According to Slawik, Dremio proved more capable thanks to very fast performance and a highly functional user interface that allows users to track data lineage visually. Moonfare selected Dremio in a proof-of-concept runoff with AWS Athena, an interactive query service that enables SQL queries on S3 data. “What’s super important is that it takes away ETL jobs,” he says, adding, “With Dremio, if the data is in S3, you can query what you want.” Users coming from a data warehouse environment shouldn’t care where the data resides,” says Angelo Slawik, data engineer at Moonfare. “You can intuitively query the data from the data lake. The lakehouse will also help with fraud prevention. Queries will include which marketing campaigns are working best with which customers and which fund managers are performing best. When the implementation goes live in the fall of 2022, business users will be able to perform self-service analytics on top of data in AWS S3. Moonfare, a private equity firm, is transitioning from a PostgreSQL-based data warehouse on AWS to a Dremio data lakehouse on AWS for business intelligence and predictive analytics. Many organizations, however, are finding this paradigm of relying on two separate systems of insight untenable. With the advent of big data, a second system of insight, the data lake, appeared to serve up artificial intelligence and machine learning (AI/ML) insights. Traditionally, organizations have maintained two systems as part of their data strategies: a system of record on which to run their business and a system of insight such as a data warehouse from which to gather business intelligence (BI). The result is an emerging paradigm shift in how enterprises surface insights, one that sees them leaning on a new category of technology architected to help organizations maximize the value of their data. Those challenges are well-known to many organizations as they have sought to obtain analytical knowledge from their vast amounts of data. Previously, Walgreens was attempting to perform that task with its data lake but faced two significant obstacles: cost and time. The giant pharmaceutical chain had put its lakehouse in place to address just such challenges in its quest, to, as Guadagno puts it, “To get the right product in the right place for the right patient.” For Guadagno, the need to match vaccine availability with patient demand came at the right moment, technologically speaking. “We leveraged the lakehouse to understand the moment,” he says. To find out, he queried Walgreens’ data lakehouse, implemented with Databricks technology on Microsoft Azure. With 65 million vaccine doses to administer at the height of the COVID-19 pandemic, Luigi Guadagno, vice president of pharmacy renewal and healthcare platform technology at Walgreens, needed to know where to send them.
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