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July 31, 2018

Cisco Backs Data Startup Dremio

Dremio Corp., the startup launched by the creators of Apache Arrow development platform for in-memory data, continues to attract investors to its data platform.

The three-year-old company based in Mountain View, Calif., announced additional funding by Cisco Investments, extending its Series B funding round in January to $25 million. The startup has so far raised $45 million. Previous investors include Norwest Venture Partners, Lightspeed Venture Partners and Redpoint Ventures.

Dremio said Tuesday (July 31) it would use the new funding to extend its self-service data access tool to Cisco Systems’ (NASDAQ: CSCO) customers.

Dremio’s framework based on Apache Arrow is billed as eliminating the need for data warehouses or proprietary extracts, instead connecting data analytics tools to data sources stored in Hadoop clusters, the cloud of within Kubernetes cluster orchestrators. That capability along with Apache Arrow helps accelerate queries on a range of data sources and relational databases.

The open source data platform, which is accessed via web browser, is designed to eliminate the extensive data engineering effort required to connect users with data residing in Hadoop, Amazon Web Services (NASDAQ: AMZN) cloud storage and other big data repositories.

The startup said it has expanded to more than 70 employees after tripling its staff in 2017.

Dremio recently launched a new licensed open source technology, The Gandiva Initiative for Apache Arrow. Apache Arrow provides the core data building block for heterogeneous data infrastructures and tools, including Python, R, Spark, RDBMS, NoSQL databases and file systems.

Dremio emerged from stealth mode last July with an eye toward reducing the complexity of accessing large data sets. Apache Arrow, the in-memory data layer that forms the digital glue to connect disparate big data engines, was developed by Dremio founder and CTO Jacques Nadeau. Introduced in 2016, Arrow is designed to scale to thousands of Hadoop nodes via native deployments on YARN.

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