How does Ideata Analytics work?

Architecture

Ideata Analytics makes it easy to connect, combine and analyze data from private big data sources, web & premium data sources to derive actionable insights

Analytics Platform Powered by Apache Spark

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Lighting Fast Processing

When comes to BigData processing speed always matters. We always look for processing our huge data as fast as possible. Spark enables our platform to run up to 100x faster in memory, and 10x faster even when running on disk.

Scalable

Join data across disparate data sources for meaningful analysis and hidden insights. The ability to query and use data across multiple systems without the physical reconciliation or movement of source data.

Sophisticated Analytics

Same platform for real time and batch processing. We support SQL queries, streaming data, and complex analytics such as machine learning and graph algorithms out-of-the-box. Not only that, users can combine all these capabilities seamlessly in a single workflow.

Know How

  • Ideata Analytics Visualization Engine

    • Drag and drop interface to generate analysis and dashboards.
    • Visual data preparation capabilities to support business users to perform data preparation operations.
    • Visual interface to use pre-packaged analytic applications with user specific data.
    • Interactive dashboard and analysis with multiple charting options.
  • Ideata Analytics Data Interface

    • Supports multiple input sources including Hadoop, NoSQL, RDBMS and leading web data sources (public and premium).
    • In house datasets are accessed without moving data to another persist storage and thereby saving cost, time and effort.
    • Web streaming and files datasets are loaded in Hadoop or Cassandra for further operations.
    • Ability to schedule data ingestion and refresh activities.
    • Multi-tenant architecture to ensure strict access control and data confidentiality between multiple user groups.
  • In Memory data units

    • Leverage Apache Spark which is an in-memory data processing application to perform extremely fast data computation.
    • Support full data lineage.
    • Ability to merges datasets on the fly.
    • Pre computation of Spark datasets to provide faster data access.
  • Data Quality and Transformation Engine

    • Performs preprocessing, parsing and transformation on data to load data in distributed in-memory units.
    • Auto discovery of unstructured data format to reach faster analysis.
    • In memory processing helps situational analysis on live streams of data.
    • Supports number of operations including aggregations, joins, and statistical operators.
    • Advanced machine learning algorithm support.
  • Analytic Apps

    • Prepackaged applications which users can use to connect to their data and get instant insights.
    • Each analytic cookbook supports data preparation, generation of analysis and dashboards for the user to get started quickly.
    • Future releases will have a visual interface to build a custom analytic cookbook. Currently, these cookbooks are stored in JSON format.
  • Distributed in-memory Execution

    • Highly available, scalable and resilient distributed in memory engine.
    • Supports job scheduling and iterative algorithms. Jobs have the ability to resumed from any stage of failure.
    • Configurable throughput.
    • Multiple job scheduling options (fair, parallel etc) available .
    • Multiple underlying file system support to spill data partitions that do not fit in-memory.
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