Fivetran hauls in $565M on $5.6B valuation, acquires competitor HVR for $700M

Fivetran, the data connectivity startup, had a big day today. For starters it announced a $565 million investment on $5.6 billion valuation, but it didn’t stop there. It also announced its second acquisition this year, snagging HVR, a data integration competitor that had raised over $50M, for $700 million in cash and stock.

The company last raised a $100 million Series C on a $1.2 billion valuation, increasing the valuation by over 5x. As with that Series C, Andreessen Horowitz was back leading the round with participation from other double dippers General Catalyst, CEAS Investments, Matrix Partners and other unnamed firms or individuals. New investors ICONIQ Capital, D1 Capital Partners and YC Continuity also came along for the ride. The company reports it has now raised $730 million.

The HVR acquisition represents a hefty investment for the startup, grabbing a company for a price that is almost equal to all the money it has raised to date, but it provides a way to expand its market quickly by buying a competitor. Earlier this year Fivetran acquired Teleport Data as it continues to add functionality and customers via acquisition.

“The acquisition — a cash and stock deal valued at $700 million — strengthens Fivetran’s market position as one of the data integration leaders for all industries and all customer types,” the company said in a statement.

While that may smack of corporate marketing speak, there is some truth to it, as pulling data from multiple sources, sometimes in siloed legacy systems is a huge challenge for companies and both Fivetran and HVR have developed tools to provide the pipes to connect various data sources and put it to work across a business.

Data is central to a number of modern enterprise practices including customer experience management, which takes advantage of customer data to deliver customized experiences based on what you know about them, and data is the main fuel for machine learning models, which use it to understand and learn how a process works. Fivetran and HVR provide the nuts and bolts infrastructure to move the data around to where it’s needed, connecting to various applications like Salesforce, Box or Airtable, databases like Postgres SQL or data repositories like Snowflake or Databricks.

Whether bigger is better remains to be seen, but Fivetran is betting that it will be in this case as it makes its way along the startup journey. The transaction has been approved by both company’s boards. The deal is still subject to standard regulatory approval, but Fivetran is expecting it to close in October

#andreessen-horowitz, #cloud, #data-pipelines, #enterprise, #exit, #fivetran, #fundings-exits, #ma, #mergers-and-acquisitions, #recent-funding, #startups

With the right tools, predicting startup revenue is possible

For a long time, “revenue” seemed to be a taboo word in the startup world. Fortunately, things have changed with the rise of SaaS and alternative funding sources such as revenue-based investing VCs. Still, revenue modeling remains a challenge for founders. How do you predict earnings when you are still figuring it out?

The answer is twofold: You need to make your revenue predictable, repeatable and scalable in the first place, plus make use of tools that will help you create projections based on your data. Here, we’ll suggest some ways you can get more visibility into your revenue, find the data that really matter and figure out how to put a process in place to make forecasts about it.

You need to make your revenue predictable, repeatable and scalable in the first place, plus make use of tools that will help you create projections based on your data.

Base projections on repeatable, scalable results

Aaron Ross is a co-author of “Predictable Revenue,” a book based on his experience of creating a process and team that helped grow Salesforce’s revenue by more than $100 million. “Predictable” is the key word here: “You want growth that doesn’t require guessing, hope and frantic last-minute deal-hustling every quarter- and year-end,” he says.

This makes recurring revenue particularly desirable, though it is by no means the be-all-end-all of predictable revenue. On one hand, there is always the risk that recurring revenue won’t last, as customers may churn and organic growth runs out of gas. On the other, there is a broader picture for predictable revenue that goes beyond subscription-based models.

Ross and his co-author, Marylou Tyler, outline three steps to predictable revenue: predictable lead generation, a dedicated sales development team and consistent sales systems. They wrote an entire book about it, so it would be hard to sum it up here. So what’s the takeaway? You shouldn’t base your projections on processes and results that aren’t repeatable and scalable.

Cross the hot coals

In their early days, startups usually grow via word of mouth, luck and sheer hustle. The problem is that it likely won’t lead to sustainable growth; as the saying goes, what got you here won’t get you there. In between, there is typically a phase of uncertainty and missed results that Ross refers to as “the hot coals.”

Before the hot coals, predicting revenue is vain at best, and oftentimes impossible. I, for one, remember being at a loss when an old-school investor asked me for five-year profit-and-loss projections when my now-defunct startup was nowhere near a stable money-making path. Not all seed investors expect this, so there was obviously a mismatch here, but the challenge is still the same for most founders: How do you bridge the gap between traditional projections and the reality of a startup?

#analytics, #business-intelligence, #chargebee, #chartmogul, #finance, #fishtown-analytics, #fivetran, #forecasting, #saas, #salesforce, #segment, #startups, #stitch, #tc, #y-combinator-alumni, #zuora

Census raises $16M Series A to help companies put their data warehouses to work

Census, a startup that helps businesses sync their customer data from their data warehouses to their various business tools like Salesforce and Marketo, today announced that it has raised a $16 million Series A round led by Sequoia Capital. Other participants in this round include Andreessen Horowitz, which led the company’s $4.3 million seed round last year, as well as several notable angles, including Figma CEO Dylan Field, GitHub CTO Jason Warner, Notion COO Akshay Kothari and Rippling CEO Parker Conrad.

The company is part of a new crop of startups that are building on top of data warehouses. The general idea behind Census is to help businesses operationalize the data in their data warehouses, which was traditionally only used for analytics and reporting use cases. But as businesses realized that all the data they needed was already available in their data warehouses and that they could use that as a single source of truth without having to build additional integrations, an ecosystem of companies that operationalize this data started to form.

The company argues that the modern data stack, with data warehouses like Amazon Redshift, Google BigQuery and Snowflake at its core, offers all of the tools a business needs to extract and transform data (like Fivetran, dbt) and then visualize it (think Looker).

Tools like Census then essentially function as a new layer that sits between the data warehouse and the business tools that can help companies extract value from this data. With that, users can easily sync their product data into a marketing tool like Marketo or a CRM service like Salesforce, for example.

Image Credits: Census

Three years ago, we were the first to ask, ‘Why are we relying on a clumsy tangle of wires connecting every app when everything we need is already in the warehouse? What if you could leverage your data team to drive operations?’ When the data warehouse is connected to the rest of the business, the possibilities are limitless.” Census explains in today’s announcement. “When we launched, our focus was enabling product-led companies like Figma, Canva, and Notion to drive better marketing, sales, and customer success. Along the way, our customers have pulled Census into more and more scenarios, like auto-prioritizing support tickets in Zendesk, automating invoices in Netsuite, or even integrating with HR systems.

Census already integrates with dozens of different services and data tools and its customers include the likes of Clearbit, Figma, Fivetran, LogDNA, Loom and Notion.

Looking ahead, Census plans to use the new funding to launch new features like deeper data validation and a visual query experience. In addition, it also plans to launch code-based orchestration to make Census workflows versionable and make it easier to integrate them into enterprise orchestration system.

#andreessen-horowitz, #business-intelligence, #canva, #ceo, #clearbit, #computing, #crm, #cto, #data-management, #data-warehouse, #dylan-field, #enterprise, #figma, #fivetran, #github, #google, #information, #information-technology, #logdna, #looker, #loom, #marketo, #netsuite, #notion, #parker-conrad, #recent-funding, #salesforce, #sequoia-capital, #startups, #tc, #warehouse, #zendesk

Databricks launches SQL Analytics

AI and data analytics company Databricks today announced the launch of SQL Analytics, a new service that makes it easier for data analysts to run their standard SQL queries directly on data lakes. And with that, enterprises can now easily connect their business intelligence tools like Tableau and Microsoft’s Power BI to these data repositories as well.

SQL Analytics will be available in public preview on November 18.

In many ways, SQL Analytics is the product Databricks has long been looking to build and that brings its concept of a ‘lake house’ to life. It combines the performance of a data warehouse, where you store data after it has already been transformed and cleaned, with a data lake, where you store all of your data in its raw form. The data in the data lake, a concept that Databrick’s co-founder and CEO Ali Ghodsi has long championed, is typically only transformed when it gets used. That makes data lakes cheaper, but also a bit harder to handle for users.

Image Credits: Databricks

“We’ve been saying Unified Data Analytics, which means unify the data with the analytics. So data processing and analytics, those two should be merged. But no one picked that up,” Ghodsi told me. But ‘lake house’ caught on as a term.

“Databricks has always offered data science, machine learning. We’ve talked about that for years. And with Spark, we provide the data processing capability. You can do [extract, transform, load]. That has always been possible. SQL Analytics enables you to now do the data warehousing workloads directly, and concretely, the business intelligence and reporting workloads, directly on the data lake.”

The general idea here is that with just one copy of the data, you can enable both traditional data analyst use cases (think BI) and the data science workloads (think AI) Databricks was already known for. Ideally, that makes both use cases cheaper and simpler.

The service sits on top of an optimized version of Databricks’ open-source Delta Lake storage layer to enable the service to quickly complete queries. In addition, Delta Lake also provides auto-scaling endpoints to keep the query latency consistent, even under high loads.

While data analysts can query these data sets directly, using standard SQL, the company also built a set of connectors to BI tools. Its BI partners include Tableau, Qlik, Looker and Thoughtspot, as well as ingest partners like Fivetran, Fishtown Analytics, Talend and Matillion.

Image Credits: Databricks

“Now more than ever, organizations need a data strategy that enables speed and agility to be adaptable,” said Francois Ajenstat, Chief Product Officer at Tableau. “As organizations are rapidly moving their data to the cloud, we’re seeing growing interest in doing analytics on the data lake. The introduction of SQL Analytics delivers an entirely new experience for customers to tap into insights from massive volumes of data with the performance, reliability and scale they need.”

In a demo, Ghodsi showed me what the new SQL Analytics workspace looks like. It’s essentially a stripped-down version of the standard code-heavy experience that Databricks users are familiar with. Unsurprisingly, SQL Analytics provides a more graphical experience that focuses more on visualizations and not Python code.

While there are already some data analysts on the Databricks platform, this obviously opens up a large new market for the company — something that would surely bolster its plans for an IPO next year.

#ali-ghodsi, #analytics, #apache-spark, #artificial-intelligence, #business-intelligence, #cloud, #data-analysis, #data-lake, #data-management, #data-processing, #data-science, #data-warehouse, #databricks, #democrats, #enterprise, #fishtown-analytics, #fivetran, #information, #looker, #machine-learning, #python, #sql, #tableau, #talend

Fivetran snares $100M Series C on $1.2B valuation for data connectivity solution

A big problem for companies these days is finding ways to connect to various data sources to their data repositories, and Fivetran is a startup with a solution to solve that very problem. No surprise then that even during a pandemic, the company announced today that it has raised $100 million Series C on a $1.2 billion valuation.

The company didn’t mess around with top flight firms Andreessen Horowitz and General Catalyst leading the investment with participation from existing investors CEAS Investments and Matrix Partners. Today’s money brings the total raised so far to $163 million, according to the company.

Martin Cassado from a16z described the company succinctly in a blog post he wrote after its $44 million Series B in September 2019, which his firm also participated in. “Fivetran is a SaaS service that connects to the critical data sources in an organization, pulls and processes all the data, and then dumps it into a warehouse (e.g., Snowflake, BigQuery or RedShift) for SQL access and further transformations, if needed. If data is the new oil, then Fivetran is the pipes that get it from the source to the refinery,” he wrote.

Writing in a blog post today announcing the new funding, CEO George Fraser added that in spite of current conditions, the company has continued to add customers. “Despite recent economic uncertainty, Fivetran has continued to grow rapidly as customers see the opportunity to reduce their total cost of ownership by adopting our product in place of highly customized, in-house ETL pipelines that require constant maintenance,” he wrote.

In fact, the company reports 75% customer growth over the prior 12 months. It now has over 1100 customers, which is a pretty good benchmark for a Series C company. Customers include Databricks, DocuSign, Forever 21, Square, Udacity and Urban Outfitters, crossing a variety of verticals.

Fivetran hopes to continue to build new data connectors as it expands the reach of its product and to push into new markets, even in the midst of today’s economic climate. With $100 million in the bank, it should have enough runway to ride this out, while expanding where it makes sense.

#andreessen-horowitz, #cloud, #data-pipelines, #enterprise, #fivetran, #funding, #martin-casado, #startups, #tc