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December 15, 2016

Spark Gains Momentum With Latest Investment

George Leopold

(Leigh Prather/Shutterstock)

Apache Spark continues to attract data technology investors, with project creators at Databricks announcing completion of another funding round totaling $60 million.

Databricks said Thursday (Dec. 15) the Series C funding round was led by New Enterprise Associates (NEA) and joined by existing investor Andreeson Horowitz. So far, the San Francisco-based company has raised $107.5 million.

The startup said it would use the cash infusion to accelerate investments in a commercial product based on Apache Spark engine, pitched as a “just-in-time data platform.”

Databricks claims surging customer adoption of Spark, including more than 400 customers. The new investment will be used to expand product development and customer support along with sales and marketing, the company said.

“This funding will advance our engineering and go-to-market strategies to address all of our customer’s pain points as we continue to grow the Spark community,” stated Ali Ghodsi, Databricks CEO and co-founder.

The company released a user survey in September revealing steady momentum as the Spark user community more than tripled over the past year to 225,000 members. “The results indicate that Spark has moved well beyond the early-adopter phase at high-tech companies and is now mainstream in large data-driven enterprises,” the startup asserted.

Along with Spark-based streaming and machine learning applications entering production, the Databricks survey found that deployment of other Spark components such as DataFrames more than doubled over the last year. Introduced in February 2015, DataFrames is a distributed data collection organized in named columns. The survey found that production deployments rose to 38 percent over the last year.

Analysts note that Spark’s flexibility is its main strength among developers. The recent Databricks survey results stressed this attribute, noting that Spark’s simplicity, a characteristic found lacking in another recent industry survey that cited “inflexibility” in current data analytics infrastructure as a key reason for many failed big data projects.

Investors are meanwhile betting that ease of use as the size of data sets soar augur well for the analytics startup. “In 2016, we witnessed an acceleration in data processing workloads moving to the cloud,” Pete Sonsini, general partner at NEA noted in a statement. “Databricks is well-positioned to further drive this revolution.”

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