Tamr Sparks Boosts Compatibility with Apache Spark and Partners with DataPRM

By CIOReview | Wednesday, March 30, 2016
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FREMONT, CA: Big data analytics company, Tamr has announced compatibility with Apache Spark, an open source cluster computing framework. The firm currently works with clients such as GE, Toyota Motors Europe, GlaxoSmithKline and others aiming to generate unified data for making better decisions. Spark’s in-memory architecture eases the scalable machine learning process and promotes Tamr’s machine based approach for effectively preparing enterprise data. The firm also dessiminates the collaboration with DataRPM, a cognitive data science company. The alliance helps DataRPM to automate data science and machine learning from ingestion to insights.

“We are thrilled to add DataRPM to our list of strategic partners. Together we are working with some of the largest corporations in the world to help them view data volume and data variety not as obstacles, but as a strategic advantage and differentiator,” says Nidhi Aggarwal, Head of Product and Strategy, Tamr.

Apache Spark is a fast and general engine used for large scale data processing. Tamr effectively utilizes the product compatibility of Spark by developing core components and open interfaces to support the data curation solutions. The toolset will pave a path towards a scalable application development scenario for verticals such as life sciences, customer data integration, procurement and many more.

Based in Cambridge, Massachusetts, Tamr offers innovative solutions in areas such as Clinical Data Conversion (CDISC), Media Analytics and many others. Tamr is deployed in the production process at various companies which includes pharmaceutical firms, information services providers and retailers.

“Our customers are typically operating with hundreds of data sources and have dozens of consumers for that data. Adding Spark compatibility to Tamr ensures that we can continue to scale with our customers, differentiating us from data preparation tools designed for individual users working with a handful of data sources,” adds Aggarwal.