Data Warehousing & Cloud BI

Move. Store. Report.  

Operational and transactional systems grow in any large enterprise and form the silos of information that are hard to report on. Building a Data Warehouse enables separation of reporting from transactional data stores, organizes the data suitable for query and allows slicing/dicing. Data from disparate sources is brought together via Extract Transform Load (ETL) and ingestion through Map Reduce (Hadoop) tool-set into the warehouse. Business Intelligence (BI) front-end tools enable the consumers of data query the data.

Volume

The analysis of outcomes begins in part with analyzing the volume of expected data from each data source and its projected growth over time. Terabytes are very common.

Velocity

The rate at which data is being generated determines the rate at which the consumption and ingestion of data must scale to.

Variety

Variation in terms of values, cases and business rules also is crucial for understanding and ingesting the data into the data warehouse to allow the business to succeed with good results

Technology

Hadoop


MongoDB


Talend


Tableau


Informatica


MS SQL


Lucene


Oracle


Pentaho


R


Solr


Birst


Cassandra


HBase


Industry Expertise


Media & Advertising


Education & Publishing


Healthcare


Real Estate


Life Science


Mobile Analytics


Financial Services


Retail


High Tech/SaaS

Our Approach


Discovery


Define Outcomes


Warehouse Design


Data Collection & Ingestion


Quality Assurance


BI & Analytics Views

Some of our Clients

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MobilityWare
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