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 U.S.-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 firsthand 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 their 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, rearchitecting 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 are fitting answers for that issue, he 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.”

Diversity and inclusion have become central topics in the world of work. In the best considerations, improving them is a holistic effort, involving not just conceiving of products with this in mind, but hiring and managing talent in a diverse and inclusive way, too. A new startup called Pequity, which has built a product to help with the latter of these areas, specifically in equitable compensation, has now raised some funding — a sign of the demand in the market, as well as how tech is being harnessed in aid of helping it.

The San Francisco-based startup has raised $19 million in a Series A led by Norwest Venture Partners. First Round Capital, Designer Fund, and Scribble Ventures also participated in the fundraise, which will be used to continue investing in product and also hiring: the company has 20 on its own books now and will aim to double that by the end of this year, on the heels of positive reception in the market.

Since launching officially last year, Pequity has picked up over 100 customers, with an initial focus on fast-scaling companies in its own backyard, a mark of how D&I have come into focus in the tech industry in particular. Those using Pequity to compare and figure out compensation include Instacart, Scale.ai and ClearCo, and the company said that in the last four months, the platform’s been used to make more then 5,000 job offers.

Image Credits: Pequity (opens in a new window)

Kaitlyn Knopp, the CEO who co-founded the company with Warren Lebovics (both pictured, right), came up for the idea for Pequity in much the same way that many innovations in the world of enterprise IT come to market: through her own first-hand experience.

She spent a decade working in employment compensation in the Bay Area, with previous roles at Google, Instacart, and Cruise. In that time, she found the tools that many companies used were lacking and simply “clunky” when it came to compensation analysis.

“The way the market has worked so far is that platforms had compensation as an element but not the focus,” she said. “It was the end of the tagline, the final part of a ‘CRM for candidates.’ But you still have to fill in all the gaps, you have to set the architecture the right way. And with compensation, you have to bake in your own analytics, which implies that you have to have some expertise.”

Indeed, as with other aspects of enterprise software, she added that the very biggest tech companies sometimes worked on their own tools, but not only does that leave smaller or otherwise other-focused businesses out of having better calculation tools, but it also means that those tools are siloed and miss out on being shaped by a bigger picture of the world of work. “We wanted to take that process and own it.”

The Pequity product essentially works by plugging into all of the other tools that an HR professional might be using — HRIS, ATS, and payroll products — to manage salaries across the whole of the organization in order to analyse and compare how compensation could look for existing and prospective employees. It combines a company’s own data and then compares it to data from the wider market, including typical industry ranges and market trends, to provide insights to HR teams.

All of this means that HR teams are able to make more informed decisions, which is step number one in being more transparent and equitable, but is also something that Pequity is optimized to cover specifically in how it measures compensation across a team.

And in line with that, there is another aspect of the compensation mindset that Knopp also wanted to address in a standalone product, and that is the idea of building a tool with a mission, one of providing a platform that can bring in data to make transparent and equitable decisions.

“A lot of the comp tools that I’ve interacted with are reactive,” she said. “You may have to do, say, a pay equity test, you do your promotion and merit cycles, and then you find all these issues that you have to solve. We’re flagging those things proactively with our analytics, because we’re plugging into those systems, which will give you those alerts before the decisions need to be made.”

As an added step in that direction, Knopp said that ultimately she believes the tool should be something that those outside of HR, such as managers and emploiyees themselves, should be able to access to better understand the logic of their own compensation and have more information going into any kind of negotiation.

Ultimately, it will be interesting to see whether modernized products like Pequity, which are tackling old problems with a new approach and point of view, find traction in the wider market. If one purpose in HR is to address diversity and inclusion, and part of the problem has been that the tools are just not fit for that purpose, then it seems a no-brainer that we’ll see more organizations trying out new things to see if they can help them in their own race to secure talent.

“Compensation reflects a company’s values, affects its ability to hire talent, and is the biggest expense on its P&L. And yet, most comp teams run on spreadsheets and emails,” said Parker Barrile, Partner at Norwest, in a statement. “Pequity empowers comp teams to design and manage equitable compensation programs with modern software designed by comp professionals, for comp professionals.”

Time is your most valuable asset — as the saying goes — and today a startup called Memory.ai, which is building AI-based productivity tools to help you with your own time management, is announcing some funding to double down on its ambitions: It wants not only to help manage your time, but to, essentially, provide ways to use it better in the future.

The startup, based out of Oslo, Norway, initially made its name with an app called Timely, a tool for people to track time spent doing different tasks. Aimed not just at people who are quantified self geeks, but those who need to track time for practical reasons, such as consultants or others who work on the concept of billable hours. Timely has racked up 500,000 users since 2014, including more than 5,000 paying businesses in 160 countries.

Now, Memory.ai has raised $14 million as it gears up to launch its next apps, Dewo (pronounced “De-Voh”), an app that is meant to help people do more “deep work” by learning about what they are working on and filtering out distractions to focus better; and Glue, described as a knowledge hub to help in the creative process. Both are due to be released later in the year.

The funding is being led by local investors Melesio and Sanden, with participation from Investinor, Concentric and SNÖ Ventures, who backed Memory.ai previously.

“Productivity apps” has always been something of a nebulous category in the world of connected work. They can variously cover any kind of collaboration management software ranging from Asana and Jira through to Slack and Notion; or software that makes doing an existing work task more efficient than you did it before (e.g. Microsoft has described all of what goes into Microsoft 365 — Excel, Word, PowerPoint, etc. — as “productivity apps”); or, yes, apps like those from Memory.ai that aim to improve your concentration or time management.

These days, however, it feels like the worlds of AI and advances in mobile computing are increasingly coming together to evolve that concept once again.

If the first wave of smartphone communications and the apps that are run on smartphone devices — social, gaming, productivity, media, information, etc. — have led to us getting pinged by a huge amount of data from lots of different places, all of the time, then could it be that the second wave is quite possibly going to usher in a newer wave of tools to handle all that better, built on the premise that not everything is of equal importance? No-mo FOMO? We’ll see.

In any case, some bigger platform players also helping to push the agenda of what productivity means in this day and age.

For example, in Apple’s recent preview of iOS 15 (due to come out later this year) the company gave a supercharge to its existing “do not disturb” feature on its phones, where it showed off a new Focus mode, letting users customize how and when they want to receive notifications from which apps, and even which apps they want to have displayed, all organized by different times of day (e.g. work time), place, calendar items and so on.

“Today, iPhone plays so many roles in our lives. It’s where we get information, how people reach us, and where we get things done. This is great, but it means our attention is being pulled in so many different directions and finding that balance between work and life can be tricky,” said Apple’s Craig Federighi in the WWDC keynote earlier this month. “We want to free up space to focus and help you be in the moment.” How well that gets used, and how much other platforms like Google follow suit, will be interesting to see play out. It feels, in any case, like it could be the start of something.

And, serendipitously — or maybe because this is some kind of zeitgeist — this is also playing into what Memory.ai has built and is building. 

Mathias Mikkelsen, the Oslo-based founder of Memory.ai, first came up with his idea for Timely (which had also been the original name of the whole startup) when he was working as a designer in the ad industry, one of those jobs that needed to track what he was working on, and for how long, in order to get paid.

He said he knew the whole system as it existed was inefficient: “I just thought it was insane how cumbersome and old it was. But at the same time how important it was for the task,” he said.

The guy had an entrepreneurial itch that he was keen to scratch, and this idea would become the salve to help him. Mikkelsen was so taken with building a startup around time management, that he sold his apartment in Oslo and moved himself to San Francisco to be where he believed was the epicenter of startup innovation. He tells me he lived off the proceeds of his flat for two years “in a closet” in a hacker house, bootstrapping Timely, until eventually getting into an accelerator (500 Startups) and subsequently starting to raise money. He eventually moved back to Oslo after two years to continue growing the business, as well as to live somewhere a little more spacious.

The startup’s big technical breakthrough with Timely was to figure out an efficient way of tracking time for different tasks, not just time worked on anything, without people having to go through a lot of data entry.

The solution: to integrate with a person’s computer, plus a basic to-do schedule for a day or week, and then match up which files are open when to determine how long one works for one client or another. Phone or messaging conversations, for the moment, are not included, and neither are the contents of documents — just the titles of them. Nor is data coming from wearable devices, although you could see how that, too, might prove useful.

The basic premise is to be personalised, so managers and others cannot use Timely to track exactly what people are doing, although they can track and bill for those billable hours. All this is important, as it also will feed into how Dewo and Glue will work.

The startup’s big conceptual breakthrough came around the same time: Getting time tracking or any productivity right “has never been a UI problem,” Mikkelsen said. “It’s a human nature problem.” This is where the AI comes in, to nudge people towards something they identify as important, and nudge them away from work that might not contribute to that. Tackling bigger issues beyond time are essential to improving productivity overall, which is why Memory.ai now wants to extend to apps for carving out time for deep thinking and creative thinking.

While it might seem to be a threat that a company like Apple has identified the same time management predicament that Memory.ai has, and is looking to solve that itself, Mikkelsen is not fazed. He said he thinks of Focus as not unlike Apple’s work on Health: there will be ways of feeding information into Apple’s tool to make it work better for the user, and so that will be Memory.ai’s opportunity to hopefully grow, not cannibalize, its own audience with Timely and its two new apps. It is, in a sense, a timely disruption.

“Memory’s proven software is already redefining how businesses around the world track, plan and manage their time. We look forward to working with the team to help new markets profit from the efficiencies, insights and transparency of a Memory-enabled workforce,” said Arild Engh, a partner at Melesio, in a statement.

Kjartan Rist, a partner at Concentric, added: “We continue to be impressed with Memory’s vision to build and launch best-in-class products for the global marketplace. The company is well on its way to becoming a world leader in workplace productivity and collaboration, particularly in light of the remote and hybrid working revolution of the last 12 months. We look forward to supporting Mathias and the team in this exciting new chapter.”