[segah]

@akalitenya are you even in the clear with this? Some of the Old LookML syntax is an exact copy.

But more importantly, the challenge for any such tool is to go beyond use by 2-3 people. At 2-3 people anything will work. Where BI tools (open source and close source) struggle is scale: having all the right features for, essentially, a group of users who actually don't know how to work with data (did I just say that aloud?). Chartio caps at 20 people. RJ capped at 50-100 (and later became Stitch for that reason). We haven't seen where Metabase caps, but I bet it is in a similar range. Very few BI products have actually surpassed 100 users at target installations. And beyond 1,000 is a real challenge that only few, and even then with a lot of assistance, can support: Tableau, Looker, Microstrategy, maybe Birst, maybe Domo.

Also, a combination of BI with LookML is a complicated product. During my days at Looker, we were handling 50+ bugs / week, and filing 1,000+ tickets. Every day we were filing over a 100 new features.

So with all that, the question is, is it really worth the struggle? What's the end vision for supporting this? Why should someone who implements BI for a living bet on this product?

[akalitenya]

> Some of the Old LookML syntax is an exact copy.

I met the online Looker demo a few years ago when I was looking for a business intelligence tool for another project.

Looker has a closed source code, so I did not see what algorithm is used to build queries.

I kept those LookML features that I understood and liked. However, in some places LookML is confusing (for example - references and aliases). I made them differently.

Later Looker quit using YAML.

> Very few BI products have actually surpassed 100 users at target installations. And beyond 1,000 is a real challenge that only few, and even then with a lot of assistance, can support: Tableau, Looker, Microstrategy, maybe Birst, maybe Domo.

Scaling that size is not a top priority right now.

> Also, a combination of BI with LookML is a complicated product.

You should have told me this a couple of years ago. It seemed pretty simple.

> So with all that, the question is, is it really worth the struggle?

Yes, If people will use it.

> What's the end vision for supporting this?

In future - maybe "skinny" or "thin" option mentioned here - https://medium.com/open-consensus/2-open-core-definition-exa...

>Why should someone who implements BI for a living bet on this product?

If you can not afford Looker (like me) and want to use similar product.

[endlessvoid94]

I used Looker for years and have always wanted a product priced less for the enterprise and more for developers. I even prototyped a similar tool myself.

Congratulations on the launch. I will be using mprove and will give you feedback along the way.

EDIT: I would love to get in touch with you and learn more about what your plans are. Possibly collaborate if you'd be open to that. I'm [e-mail] -- I couldn't find your contact info anywhere!

[akalitenya]

Thank you, I sent you an email. I am open to any suggestions - [e-mail].

[__coaxialcabal]

> Very few BI products have actually surpassed 100 users at target installations. And beyond 1,000 is a real challenge that only few, and even then with a lot of assistance, can support: Tableau, Looker, Microstrategy, maybe Birst, maybe Domo.

At one point Looker was essentially a web application with a query service that could be scaled by adding more servers behind a load balancer. Each end user is essentially working in a shared nothing environment and the query engine is driven by metadata stored in a git repository. Looker itself did not manage cache or analytical processes. All of the real effort to scale was in the database backend.

[segah]

"scale" meant adoption, not performance. Scaling performance can generally be solved with hardware. Adoption, not so much.

[zackt]

Looker is easy to cr*ck (terrible security) and you can deploy it everywhere for your BI needs. So we dont need similar product.

[MechanicalTwerk]

I agree with pretty much everything you've said. However, as someone who has used Chartio extensively, I'd say 20 is wayyy too low. It can definitely handle 100s. But, like you said, 1,000s is a struggle for anyone.

Also, if you think this is a rip off of LookML, you should take a look at what GitLab is doing with Meltano. They completely jacked LookML.

[g14i]

Please, do you mind sharing what exactly makes a BI solution difficult/struggling for 1,000+ users?

Is it something technically related or more business/feature related?

[segah]

mmmm, both!

Tech-wise, data stacks are complicated. Hundreds of pre-existing tools, with many owners and diverse interests. BI is where they all meet. With some organizations its easy to build a central data warehouse, but in a large corporate environment that's a dream. Imagine how many data warehouses an organization like Microsoft might have? OK, MS is too large - how about Expedia or Zillow? Thousands? So you connect a tool like Looker--that was designed for 2-10 connections--to a thousand? How do you even administer all the connections? How do you govern access? Access permissions at Looker (and other BI tools) are great, but it would take you 10 years to set them up for 1,000 DBs. And what about tool access? Some want SSH tunneling, others want complete on-prem. Some need Google authentication, while others want one through a pre-existing corporate login. Most large organizations typically end up building--or hiring someone to build--their own custom solutions on top of these tools. Such custom solutions are bigger projects than the implementation of a BI tool to begin with. Oh, and most also fail. The success of a BI in an enterprise environment is partly good sales/marketing/support, but a big part is due to the product achieving some form of maturity with all this side tech--the stuff totally not core to the analytics itself.

Business-wise, try getting people with diverse backgrounds agree on common terminology. Let's take marketing for instance. Should be easy, no? But hey, marketing is actually like 10-20 different kinds of people - some know SQL, others can't put 2+2 together and arrive at anything other than the word "magic". Some think in terms of stories--others think in terms of conversion funnels. OK, so you've put together some data dictionary, did some training, segmented users into 1) technical ones (SQL/LookML/BackML...), 2) business (explore), 3) consumers (dashboards/pretty charts). But now it turns out that much of the data they rely on is generated by a different team--say, operations or sales. Again, you've got 10-20 different kinds of personalities there. Somehow all these people have to agree. How do you make them all agree? Short answer: you cannot. No one can. They don't even like speaking to one another - and now you are going to come in and make them agree on what kind of KPI is going to determine their success => bonus. Hell no!

There are ways to make progress on both fronts. And, no doubt, an open source project has some chance. But it is not easy. And no one has really done it in 30 years. Many have tried.

Full disclosure: I love BigQuery (Google Cloud partner). Love Looker. Rely on Open Source constantly. Just trying to demonstrate a realistic view of how hard this problem is.

[g14i]

Wow. Thanks for putting great energy for this answer. Great perspective.

Again, if you don't mind sharing, what would be a dream solution for you, something that could solve most of the problems you mentioned?

I understand that some of them are cultural problems (like people no talking to each other), but maybe somehow a technology could solve this?

If you could think of a new approach, what would that be?

[thingsilearned]

Hey @segah, founder of Chartio here. I can deeply second all the comments on how long of a feature tail BI is, and the amount of work it takes to have real product depth and stability vs. an impressive demo. It takes years, and ongoing maintenance that is impossible to estimate in the beginning.

Also, it's definitely a challenge to support 100's and 1000's of users digesting data, especially in the democratized fashion that we're typically used in. It takes good data governance, support, and admin tools. I gotta chime in and say for the record though that Chartio well supports many such customers.

It may be weird for you to chat as you worked at Looker, but I'd love to hear anytime on why you see Chartio capping out at a lower # than the others listed. You can reach me at dave-at-chartio.com!

[yellowapple]

I don't think ripping off LookML is a bad thing; on the contrary, it's probably an awesome thing for Looker to be in the position of shaping how companies model their data and the relationships thereof. At the very least, it means Looker can turn around and say "hey, if you're outgrowing this smaller tool that uses LookML, why not switch to a platform that can meet your scale (like, say, us)?". The more smaller-scale "competitors" aping LookML, the more teams outgrowing those platforms and looking for larger-scale platforms where they can `git pull $old-platform`, `git push $new-platform`, copy over some data sources / credentials, and call it a day. If $new-platform=Looker, then congrats, Looker now has a decently-sized customer.

In other words: Looker ought to embrace the idea of LookML being the data modelling language. Encourage other implementations of it. Treat them as customer sources instead of customer sinks. That's how they can take over the world :)

[morenoh149]

Why discourage an alternative? We've already seen there is demand for libre analytics tools as an alternative to google analytics. I'd use this product in the lower range as well <100 person company. I say go for it, looker is doing fine they won't care or be phased by this at all.

[sbr464]

By support do you mean performance/load or array of features/team tools to be effective, support different use cases? Curious what you mean by 20 user cap for Chartio.

[pplonski86]

What is the main reason that LookML is complicated product?

[akalitenya]

LookML supports different databases as sources. Sometimes Looker adds its own LookML parameters for database - which makes it difficult to read the documentation.

For analysts who work with SQL queries, any wrapper on a language seems complicated. But in the case of LookML and BlockML, in a couple of days you already begin to understand how this works, and after a week you can not tear yourself away from using the product.

Looker and Mprove are in the middle between fully custom DIY solutions and simple query generators tools.

[segah]

@akalitenya the way I used to explain it to the technical audience is: would you rather program in a low-level language like C or in a high-level language like Java, Python, or Node? Well, if you are writing compilers, you might choose C, but if you are doing almost everything else, you want to choose a language that fits the purpose: python for data science, Node for web backend, etc. Same with LookML - it provides a language that is ideal for doing analytics in-database. SQL is a language for extracting and summarizing data - it is not an ideal language to preserve business logic in just as C would not be an ideal language to preserve backend web dev. work in.

[akalitenya]

@segah Good point. Based on your consulting experience, can you tell if there have been any cases when your clients chose another Business Intelligence tool instead of Looker, what are these tools and what are the reasons?

[segah]

that's the wrong question. People choose different tools for all kinds of reasons. And many of these reasons are totally valid. A tool, Looker or otherwise, is no panacea.