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Doolytic

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Solutions Gallery > Doolytic

Solution overview

Doolytic is a data discovery web application enabling Business Intelligence methodologies on Cloudera CDH environments. Where data can be collected from both structured and unstructured sources and users expect an agile experience, with no predefined paths or constraints, Doolytic offers its novel approach to analytics, by:

  • Exposing the enterprise data in a ready to click interface
  • Enabling users to locate the right asset through its navigation interface
  • Querying data with filters that persist and propagate to the whole interface
  • Combining and aggregating assets, leveraging the power of Spark, Hive and Impala
  • Visualizing data as text or charts that can be embedded in external web applications
  • Storing its results back on Hadoop

Finding a path through information is the very first obstacle to overcome when facing a data lake. Doolytic knows this very well and offers its data lookup engine to locate matching information across all the assets, discovering relations and guiding the user through its journey. And when data has been finally mastered, Doolytic dashboard editor composes charts and tables in a visual, JavaScript free experience.

Doolytic requires no previous knowledge of Hive, Spark, Impala nor MapReduce, but experienced users can take advantage of its query engine, to combine and refine data with an SQL-like language.

Being at the front of the Cloudera CDH cluster, Doolytic is the perfect environment to rapidly prototype any Big Data application, by combining its data transformation and presentation features. The rich web service API offered by Doolytic is a solid starting point for data governance and data delivery to third party solutions and any other enterprise consumer.

Positive Business Outcomes

  • Enables users without technical preparation to big data access
  • Masks Hadoop complexities, leveraging Spark, Hive, Impala and Solr for the end user
  • Brings the typical business intelligence experience to the world of Cloudera CDH
  • Shorten the time required to deliver insights on analytics projects
  • Turns expert BI users into Citizen Data Scientists

Metrics and Proof Points

Case study: A Big Telco selected Doolytic to replace a former analytics tool, unable to keep the pace with data rates and volumes. With Doolytic on top of Cloudera CDH, the Telco ensured to its internal Citizen Data Scientists proper performance and capabilities on three analytic environments, aggregated by 4 hours, 1 hour and 5 minutes.

Doolytic Volumi

The former tool kept on-line no more than 3 billion records for 4h aggregations, 2 billions for 1h aggregations and was unable to fulfill the requirements for 5 minute aggregations. With Doolytic, 172 billion records are available as 4h aggregations, 34 billions for 1h aggregations and 30 billions for 5m aggregations, with a retention of 14 months, 1 month and 1 week respectively.

Another 12 billion records assets are computed to ease analytic tasks.

 

Key highlights

Category
Drive Customer Insights

About Nevis 
Nevis is a Horsa Group company focused on Business Analytics. Horsa Spa is an Italian System Integrator with 550+ employee focused on different industry, from Manufacturing, to Retail, Fashion, Telco and Financial Services (Insurance and Banking). The Business Analytics Unit has more than 300 clients and 100+ Consultants. We are 100% analytics focused with functional and technical skill on BI, CPM, Machine Learning, AI and Big Data.

Required Capabilities

  • Doolytic requires at least a Cloudera CDH cluster with the following services: HDFS, YARN, Spark with SparkSQL and Impala. 
  • Solr is optionally required to enable Doolytic data discovery features.
  • No previous experience on Cloudera CDH is required to exploit Doolytic advantage, but previous SQL experience can be used through the query engine.

Differentiators

  • Click only interface for search, aggregation and charting
  • Whole interface filters
  • Data abstraction
  • Data cross-search on whole data thanks to Solr

Learn more about the solution

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