Dremio to Showcase Self-Service Data Platform at Strata Data Conference New York

MOUNTAIN VIEW, Calif., Sept. 19, 2017 -- Dremio, the self-service data company, announced today it will publicly demonstrate, for the first time, its Self-Service Data Platform at Strata Data Conference NYC 2017 in booth #P13 at the Jacob Javitz Convention Center. Company representatives will showcase capabilities for a new approach to data analytics – empowering business users to curate precisely the data they need, from any data source, then accelerate analytical processing for BI tools, machine learning, data science, and SQL clients. Working with existing data sources and business intelligence tools, Dremio starts to deliver value in minutes, enabling data engineers, analysts, and data scientists to be more productive.​

Making its entrance into the data analytics market in July 2017, Dremio’s software is being used by leading organizations across 50 countries and 6 continents, including Daimler, a leading producer of premium cars and the world’s largest manufacturer of commercial vehicles, Intel and OVH, Europe’s leading cloud provider.

“We started Dremio to shatter a thirty year-old data paradigm that holds virtually every company back,” said Tomer Shiran, co-founder and CEO, Dremio. “Dremio is the world’s first self-service solution that works with existing data sources – relational databases, data lakes, NoSQL, etc., as well as your favorite BI, visualization, and data science tools, like Tableau, Python, and R. We give data consumers the ability to accelerate any source, of any size, at any time, so analysts, data scientists, and data engineers can innovate, ask new questions, and test hypothesis faster.”

Dremio provides an increased leap in performance, based on four areas of innovation:

• Dremio Reflections™. Dremio accelerates processing and isolates operational systems from analytical workloads by physically optimizing data for specific query patterns. Dremio maintains multiple reflections of datasets, optimized for heterogeneous workloads that are fully transparent to users. Dremio's query planner automatically selects the best reflections to provide maximum efficiency, providing a breakthrough in performance that accelerates processing by up to a factor of 1000.
• Apache Arrow Execution Engine. Dremio is the first Apache Arrow-based distributed query execution engine. This represents a breakthrough in performance for analytical workloads as it enables extreme hardware efficiency and minimizes serialization and deserialization of in-memory data buffers between Dremio and Python, R, Spark, and other analytical tools.
• Native Query Push Downs. Dremio optimizes processing into underlying data sources, maximizing efficiency and minimizing demands on operational systems. Dremio rewrites SQL in the native query language of each data source, such as Elasticsearch, MongoDB, and HBase, and optimizes processing for file systems such as Amazon S3 and HDFS.
•  Universal Relational Algebra. Cost-based query planner automatically substitutes plans to make optimal use of Dremio Reflections™.

Join the Strata Data Conference conversation on Twitter @dremiohq and @strataconf

About Dremio
Dremio reimagines analytics for modern data. Created by veterans of open source and big data technologies, Dremio is a fundamentally new approach that dramatically simplifies and accelerates time to insight. Dremio empowers business users to curate precisely the data they need, from any data source, then accelerate analytical processing for BI tools, machine learning, data science, and SQL clients. Dremio starts to deliver value in minutes, and learns from your data and queries, making your data engineers, analysts, and data scientists more productive.​ For more information, visit www.dremio.com.

Founded in 2015, Dremio is headquartered in Mountain View, CA. Investors include Lightspeed Venture Partners and Redpoint. Connect with Dremio on GitHub, LinkedIn, Twitter and Facebook

Media Contact:
Kim Pegnato
Winkowski Public Relations
(781) 620-0016
[email protected]