Data Engineering

Big Data is commercial boon for pharma – how can you unlock its power? Big Data is no fad, it is fundamentally transforming how Pharma Companies operate and paving way for better commercial results. What are the top opportunities? How can the commercial benefits of Big Data come to fruition. We have worked on many use cases and realized practical benefits of Big Data in the Pharma Commercial Effectiveness World

Hadoop Ecosystem & Components for Pharma

Spark, Pig and Hive are three of the best-known Apache Hadoop projects commonly deployed in Pharma commercial applications. While there are a lot of articles and discussions about whether Spark, Hive or Pig is better, in practice many pharma organizations do not only use a single one because each is optimized for specific functions.

Amazon Ecosystem for Pharma

Amazon big data ecosystem is evolving rapidly and find many applications in the pharma commercial and scientific environments. Some of the key components that we actively use :

AWS provide good stuffs for creating Big Data infrastructure on cloud. Many big companies, like Netflix, trust AWS for their Big Data. However, many people also don’t use AWS big data infrastructure. Most of their reason is because there are not many people can operate AWS’ ‘BigData’ tools and also these tools from AWS are quite specific to AWS and they cannot have the same tools in other cloud computing service like Rackspace or DigitalOcean. That’s why they prefer the open source version of Big Data like Hadoop, Cassandra, etc.

ETL for Pharma

Organizations challenged with overburdened EDWs need solutions that can offload the heavy lifting of ETL processing from the data warehouse to an alternative environment that is capable of managing today’s data sets. The first question is always, “how can this be done in a simple, cost-effective manner that doesn’t require specialized skill sets?” Let’s start with Hadoop. As previously mentioned, many pharma organizations deploy Hadoop to offload their data warehouse processing functions. After all, Hadoop is a cost-effective, highly scalable platform that can store volumes of structured, semi-structured, and unstructured data sets. Hadoop can also help accelerate the ETL process, while significantly reducing costs in comparison to running ETL jobs in a traditional data warehouse. However, while the benefits of Hadoop are appealing, the complexity of this platform continues to hinder adoption at many organizations. It has been our goal to find a better solution.