Krunal talked about the architecture of the product and explained various components and how they are powered by AWS. He was in conversation with Santiago Cardenas, Partner Solution Architect, AWS.

Here is an overview of the segment in Krunal’s own words:

It was a fantastic opportunity to be invited by AWS to talk about Saama’s Life Science Analytics Cloud (LSAC). We have built a revolutionary product using AWS cloud, and I was delighted to talk about it on a platform that celebrates innovative architecture.

We began by talking about a new and innovative trends in the pharmaceutical industry where companies with big-data-based data lakes are moving to a completely cloud-based SaaS environment. My host, Santiago, and I discussed how this offers them more flexibility and scalability and uses best of both worlds – multitenancy for API driven data services and separated data storage for each customer instance.

We then discussed various components of the architecture, such as the orchestrator, data visualization charts, S3 data lake, and the application of various Amazon components – AWS Lambda (Serverless Computing), Amazon EMR, Amazon Glacier, and Amazon Redshift.

I described the entire journey of the data, starting from LSAC connecting to various source systems used in the clinical world and data sets getting ingested into the S3 data lake, where it went through various sanity checks and Change Data Capture using custom-built algorithms.

I highlighted the fact that we use EMR in transient mode – which means we spin up EMR Hadoop cluster when we need to process and transform large volumes of data and we shut it off after that. This action helps in scaling our solution as and when needed while keeping the costs to a minimum.

I went on to explain the importance of compliance in our work as the pharmaceutical and life sciences industries are subject to rigorous compliance and regulations.

I added that as we have to consider compliance every step of the way, we have used Amazon Glacier for building durable archives.

One of the most interesting points of the conversation was the importance of the virtual assistant that uses deep learning capability to answer direct queries such as, “why certain sites are missing milestones?” and comes back with relevant answers, insights, and recommendations.

My host immediately recognized the solution’s appeal and said that it took self-service to the next level.

Learn more about the LSAC suite of solutions on our website: Life Science Analytics Cloud

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