BLUEPRINT FOR BIG DATA SUCCESS:
Streamlined Data Refinery
Create a single ‘refinery’ by streamlining all your data sources through a scalable big data processing hub. Using Hadoop for transformations, refined data is query-ready and can be immediately pushed downstream to an analytical database for low-latency analytics.
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Turn Hadoop into a Valuable Multi-source Business Information Hub, Just Waiting to be Queried
Pentaho’s agile data integration and analytics platform allows you to stream data through Hadoop for transformation processing and immediately push the refined data to any of today’s popular analytic databases (such as Vertica or Greenplum). For the end-user, a rich set of data discovery, reports, dashboards and visualizations are immediately available.
Diverse data, at a high volume, analytics-ready - thanks to faster queries, rapid ingestion and powerful processing:
- Flexible data integration allows data to be seen as it is transformed shaving days/weeks off the development cycle.
- 15X faster than hard coding, Pentaho’s GUI for MapReduce integration allows data to be moved and processed between Hadoop and ANY data source or system.
- Broad data integration accommodates and grows with your existing architecture.
- Powerful array of self-service analytics and visualization for all end users - business users, analysts, and data scientists.
A common use case can be best seen in the following example:
- This electronic marketing firm has created a refinery architecture for delivering personalized offers.
- Online campaign, enrollment, and transaction data is ingested via Hadoop, processed and then sent on to an analytic database.
- A business analytics front-end includes reporting and ad hoc analysis for business users.
The End Result
- Business users have immediate insight into ALL data
- IT can scale ETL and data management ensuring cost savings
- Engineer new data sets on-the-fly for prediction and trends
Paytronix, Maximizing Loyalty Programs
Improving top and bottom line for restaurant loyalty programs.
Big Data Goal:
- Data driven insight into patron preferences
- 80% reduction in processing time for faster insights
- Analyze restaurant patron purchases to improve loyalty
- Gain insight into patron buying patterns from over 8,000 restaurants