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

morganstanley-logo
emc-logo
humana-logo
tvguide-logo
stpaul-logo
johnhopokins-logo
MobilityWare
Back to Top