Alpine Data Labs, a leader in Advanced Analytics Software on Big Data and Hadoop, announced today that it is one of the first Enterprise Advanced Analytics Platforms to be certified by Databricks on Apache Spark. Databricks, the company founded by the creators of Spark, recently announced the new Spark Certification as a means to encourage new development on the distributed, in-memory cluster computing framework. Alpine has a global customer base and was recently positioned in “Gartner’s Magic Quadrant for Advanced Analytics Platforms.”
We are thrilled to see innovators like Alpine Data Labs further the popularity of Spark and extend its value to business users and enterprise customers” says Ion Stoica, co-founder and CEO at Databricks.
Alpine Chorus, the company’s flagship product, is a leading analytics platform for Big Data. The company’s modern architecture makes it easy for business users and IT departments to adopt. It is designed for scalability and its end-to-end capabilities make it an excellent fit for enterprises eager to make analytics part of their culture.
Spark has brought new levels of speed and agility to the world of Big Data and we couldn’t more excited to be one of the earlier adopters of this technology”, says Steven Hillion, Alpine Data Labs’ Chief Product Officer. “Companies have been struggling to deliver on the full promise of Hadoop. Hadoop is falling behind the pace of business because it is too complicated and slow. In order to allow business users to gain deep insights out of Big Data, we need analytics software that is faster, user-friendly and more agile. At Alpine Data Labs, we are committed to this vision and it is embedded in everything we do.”
Beyond data-related issues, Big Data fails to impact people’s decisions rapidly. According to a recent Bain & Co research report, only 4% of companies attribute better decision making and performance to the use of Big Data analytics.
This shows a clear disconnect between the multiple actors of the analytics pipeline: current solutions make it really hard for data scientists, business users and engineers to work together effectively,” Hillion said. ” This creates fragmented and inefficient processes” he concludes. “That’s why we’ve developed an analytics platform on top of a web-based collaborative environment where everything, from data discovery to data transformation to analytics, can be centralized, in a transparent and secure way. We’ve found that this is the best way to encourage the sharing of data, analytics, insights and best practices”.
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