Enterprise Grid® for Big Data Analytics Software
Big Data Analytics Software Definition
Big data analytics software provides insights into large, complex data sets collected from big data clusters, helping business users understand data trends, patterns, and anomalies through visualizations, reports, and dashboards, often requiring query languages to extract data from unstructured file systems.
Core Capabilities of Big Data Analytics Software
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
Common Use Cases for Big Data Analytics Software
Data engineers, analysts, and business intelligence teams use big data analytics software to extract value from large-scale, unstructured data environments. Common use cases include:
- Querying and analyzing large Hadoop or distributed data clusters to surface business insights
- Detecting patterns and anomalies in high-volume data sets for operational or strategic decision-making
- Building self-service charts and dashboards for non-technical stakeholders from big data sources
How Big Data Analytics Software Differs from Other Tools
Big data analytics software is solely focused on manipulating complex, large-scale data clusters into understandable visualizations, differentiating it from analytics platforms, which support a wide range of data sources and connectors beyond big data. The two categories are mutually exclusive. Big data analytics tools are commonly used at companies running Hadoop in conjunction with big data processing and distribution software and integrate with data warehouse software as the central hub for integrated data. Some solutions also leverage machine learning and natural language processing to enable natural language querying.
Insights from G2 on Big Data Analytics Software
Based on category trends on G2, query flexibility and scalability for large data sets stand out as standout capabilities. Faster insight generation from complex data environments stand out as the primary benefit of adoption.
Enterprise Big Data Analytics Grid® Scoring Description
Products shown on the Enterprise Grid® for Big Data Analytics have received a minimum of 10 reviews/ratings in
data gathered by March 07, 2023. Products are ranked by customer satisfaction (based on user reviews) and market
presence (based on market share, seller size, and social impact) and placed into four categories on the
Grid®:
- Products in the Leader quadrant are rated highly by G2 users and have substantial Market Presence scores. Leaders include: Databricks Lakehouse Platform, Alteryx, Azure Databricks, Google Cloud BigQuery, MATLAB, Starburst, Qubole, and Dremio
- High Performing products have high customer Satisfaction scores and low Market Presence compared to the rest of the category.
- Contender products have relatively low customer Satisfaction scores and high Market Presence compared to the rest of the category. While they may have positive reviews, they do not have enough reviews to validate those ratings. Contenders include: Azure Data Lake Analytics, Confluent, IBM Cloud Pak for Data, and DIAdem
- Niche products have relatively low Satisfaction scores and low Market Presence compared to the rest of the category. While they may have positive reviews, they do not have enough reviews to validate those ratings. Niche products include: Apache Pig and Arcadia Enterprise
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in any form without G2’s prior written permission. While the information in this report has been
obtained from sources believed to be reliable, G2 disclaims all warranties as to the accuracy,
completeness, or adequacy of such information and shall have no liability for errors, omissions, or inadequacies in such information.
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