Best Big Data Analytics Softwares

Data analysis can provide users with a deeper understanding of their customers, businesses, and industries. Big data analysis involves a complex process that spans business management, data scientists, developers, and production teams. Let’s have a review of some of the best big data analytics softwares of 2019.


Birst focuses on one of the thorniest challenges in data analysis — building trust in data from many different sources within an enterprise. Birst’s user data layer automatically retrieves, maps, and integrates data sources and provides users with a unified view of the data. Second, with Birst’s adaptive user experience, breaking the silos between data discovery and dashboards, users can access unified data sources and develop analytics without coding or IT intervention.


Dataiku DSS integrates most of the data analysis lifecycle into one tool. It enables data analysts to capture and prepare data, build prediction models, integrate with data mining tools, and develop visualizations for end users. The collaborative environment of DSS enables different users to work together and share knowledge within the DSS platform. DSS focuses on data science and tends to provide in-depth analysis use cases, such as churn analysis, demand forecasting, fraud detection, spatial analysis, and lifetime value optimization.


Domo focuses on providing quick insight to low-tech users. It has more than 500 built-in data connectors and visual data preparation interface, which can accelerate data procurement and transformation. Its powerful business intelligence capabilities enable visualization and social commentary to facilitate collaboration. Domo also has native mobile device support, with the same analytics, annotations, and collaboration experience as desktop. Domo simplifies remote embedding analysis with “Cards” or deployable interactive visual portlets. These components integrate with Web applications using JavaScript APIs and iframes and can track utilization by unique endpoints.


KNIME is an open-source enterprise analytics platform designed for data scientists. KNIME’s visual interface includes all nodes from extraction to rendering of data, with an emphasis on statistical models. KNIME integrates with several other data science tools, including R, Python, Hadoop, and H2O, as well as many structured and unstructured data types. KNIME supports cutting-edge data science use cases such as social media sentiment analysis, medical claims outline testing, market basket analysis, and text mining.


Looker aims to provide a unified data environment and centralized data governance and to be a reusable component for data analysts. With the extract/load/transform (ELT) approach, Looker enables users to model and transform data on demand. Looker also has a proprietary LookML language that leverages SQL in a visual and reusable way. The concept of reusability extends to Looker Block components, which are reusable data linking, analysis, visualization, and distribution utilities. Finally, Looker can easily integrate popular collaboration and workflow tools like Jira, Slack, and Segment.


Microstrategy, founded in 1989, is one of the older data analysis platforms, with expectations for this mature toolset. Microstrategy connects to many enterprise assets such as ERP and cloud data providers and integrates with multiple common user clients such as Android, iOS, and Windows. It also provides a variety of common services, such as alerts, distribution, and security, and supports many BI functions, such as data enrichment, visualization, and user management.


Pentaho emphasizes Internet of Things (IoT) data collection and integration with other data sources such as ERP and CRM systems, as well as big data tools such as Hadoop and NoSQL. Its built-in IoT endpoint integration and unique metadata injection capabilities accelerate data collection from multiple sources. Pentaho’s visualization capabilities include basic reporting and sophisticated predictive models. Pentaho is actively approaching embedded analysis. In addition to investing in integrated components like the REST API, Pentaho’s thorough training and project management approach helps ensure that customers succeed through embedded analytics.


Qlik emphasizes speed by automating the relationship between data discovery and multiple data sources during data acquisition and preparation. Instead of using the traditional query-based approach to retrieve data, Qlik’s correlation engine automatically analyzes data from all inbound sources, identifies links, and presents this combined data set to the user. Thanks to Qlik’s memory processing architecture, which includes compressed binary indexes, logical reasoning, and dynamic computing, multiple concurrent users can quickly explore large and diverse data sets. Qlik supports RESTful API as well as HTML5 and JavaScript. This supports the integration of Web, business applications, and mobile platforms for enterprise-level embedded analytics.

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