It is indeed a challenge to analyze and make sense out of large volume data. Deriving intelligence from such data heaps through manual analysis is cumbersum and time consuming. AD2 helps in interpreting and understanding such data through easy to use, flexible and interactive visualization techniques. AD2 engages sampling, filtering, binning, clustering, aggregation etc to contain data explotion and help user derive sensible information from large data masses. Simple drag and drop facility will cut down the learning curve and allows even a novice user to handle various tool sets provided in AD2 in a short time interval. AD2 will also have built in machine learning components in its tool set that would help users extract hidden patterns. AD2 provides connectors to various data sources like HDFS, Flat Files, FTP, RDBMS, Streaming Queues like MQTT, ZMQ, KAFKA etc. Users can merge data from diverse data sources and perform complex analysis. Supports streaming data analysis from IoT devices.
AD2 can handle both static and streaming data and dynamic interact over the derived dataset. Built in capability to generate data histograms helps user to cleanse the data before analysis. Charts extends dynamically avoiding cramping of information with the growing data volume which otherwise would be unreadable. Common features like zooming, panning etc. brings about better interactivity and reduced effort in making sense out of large volume data. Dynamic Interaction ensures that data can be viewed from different perspectives thereafter can be tuned and shaped to fit the users need. AD2 can integrate data from diverse data sources with no user level data conversions expected. SQL Builder a dynamic GUI based SQL development interface aid rapid development of complex queries across multiple tables from diverse sources thereby eliminating the need to have SQL development skills.