UPDATED 09:00 EDT / FEBRUARY 05 2019

BIG DATA

Doubling down on data science, Databricks lands massive $250M round

Big-data company Databricks Inc. is topping up its war chest with a hefty $250 million late-stage round of funding.

The Series E round was led by Andreessen Horowitz. Coatue Management, Microsoft Corp. and New Enterprise Associates also participated in the round, which brings Databricks’ total capital raised to date to $498.5 million.

Databricks sells a Unified Analytics Platform based on the open-source Apache Spark big-data framework that’s used by enterprises to analyze data, build data pipelines across siloed storage systems and prepare labeled datasets for model building. The idea is that organizations can then use the Unified Analytics Platform to train machine learning and other artificial intelligence models using their existing data.

The platform also provides collaborative features intended to foster closer communication between data scientists and engineers who need to work together in order to build better AI models.

Led by the founders of Apache Spark, Databricks has helped to drive massive adoption of that open-source platform in the last few years. In its press release, the privately owned company said it managed to top $100 million in annual recurring revenue during 2018 while tripling year-over-year growth in its subscription revenue.

Ali Ghodsi (pictured), Databricks’ cofounder and chief executive officer, said those numbers make Databricks one of the fastest-growing enterprise software companies around.

“What’s driving this incredible growth is the market’s massive appetite for Unified Analytics,” Ghodsi said in a statement. “Organizations need to achieve success with their AI initiatives and this requires a Unified Analytics Platform that bridges the divide between big data and machine learning.”

Databricks’ growth has been helped by some useful updates over the past year that aimed to expand the utility of its Unified Analytics Platform. Back in March for example, the company launched a cloud-based offering called Microsoft Azure Databricks that can easily be deployed on Microsoft’s Azure cloud and integrated with services such as the Azure SQL Data Warehouse.

Other new features added over the last year include Databricks Runtime For ML, which provides preconfigured modeling and distributed training environments. Tightly integrated with such popular AI frameworks and libraries as TensorFlow, Scikit-Learn, Keras and XGBoost, they serve to accelerate environment provisioning and configuration management.

The company also introduced Databricks MLflow, which provides an open source toolkit for simplifying ML modeling, training and operationalization across multiclouds. The offering integrates closely with Apache Spark, SciKit-Learn, TensorFlow and other open-source frameworks.

Such progress is positive, but in an article last summer, analyst James Kobielus of Wikibon, owned by the same company as SiliconANGLE, pointed out that Databricks faces significant challenges in trying to sustain its growth in a market where Apache Spark’s adoption is likely to plateau in a similar fashion to how Hadoop’s has. Kobielus notes that Apache Spark is starting to show its age, and feels increasingly like a legacy technology in many AI shops. Moreover, he said, its recent new offerings don’t signal a clear differentiation for Databricks in a market that has become saturated with AI DevOps, modeling and training tools offered by the likes of Amazon Web Services Inc. and Google LLC, among others.

Still, Spark remains the industry’s most dominant runtime engine and library for in-memory, parallel machine learning, as well as a useful tool in many AI platforms for efficient data preparation and training for many modeling initiatives, Kobielus said. As such, the analyst said, he believes Databricks will be able to use today’s funding to innovate further and overcome the the challenges he highlighted.

“Databricks is able to attract so much funding because the larger data-science workbench market, aka ‘AI DevOps,’ is booming,” Kobielus said. He noted that Databricks now offers a “full-featured data science workbench” that’s not only fairly mature in comparison to others, but also provides a smooth upgrade path for data scientists working on sophisticated AI projects.

Those sentiments were shared by analyst Doug Henschen of Constellation Research Inc., who told SiliconANGLE that he believes Spark will see yet more adoption in the enterprise both for its data-processing and data-transformation capabilities, and for the advanced analytical work it performs.

“Databricks faces competition both from third-party Spark services and emerging analytics platforms and studios such as Amazon SageMaker and Google AI Hub,” Henschen said. “The higher value is in the model-development work, and that’s where lots of alternatives to Databricks are emerging.”

Kobielus also noted the strength of Databrick’s rivals’ offerings but said the company remains a pacesetter in the data science market.

“It’s an exceptionally solid brand run by dynamic visionaries who are doing excellent work and have loyal customers,” Kobielus said. “My sense is that they’re more likely to prevail going forward in this changed market than the combined Cloudera/Hortonworks is, based on the strengths of their respective machine learning, deep learning and artificial intelligence development platforms.”

Databricks certainly has a plan to do just that. In a statement to SiliconANGLE, the company said it’s aiming to use its new funds to double down on its growth in Asia, Europe and the Middle East, and the Americas. This includes expanding its sales teams in those regions, more investment in engineering, and opening new offices in several countries across the globe.

Photo: SiliconANGLE

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