Big data analytics software provides insights into large data sets that are collected from big data clusters. These tools help business users digest data trends, patterns, and anomalies and synthesize the information into understandable data visualizations, reports, and dashboards. Because of the unstructured nature of big data clusters, these analytics solutions often require a query language to pull the data out of the file system. Some solutions may offer self-service features so that non-technical employees can assemble their own charts and graphs from big data sets.
Some big data analytics solutions offer features powered by machine learning, such as natural language processing, allowing the user to query company data in a natural manner. Big data analytics software is commonly used at companies running Hadoop in conjunction with big data processing and distribution software to collect and store data. In addition, these products typically integrate with data warehouse software, the central storage hub for a company’s integrated data.
Big data analytics software differs from analytics platforms inasmuch as the former are solely focused on the manipulation of complex and large scale big data clusters into understandable visualizations, while the latter are geared toward a wide range of data sources and connectors. The two categories are mutually exclusive, and those products which are solely focused on big data use cases are only categorized in the big data analytics category.
To qualify for inclusion in the Big Data Analytics category, a product must:
Consume data, query file systems, and connect directly to big data clusters
Allow users to prepare complex big data sets into helpful and understandable data visualizations
Create business-applicable reports, visualizations, and dashboards based on discoveries inside the data sets
