In the world of big data, Databricks has emerged as one of the leading companies providing innovative solutions for data engineering, data science, and machine learning. Founded in 2013, the company has grown rapidly, thanks to its cloud-based data processing and analytics platform that leverages Apache Spark. In this article, we will explore the growth story of Databricks, from its inception as a Spark project to its current status as a pre-IPO company. Read our complete Databricks guide here.
The Early Days of Databricks
Databricks was founded in 2013 by Ali Ghodsi, Matei Zaharia, Patrick Wendell, Reynold Xin, and Andy Konwinski. The founders were all involved in the development of Apache Spark, an open-source distributed computing system that is used to process large datasets. They realized that Spark had the potential to revolutionize the way organizations manage and analyze their data, but that it needed to be made more accessible to a wider audience.
To address this need, the founders decided to create a cloud-based platform that would make it easy for data scientists and engineers to use Spark without having to worry about the complexities of infrastructure management. They called the platform Databricks, and it quickly gained popularity in the data science community.
Databricks: Growth and Expansion
In the early days, Databricks was focused on building out its platform and establishing itself in the market. The company raised $13.9 million in a Series A funding round led by Andreessen Horowitz in 2014, and $33 million in a Series B funding round led by NEA in 2015. These funds were used to expand the platform and to hire additional staff.
Over time, Databricks continued to grow its customer base, attracting organizations across a wide range of industries, including finance, healthcare, and technology. The company also expanded its product offerings, adding new features and capabilities to the platform, such as Delta Lake, a data lake management system, and MLflow, an open-source machine learning platform.
In 2019, Databricks raised $400 million in a Series F funding round led by Andreessen Horowitz, bringing its total funding to over $900 million. The company used the funds to further expand its platform and to accelerate its growth.
In June 2021, Databricks announced that it had raised $1.6 billion in a Series H funding round, at a valuation of $38 billion, making it one of the most valuable privately held companies in the world. This funding round was led by Counterpoint Global, and included participation from other investors such as Franklin Templeton, Fidelity, and Whale Rock. The funds will be used to further expand the platform and accelerate Databricks’ growth.
The company has also made several strategic moves that suggest it is preparing for a public offering in the near future. In late 2020, Databricks hired a new CFO, Tim Bixby, who has extensive experience leading finance teams at publicly traded companies. The company has also recently added several high-profile executives to its leadership team, including former AWS executive Stephanie Buscemi, who joined as Chief Marketing Officer in February 2021.
With its impressive valuation and growing executive team, Databricks is well positioned to continue its rapid growth and expansion. While the company has not yet announced any specific plans for an IPO, many analysts believe that it is only a matter of time before Databricks goes public.
Looking to the Future of Databricks Growth
As Databricks continues to grow and expand, it is poised to play a leading role in the future of data science and big data. The company’s cloud-based platform and its focus on accessibility and collaboration have made it a popular choice among data scientists and engineers, and its machine learning capabilities are among the most advanced in the industry.
With its recent appointment of a new CEO and its growing executive team, Databricks is likely to continue its rapid growth and expansion in the years ahead. Whether it eventually goes public or remains privately held, the company