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HomeBig DataThe Way forward for the Fashionable Knowledge Stack in 2022 - Atlan

The Way forward for the Fashionable Knowledge Stack in 2022 – Atlan

That includes the 6 large concepts you need to know from 2021

As the information world slowed down for the vacations, I received some downtime to step again and take into consideration the final 12 months. And I can’t assist however assume, wow, what a 12 months it’s been!

Is it simply me, or did knowledge undergo 5 years’ price of change in 2021?

It’s partially COVID time, the place a month seems like a day and a 12 months on the similar time. You’d blink, and immediately there can be a brand new buzzword dominating Knowledge Twitter. It’s additionally partially the deluge of VC cash and loopy startup rounds, which added gasoline to the 12 months’s knowledge hearth.

With a lot hype, it’s onerous to know what traits are right here to remain and which can disappear simply as rapidly as they arose.

This weblog breaks down the six concepts you need to know in regards to the trendy knowledge stack going into 2022 — those that exploded within the knowledge world final 12 months and don’t appear to be going away.

Future of the modern data stack in 2022: Data Mesh

You in all probability know this time period by now, even you don’t precisely know what it means. The concept of the “knowledge mesh” got here from two 2019 blogs by Zhamak Dehghani, Director of Rising Applied sciences at Thoughtworks:

  1. Find out how to Transfer Past a Monolithic Knowledge Lake to a Distributed Knowledge Mesh
  2. Knowledge Mesh Ideas and Logical Structure

Its core thought is that firms can turn into extra data-driven by transferring from centralized knowledge warehouses and lakes to a “domain-oriented decentralized knowledge possession and structure” pushed by self-serve knowledge and “federated computational governance”.

As you possibly can see, the language across the knowledge mesh will get advanced quick, which is why there’s no scarcity of “what really is a knowledge mesh?” articles.

The concept of the information mesh has been quietly rising since 2019, till immediately it was in all places in 2021. The Thoughtworks Expertise Radar moved Knowledge Mesh’s standing from “Trial” to “Assess” in only one 12 months. The Knowledge Mesh Studying Neighborhood launched, and their Slack group received over 1,500 signups in 45 days. Zalando began doing talks about the way it moved to a knowledge mesh.

Quickly sufficient, scorching takes had been flying backwards and forwards on Twitter, with knowledge leaders arguing over whether or not the information mesh is revolutionary or ridiculous.

Future of the modern data stack in 2022: Data Mesh

In 2022, I feel we’ll see a ton of platforms rebrand and provide their companies because the “final knowledge mesh platform”. However the factor is, the information mesh isn’t a platform or a service that you may purchase off the shelf. It’s a design idea with some fantastic ideas like distributed possession, domain-based design, knowledge discoverability, and knowledge product transport requirements — all of that are price attempting to operationalize in your group.

So right here’s my recommendation: As knowledge leaders, you will need to persist with the primary rules at a conceptual degree, slightly than purchase into the hype that you simply’ll inevitably see out there quickly. I wouldn’t be stunned if some groups (particularly smaller ones) can obtain the information mesh structure by a completely centralized knowledge platform constructed on Snowflake and dbt, whereas others will leverage the identical rules to consolidate their “knowledge mesh” throughout advanced multi-cloud environments.

Future of the modern data stack in 2022: Metrics Layer

Metrics are important to assessing and driving an organization’s progress, however they’ve been struggling for years. They’re usually break up throughout totally different knowledge instruments, with totally different definitions for a similar metric throughout totally different groups or dashboards.

In 2021, individuals lastly began speaking about how the fashionable knowledge stack might repair this concern. It’s been referred to as the metrics layermetrics retailerheadless BI, and much more names than I can checklist right here.

It began in January, when Base Case proposed “Headless Enterprise Intelligence”, a brand new strategy to fixing metrics issues. A pair months later, Benn Stancil from Mode talked in regards to the “lacking metrics layer” in as we speak’s knowledge stack.

That’s when issues actually took off. 4 days later, Mona Akmal and Aakash Kambuj from Falkon printed articles about making metrics first-class residents and the “trendy metrics stack”.

Two days after that, Airbnb introduced that it had been constructing a home-grown metrics platform referred to as Minerva to resolve this concern. Different distinguished tech firms quickly adopted swimsuit, together with LinkedIn’s Unified Metrics Platform, Uber’s uMetric, and Spotify’s metrics catalog of their “new experimentation platform”.

Simply after we thought this fervor had died down, Drew Banin (CPO and Co-Founding father of dbt) opened a PR on dbtcore in October. He hinted that dbt can be incorporating a metrics layer into its product, and even included hyperlinks to these foundational blogs by Benn and Base Case. The PR blew up and reignited the dialogue round constructing a greater metrics layer within the trendy knowledge stack.

In the meantime, a bunch of early stage startups have launched to compete for this area. Rework might be the largest identify to this point, however MetriqlLightdashSupergrain, and Metlo additionally launched this 12 months. Some greater names are additionally pivoting to compete within the metrics layer, comparable to GoodData’s foray into Headless BI.

Future of the modern data stack in 2022: Metrics Layer

I’m extraordinarily excited in regards to the metrics layer lastly turning into a factor. A couple of months in the past, George Fraser from Fivetran had an unpopular opinion that all metrics shops will evolve into BI instruments. Whereas I don’t absolutely agree, I do imagine {that a} metrics layer that isn’t tightly built-in with BI is unlikely to ever turn into commonplace.

Nonetheless, current BI instruments aren’t actually incentivized to combine an exterior metrics layer into their instruments… which makes this a hen and egg drawback. Standalone metrics layers will battle to encourage BI instruments to undertake their frameworks, and might be compelled to construct BI like Looker was compelled to a few years in the past.

Because of this I’m actually enthusiastic about dbt saying their foray into the metrics layer. dbt already has sufficient distribution to encourage a minimum of the fashionable BI instruments (e.g. Preset, Mode, Thoughtspot) to combine deeply into the dbt metrics API, which can create aggressive strain for the bigger BI gamers.

I additionally assume that metrics layers are so deeply intertwined with the transformation course of that intuitively this is smart. My prediction is that we’ll see metrics turn into a first-class citizen in additional transformation instruments in 2022.

Future of the modern data stack in 2022: Reverse ETL

For years, ETL (Extract, Rework, Load) was how knowledge groups populated their methods. First, they’d pull knowledge from third-party methods, clear it up, after which load it into their warehouses. This was nice as a result of it saved knowledge warehouses clear and orderly, nevertheless it additionally meant that it took ceaselessly to get knowledge into warehouses. Typically, knowledge groups simply needed to dump uncooked knowledge into their methods and take care of it later.

That’s why many firms moved from ETL to ELT (Extract, Load, Rework) a few years in the past. As an alternative of remodeling knowledge first, firms would ship uncooked knowledge into a knowledge lake, then rework it later for a selected use case or drawback.

In 2021, we received one other main evolution on this thought — reverse ETL. This idea first began getting consideration in February, when Astasia Myers (Founding Enterprise Associate at Quiet Capital) wrote an article in regards to the emergence of reverse ETL.

Since then, Hightouch and Census (each of which launched in December 2020) have set off a firestorm as they’ve battled to personal the reverse ETL area. Census introduced that it raised a $16 million Sequence A in February and printed a collection of benchmarking experiences concentrating on Hightouch. Hightouch countered with three raises of a complete $54.2 million in lower than 12 months.

Hightouch and Census have dominated the reverse ETL dialogue this 12 months, however they’re not the one ones within the area. Different notable firms are GrouparooHeadsUp, PolytomicRudderstack, and Workato (who closed a $200m Sequence E in November). Seekwell even received acquired by Thoughtspot in March.

Future of the modern data stack in 2022: Reverse ETL

I’m fairly enthusiastic about all the things that’s fixing the “final mile” drawback within the trendy knowledge stack. We’re now speaking extra about easy methods to use knowledge in day by day operations than easy methods to warehouse it — that’s an unbelievable signal of how mature the basic constructing blocks of the information stack (warehousing, transformation, and so on) have turn into!

What I’m not so positive about is whether or not reverse ETL ought to be its personal area or simply be mixed with a knowledge ingestion software, given how related the basic capabilities of piping knowledge out and in are. Gamers like Hevodata have already began providing each ingestion and reverse ETL companies in the identical product, and I imagine that we would see extra consolidation (or deeper go-to-market partnerships) within the area quickly.

Future of the modern data stack in 2022: Active Metadata & Third-Gen Data Catalogs

Within the final couple of years, the talk round knowledge catalogs was, “Are they out of date?” And it might be straightforward to assume the reply is sure. In a few well-known articles, Barr Moses argued that knowledge catalogs had been lifeless, and Michael Kaminsky argued that we don’t want knowledge dictionaries.

Then again, there’s by no means been a lot buzz about knowledge catalogs and metadata. There are such a lot of knowledge catalogs that Rohan from our crew created, a “catalog of catalogs”, which feels each ridiculous and fully essential. So which is it — are knowledge catalogs lifeless or stronger than ever?

This 12 months, knowledge catalogs received new life with the creation of two new ideas — third-generation knowledge catalogs and energetic metadata.

Initially of 2021, I wrote an article on trendy metadata for the fashionable knowledge stack. I launched the concept we’re coming into the third-generation of knowledge catalogs, a elementary transformation from the prevalent old-school, on-premise knowledge catalogs. These new knowledge catalogs are constructed round numerous knowledge belongings, “large metadata”, end-to-end knowledge visibility, and embedded collaboration.

This concept received amplified by a enormous transfer Gartner made this 12 months — scrapping its Magic Quadrant for Metadata Administration Options and changing it with the Market Information for Lively Metadata. In doing this, they launched “energetic metadata” as a brand new class within the knowledge area.

What’s the distinction? Outdated-school knowledge catalogs accumulate metadata and convey them right into a siloed “passive” software, aka the normal knowledge catalog. Lively metadata platforms act as two-way platforms — they not solely deliver metadata collectively right into a single retailer like a metadata lake, but additionally leverage “reverse metadata” to make metadata obtainable in day by day workflows.

For the reason that first time we wrote about third-generation catalogs, they’ve turn into a part of the discourse round what it means to be a contemporary knowledge catalog. We even noticed the phrases pop up in RFPs!

Third-Gen Data Catalog RFP
Snippet of an anonymized RPF

On the similar time, VCs have been keen to speculate on this new area. Metadata administration has grown a ton with raises throughout the board — e.g. Collibra’s $250m Sequence GAlation’s $110m Sequence D, and our $16m Sequence A at Atlan. Seed-stage firms like Stemma and Acryl Knowledge additionally launched to construct managed metadata options on current open-source tasks.

Future of the modern data stack in 2022: Active Metadata & Third-Gen Data Catalogs

The info world will all the time be numerous, and that range of individuals and instruments will all the time result in chaos. I’m in all probability biased, on condition that I’ve devoted my life to constructing an organization within the metadata area. However I actually imagine that the important thing to bringing order to the chaos that’s the trendy knowledge stack lies in how we will use and leverage metadata to create the fashionable knowledge expertise.

Gartner summarized the way forward for this class in a single sentence: “The stand-alone metadata administration platform might be refocused from augmented knowledge catalogs to a metadata ‘wherever’ orchestration platform.”

The place knowledge catalogs within the 2.0 era had been passive and siloed, the three.0 era is constructed on the precept that context must be obtainable wherever and each time customers want it. As an alternative of forcing customers to go to a separate software, third-gen catalogs will leverage metadata to enhance current instruments like Looker, dbt, and Slack, lastly making the dream of an clever knowledge administration system a actuality.

Whereas there’s been a ton of exercise and funding within the area in 2021, I’m fairly positive we’ll see the rise of a dominant and really third-gen knowledge catalog (aka an energetic metadata platform) in 2022.

Future of the modern data stack in 2022: Data Teams as Product Teams

As the fashionable knowledge stack goes mainstream and knowledge turns into an even bigger a part of day by day operations, knowledge groups are evolving to maintain up. They’re not “IT of us”, working individually from the remainder of the corporate. However this raises the query, how ought to knowledge groups work with the remainder of the corporate? Too usually, they get caught within the “service lure” — unending questions and requests for creating stats, slightly than producing insights and driving impression by knowledge.

Emilie Schario - Service Trap
Emilie Schario’s iconic picture on the truth of engaged on a knowledge crew. (Picture from MDSCON 2021.)

In 2021, Emilie Schario from Amplify CompanionsTaylor Murphy from Meltano, and Eric Weber from Sew Repair talked a few method to break knowledge groups out of this lure — rethinking knowledge groups as product groups. They first defined this concept with a weblog on Regionally Optimistic, adopted by nice talks at conferences like MDSCONdbt Coalesce, and Future Knowledge.

A product isn’t measured on what number of options it has or how rapidly engineers can quash bugs — it’s measured on how effectively it meets prospects’ wants. Equally, knowledge product groups ought to be centered on the customers (i.e. knowledge customers all through the corporate), slightly than questions answered or dashboards constructed. This permits knowledge groups to give attention to expertise, adoption, and reusability, slightly than ad-hoc questions or requests.

This give attention to breaking out of the service lure and reorienting knowledge groups round their customers actually resonated with the information world this 12 months. Extra individuals have began speaking about what it means to construct “knowledge product groups”, together with loads of scorching takes on who to rent and easy methods to set targets.

Future of the modern data stack in 2022: Data Teams as Product Teams

Of all of the hyped traits in 2021, that is the one I’m most bullish on. I imagine that within the subsequent decade, knowledge groups will emerge as probably the most essential groups within the group cloth, powering the fashionable, data-driven firms on the forefront of the financial system.

Nonetheless, the truth is that knowledge groups as we speak are caught in a service lure, and solely 27% of their knowledge tasks are profitable. I imagine the important thing to fixing this lies within the idea of the “knowledge product” mindset, the place knowledge groups give attention to constructing reusable, reproducible belongings for the remainder of the crew. This can imply investing in consumer analysis, scalability, knowledge product transport requirements, documentation, and extra.

Future of the modern data stack in 2022: Data Observability

This concept got here out of “knowledge downtime”, which Barr Moses from Monte Carlo first spoke about in 2019 saying, “Knowledge downtime refers to durations of time when your knowledge is partial, misguided, lacking or in any other case inaccurate”. It’s these emails you get the morning after an enormous challenge, saying “Hey, the information doesn’t look proper…”

Knowledge downtime has been part of regular life on a knowledge crew for years. However now, with many firms counting on knowledge for actually each facet of their operations, it’s an enormous deal when knowledge stops working.

But everybody was simply reacting to points as they cropped up, slightly than proactively stopping them. That is the place knowledge observability — the concept of “monitoring, monitoring, and triaging of incidents to forestall downtime” — got here in.

I nonetheless can’t imagine how rapidly knowledge observability has gone from being simply an thought to a key a part of the fashionable knowledge stack. (Lately, it’s even began being referred to as “knowledge reliability” or “knowledge reliability engineering”.)

The area went from being non-existent to internet hosting a bunch of firms, with a collective $200m of funding raised in 18 months. This contains AcceldataAnomaloBigeyeDatabandDatafoldMetaplaneMonteCarlo, and Soda. Folks even began creating lists of latest “knowledge observability firms” to assist maintain monitor of the area.

Future of the modern data stack in 2022: Data Observability

I imagine that previously two years, knowledge groups have realized that tooling to enhance productiveness isn’t a good-to-have however vital. In any case, knowledge professionals are probably the most sought-after hires you’ll ever make, so that they shouldn’t be losing their time on troubleshooting pipelines.

So will knowledge observability be a key a part of the fashionable knowledge stack sooner or later? Completely. However will knowledge observability live on as its personal class or will it’s merged right into a broader class (like energetic metadata or knowledge reliability)? That is what I’m not so positive about.

Ideally, in case you have all of your metadata in a single open platform, you need to be capable of leverage it for quite a lot of use instances (like knowledge cataloging, observability, lineage and extra). I wrote about that concept final 12 months in my article on the metadata lake.

That being stated, as we speak, there’s a ton of innovation that these areas want independently. My sense is that we’ll proceed to see fragmentation in 2022 earlier than we see consolidation within the years to come back.

Future of the modern data stack in 2022: Last thoughts

It could really feel chaotic and loopy at instances, however as we speak is a golden age of knowledge.

Within the final eighteen months, our knowledge tooling has grown exponentially. All of us make loads of fuss in regards to the trendy knowledge stack, and for good cause — it’s so significantly better than what we had earlier than. The sooner knowledge stack was frankly as damaged as damaged might get, and this gigantic leap ahead in tooling is precisely what knowledge groups wanted.

For my part, the following “delta” on the horizon for the information world is the trendy knowledge tradition stack — the very best practices, values, and cultural rituals that may assist us numerous people of knowledge collaborate successfully and up our productiveness as we deal with our new knowledge stacks.

Nonetheless, we will solely take into consideration working collectively higher with knowledge after we’ve nailed, effectively, working with knowledge. We’re on the cusp of getting the fashionable knowledge stack proper, and we will’t wait to see what new developments and traits 2022 will deliver!

This text was initially printed on In direction of Knowledge Science.

Header picture: Mike Kononov on Unsplash



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