Tag Archives: big data

Firebolt raises $127M more for its new approach to cheaper and more efficient big data analytics

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Snowflake changed the conversation for many companies when it comes to the potentials of data warehousing. Now one of the startups that’s hoping to disrupt the disruptor is announcing a big round of funding to expand its own business.

Firebolt, which has built a new kind of cloud data warehouse that promises much more efficient, and cheaper, analytics around whatever is stored within it, is announcing a major Series B of $127 million on the heels of huge demand for its services.

The company, which only came out of stealth mode in December, is not disclosing its valuation with this round, which brings the total raised by the Israeli company to $164 million. New backers Dawn Capital and K5 Global are in this round, alongside previous backers Zeev Ventures, TLV Partners, Bessemer Venture Partners, and Angular Ventures.

Nor is it disclosing many details about its customers at the moment. CEO and co-founder Eldad Farkash told me in an interview that most of them are US-based, and that the numbers have grown from the dozen or so that were using Firebolt when it was still in stealth mode (it worked quietly for a couple of years building its product and onboarding customers before finally launching six months ago). They are all migrating from existing data warehousing solutions like Snowflake or BigQuery. In other words, its customers are already cloud-native, big-data companies: it’s not trying to proselytize on the basic concept but work with those who are already in a specific place as a business.

“If you’re not using Snowflake or BigQuery already, we prefer you come back to us later,” he said. Judging by the size and quick succession of the round, that focus is paying off.

The challenge that Firebolt set out to tackle is that while data warehousing has become a key way for enterprises to analyze, update and manage their big data stores — after all, your data is only as good as the tools you have to parse it and keep it secure — typically data warehousing solutions are not efficient, and they can cost a lot of money to maintain.

The challenge was seen first-hand by the three founders of Firebolt, Farkash (CEO), Saar Bitner (COO) and Ariel Yaroshevich (CTO) when they were at a previous company, the business intelligence powerhouse Sisense, where respectively they were one of its co-founders and two members of its founding team. At Sisense, the company continually came up against an issue: When you are dealing in terabytes of data, cloud data warehouses were straining to deliver good performance to power its analytics and other tools, and the only way to potentially continue to mitigate that was by piling on more cloud capacity. And that started to become very expensive.

Firebolt set out to fix that by taking a different approach, re-architecting the concept. As Farkash sees it, while data warehousing has indeed been a big breakthrough in big data, it has started to feel like a dated solution as data troves have grown.

“Data warehouses are solving yesterday’s problem, which was, ‘How do I migrate to the cloud and deal with scale?’ ” he told me back in December. Google’s BigQuery, Amazon’s RedShift and Snowflake as fitting answers for that issue, believes, but “we see Firebolt as the new entrant in that space, with a new take on design on technology. We change the discussion from one of scale to one of speed and efficiency.”

The startup claims that its performance is up to 182 times faster than that of other data warehouses with a SQL-based system that works on academic research that had yet to be applied anywhere, around how to handle data in a lighter way, using new techniques in compression and how data is parsed. Data lakes in turn can be connected with a wider data ecosystem, and what it translates to is a much smaller requirement for cloud capacity. And lower costs.

Fast forward to today, and the company says the concept is gaining a lot of traction with engineers and developers in industries like business intelligence, customer-facing services that need to parse a lot of information to serve information to users in real-time, and back-end data applications. That is proving out what investors suspected would be a shift before the startup even launched, stealthily or otherwise.

“I’ve been an investor at Firebolt since their Series A round and before they had any paying customers,” said Oren Zeev of Zeev Ventures. “What had me invest in Firebolt is mostly the team. A group of highly experienced executives mostly from the big data space who understand the market very well, and the pain organizations are experiencing. In addition, after speaking to a few of my portfolio companies and Firebolt’s initial design partners, it was clear that Firebolt is solving a major pain, so all in all, it was a fairly easy decision. The market in which Firebolt operates is huge if you consider the valuations of Snowflake and Databricks. Even more importantly, it is growing rapidly as the migration from on-premise data warehouse platforms to the cloud is gaining momentum, and as more and more companies rely on data for their operations and are building data applications.”


By Ingrid Lunden

With buyout, Cloudera hunts for relevance in a changing market

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When Cloudera announced its sale to a pair of private equity firms yesterday for $5.3 billion, along with a couple of acquisitions of its own, the company detailed a new path that could help it drive back towards relevance in the big data market.

When the company launched in 2008, Hadoop was in its early days. The open source project developed at Yahoo three years earlier was built to deal with the large amounts of data that the Internet pioneer generated. It became increasingly clear over time that every company would have to deal with growing data stores, and it seemed that Cloudera was in the right market at the right time.

And for a while things went well. Cloudera rode the Hadoop startup wave, garnering a cool billion in funding along the way, including a stunning $740 million check from Intel Capital in 2014. It then went public in 2018 to much fanfare.

But the markets had already started to shift by the time of its public debut. Hadoop, a highly labor-intensive way to manage data, was being supplanted by cheaper and less complex cloud-based solutions.

“The excitement around the original promise of the Hadoop market has contracted significantly. It’s incredibly expensive and complex to get it working effectively in an enterprise context,” Casey Aylward, an investor at Costanoa Ventures told TechCrunch.

The company likely saw that writing on the wall when it merged with another Hadoop-based company, Hortonworks in 2019. That transaction valued the combined entity at $5.2 billion, almost the same amount it sold for yesterday, two years down the road. The decision to sell and go private may also have been spurred by Carl Icahn buying an 18% stake in the company that same year.

Looking to the future, Cloudera’s sale could provide the enterprise unicorn room as it regroups.

Patrick Moorhead, founder and principal analyst at Moor Insight & Strategies sees the deal as a positive step for the company. “I think this is good news for Cloudera because it now has the capital and flexibility to dive head first into SaaS. The company invented the entire concept of a data life cycle, implemented initially on premises, then extended to private and public clouds,” Moorhead said.

How to ensure data quality in the era of Big Data

Adam Ronthal, Gartner Research VP agrees that it at least gives Cloudera more room to make necessary adjustments its market strategy as long as it doesn’t get stifled by its private equity overlords. “It should give Cloudera an opportunity to focus on their future direction with increased flexibility — provided they are able to invest in that future and that this does not just focus on cost cutting and maximizing profits. Maintaining a culture of innovation will be key,” Ronthal said.

Which brings us to the two purchases Cloudera also announced as part of its news package.

If you want to change direction in a hurry, there are worse ways than via acquisitions. And grabbing Datacoral and Cazena should help Cloudera alter its course more quickly than it could have managed on its own.

“[The] two acquisitions will help Cloudera capture some of the value on top of the lake storage layer — perhaps moving into different data management features and/or expanding into the compute layer for analytics and AI/ML use cases, where there has been a lot of growth and excitement in recent years,” Alyward said.

Chandana Gopal, Research Director for the future of intelligence at IDC agrees that the transactions give Cloudera some more modern options that could help speed up the data wrangling process. “Both the acquisitions are geared towards making the management of cloud infrastructure easier for end-users. Our research shows that data prep and integration takes 70%-80% of an analyst’s time versus the time spent in actual analysis. It seems like both these companies’ products will provide technology to improve the data integration/preparation experience,” she said.

The company couldn’t stay on the path it was on forever, certainly not with an activist investor breathing down its neck. Its recent efforts could give it the time away from public markets it needs to regroup. How successful Cloudera’s turnaround proves to be will depend on whether the private equity companies buying it can both agree on the direction and strategy for the company, while providing the necessary resources to push the company in a new direction. All of that and more will determine if these moves pay off in the end.

Cloudera to go private as KKR & CD&R grab it for $5.3B


By Ron Miller

SingleStore, formerly MemSQL, raises $80M to integrate and leverage companies’ disparate data silos

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While the enterprise world likes to talk about “big data”, that term belies the real state of how data exists for many organizations: the truth of the matter is that it’s often very fragmented, living in different places and on different systems, making the concept of analysing and using it in a single, effective way a huge challenge.

Today, one of the big up-and-coming startups that has built a platform to get around that predicament is announcing a significant round of funding, a sign of the demand for its services and its success so far in executing on that.

SingleStore, which provides a SQL-based platform to help enterprises manage, parse and use data that lives in silos across multiple cloud and on-premise environments — a key piece of work needed to run applications in risk, fraud prevention, customer user experience, real-time reporting and real-time insights, fast dashboards, data warehouse augmentation, modernization for data warehouses and data architectures and faster insights — has picked up $80 million in funding, a Series E round that brings in new strategic investors alongside its existing list of backers.

The round is being led by Insight Partners, with new backers Dell Technologies Capital, Hercules Capital; and previous backers Accel, Anchorage, Glynn Capital, GV (formerly Google Ventures) and Rev IV also participating.

Alongside the investment, SingleStore is formally announcing a new partnership with analytics powerhouse SAS. I say “formally” because they two have been working together already and it’s resulted in “tremendous uptake,” CEO Raj Verma said in an interview over email.

Verma added that the round came out of inbound interest, not its own fundraising efforts, and as such, it brings the total amount of cash it has on hand to $140 million. The gives the startup money to play with not only to invest in hiring, R&D and business development, but potentially also M&A, given that the market right now seems to be in a period of consolidation.

Verma said the valuation is a “significant upround” compared to its Series D in 2018 but didn’t disclose the figure. PitchBook notes that at the time it was valued at $270 million post-money.

When I last spoke with the startup in May of this year — when it announced a debt facility of $50 million — it was not called SingleStore; it was MemSQL. The company rebranded at the end of October to the new name, but Verma said that the change was a long time in the planning.

“The name change is one of the first conversations I had when I got here,” he said about when he joined the company in 2019 (he’s been there for about 16 months). “The [former] name didn’t exactly flow off the tongue and we found that it no longer suited us, we found ourselves in a tiny shoebox of an offering, in saying our name is MemSQL we were telling our prospects to think of us as in-memory and SQL. SQL we didn’t have a problem with but we had outgrown in-memory years ago. That was really only 5% of our current revenues.”

He also mentioned the hang up many have with in-memory database implementations: they tend to be expensive. “So this implied high TCO, which couldn’t have been further from the truth,” he said. “Typically we are ⅕-⅛ the cost of what a competitive product would be to implement. We were doing ourselves a disservice with prospects and buyers.”

The company liked the name SingleStore because it is based a conceptual idea of its proprietary technology. “We wanted a name that could be a verb. Down the road we hope that when someone asks large enterprises what they do with their data, they will say that they ‘SingleStore It!’ That is the vision. The north star is that we can do all types of data without workload segmentation,” he said.

That effort is being done at a time when there is more competition than ever before in the space. Others also providing tools to manage and run analytics and other work on big data sets include Amazon, Microsoft, Snowflake, PostgreSQL, MySQL and more.

SingleStore is not disclosing any metrics on its growth at the moment but says it has thousands of enterprise customers. Some of the more recent names it’s disclosed include GE, IEX Cloud, Go Guardian, Palo Alto Networks, EOG Resources, SiriusXM + Pandora, with partners including Infosys, HCL and NextGen.

“As industry after industry reinvents itself using software, there will be accelerating market demand for predictive applications that can only be powered by fast, scalable, cloud-native database systems like SingleStore’s,” said Lonne Jaffe, managing director at Insight Partners, in a statement. “Insight Partners has spent the past 25 years helping transformational software companies rapidly scale-up, and we’re looking forward to working with Raj and his management team as they bring SingleStore’s highly differentiated technology to customers and partners across the world.”

“Across industries, SAS is running some of the most demanding and sophisticated machine learning workloads in the world to help organizations make the best decisions. SAS continues to innovate in AI and advanced analytics, and we partner with companies like SingleStore that share our curiosity about how data and analytics can help organizations reimagine their businesses and change the world,” said Oliver Schabenberger, COO and CTO at SAS, added. “Our engineering teams are integrating SingleStore’s scalable SQL-based database platform with the massively parallel analytics engine SAS Viya. We are excited to work with SingleStore to improve performance, reduce cost, and enable our customers to be at the forefront of analytics and decisioning.”


By Ingrid Lunden

Data virtualization service Varada raises $12M

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Varada, a Tel Aviv-based startup that focuses on making it easier for businesses to query data across services, today announced that it has raised a $12 million Series A round led by Israeli early-stage fund MizMaa Ventures, with participation by Gefen Capital.

“If you look at the storage aspect for big data, there’s always innovation, but we can put a lot of data in one place,” Varada CEO and co-founder Eran Vanounou told me. “But translating data into insight? It’s so hard. It’s costly. It’s slow. It’s complicated.”

That’s a lesson he learned during his time as CTO of LivePerson, which he described as a classic big data company. And just like at LivePerson, where the team had to reinvent the wheel to solve its data problems, again and again, every company — and not just the large enterprises — now struggles with managing their data and getting insights out of it, Vanounou argued.

Image Credits: Varada

The rest of the founding team, David Krakov, Roman Vainbrand and Tal Ben-Moshe, already had a lot of experience in dealing with these problems, too, with Ben-Moshe having served at the Chief Software Architect of Dell EMC’s XtremIO flash array unit, for example. They built the system for indexing big data that’s at the core of Varada’s platform (with the open-source Presto SQL query engine being one of the other cornerstones).

Image Credits: Varada

Essentially, Varada embraces the idea of data lakes and enriches that with its indexing capabilities. And those indexing capabilities is where Varada’s smarts can be found. As Vanounou explained, the company is using a machine learning system to understand when users tend to run certain workloads and then caches the data ahead of time, making the system far faster than its competitors.

“If you think about big organizations and think about the workloads and the queries, what happens during the morning time is different from evening time. What happened yesterday is not what happened today. What happened on a rainy day is not what happened on a shiny day. […] We listen to what’s going on and we optimize. We leverage the indexing technology. We index what is needed when it is needed.”

That helps speed up queries, but it also means less data has to be replicated, which also brings down the cost. AÅs Mizmaa’s Aaron Applebaum noted, since Varada is not a SaaS solution, the buyers still get all of the discounts from their cloud providers, too.

In addition, the system can allocate resources intelligently to that different users can tap into different amounts of bandwidth. You can tell it to give customers more bandwidth than your financial analysts, for example.

“Data is growing like crazy: in volume, in scale, in complexity, in who requires it and what the business intelligence uses are, what the API uses are,” Applebaum said when I asked him why he decided to invest. “And compute is getting slightly cheaper, but not really, and storage is getting cheaper. So if you can make the trade-off to store more stuff, and access things more intelligently, more quickly, more agile — that was the basis of our thesis, as long as you can do it without compromising performance.”

Varada, with its team of experienced executives, architects and engineers, ticked a lot of the company’s boxes in this regard, but he also noted that unlike some other Israeli startups, the team understood that it had to listen to customers and understand their needs, too.

“In Israel, you have a history — and it’s become less and less the case — but historically, there’s a joke that it’s ‘ready, fire, aim.’ You build a technology, you’ve got this beautiful thing and you’re like, ‘alright, we did it,’ but without listening to the needs of the customer,” he explained.

The Varada team is not afraid to compare itself to Snowflake, which at least at first glance seems to make similar promises. Vananou praised the company for opening up the data warehousing market and proving that people are willing to pay for good analytics. But he argues that Varada’s approach is fundamentally different.

“We embrace the data lake. So if you are Mr. Customer, your data is your data. We’re not going to take it, move it, copy it. This is your single source of truth,” he said. And in addition, the data can stay in the company’s virtual private cloud. He also argues that Varada isn’t so much focused on the business users but the technologists inside a company.

 


By Frederic Lardinois

Mode raises $33M to supercharge its analytics platform for data scientists

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Data science is the name of the game these days for companies that want to improve their decision making by tapping the information they are already amassing in their apps and other systems. And today, a startup called Mode Analytics, which has built a platform incorporating machine learning, business intelligence and big data analytics to help data scientists fulfil that task, is announcing $33 million in funding to continue making its platform ever more sophisticated.

Most recently, for example, the company has started to introduce tools (including SQL and Python tutorials) for less technical users, specifically those in product teams, so that they can structure queries that data scientists can subsequently execute faster and with more complete responses — important for the many follow up questions that arise when a business intelligence process has been run. Mode claims that its tools can help produce answers to data queries in minutes.

This Series D is being led by SaaS specialist investor H.I.G. Growth Partners, with previous investors Valor Equity Partners, Foundation Capital, REV Venture Partners, and Switch Ventures all participating. Valor led Mode’s Series C in February 2019, while Foundation and REV respectively led its A and B rounds.

Mode is not disclosing its valuation, but co-founder and CEO Derek Steer confirmed in an interview that it was “absolutely” an up-round.

For some context, PitchBook notes that last year its valuation was $106 million. The company now has a customer list that it says covers 52% of the Forbes 500, including Anheuser Busch, Zillow, Lyft, Bloomberg, Capital One, VMWare, and Conde Nast. It says that to date it has processed 830 million query runs and 170 million notebook cell runs for 300,000 users. (Pricing is based on a freemium model, with a free “Studio” tier and Business and Enterprise tiers priced based on size and use.)

Mode has been around since 2013, when it was co-founded by Steer, Benn Stancil (Mode’s current president) and Josh Ferguson (initially the CTO and now chief architect).

Steer said the impetus for the startup came out of gaps in the market that the three had found through years of experience at other companies.

Specifically, when all three were working together at Yammer (they were early employees and stayed on after the Microsoft acquisition), they were part of a larger team building custom data analytics tools for Yammer. At the time, Steer said Yammer was paying $1 million per year to subscribe to Vertica (acquired by HP in 2011) to run it.

They saw an opportunity to build a platform that could provide similar kinds of tools — encompassing things like SQL Editors, Notebooks, and reporting tools and dashboards — to a wider set of users.

“We and other companies like Facebook and Google were building analytics internally,” Steer recalled, “and we knew that the world wanted to work more like these tech companies. That’s why we started Mode.”

All the same, he added, “people were not clearly exactly about what a data scientist even was.”

Indeed, Mode’s growth so far has mirrored that of the rise of data science overall, as the discipline of data science, and the business case for employing data scientists to help figure out what is “going on” beyond the day to day, getting answers by tapping all the data that’s being amassed in the process of just doing business. That means Mode’s addressable market has also been growing.

But even if the trove of potential buyers of Mode’s products has been growing, so has the opportunity overall. There has been a big swing in data science and big data analytics in the last several years, with a number of tech companies building tools to help those who are less technical “become data scientists” by introducing more intuitive interfaces like drag-and-drop features and natural language queries.

They include the likes of Sisense (which has been growing its analytics power with acquisitions like Periscope Data), Eigen (focusing on specific verticals like financial and legal queries), Looker (acquired by Google) and Tableau (acquired by Salesforce).

Mode’s approach up to now has been closer to that of another competitor, Alteryx, focusing on building tools that are still aimed primary at helping data scientists themselves. You have any number of database tools on the market today, Steer noted, “Snowflake, Redshift, BigQuery, Databricks, take your pick.” The key now is in providing tools to those using those databases to do their work faster and better.

That pitch and the success of how it executes on it is what has given the company success both with customers and investors.

“Mode goes beyond traditional Business Intelligence by making data faster, more flexible and more customized,” said Scott Hilleboe, MD, H.I.G. Growth Partners, in a statement. “The Mode data platform speeds up answers to complex business problems and makes the process more collaborative, so that everyone can build on the work of data analysts. We believe the company’s innovations in data analytics uniquely position it to take the lead in the Decision Science marketplace.”

Steer said that fundraising was planned long before the coronavirus outbreak to start in February, which meant that it was timed as badly as it could have been. Mode still raised what it wanted to in a couple of months — “a good raise by any standard,” he noted — even if it’s likely that the valuation suffered a bit in the process. “Pitching while the stock market is tanking was terrifying and not something I would repeat,” he added.

Given how many acquisitions there have been in this space, Steer confirmed that Mode too has been approached a number of times, but it’s staying put for now. (And no, he wouldn’t tell me who has been knocking, except to say that it’s large companies for whom analytics is an “adjacency” to bigger businesses, which is to say, the very large tech companies have approached Mode.)

“The reason we haven’t considered any acquisition offers is because there is just so much room,” Steer said. “I feel like this market is just getting started, and I would only consider an exit if I felt like we were handicapped by being on our own. But I think we have a lot more growing to do.”


By Ingrid Lunden

Quantexa raises $64.7M to bring big data intelligence to risk analysis and investigations

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The wider field of cyber security — not just defending networks, but identifying fraudulent activity — has seen a big boost in activity in the last few months, and that’s no surprise. The global health pandemic has led to more interactions and transactions moving online, and the contractions we’re feeling across the economy and society have led some to take more desperate and illegal actions, using digital challenges to do it.

Today, a UK company called Quantexa — which has built a machine learning platform branded “Contextual Decision Intelligence” (CDI) that analyses disparate data points to get better insight into nefarious activity, as well as to (more productively) build better profiles of a company’s entire customer base — is raising a growth round of funding to address that opportunity.

The London-based startup has picked up $64.7 million, a Series C the it will be using to continue building out both its tools and the use cases for applying them, as well as expanding geographically, specifically in North America, Asia-Pacific and more European territories.

The mission, said Vishal Marria, Quantexa’s founder and CEO, is to “connect the dots to make better business decisions.”

The startup built its business on the back of doing work for major banks and others in the financial services sector, and Marria added that the plan will be to continue enhancing tools for that vertical while also expanding into two growing opportunities: working with insurance and government/public sector organizations.

The backers in this round speak to how Quantexa positions itself in the market, and the traction it’s seen to date for its business. It’s being led by Evolution Equity Partners — a VC that specialises in innovative cybersecurity startups — with participation also from previous backers Dawn Capital, AlbionVC, HSBC and Accenture, as well as new backers ABN AMRO Ventures. HSBC, Accenture and ABN AMRO are all strategic investors working directly with the startup in their businesses.

Altogether, Quantexa has “thousands of users” across 70+ countries, it said, with additional large enterprises including Standard Chartered, OFX and Dunn & Bradstreet.

The company has now raised some $90 million to date, and reliable sources close to the company tell us that the valuation is “well north” of $250 million — which to me sounds like it’s between $250 million and $300 million.

Marria said in an interview that he initially got the idea for Quantexa — which I believe may be a creative portmanteau of “quantum” and “context” — when he was working as an executive director at Ernst & Young and saw “many challenges with investigations” in the financial services industry.

“Is this a money launderer?” is the basic question that investigators aim to answer, but they were going about it, “using just a sliver of information,” he said. “I thought to myself, this is bonkers. There must be a better way.”

That better way, as built by Quantexa, is to solve it in the classic approach of tapping big data and building AI algorithms that help, in Marria’s words, connect the dots.

As an example, typically, an investigation needs to do significantly more than just track the activity of one individual or one shell company, and you need to seek out the most unlikely connections between a number of actions in order to build up an accurate picture. When you think about it, trying to identify, track, shut down and catch a large money launderer (a typical use case for Quantexa’s software) is a classic big data problem.

While there is a lot of attention these days on data protection and security breaches that leak sensitive customer information, Quantexa’s approach, Marria said, is to sell software, not ingest proprietary data into its engine to provide insights. He said that these days deployments typically either are done on premises or within private clouds, rather than using public cloud infrastructure, and that when Quantexa provides data to complement its customers’ data, it comes from publicly available sources (for example Companies House filings in the UK).

There are a number of companies offering services in the same general area as Quantexa. They include those that present themselves more as business intelligence platforms that help detect fraud (such as Looker) through to those that are secretive and present themselves as AI businesses working behind the scenes for enterprises and governments to solve tough challenges, such as Palantir, through to others focusing specifically on some of the use cases for the technology, such as ComplyAdvantage and its focus on financial fraud detection.

Marria says that it has a few key differentiators from these. First is how its software works at scale: “It comes back to entity resolution that [calculations] can be done in real time and at batch,” he said. “And this is a platform, software that is easily deployed and configured at a much lower total cost of ownership. It is tech and that’s quite important in the current climate.”

And that is what has resonated with investors.

“Quantexa’s proprietary platform heralds a new generation of decision intelligence technology that uses a single contextual view of customers to profoundly improve operational decision making and overcome big data challenges,” said Richard Seewald, founding and managing partner of Evolution, in a statement. “Its impressive rapid growth, renowned client base and potential to build further value across so many sectors make Quantexa a fantastic partner whose team I look forward to working with.” Seewald is joining the board with this round.


By Ingrid Lunden

SEC filing indicates big data provider Palantir is raising $961M, $550M of it already secured

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Palantir, the secretive big data and analytics provider that works with governments and other public and private organizations to power national security, health and a variety of other services, has reportedly been eyeing up a public listing this autumn. But in the meantime it’s also continuing to push ahead in the private markets.

The company has filed a Form D indicating that it is in the process of raising nearly $1 billion — $961,099,010, to be exact — with $549,727,437 of that already sold, and a further $411,371,573 remaining to be raised.

The filing appears to confirm a report from back in September 2019 that the company was seeking to raise between $1 billion and $3 billion, its first fundraising in four years. That report noted Palantir was targeting a $26 billion valuation, up from $20 billion four years ago. A Reuters article from June put its valuation on secondary market trades at between $10 billion and $14 billion.

The bigger story of that Reuters report was that Palantir confirmed two fundraises from strategic investors that both work with the company: $500 million in funding from Japanese insurance company Sompo Holdings, and $50 million from Fujitsu. Together, it seems like these might account for $550 million already sold on the Form D.

It’s not clear if this fundraise would essentially mean a delay to a public listing, or if it would complement it.

To date Palantir has raised $3.3 billion in funding, according to PitchBook data, with no less than 108 investors on its cap table. But if you dig into the PitchBook data (some of which is behind a paywall) it also seems that Palantir has raised a number of other rounds of undisclosed amounts. Confusingly (but probably apt for a company famous for being secretive) some of that might also be part of this Form D amount.

We have reached out to Palantir to ask about the Form D and will update this post as we learn more.

While Palantir was last valued at $20 billion when it last raised money four years ago, there are some data points that point to a bigger valuation today.

In April, according to a Bloomberg report, the company briefed investors with documents showing that it expects to make $1 billion in revenues this year, up 38% on 2019, and breaking even in the first time since being founded 16 years ago by Peter Thiel, Nathan Gettings, Joe Lonsdale, Stephen Cohen, and current CEO, Alex Karp.

(The Bloomberg report didn’t explain why Palantir was briefing investors, whether for a potential public listing, or for the fundraise we’re reporting on here, or something else.)

On top of that, the company has been in the news a lot around the global novel coronavirus pandemic. Specifically, it’s been winning business, in the form of projects in major markets like the UK (where it’s part of a consortium of companies working with the NHS on a COVID-19 data trove) and the US (where it’s been working on a COVID-19 tracker for the federal government and a project with the CDC), and possibly others. Those projects will presumably need a lot of upfront capital to set up and run, possibly one reason raising money now.


By Ingrid Lunden

Collibra nabs another $112.5M at a $2.3B valuation for its big data management platform

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GDPR and other data protection and privacy regulations — as well as a significant (and growing) number of data breaches and exposées of companies’ privacy policies — have put a spotlight on not just on the vast troves of data that businesses and other organizations hold on us, but also how they handle it. Today, one of the companies helping them cope with that data trove in a better and legal way is announcing a huge round of funding to continue that work. Collibra, which provides tools to manage, warehouse, store and analyse data troves, is today announcing that it has raised $112.5 million in funding, at a post-money valuation of $2.3 billion.

The funding — a Series F from the looks of it — represents a big bump for the startup, which last year raised $100 million at a valuation of just over $1 billion. This latest round was co-led by ICONIQ Capital, Index Ventures, and Durable Capital Partners LIP, with previous investors CapitalG (Google’s growth fund), Battery Ventures, and Dawn Capital also participating.

Collibra, originally a spin-out from Vrije Universiteit in Brussels, Belgium, today works with some 450 enterprises and other large organizations — customers include Adobe, Verizon (which owns TechCrunch), insurers AXA, and a number of healthcare providers. Its products cover a range of services focused around company data, including tools to help customers comply with local data protection policies, store it securely, and to run analytics and more.

These are all tools that have long had a place in enterprise big data IT, but have become increasingly more used and in-demand both as data policies have expanded, and as the prospects of what can be discovered through big data analytics have become more advanced. With that growth, many companies have realised that they are not in a position to use and store their data in the best possible way, and that is where companies like Collibra step in.

“Most large organizations are in data chaos,” Felix Van de Maele, co-founder and CEO, previously told us. “We help them understand what data they have, where they store it and [understand] whether they are allowed to use it.”

As you would expect with a big IT trend, Collibra is not the only company chasing this opportunity. Competitors include Informatica, IBM, Talend, Egnyte, among a number of others, but the market position of Collibra, and its advanced technology, is what has continued to impress investors.

“Durable Capital Partners invests in innovative companies that have significant potential to shape growing industries and build larger companies,” said Henry Ellenbogen, founder and chief investment officer for Durable Capital Partners LP, in a statement (Ellenbogen is formerly an investment manager a T. Rowe Price, and this is his first investment in Collibra under Durable). “We believe Collibra is a leader in the Data Intelligence category, a space that could have a tremendous impact on global business operations and a space that we expect will continue to grow as data becomes an increasingly critical asset.”

“We have a high degree of conviction in Collibra and the importance of the company’s mission to help organizations benefit from their data,” added Matt Jacobson, general partner at ICONIQ Capital and Collibra board member, in his own statement. “There is an increasing urgency for enterprises to harness their data for strategic business decisions. Collibra empowers organizations to use their data to make critical business decisions, especially in uncertain business environments.”


By Ingrid Lunden

BackboneAI scores $4.7M seed to bring order to intercompany data sharing

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BackboneAI, an early-stage startup that wants to help companies dealing with lots of data, particularly coming from a variety of external sources, announced a $4.7 million seed investment today.

The round was led by Fika Ventures with participation from Boldstart Ventures, Dynamo Ventures, GGV Capital, MetaProp, Spider VC and several other unnamed investors.

Company founder Rob Bailey says he has spent a lot of time in his career watching how data flows in organizations. There are still a myriad of challenges related to moving data between organizations, and that’s what his company is trying to solve. “BackboneAI is an AI platform specifically built for automating data flows within and between companies,” he said.

This could involve any number of scenarios from keeping large, complex data catalogues up-to-date to coordinating the intricate flow of construction materials between companies or content rights management across an entertainment industry.

Bailey says that he spent 18 months talking to companies before he built the product. “What we found is that every company we talked to was, in some way or another, concerned about an absolute flood of data from all these different applications and from all the companies that they’re working with externally,” he explained.

The BackboneAI platform aims to solve a number of problems related to this. For starters, it automates the acquisition of this data, usually from third parties like suppliers, customers, regulatory agencies and so forth. Then it handles ingestion of the data, and finally it takes care of a lot of actual processing from external sources, while mapping it to internal systems like the company ERP system.

As an example, he uses an industrial supply company that may deal with a million SKUs across a couple of dozen divisions. Trying to track that with manual or even legacy systems is difficult. “They take all this product data in [from external suppliers], and then process the information in their own [internal] product catalog, and then finally present that data about those products to hundreds of thousands of customers. It’s an incredibly large and challenging data problem as you’re processing millions and millions of SKUs and orders, and you have to keep that data current on a regular basis,” he explained.

The company is just getting started. It spent 2019 incubating inside of Boldstart Ventures . Today the company has close to 20 employees in New York City, and it has signed its first Fortune 500 customer. Bailey says they have 15 additional Fortune 500 companies in the pipeline. With the seed money, he hopes to build on this initial success.


By Ron Miller

Databricks brings its Delta Lake project to the Linux Foundation

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Databricks, the big data analytics service founded by the original developers of Apache Spark, today announced that it is bringing its Delta Lake open-source project for building data lakes to the Linux Foundation and under an open governance model. The company announced the launch of Delta Lake earlier this year and even though it’s still a relatively new project, it has already been adopted by many organizations and has found backing from companies like Intel, Alibaba and Booz Allen Hamilton.

“In 2013, we had a small project where we added SQL to Spark at Databricks […] and donated it to the Apache Foundation,” Databricks CEO and co-founder Ali Ghodsi told me. “Over the years, slowly people have changed how they actually leverage Spark and only in the last year or so it really started to dawn upon us that there’s a new pattern that’s emerging and Spark is being used in a completely different way than maybe we had planned initially.”

This pattern, he said, is that companies are taking all of their data and putting it into data lakes and then do a couple of things with this data, machine learning and data science being the obvious ones. But they are also doing things that are more traditionally associated with data warehouses, like business intelligence and reporting. The term Ghodsi uses for this kind of usage is ‘Lake House.’ More and more, Databricks is seeing that Spark is being used for this purpose and not just to replace Hadoop and doing ETL (extract, transform, load). “This kind of Lake House patterns we’ve seen emerge more and more and we wanted to double down on it.”

Spark 3.0, which is launching today, enables more of these use cases and speeds them up significantly, in addition to the launch of a new feature that enables you to add a pluggable data catalog to Spark.

Data Lake, Ghodsi said, is essentially the data layer of the Lake House pattern. It brings support for ACID transactions to data lakes, scalable metadata handling, and data versioning, for example. All the data is stored in the Apache Parquet format and users can enforce schemas (and change them with relative ease if necessary).

It’s interesting to see Databricks choose the Linux Foundation for this project, given that its roots are in the Apache Foundation. “We’re super excited to partner with them,” Ghodsi said about why the company chose the Linux Foundation. “They run the biggest projects on the planet, including the Linux project but also a lot of cloud projects. The cloud-native stuff is all in the Linux Foundation.”

“Bringing Delta Lake under the neutral home of the Linux Foundation will help the open source community dependent on the project develop the technology addressing how big data is stored and processed, both on-prem and in the cloud,” said Michael Dolan, VP of Strategic Programs at the Linux Foundation. “The Linux Foundation helps open source communities leverage an open governance model to enable broad industry contribution and consensus building, which will improve the state of the art for data storage and reliability.”


By Frederic Lardinois

Satya Nadella looks to the future with edge computing

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Speaking today at the Microsoft Government Leaders Summit in Washington DC, Microsoft CEO Satya Nadella made the case for edge computing, even while pushing the Azure cloud as what he called “the world’s computer.”

While Amazon, Google and other competitors may have something to say about that, marketing hype aside, many companies are still in the midst of transitioning to the cloud. Nadella says the future of computing could actually be at the edge where computing is done locally before data is then transferred to the cloud for AI and machine learning purposes. What goes around, comes around.

But as Nadella sees it, this is not going to be about either edge or cloud. It’s going to be the two technologies working in tandem. “Now, all this is being driven by this new tech paradigm that we describe as the intelligent cloud and the intelligent edge,” he said today.

He said that to truly understand the impact the edge is going to have on computing, you have to look at research, which predicts there will be 50 billion connected devices in the world by 2030, a number even he finds astonishing. “I mean this is pretty stunning. We think about a billion Windows machines or a couple of billion smartphones. This is 50 billion [devices], and that’s the scope,” he said.

The key here is that these 50 billion devices, whether you call them edge devices or the Internet of Things, will be generating tons of data. That means you will have to develop entirely new ways of thinking about how all this flows together. “The capacity at the edge, that ubiquity is going to be transformative in how we think about computation in any business process of ours,” he said. As we generate ever-increasing amounts of data, whether we are talking about public sector kinds of use case, or any business need, it’s going to be the fuel for artificial intelligence, and he sees the sheer amount of that data driving new AI use cases.

“Of course when you have that rich computational fabric, one of the things that you can do is create this new asset, which is data and AI. There is not going to be a single application, a single experience that you are going to build, that is not going to be driven by AI, and that means you have to really have the ability to reason over large amounts of data to create that AI,” he said.

Nadella would be more than happy to have his audience take care of all that using Microsoft products, whether Azure compute, database, AI tools or edge computers like the Data Box Edge it introduced in 2018. While Nadella is probably right about the future of computing, all of this could apply to any cloud, not just Microsoft.

As computing shifts to the edge, it’s going to have a profound impact on the way we think about technology in general, but it’s probably not going to involve being tied to a single vendor, regardless of how comprehensive their offerings may be.

Edge computing could push the cloud to the fringe


By Ron Miller

Data storage company Cloudian launches a new edge analytics subsidiary called Edgematrix

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Cloudian, a company that enables businesses to store and manage massive amounts of data, announced today the launch of Edgematrix, a new unit focused on edge analytics for large data sets. Edgematrix, a majority-owned subsidiary of Cloudian, will first be available in Japan, where both companies are based. It has raised a $9 million Series A from strategic investors NTT Docomo, Shimizu Corporation and Japan Post Capital, as well as Cloudian co-founder and CEO Michael Tso and board director Jonathan Epstein. The funding will be used on product development, deployment and sales and marketing.

Cloudian itself has raised a total of $174 million, including a $94 million Series E round announced last year. Its products include the Hyperstore platform, which allows businesses to store hundreds of petrabytes of data on premise, and software for data analytics and machine learning. Edgematrix uses Hyperstore for storing large-scale data sets and its own AI software and hardware for data processing at the “edge” of networks, closer to where data is collected from IoT devices like sensors.

The company’s solutions were created for situations where real-time analytics is necessary. For example, it can be used to detect the make, model and year of cars on highways so targeted billboard ads can be displayed to their drivers.

Tso told TechCrunch in an email that Edgematrix was launched after Cloudian co-founder and president Hiroshi Ohta and a team spent two years working on technology to help Cloudian customers process and analyze their data more efficiently.

“With more and more data being created at the edge, including IoT data, there’s a growing need for being able to apply real-time data analysis and decision-making at or near the edge, minimizing the transmission costs and latencies involved in moving the data elsewhere,” said Tso. “Based on the initial success of a small Cloudian team developing AI software solutions and attracting a number of top-tier customers, we decided that the best way to build on this success was establishing a subsidiary with strategic investors.”

Edgematrix is launching in Japan first because spending on AI systems there is expected to grow faster than in any other market, at a compound annual growth rate of 45.3% from 2018 to 2023, according to IDC.

“Japan has been ahead of the curve as an early adopter of AI technology, with both the governmetn and private sector viewing it as essential to boosting productivity,” said Tso. “Edgematrix will focus on the Japanese market for at least the next year, and assuming that all goes well, it would then expand to North America and Europe.”


By Catherine Shu

Microsoft acquires data privacy and governance service BlueTalon

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Microsoft today announced that it has acquired BlueTalon, a data privacy and governance service that helps enterprises set policies for how their employees can access their data. The service then enforces those policies across most popular data environments and provides tools for auditing policies and access, too.

Neither Microsoft nor BlueTalon disclosed the financial details of the transaction. Ahead of today’s acquisition, BlueTalon had raised about $27.4 million, according to Crunchbase. Investors include Bloomberg Beta, Maverick Ventures, Signia Venture Partners and Stanford’s StartX fund.

BlueTalon Policy Engine How it works

“The IP and talent acquired through BlueTalon brings a unique expertise at the apex of big data, security and governance,” writes Rohan Kumar, Microsoft’s corporate VP for Azure Data. “This acquisition will enhance our ability to empower enterprises across industries to digitally transform while ensuring right use of data with centralized data governance at scale through Azure.”

Unsurprisingly, the BlueTalon team will become part of the Azure Data Governance group, where the team will work on enhancing Microsoft’s capabilities around data privacy and governance. Microsoft already offers access and governance control tools for Azure, of course. As virtually all businesses become more data-centric, though, the need for centralized access controls that work across systems is only going to increase and new data privacy laws aren’t making this process easier.

“As we began exploring partnership opportunities with various hyperscale cloud providers to better serve our customers, Microsoft deeply impressed us,” BlueTalon CEO Eric Tilenius, who has clearly read his share of “our incredible journey” blog posts, explains in today’s announcement. “The Azure Data team was uniquely thoughtful and visionary when it came to data governance. We found them to be the perfect fit for us in both mission and culture. So when Microsoft asked us to join forces, we jumped at the opportunity.”


By Frederic Lardinois

With Tableau and Mulesoft, Salesforce gains full view of enterprise data

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Back in the 2010 timeframe, it was common to say that content was king, but after watching Google buy Looker for $2.6 billion last week and Salesforce nab Tableau for $15.7 billion this morning, it’s clear that data has ascended to the throne in a business context.

We have been hearing about Big Data for years, but we’ve probably reached a point in 2019 where the data onslaught is really having an impact on business. If you can find the key data nuggets in the big data pile, it can clearly be a competitive advantage, and companies like Google and Salesforce are pulling out their checkbooks to make sure they are in a position to help you out.

While Google, as a cloud infrastructure vendor, is trying to help companies on its platform and across the cloud understand and visualize all that data, Salesforce as a SaaS vendor might have a different reason — one that might surprise you — given that Salesforce was born in the cloud. But perhaps it recognizes something fundamental. If it truly wants to own the enterprise, it has to have a hybrid story, and with Mulesoft and Tableau, that’s precisely what it has — and why it was willing to spend around $23 billion to get it.

Making connections

Certainly, Salesforce chairman Marc Benioff has no trouble seeing the connections between his two big purchases over the last year. He sees the combination of Mulesoft connecting to the data sources and Tableau providing a way to visualize as a “beautiful thing.”


By Ron Miller

Tealium, a big data platform for structuring disparate customer information, raises $55M led by Silver Lake

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The average enterprise today uses about 90 different software packages, with between 30-40 of them touching customers directly or indirectly. The data that comes out of those systems can prove to be very useful — to help other systems and employees work more intelligently, to help companies make better business decisions — but only if it’s put in order: now, a startup called Tealium, which has built a system precisely to do just that and works with the likes of Facebook and IBM to help manage their customer data, has raised a big round of funding to continue building out the services it provides.

Today, it is announcing a $55 million round of funding — a Series F led by Silver Lake Waterman, the firm’s late-stage capital growth fund; with ABN AMRO, Bain Capital, Declaration Partners, Georgian Partners, Industry Ventures, Parkwood, and Presidio Ventures also participating.

Jeff Lunsford, Tealium’s CEO, said that the company is not disclosing valuation, but he did say that it was “substantially” higher than when the company was last priced three years ago. (For context, that valuation was $305 million in 2016, according to PitchBook — a figure Lunsford didn’t dispute when I spoke with him about it.)

He added that the company is close to profitability and is projected to make $100 million in revenues this year, and that this is being considered the company’s “final round” — presumably a sign that it will either no longer need external funding and that if it does, the next step might be either getting acquired or going public.

This brings the total raised by Tealium to $160 million.

The company’s rise over the last eight years has dovetailed with the rapid growth of big data. The movement of services to digital platforms has resulted in a sea of information. Much of that largely sits untapped, but those who are able to bring it to order can reap the rewards by gaining better insights into their organizations.

Tealium had its beginnings in amassing and ordering tags from internet traffic to help optimise marketing and so on — a business where it competes with the likes of Google and Adobe.

Over time, it has expanded and capitalised to a much wider set of data sources that range well beyond web and commerce, and one use of the funding will be to continue expanding those data sources, and also how they are used, with an emphasis on using more AI, Lunsford said.

“There are new areas that touch customers like smart home and smart office hardware, and each requires a step up in integration for a company like us,” he said. “Then once you have it all centralised you could feed machine learning algorithms to have tighter predictions.”

That vast potential is one reason for the investor interest.

“Tealium enables enterprises to solve the customer data fragmentation problem by integrating and enriching data across sources,in real-time to create audiences while providing data governance and fidelity” said Shawn O’Neill, Managing Director, of Silver Lake Waterman, in a statement. “Jeff and his team have built a great platform and we are excited to support the company’s continued growth and investment in innovation.”

The rapid growth of digital services has already seen the company getting a big boost in terms of the data that is passing through its cloud-based platform: it has had a 300 percent year-over-year increase in visitor profiles created, with current tech customers including the likes of Facebook, IBM, Visa and others from across a variety of sectors, such as healthcare, finance and more.

“You’d be surprised how many big tech companies use Telium,” Lunsford said. “Even they have a limited amount of bandwidth when it comes to developing their internal platforms.”

People like to say that “data is the new oil”, but these days that expression has taken on perhaps an unintended meaning: just like the overconsumption of oil and fossil fuels in general is viewed as detrimental to the long-term health of our planet, the overconsumption of data has also become a very problematic spectre in our very pervasive world of tech.

Governments — the European Union being one notable example — are taking up the challenge of that latter issue with new regulations, specifically GDPR. Interestingly, Lunsford says this has been a good thing rather than a bad thing for his company, as it gives a much clearer directive to companies about what they can use, and how it can be used.

“They want to follow the law,” he said of their clients, “and we give them the data freedom and control to do that.” It’s not the only company tackling the business opportunity of being a big-data repository at a time when data misuse is being scrutinised more than ever: InCountry, which launched weeks ago, is also banking on this gap in the market.

I’d argue that this could potentially be one more reason why Tealium is keen on expanding to areas like IoT and other sources of customer information: just like the sea, the pool of data that’s there for the tapping is nearly limitless.


By Ingrid Lunden