Tuesday, July 5, 2022
HomeBig DataWay forward for the Metrics Layer with Drew Banin (dbt) and Nick Handel...

Way forward for the Metrics Layer with Drew Banin (dbt) and Nick Handel (Rework) – Atlan

Sizzling takes on what we get incorrect in regards to the metrics layer and the place it suits within the trendy knowledge stack

The metrics layer has been all the fad in 2022. It’s simply forming within the knowledge stack, however I’m so excited to see it coming alive. Not too long ago dbt Labs integrated a metrics layer into their product, and Rework open-sourced MetricFlow (their metric creation framework).

A couple of weeks in the past, I used to be fortunate sufficient to speak in regards to the metrics layer with two most prolific product thinkers within the house — Drew Banin (Co-founder of dbt Labs) and Nick Handel (Co-founder of Rework).

We lined all the pieces from the fundamentals of a metrics layer and what individuals get incorrect about it to real-life use instances and its place within the trendy knowledge stack.

Earlier than we start… WTF really is a metrics layer? At present metrics are sometimes cut up throughout completely different knowledge instruments, and completely different groups or dashboards find yourself utilizing completely different definitions for a similar metric. The metrics layer goals to repair this by creating a standard set of metrics and their definitions.

Drew and Nick dove extra into this definition, so let’s soar proper into all of their insights and fiery takes. We talked for over an hour, so it is a condensed, edited model of our dialogue. (Take a look at the total recording right here.)

How would you clarify the metrics layer to a newbie knowledge analyst?

Because it’s a brand new idea, there’s loads of confusion about what actually the metrics layer is. Drew and Nick reduce by the confusion with succinct definitions about creating a standard supply of fact for metrics.

Drew Banin: “The shortest model I can consider is…”

Outline your metrics as soon as and reference them in all places in order that in case your metrics ever change, you get up to date outcomes in all places you have a look at knowledge.

Nick Handel: “The way in which that I’ve defined it to household and people who find themselves completely out of the house is simply, companies have knowledge. They use that knowledge to measure their operations. The purpose of this software program is principally to make it very easy for the information analysts (the people who find themselves liable for measuring that knowledge) to outline these metrics, and make it simple for the remainder of the enterprise to devour that single right approach to measure that knowledge.”

What’s the actual downside the metrics layer is seeking to clear up?

Nick and Drew defined that the metrics layer is motivated by two key concepts: precision and belief.

Nick: “I feel we’re all fairly satisfied in regards to the worth of information. Now we have every kind of various, attention-grabbing issues that we are able to do with knowledge, and the price of doing these issues is pretty excessive. There’s a bunch of labor to get the information into the place the place we are able to go and do something that’s actually attention-grabbing and helpful.

“Why does this matter? It’s speculated to make that complete technique of getting the information prepared for that supply of worth a lot simpler and in addition extra reliable.”

It comes all the way down to these two issues: productiveness and belief. Is it simple to provide the metric, and is it the best metric? And might you place it into no matter software you’re attempting to serve?

Drew: “That’s actually good framing. I simply look inwards at our group. The very first metric we ever created was weekly lively tasks — what number of dbt tasks had been run within the earlier seven days? Now we’re about 250 individuals and we’re measuring so many issues throughout the enterprise with a lot of new individuals round.”

We’re attempting to ensure that when somebody says ‘weekly lively accounts’ or ‘MRR’ or ‘MRR cut up by handle versus self-service’, all of us imply precisely the identical factor.

Drew and Nick additionally emphasised change administration as each a significant problem and use case for the metrics layer.

Drew: “I feel a lot in regards to the change administration a part of it. When you get the best individuals collectively, you’ll be able to exactly outline a metric at that time limit. However inevitably what you are promoting or product will evolve. How do you retain it in sync in perpetuity? That’s the exhausting half.”

Nick: “I actually agree with that. Particularly if change administration is occurring when there are only some individuals within the room, and different people who find themselves relying on the identical metrics weren’t part of that dialog.”

How ought to we take into consideration the metrics layer, and the way ought to it interaction with different parts of the trendy knowledge stack?

Nick broke the metrics layer down into 4 key parts (semantics, efficiency, querying, and governance), whereas Drew centered on its function as a community connecting a various set of information instruments.

Nick: “The way in which that I take into consideration the metrics layer is principally 4 items. There are the semantics: How do I am going and outline this metric? This could vary from ‘Right here’s a SQL snippet’ or ‘That is the definition of the metric’ to a full semantic layer that has entities and measures and dimensions and relations.

“Then there’s efficiency. Nice, now I’ve this semantic mannequin. How do I am going and construct logic towards it, executed towards some compute setting (whether or not it’s a warehouse or only a compute engine on an information lake)?

“Then there’s, how do I question this factor? What are the interfaces that I exploit to tug it out of the information warehouse or knowledge lake, resolve it into this quantitative object that I can then go and use in some evaluation. That features each broad methods of consuming knowledge (like a Python interface or GraphQL or a SQL interface) in addition to direct integrations (a device that builds a customized wrapper round a REST or GraphQL API and builds a very first-class expertise).

“Then the final piece is governance. There’s organizational governance and technical governance. Organizational governance which means, does the finance chief agree on the human-understandable definition of income in the identical means that the technical one that’s defining the logic defines that code?”

Drew: “Simply to supply an alternate framing: We are able to consider it by way of the expertise for the one that desires to devour knowledge to reply some query or clear up some downside, after which additionally the individuals constructing the instruments the place these of us are consuming the information.

“It’s slightly bit at odds with one another, as a result of the enterprise shoppers wish to see the very same metric in each single device and so they need all of it to replace in actual time. So you’ve got this large community of various instruments that conceivably want to speak to one another. That’s a tough factor to arrange and make occur in follow.

That’s why the concept we name this the ‘metrics layer’ is smart. It’s a single abstraction layer that all the pieces can interface with so as to get exact and constant definitions in each single device.

“To me, that’s the place metadata actually shines. Like, that is the metric, that is the way it’s outlined, that is its provenance, right here’s the place it’s used. This isn’t really the information itself. It’s attributes of the information. That’s the knowledge that may synchronize all these completely different instruments collectively round shared knowledge definitions.”

What metadata ought to we be monitoring about our metrics, and why?

Nick and Drew shared that metadata is essential for understanding metrics as a result of corporations lose necessary tribal information about knowledge outages and anomalies over time as workers modifications.

Nick: “The metric is likely one of the most constant objects in a corporation’s life.

Merchandise change, tables change, all the pieces modifications. Even the definitions of those metrics evolve. However most companies find yourself monitoring the identical North Star metrics from the very early days. When you can connect metadata to it, that’s extremely precious.

“At Airbnb, we tracked nights booked. It was necessary from the very early days when BI was actually a printed-off graph that they placed on the wall, and it’s nonetheless a very powerful metric that the corporate talks about within the public earnings calls. If we had been monitoring necessary metadata by time of what was occurring to that metric, there can be a wealth of information that the corporate may use.”

They defined that these modifications are why it’s essential for the metrics layer to work together with each the information layer and the enterprise layer — to seize context that impacts knowledge evaluation and high quality.

Nick: “Airbnb had a giant product launch, and completely different metrics spiked in all completely different instructions. At present, I’m undecided {that a} knowledge scientist at Airbnb may actually perceive what occurred. They’re attempting to make use of historic knowledge to know issues, and so they simply don’t have that context. If something, they actually solely have context for the final two or three years, when there was someone who’s within the enterprise who remembers what occurred, who did the evaluation, and so on.”

Drew: “There’s loads of this that finally ends up being technical — by way of how instruments combine with one another, and the way you outline the metrics and model them. However a lot of it’s certainly the social and enterprise context.

In follow, the individuals which were round for the longest time have probably the most context and possibly know greater than any of our precise techniques do.

“We had a interval the place we had slightly bit of information loss for some occasions we had been monitoring. It appeared like, I feel it was, Might 2021 was the worst month ever. However actually it was similar to, no, we didn’t acquire the information.

“How would you realize that? The place does that info dwell? Is it a property of the supply dataset that propagates by to the metrics? Who’s liable for encoding that?”

What are the actual use instances for a metrics layer?

Drew and Nick referred to as out loads of potential purposes for the metrics layer — e.g. enhancing BI and analytics for early-stage knowledge groups, serving to enterprise and knowledge individuals use knowledge fashions in the identical means, and making precious however time-consuming purposes (like experimentation, forecasting, and anomaly detection) potential for all corporations.

Drew: “I feel among the use instances round BI and analytics are probably the most clear, apparent, and current for lots of corporations.

Many corporations on the market are usually not on the knowledge science and machine studying a part of their journeys but. Issues that make enterprise intelligence and reporting higher (extra exact and extra constant) cowl 90% of the issues that they’re attempting to unravel with knowledge.

“Casting our minds ahead, I feel that there could possibly be a ton of advantages to leveraging metrics for knowledge science use instances.

“Particularly, one of many issues that we’ve seen individuals do with dbt that was actually formative for me — they’d construct these knowledge fashions after which use them each for BI reporting and in addition to energy knowledge science purposes and modeling. The truth that the information scientist and the BI analysts are utilizing the identical knowledge units signifies that it’s much more probably that they’re consuming the identical knowledge in the identical means. Once you prolong it to metrics, there’s like a very pure approach to make that occur too.”

Nick: “I do partly agree with that. But in addition there are loads of knowledge science and machine studying purposes that require very completely different datasets than what a metric retailer produces.

“In analytics purposes, you attempt to embrace as a lot related info as potential. When you’ve got an ecommerce retailer, individuals can browse it logged out. So that you attempt to dedupe customers and establish as customers log into gadgets. There’s a complete follow of attempting to determine which entities are utilizing your service. That’s actually necessary for analytics as a result of it permits us to get a a lot clearer image. However you don’t wish to do this for machine studying, as a result of that’s all info leakage and that can destroy your fashions.

With machine studying, you attempt to get as near the uncooked knowledge units as potential. With analytical purposes, you attempt to course of that info into the clearest and greatest image of the world.

“One of many purposes that I at all times take into consideration is experimentation. The explanation we constructed a metrics repo initially was experimentation.

“There have been 15–20 individuals on the information crew on the time. We had been attempting to run extra product experiments, and we had been doing all the pieces manually. It was actually time intensive to go and take task logs and metric definitions and be a part of them collectively.

Principally, we wanted some programmatic approach to go and assemble metrics. It’s a massively precious software for corporations that do it, however only a few corporations have the infrastructure or construct the tooling to do that. I feel that that’s actually unlucky. And it’s in all probability the factor that I’m most excited in regards to the metrics layer.

“If you consider each knowledge software as having some value and a few profit — the extra you’ll be able to scale back the price of pursuing that software, the extra clearly the justification turns into to pursue some new software.

“I feel experimentation is one among these examples. I additionally take into consideration anomaly detection or forecasting. These are issues that I feel most corporations don’t do — not as a result of they’re not precious, however simply because producing the datasets to even get began on these purposes is actually exhausting.”

Let’s soar into some questions in regards to the metric layer and the trendy knowledge stack.

First, let’s discuss bundling vs unbundling. Ought to the metrics layer even be a separate layer, or ought to it’s a part of an present layer within the stack?

As with each debate within the knowledge ecosystem, we ended up simply answering, it relies upon. Drew and Nick defined that how we clear up this downside is finally extra necessary than how we outline that resolution.

Drew: “I’m not in love with the best way that we as an ecosystem discuss new instruments as being layers, just like the lacking layer of the information stack. That’s the incorrect framing.

“Folks that construct purposes don’t give it some thought that means. They’ve providers, and the providers can discuss to one another. Some are inside providers and a few are SaaS providers. It turns into a community of related instruments moderately than precisely, say, 4 layers. Nobody runs an software anymore with precisely the Linux, Apache, MySQL, and PHP (LAMP) stack, proper? We’re previous that.

The phrase ‘layer’ is smart solely insofar because it’s a layer of abstraction. However in any other case, I reject the terminology, though I can’t consider something too a lot better than that.

“The very last thing I’m going to say on bundling and unbundling… For this factor to work, it does should be an middleman between a really large community of various instruments. Treating it as a boundary like that motivates which instruments can construct it and supply it. It’s not one thing you’d see from a BI device, as a result of it’s not likely in a BI device’s curiosity to supply the layer to each different BI device — which is just like the factor that you really want from this.”

Nick: “I feel I usually agree with that.

Principally, individuals have issues, and firms construct applied sciences to unravel issues. If individuals have issues and there’s a precious expertise to construct, then I feel it’s price taking a shot and attempting to construct that expertise and voicing these opinions.

“Finally, I feel that there are good factors there of the connection to completely different organizational workflows. This isn’t one thing that I feel we’ve performed job of explaining, however I feel that the metrics retailer and the metrics layer are two completely different ideas.

“The metrics retailer extends the metrics layer to incorporate this piece of organizational governance — how do you get a bunch of various enterprise customers concerned on this dialog, and really give them a task in one thing that, frankly, they’ve an enormous stake in? I feel that that’s one thing that isn’t actually caught on this dialog across the metrics layer, or headless BI, or any of those completely different phrases. Nevertheless it’s actually, actually necessary.”

For a standard firm that already has an information warehouse and BI layer, the place does the metrics layer match into their stack?

Once more, the reply is that it relies upon — sigh. The metrics layer would dwell between the information warehouse and BI device. Nonetheless, each BI device is completely different and a few are friendlier to this integration than others.

Nick: “The metrics layer sits on high of the information warehouse and principally wraps it with semantic info. It then permits completely different endpoints to be consumed from and principally pushes metrics to these completely different locations, whether or not they’re generic or direct integrations to these instruments.”

Drew: “It finally ends up being very BI device–dependent. There are some BI instruments the place it is a very pure kind of factor to do, and others the place it’s really fairly unnatural.”

If an organization has already outlined a ton of metrics inside their BI device, what ought to they do?

Nick and Drew defined that sluggish and regular wins the race once you aren’t ranging from scratch. As an alternative of planning an enormous overhaul, begin with one crew or device, combine a greater metrics layer, and take a look at the way it works on your group.

Nick: “I might advocate for not an enormous ‘change all the pieces ’. I might advocate for, outline some metrics, push these by the APIs and integrations, construct one thing new, probably exchange one thing outdated that was exhausting to handle, after which go from there when you’ve seen the way it works and consider in that philosophy.”

Drew: “I’m with you. I feel one thing domain-driven makes loads of sense. You may validate it after which develop. I’d in all probability begin with… it will depend on your tolerance, however the government dashboard that goes to the CEO. Is that the perfect place to kick the tires? Perhaps not. But when it really works there, it’ll work in all places.”

Can’t a metrics layer simply be a part of a characteristic retailer?

Since Nick has constructed a number of characteristic shops and metrics layers, he had a robust opinion on this matter — whereas the metrics layer and options retailer are related, they’re too basically completely different to merge proper now.

Nick: “I’ve a very sturdy opinion about this one as a result of I’ve constructed two characteristic shops and three metrics layers. These two issues are completely completely different.

“On the core, they’re each derived knowledge. However there are such a lot of nuances to constructing characteristic shops and so many nuances to constructing metric shops. I’m not saying that these two issues won’t ever merge — the concept of a derived knowledge repository or one thing like that sounds great. However I simply don’t see it occurring within the brief time period.

Everybody desires options to be particular to their mannequin. No person desires metrics to be particular to their crew or their consumption. Folks need metrics to be constant. Folks need options to be distinctive and no matter advantages their mannequin.

“Actual-time versus batch — it is a tremendous difficult downside within the characteristic house. Organizational governance is means necessary for the metrics layer. The technical definitions are sometimes completely different. The extent of granularity is completely different for options — you go means finer with options than you do metrics.”

Do you consider a caching layer is crucial for a metrics layer?

This was a powerful YES from each Drew and Nick. Caching makes the metrics layer quick, which is crucial for making certain that knowledge practitioners really use it. Nonetheless, it’s necessary that this caching doesn’t replicate knowledge.

Drew: “I feel that the velocity with which you’ll ask a query and get a solution again is actually crucial.

The distinction between one thing taking a minute plus to return again and never coming again in any respect is negligible in loads of instances. So, conceptually, I’m very aligned with the concept of caching metric knowledge and having the ability to serve it up actually shortly.

“I’ll simply say — and I feel we’ve been open about this up to now — we in all probability received’t do this for V1 of metrics inside dbt. However conceptually, I’m fairly aligned with that being an necessary a part of the system long-term.”

Nick: “Caching is tremendous necessary. Efficiency issues a ton, particularly to enterprise customers. Even 10 seconds is lower than a really perfect expertise.

“I feel that there are two necessary nuances to caching. One is, what do I do know forward of time that I would like, and the way do I pre-compute that and make that basically snappy? After which if I do compute one thing, how do I then reuse it in order that it’s quick subsequent time? I feel that’s the level of a caching layer.

“The opposite one is, I don’t suppose that caching must occur outdoors of the cloud knowledge warehouse or the information lake. I feel that you should utilize these techniques. The replication of information, in my thoughts, is simply so expensive and so exhausting to handle.”

Lastly, when you had been handed a megaphone and will blast out a message for all the knowledge world, what would you say?


There are loads of issues in knowledge that you may clear up with expertise, however among the hardest and most necessary ones you need to clear up with conversations and folks and alignment and generally whiteboards. Realizing which type of downside you’re attempting to unravel at any given time goes that will help you choose the proper of resolution.


I feel the metrics layer is principally a semantic layer with a further idea of a metric, which is tremendous necessary. So I might simply say, the metrics layer needs to be backed by a general-purpose semantic layer. The spec and the definition of that semantic layer and the abstractions is so unbelievably necessary.

Aspect notice: I’m personally tremendous enthusiastic about how a metrics layer can work together with an lively metadata platform to supercharge information administration for knowledge groups. It’s been tremendous thrilling to see the metrics layer grow to be extra mainstream, which was a prediction I’d made at first of this 12 months.

Study extra in regards to the metrics layer and my six large concepts within the knowledge world this 12 months.

Report: The Way forward for the Trendy Information Stack in 2022

Obtain right here →



Please enter your comment!
Please enter your name here

- Advertisment -
Google search engine

Most Popular

Recent Comments