Within the ever vibrant world of the “Fashionable Knowledge Stack” (an ecosystem of largely younger tech startups that characterize the rising era of knowledge software program distributors, and combine nicely with each other), Hex has been getting growing visibility and momentum. At its core, Hex is a collaborative knowledge platform the place groups can discover, analyze, and share. It goals to carry collectively the very best of notebooks, BI & docs right into a seamless, collaborative UI.
The corporate was based in 2019 and also you raised a complete of $73.5 million in enterprise capital up to now, together with most not too long ago a $52 million Collection B.
CEO Barry McCardel joined us at Knowledge Pushed NYC for a deep dive in to the product, the corporate, the info house and his journey from doing “unholy issues in Excel” as a younger marketing consultant to constructing an awesome startup.
Beneath is the video and full transcript.
(As at all times, Knowledge Pushed NYC is a workforce effort – many due to my FirstMark colleagues Jack Cohen, Karissa Domondon Diego Guttierez)
VIDEO:
TRANSCRIPT [edited for clarity and brevity]:
[Matt Turck] Why does the world want a collaborative workspace for knowledge groups? What’s the large drawback that you simply’re engaged on fixing?
I’ve been working in knowledge successfully my complete profession. I began as an undergrad, actually, doing a bunch of stuff, simply writing R scripts and all this analysis stuff earlier than knowledge science was even a factor, actually. After which was doing unholy issues in Excel as a marketing consultant. Then I used to be at Palantir for 5 years, the place I received publicity all throughout a bunch of various technical issues. Then I labored actually intently with our knowledge workforce at my final firm. And all alongside the way in which I principally noticed the identical set of issues. In essence, Hex is supposed to resolve these.
So the very first thing we actually set out on and the factor that was a extremely acute drawback that we wished to resolve was across the means to share work. It’s this quite common factor and we noticed it up shut at our final firm we have been at which is you could have knowledge analysts and knowledge scientists and simply folks working with knowledge everywhere in the enterprise doing actually attention-grabbing, superior issues. They’re entering into. They’re asking and answering questions. They’re driving perception.
Then the precise means to share and publish that all through the group is terrible. It’s actually a catastrophe. You’ve gotten folks screenshotting charts out of Jupyter Pocket book and pasting them in Google Docs. You’ve gotten folks exporting a CSV from a BI device to allow them to construct the appropriate match on this different factor after which put that in a deck. Then you could have folks hacking collectively scripts to attempt to construct a pipeline to place the forecast within the warehouse so you’ll be able to have a look at it within the BI device.
It was this large mess. So we began actually specializing in that drawback. The preliminary factor we have been centered on was, how will you assist knowledge scientists who’re engaged on one thing like a Jupyter Pocket book, take that and share it with others in a manner that’s interactive and helpful and usable? As we began entering into that, we realized the ache was actually a lot deeper than that. It was really like folks have been simply annoyed with the entire stack. You had people leaping round between instruments relying on whether or not they’re utilizing SQL or Python or no code. You’ve received groups actually unable to collaborate. The entire versioning and real-time collaboration for all that is only a mess. It’s very regressive in comparison with instruments in different areas, like Figma or Google Docs.
Then there’s simply an quantity of overhead and ache to getting these instruments up and working anyway that’s really actually laborious. There’s a really basic expertise the place you’ll see a brand new knowledge scientist will be a part of and the primary two weeks are actually nearly getting all the appropriate packages put in regionally of their Jupyter surroundings after which ensuring that’s synced up. You wind up with this overhead that’s each very irritating for the people who find themselves doing these workflows but in addition prevents lots of people from accessing.
So again to your query, Hex is de facto meant to be three massive issues. It’s an incredible, collaborative surroundings for with the ability to do evaluation and knowledge science. It’s received a pocket book UI that’s simply completely magical. I’ll present that to you in a bit. It’s very, very simple to take your work and share it and publish it as an interactive knowledge app that anybody can use. Then that work is then stored and arranged in what we name a Data Library which makes it very simple for anybody else within the group to find and profit from the work that the info workforce has been doing. So mission sensible, that’s actually what we’re about and we constructed a product that actually addresses that finish to finish.
Nice. And it’s, by definition, meant to be very inclusive, proper? So it’s knowledge scientists. It’s knowledge analysts. It’s enterprise folks as nicely. You’ve gotten an expression that I learn someplace, which I actually favored, which was the “analytically technical.”
Analytically technical, yeah. It’s attention-grabbing as a result of you consider a few of the massive modifications which have occurred in the previous couple of years. You see this explosion in people who find themselves knowledge literate. They’re even, I’d name them, considerably technical. And there’s extra individuals who know Python, certain. There’s much more individuals who know SQL. And lots of people have both realized SQL on the job or are available in out of undergrad with that skillset. There’s additionally this a lot larger inhabitants of those who I’d argue which might be technical in their very own manner; which if you happen to’re an Excel Energy consumer and also you’re writing deeply nested features or VBA and even just a few pivot tables or IFs, you’re principally writing code. I’d argue you’re writing code. You might be technical indirectly.
And I believe conventional knowledge science and analytics instruments have really been a excessive tower. They’re troublesome for these folks to entry. And so one of many issues that’s actually attention-grabbing for us, what we see in our prospects, is we now have quite a lot of customers. In reality, most of our prospects, a lot of the customers are largely writing SQL. And that’s very completely different than what you would possibly consider while you consider a pocket book surroundings, which is historically very related to Python and, quote-unquote, “knowledge science.” However Hex makes it very simple to commute between SQL and Python. You possibly can collaborate between these. And so it’s very inclusive.
It’s very cool for us to see that our prospects will begin with a really small variety of knowledge scientists, a pair people who find themselves migrating their workflows over from Jupyter however then will explode to the place you see all kinds of individuals utilizing Hex to ask and reply questions. That’s one thing we’re very enthusiastic about. I really feel like we’re simply nonetheless on the tip of the iceberg. And we consider it as constructing a platform that has a low flooring and a excessive ceiling. We need to have a platform that anybody can are available in and ask and reply questions. Nevertheless it doesn’t arbitrarily prime out.
And I believe that’s a giant distinction between the final era of instruments, which is like, “Okay. This can be a no-code factor. It’s received a low flooring and a low ceiling.” However the second you need to do one thing extra advanced, you’ve topped out. And now you could have a UX SqlRunner. Medium flooring, medium ceiling. After which, “Okay. Now I’m over in my Jupyter Pocket book,” excessive flooring, excessive ceiling. I problem why this must be three fragmented issues. And I believe we’ve carried out an awesome job up to now with the ability to carry a few of these extra collectively.
So to take a few of us by means of a bit and drill into the subsequent stage, so the core is a pocket book. We talked about exhibiting the product. So I’m excited for a product demo. However simply at a excessive stage earlier than we leap into the demo and perhaps to make it inclusive for everybody, so only a 10-second definition of what a pocket book really is.
Yeah certain so notebooks have been round for a very long time. As legend has it, they have been first pioneered at Mathematica. And the most typical one now’s a undertaking referred to as Jupyter. It was referred to as IPython.
That was within the ’80s, proper?
Yeah. Properly, I imply, Mathematica is an actual OG. IPython’s a little bit bit newer. After which it was rebranded as Jupyter, I believe, in 2015, one thing like that. However anyway, the pocket book format is principally you’ll have cells which have code historically. After which these cells present the output of that code. And people cells might be evaluated individually. That is completely different from a script. A script is one file. And the script is often evaluated, the entire thing, prime to backside.
And this breaking it up into cells makes it actually nice for iterative and exploratory evaluation. So you’ll be able to say, “I simply need to run this little chunk. And, oh. I need to do the aggregation a little bit bit completely different. I need to do that.” And that is all an expression of a factor referred to as literate programming. I cannot go within the deep finish on this. However principally, it’s this concept that you would be able to see your logic after which the outputs in a single place. It’s a really, very talked-about format. I imply, thousands and thousands of individuals use notebooks. However we predict that it’s really a format that much more folks must be utilizing. We’re very completely happy to see that with our consumer base and prospects.
Yeah and simply even at a better stage, a pocket book is a spot the place knowledge scientists and knowledge analysts work collectively. And it’s a mixture of code and clarification. So it’s like a piece house.
That’s proper. And it’s actually the factor. Properly, if you happen to discuss to quite a lot of the info scientists, particularly, it’s the factor they use all day. It’s the factor the place they’re going and writing code. And so they’re iterating on one thing. Now, notebooks additionally historically have quite a lot of points. There’s a well-known discuss referred to as I Don’t Like Notebooks that this man Joel Grus gave at JupyterCon. It was very simply exhibiting up within the unsuitable place to offer that discuss. However he was proper. There’s all these points.
It was like 4 years in the past or one thing like this.
Yeah. It was 2018, I believe. Nevertheless it was all these points. A part of what we’re doing at Hex is, “Properly, notebooks are nice. They’ve some points.” I believe there’s a camp of individuals which might be like, “Due to these points, everybody must be doing one thing like writing scripts or no matter.” I believe we’re looking for that synthesis of, “Properly, what if we simply repair these points with notebooks and made them superior and made them accessible to 100 occasions the folks? I believe this really may go someplace.” And that, in a extremely simplistic manner, is what we’ve been as much as on quite a lot of issues.
A part of what you have been describing was one of many key points – simply to ensure I paraphrase and I be sure I understood accurately, one massive subject of notebooks is that you would be able to have completely different definitions of a variable in a pocket book.
Yeah. We name this a state subject. So I’d get away some points with notebooks. I’d say the very first thing is accessibility. I used to be getting at this earlier, however most individuals working with knowledge in most locations have by no means used a pocket book as a result of the first step is studying computer systems. You need to determine methods to arrange a neighborhood Python surroundings and set up Jupyter. And most of the people usually are not going to try this. Factor two is state. That’s what you’re getting at. And the brief model of that is notebooks historically run in what’s referred to as a kernel. It’s principally reminiscence house the place you run one thing like, “X equals 1.” Now in reminiscence, X equals 1.
However as a result of you’ll be able to run cells out of order, it’s really you may get in these bizarre state points the place you’ll be able to’t really know what state issues are in. You’ve gotten one cell that’s X equals 1. And one other cell is X equals 20. When you ran X equals 1 earlier than X equals 20, nicely, now it’s one and vice versa. So it will get actually difficult. For individuals who aren’t acquainted, we now have a complete weblog publish about it. However the brief model is it is a ache within the ass for individuals who have been utilizing notebooks a very long time, like me.
Nevertheless it’s actually painful for people who find themselves new to it, who’re like, “What’s occurring?” You lose lots of people. And we consider this as one of many many issues which might be in that low flooring, excessive ceiling of, how will we make notebooks superior, higher for these energy customers? But in addition, how do you make it extra accessible and usable and welcoming for this larger inhabitants of those who we predict deserve nice instruments?
There’s different points with notebooks we’re engaged on too. However that state subject… we launched a characteristic final October. We referred to as it Hex 2.0. Nevertheless it was this reactive compute engine we had. And I’ll present it off in a minute. Nevertheless it’s successfully saying, “What if notebooks labored a little bit bit extra like a spreadsheet the place cells have this sense of provenance between them?” While you replace one factor, it routinely updates downstream cells. And the state is in a a lot better state. State is in a greater state.
And that is nice. That is higher for these energy customers who’re like, “Man, that is the way in which I at all times wished this labored.” And it’s nice for novice customers, who quite a lot of them have by no means used a pocket book earlier than. They’re not even conscious there’s a state subject. They simply know they don’t have that in Hex. So it’s all good. And that was the aim of that characteristic for us.
As only one final query on notebooks earlier than we leap into the demo – a part of the worth proposition as nicely is that you are able to do knowledge science within the Python world. However you can too do SQL and databases. And I believe you are able to do that in Jupyter as nicely by putting in packages. Nevertheless it all comes out of the field. Or is that not right?
Properly, you’ll be able to. I imply, this was the factor after we began out after I was like… while you’re beginning an organization, you get an thought, you’re pitching folks the concept. And it’s not unusual for folks like, “Properly, that’s already potential.” I’d be like, “Properly, what if it labored like this?” Individuals are like, “Properly, Barry, that’s already potential.” “Oh. Actually? Have I missed one thing?” “Properly, if you happen to set up these three packages and you then’re prepared to, when you have the surroundings variables all arrange accurately and you then roll your individual reference to SQLAlchemy after which write your [inaudible], yeah, you could possibly completely write SQL in notebooks.” That’s an terrible expertise. And never solely do I hate doing it as somebody who’s technically able to doing it however what about all these people who find themselves not going to combat by means of all of that ache or don’t have the flexibility to try this?
And I believe it’s the identical with the sharing factor. I used to be like, “Properly, what if it was very easy to publish your pocket book in a manner that anybody may use?” And it’s like, “Properly, that’s potential.” There’s these three open supply packages that if you happen to set up them in your JupyterHub occasion and everybody’s utilizing the appropriate model of JupyterLab and so they’re all updated. And, oh. Properly, these extensions are incompatible. However ignore that. And if you happen to do that all proper after which Mercury is aligned with Jupiter the appropriate manner, then you are able to do it. And by the way in which, you’re going to want a full-time particular person to handle all of this.
That is the kind of shit that’s solely accessible to those actually technical customers and turns lots of people off from these workflows. And we don’t assume it needs to be this manner. So whether or not it’s the SQL stuff or reactivity or stunning no-code charts, we’re simply making it actually freaking simple to share your work with anybody. We predict that there’s a technique to make this extra accessible with out dumbing it down. Our energy customers love these things too. That’s the place, I believe, there’s this false dichotomy generally of, are you constructing for low-end customers or constructing for energy customers? We predict there’s quite a lot of sensible folks. We predict there’s lots of people that, given the appropriate instruments, will interact with these knowledge workflows. And we’re all about constructing for that inhabitants.
Superior. Adore it. All proper. Let’s leap into the demo.
[DEMO]
So switching tacks a little bit bit, you guys appear to have carried out a very nice job partnering with quite a lot of corporations within the ecosystem, together with quite a lot of corporations we’ve had at this occasion through the years, together with, in your spherical not too long ago, I noticed that each Databricks and Snowflake invested within the firm. However earlier than that, you had bulletins with metric retailer corporations and different corporations, like dbt.
dbt is a giant companion. Yeah.
Yeah. So is {that a} go-to-market technique? Is {that a} product? How do you consider it?
Properly, it’s each. I believe the partnerships with Snowflake and Databricks are very attention-grabbing in that… I didn’t speak about this earlier however we’re actually constructing a product to embrace what we consider because the cloud knowledge period, which is you could have knowledge that’s a large scale, saved in cloud knowledge warehouses. And people cloud knowledge warehouses usually are not simply there for storage. Databricks and Snowflake and different corporations are additionally constructing very highly effective compute primitives whether or not it’s simply with the ability to push a question down completely different warehouse sizes and even with the ability to push Python code down. We predict they’re doing an awesome job with that. We predict that they’re going to proceed to do an awesome job with that. And we need to companion actually shut with them on that.
So the partnership makes a ton of sense as a result of when individuals are utilizing Hex, they’re going to be asking and answering questions on extra knowledge. They’re going to be pushing extra workloads right down to these knowledge warehouses, which is nice for them. And people knowledge warehouses additionally present a extremely nice scale and knowledge story for us. We really must do much less on our finish to construct out a complete compute infrastructure and ecosystem ourselves in the event that they’re doing an awesome job of that. So we predict that partnership makes a ton of sense. We see our prospects actually pulling us on, how are we integrating very intently with these applied sciences that they’re already investing in?
After which dbt? dbt is the concept you’re constructing some transformation within the pocket book?
You actually may. There’s a pair attention-grabbing angles. We really simply printed a weblog publish, one in every of our first analytics engineers, she makes use of dbt and Hex all day. She’s received a really cool workflow the place she’ll develop quite a lot of stuff in Hex, carry it over to dbt. She’s printed a weblog publish on our website about how she makes use of them collectively, which may be very cool. However going a little bit bit deeper than that, I believe, while you have a look at what dbt is doing or what corporations like Rework are doing on the metrics layer, they’re actually virtually unbundling BI on this actually attention-grabbing manner the place they’re saying, “Hey, it’s not nearly reworking knowledge as normalized tables in your warehouse. It’s about the way you’re then really turning them into metrics and measures and semantics which might be accessible to BI and analytics layer.” And so we’re very enthusiastic about what’s taking place there.
We predict it’s very a lot in its infancy. However because it matures, we predict there’s a extremely cool alternative to carry that extra in Hex the place you could possibly have folks as an alternative of getting to jot down a ton of SQL in Excel, perhaps they’re in a position to write one thing rather more concise or perhaps one thing extra UI pushed, the place they’ll simply choose a metric they need, get an information body again after which begin working in opposition to that. So we now have a ton of shared enterprise with dbt at this time. However I believe with the place they’re going and the place we’re going, there’s much more that we’re going to be doing collectively and others in that house.
Nice. So perhaps to shut, I’d like to spend two or three minutes on go-to-market gross sales. Who do you promote to? Who’s an awesome buyer? Possibly, who’re some current prospects? That facet of the enterprise.
Yeah. So we’re utilized by over… I believe the final depend was over 150 groups globally now, knowledge groups paying us for Hex, which we’re extraordinarily pleased with. We assist actually massive public corporations, like Persion Prescribed drugs, for instance. They use Hex enterprise huge to assist their analysis efforts.
We’re additionally utilized by small startups. I believe the one constant factor throughout our buyer base and the place we’re actually resonating, they’re making investments in knowledge infrastructure and knowledge. And we simply talked about Snowflake and Databricks and dbt.
If corporations are adopting these applied sciences, they’re usually then coming to Hex for, “Nice. I’ve received all this knowledge now in my warehouse. I’m reworking it with dbt. Now I really need to have the ability to ask and reply questions of it. I need to have the ability to do extra with it than I’m in a position to in legacy instruments or Jupyter Notebooks or SQL Scratchpads.”
Hex is an excellent good complement to corporations which have invested in that stack. And what we’re seeing is any firm that’s hiring of us in roles like knowledge science, analytics, analytics engineering actually wants and desires and will get a ton of worth out of Hex. So, yeah. From a buyer and goal perspective, that’s the place we’re at proper now.
Very cool. Congratulations on all of this and this journey. The corporate remains to be fairly younger. I imply, it looks like you guys are executing extremely nicely and really quick.
Yeah. We began in late, late 2019. So actually, not been at it too lengthy. I used to be very, very lucky to start out at an organization with two of us that I had labored with at Palantir, Caitlin and Glen. And we’ve had quite a lot of enjoyable and a little bit little bit of luck the final couple years constructing this out. So we’re trying to proceed that streak for a little bit bit longer and hold occurring. We’re having a superb time.
Very cool. Properly, thanks a lot, Barry, for coming to this occasion, telling us your story. Better of luck for the longer term. I hope you come again in a few years.
Yeah. Sarcastically, I’m really in New York this week, not that removed from you. However we’re nonetheless doing this digital. So perhaps subsequent time I come again, we’ll be capable of do it in particular person.
Do it in particular person. Sure. I can not look forward to that. Okay. Cool. Thanks a lot, Barry.
