in real time.
Native Big Data Analytics Application
Details how StyleBI runs natively inside Hadoop/Spark rather than merely connecting as an external layer. Enables in‑cluster dashboards, mashups, machine learning and analysis without moving data. Reduces latency and centralizes analytics within the Big Data platform.
Big Data Manipulation
Highlights StyleBI’s ability to access and combine data from Hadoop, Spark, SAP HANA, spreadsheets and relational sources in one environment. Its mashup engine removes the need for rigid schema and heavy ETL. Users can craft interactive visual analysis workflows with drag‑and‑drop ease.
Big Data Demo Visualization
Offers a live demo that showcases how StyleBI handles big data via real‑time mashups and dashboards. Shows how Data Grid Cache accelerates performance on massive datasets. Illustrates self‑service report creation with minimal technical overhead.
Big Data Analytics in Banking
Examines how financial institutions use big data to personalize services, detect fraud, and optimize risk models. Shows how StyleBI supports these insights through comprehensive analytics on transactional and behavioral data. Emphasizes improved decision‑making and customer experience.
Big Data Analytics Companies
Compares BI vendors in the Big Data sphere, positioning InetSoft as a pioneer in hybrid architecture. Explains how StyleBI’s caching and mashup tech delivers flexibility at scale. Offers evaluation resources to choose a vendor for enterprise Big Data needs.
Velocity Variety Veracity Value
Introduces the five V’s of data analytics—volume, velocity, variety, veracity and value—and why they matter for big data. Links these principles to the need for tools like StyleBI that manage scale and speed. Shows how quality and variety impact insight generation.
Six Phases of Data Analytics
Outlines the six stages—ask, prepare, process, analyze, share and act—of a data analytics lifecycle. Describes how StyleBI supports each stage from data prep to sharing dashboards. Emphasizes structured workflows even in Big Data contexts.
Using Big Data in Biotech
Explores ten biotech use cases—from genomics to pharmaceutical discovery—where Big Data drives innovation. Demonstrates StyleBI’s role in visualizing massive biotech datasets. Highlights how analytics accelerates research and agricultural and healthcare advances.