Tracking Tiger Global (Week 35)
2 Tiger deals this week: Databricks, Hu.ma.ne
Dear subscribers,
Welcome to the second edition of Tracking Tiger Global, a newsletter that provides weekly updates on Tiger Global’s latest investments.
There were ONLY 2 new VC deals with Tiger’s involvement announced this week. The dataset has been updated on the website and can be accessed here.
DATABRICKS (US | $1.6bn Series H | SaaS | Tiger participated)
HU.MA.NE (US | $100m Series B | Consumer Electronics | Tiger led)
DATABRICKS (US | $1.6bn Series H | SaaS | Tiger participated)
Founded in 2013 and headquartered in San Francisco, Databricks is a “Data + AI” company. In a nutshell, Databricks is a provider of a unified data platform that spans data science, ML, analytics and data engineering. It recently raised a $1.6bn Series H round at a $38bn post-money valuation, making it one of the most valuable private companies in the world. The round was led by Morgan Stanley’s Counterpoint Global Fund, with other new investors including Baillie Gifford and ClearBridge Investments. Several existing investors, including Tiger Global also participated in the round.
Evolution of Data Management Platforms
In order to better understand Databrick’s product suite, it is important to understand the evolution of the data management architecture, as shown below.
About a decade ago (second sequence above), enterprises began building data lakes, which are repositories for raw data in a variety of formats. While suitable for storing data in different formats in a cost-efficient manner, data lakes lack several critical features that are needed to run ML workloads efficiently. “Hence, enterprises today use multiple systems - a data lake, several data warehouses, and other specialized systems such as streaming, time-series, graph, and image databases. However, having a multitude of systems introduces complexity and, more importantly, introduces delay as data professionals invariably need to move or copy data between different systems.”
In order to solve these challenges associated with multiple systems, Databricks created the concept of a “Data Lakehouse”, which adds a transactional storage layer on top of the data lake (third sequence above). “It uses similar data structures and data management features as those in a data warehouse but runs them directly on cloud data lakes.” Hence, it combines the best of data lakes and warehouses - data reliability and governance characteristics of a data warehouse and flexibility for storing in different formats at low cost, associated with data lakes. In summary, key benefits of the Data Lakehouse solution include (a) lower compute cost, (b) higher team productivity, and (c) faster model deployment. A good comparison between data lakes, warehouses and lakehouses can be found here.
Databricks - Lakehouse Platform
An overview of Databricks’s Lakehouse platform is provided above. Firstly, Databricks support all the major cloud providers (AWS, Azure and Google Cloud). In fact, Amazon, Google and Microsoft are investors in Databricks. Secondly, different types of data (structured, semi-structured, unstructured, streaming), that come into the Lakehouse platform, are stored in the Open Data Lake. Thirdly, Delta Lake, an open-source storage layer that brings reliability to data lakes, runs on top of data lakes. It effectively represents Databricks’s lakehouse concept, introduced in the section above, and combines the advantages of data lakes and warehouses. On top of the Delta Lake layer, there are several applications (based on Azure Databricks documentation), including:
Data Engineering: “an interactive workspace that enables collaboration between data engineers, data scientists, and machine learning engineers.”
BI and SQL Analytics: “an easy-to-use platform for analysts who want to run SQL queries on their data lake, create multiple visualization types to explore query results from different perspectives, and build and share dashboards.”
Data Science and Machine Learning: “an integrated end-to-end machine learning environment incorporating managed services for experiment tracking, model training, feature development and management, and feature and model serving.”
Current traction and use of proceeds
According to CNBC, Databricks is on pace to generate +$1bn in 2022 revenue, implying 75% YoY growth. Its latest $38bn valuation, implies a 38x EV / 22E revenue. For comparison, Snowflake currently trades at c.60x EV / NTM revenue multiple, with NTM revenue estimate of $1.5bn.
Databricks serves 5,000+ enterprise clients (including >40% of the Fortune 500) in 19 countries. Based on LinkedIn data, the company employs c. 2,600 people across the globe with +33% headcount growth over the last 6 months.
According to Databricks, “the new funding will be used to accelerate the company’s lead in the massive and rapidly growing data lakehouse market. […] In addition, under Andy Kofoid’s leadership (author’s note: newly appointed President of Global Sales Field Operations, ex-Salesforce), the company will invest to accelerate the adoption of the Databricks Lakehouse Platform globally, by entering new markets, enabling and growing its partner ecosystem, and building a broad catalogue of industry solutions.”
Sources:
HU.MA.NE (US | $100m Series B | Hardware | Tiger led)
Founded in 2017, Hu.ma.ne (in stealth mode) “is an experience company that creates products for the benefit of people, crafting technology that puts people first.” It recently raised $100 in a Series B round led by Tiger Global, with participation from SoftBank, BOND, Forerunner Ventures, Qualcomm Ventures, and others.
It is not surprising that the company has attracted significant capital from high-profile backers. “The calibre of individuals working at Humane is incredibly impressive,” said Chase Coleman, Partner, Tiger Global. “These are people who have built and shipped transformative products to billions of people around the world.”. According to 9to5mac.com, “Imran Chaudhri (Chairman and President at Hu.ma.ne) previously spent over two decades working on the design at Apple, and Bethany Bongiorno (CEO) spent nearly a decade managing iPhone, iPad, and Mac software.”
While the founding team has deep roots in consumer electronics, it is unclear what exactly the company is building. One of the roles advertised on the website (excerpt below) mentions that “the Humane’s computer vision team is responsible for key technologies enabling our new class of mobile devices and services.” While not being specific, it does provide an indication of what the company is working on! ;)
The Series B round will “enable Humane to scale its operations and continue executing and expanding on its mission to deliver the next shift between humans and computing.” The company employs c.60 people and has c.40 job openings on its website.
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