Big Data & Business Intelligence Practice

Our Big Data & BI Practice is composed of two groups:

Big Data with the Hadoop

Professional Services team with experience leveraging the Hadoop Ecosystem to generate new insights from data.

End-to-End Data Warehouse & Business Intelligence

Our end-to-end data warehouse and business intelligence solution and professional services team anchors around the use of AWS Redshift as the core data warehouse, and incorporates open source toolsets from Talend and Pentaho to provide robust, capable and a highly cost effective end-to-end solution.

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Hadoop Big Data Practice

Our Hadoop team is a collection of developers and architects with skills and experience in almost every element of the Hadoop 2.0 Ecosystem; from the core Hadoop Distributed File System (HDFS) to the use of YARN to enable real-time and streaming data applications, in addition to the traditional batch-based MapReduce data processing and the data analysis tools like Hive.

CASE STUDY

Hadoop in the Pharmaceutical Industry 

Working with an international pharmaceutical organization, our team is leveraging a number of elements from the Hadoop ecosystem to accelerate the use of clinical trial and other research data.
Our team is contributing to several aspects of the program including enhancing the security architecture, enhancing the deployment processes, and enabling faster processing of research findings.

Data Warehouse & Business Intelligence Practice

Our Integrated Data Warehouse & Business Intelligence team has pulled together an end-to-end architecture that leverages cloud components, open source toolsets and specialized software solutions into a cost effective, efficient, reliable and visually compelling data insights platform for enterprises.

The integrated solutions include the following elements:


  • Amazon Redshift – as the data warehouse – a fast, fully-managed, petabyte-scale data warehouse service that enables clients to analyze data in a simple and cost-effective manner

  • Talend Open Studio – for data management and data integration – a rich and extensive open source toolset that makes Extract, Transform and Load (ETL) processes simple to develop and easy to maintain

  • Amazon Kinesis – for real-time data processing – this service enables the effective streaming of data into a data solution, and provides robustness around the underlying elements of load-balancing, services coordination, and fault-processing.

  • Amazon Simple Storage Service (Amazon S3) – as the staging layer – in the end-to-end architecture raw data pulled from source systems is staged in S3, and from there picked up by ETL jobs created with Talend Open Stuido and moved into the Redshift Data Warehouse

  • Pentaho Business Analytics – as the Business Intelligence platform – this open source BI platform provides the toolset to easily create browser-based dashboards, data visualization, and reports. And supports the use of 3rd party open sources plugins such as Pivot4J which can be used to create pivot table styled frontends for datasets organized in Pentaho OLAP servers.

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CASE STUDY

An End-to-End Data Warehouse & BI Solution 

Working for a commercial vehicles and engine manufacturing company our team developed an end-to-end data warehouse and business intelligences solution using Amazon Redshift, Amazon S3, Talend Open Studio, Pentaho Business Analytics, and Roambi Analytics to replace, modernize, enhance and extend the previous SQL Server and Microsoft Excel based solution which was falling far short of business expectations.

Bringing it all Together to Tell Your Story

Though the underlying toolsets differ, our two Data Practice groups share a passion for insights from data, and for delivering value to clients. And the two groups often work together to bring forward a better solution that either could on their own.

CASE STUDY

Creating a Hadoop-Roambi Solution

Working for a global pharmaceutical company, we are developing a solution that pulls data from Hadoop based data repositories into Roambi Flow, to enable the company to easily and quickly create rich, multi-touch publications around their research data, and tell their story more effectively.