Big Data

Extracting Actionable Insights

Deriving actionable insights from Big Data to facilitate business decision-making in real time is what delivers the greatest value for companies. While many companies have achieved the basics in terms of data management and analytics, fewer have been able to put the processes and tools in place for utilizing analytics to elevate the value delivered to customers and inform their digital transformation initiatives.

Data Engineer or Data Scientist or Both?

Further complicating their efforts is some confusion over necessary roles and responsibilities. Companies have invested in hiring Data Scientists only to find that there still gaps that can only be filled with Big Data Engineers. Big Data Engineers prepare the “big data” infrastructure, make sure data is easily accessible, and optimize the performance of the big data ecosystem. The primary function of a Data Scientist is to analyze the integrated data and identify the valuable and actionable insights.

Leave the Heavy Lifting to DMD

The Big Data Engineering team at Digital Mind Development has accumulated extensive experience and expertise in managing data on a physical infrastructure level. We have also built up a team of highly trained experts who analyze the relevant data and draw value from it. Our Big Data Engineering team helps companies stay ahead of the curve.

Tha DMD business model is similar to the data-as-a-service model. Think Big-Data-as-a-Service - BDaaS. Adopting this model relieves companies of the upfront costs associated with managing large quantities of data and building up a team that possesses the necessary expertise to operationalize Big Data and convert it into a business asset.


The most popular stack of technologies were used to solve the customer’s data challenges and execute on tasks in Big Data processing and analysis:

  • Hadoop/HDFS stack with Hbase non-relation distributive database for customer data storage
  • Batch processing with Hive infrastructure for data aggregation, querying and source of analysis
  • Cloudera Impala query engine is used to access customer’s data as a low-latency queuing technology 

If your company is ready to embark on a Big Data initiative and you need to rely on experts to get the job done, please contact us today for a free consultation and project estimate.

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