EC Enterprise Applications
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How engineers fought the CAP theorem in the global war on latency
CockroachDB was intended to be a global database from the beginning. The founders of Cockroach Labs wanted to ensure that data written in one location would be viewable immediately in another location 10,000 miles away. The use case was simple, but the work needed to make it happen was herculean.
The company is betting the farm that it can solve one of the largest challenges for web-scale applications. The approach it’s taking is clever, but it’s a bit complicated, particularly for the non-technical reader. Given its history and engineering talent, the company is in the process of pulling it off and making a big impact on the database market, making it a technology well worth understanding. In short, there’s value in digging into the details.
Using CockroachDB’s multiregion feature to segment data according to geographic proximity fulfills Cockroach Labs’ primary directive: To get data as close to the user as possible.
In part 1 of this EC-1, I provided a general overview and a look at the origins of Cockroach Labs. In this installment, I’m going to cover the technical details of the technology with an eye to the non-technical reader. I’m going to describe the CockroachDB technology through three questions:
- What makes reading and writing data over a global geography so hard?
- How does CockroachDB address the problem?
- What does it all mean for those using CockroachDB?
What makes reading and writing data over a global geography so hard?
Spencer Kimball, CEO and co-founder of Cockroach Labs, describes the situation this way:
There’s lots of other stuff you need to consider when building global applications, particularly around data management. Take, for example, the question and answer website Quora. Let’s say you live in Australia. You have an account and you store the particulars of your Quora user identity on a database partition in Australia.
But when you post a question, you actually don’t want that data to just be posted in Australia. You want that data to be posted everywhere so that all the answers to all the questions are the same for everybody, anywhere. You don’t want to have a situation where you answer a question in Sydney and then you can see it in Hong Kong, but you can’t see it in the EU. When that’s the case, you end up getting different answers depending where you are. That’s a huge problem.
Reading and writing data over a global geography is challenging for pretty much the same reason that it’s faster to get a pizza delivered from across the street than from across the city. The essential constraints of time and space apply. Whether it’s digital data or a pepperoni pizza, the further away you are from the source, the longer stuff takes to get to you.
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“Developers, as you know, do not like to pay for things”
In the previous part of this EC-1, we looked at the technical details of CockroachDB and how it provides accurate data instantaneously anywhere on the planet. In this installment, we’re going to take a look at the product side of Cockroach, with a particular focus on developer relations.
As a business, Cockroach Labs has many things going for it. The company’s approach to distributed database technology is novel. And, as more companies operate on a global level, CockroachDB has the potential to gain some significant market share internationally. The company is seven years into a typical 10-year maturity model for databases, has raised $355 million, and holds a $2 billion market value. It’s considered a double unicorn. Few database companies can say this.
The company is now aggressively expanding into the database-as-a-service space, offering its own technology in a fully managed package, expanding the spectrum of clients who can take immediate advantage of its products.
But its growth depends upon securing the love of developers while also making its product easier to use for new customers. To that end, I’m going to analyze the company’s pivot to the cloud as well as its extensive outreach to developers as it works to set itself up for long-term, sustainable success.
Cockroach Labs looks to the cloud
These days, just about any company of consequence provides services via the internet, and a growing number of these services are powered by products and services from native cloud providers. Gartner forecasted in 2019 that cloud services are growing at an annual rate of 17.5%, and there’s no sign that the growth has abated at all.
Its founders’ history with Google back in the mid-2000s has meant that Cockroach Labs has always been aware of the impact of cloud services on the commercial web. Unsurprisingly, CockroachDB could run cloud native right from its first release, given that its architecture presupposes the cloud in its operation — as we saw in part 2 of this EC-1.
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Scaling CockroachDB in the red ocean of relational databases
Most database startups avoid building relational databases, since that market is dominated by a few goliaths. Oracle, MySQL and Microsoft SQL Server have embedded themselves into the technical fabric of large- and medium-size companies going back decades. These established companies have a lot of market share and a lot of money to quash the competition.
So rather than trying to compete in the relational database market, over the past decade, many database startups focused on alternative architectures such as document-centric databases (like MongoDB), key-value stores (like Redis) and graph databases (like Neo4J). But Cockroach Labs went against conventional wisdom with CockroachDB: It intentionally competed in the relational database market with its relational database product.
While it did face an uphill battle to penetrate the market, Cockroach Labs saw a surprising benefit: It didn’t have to invent a market. All it needed to do was grab a share of a market that also happened to be growing rapidly.
Cockroach Labs has a bright future, compelling technology, a lot of money in the bank and has an experienced, technically astute executive team.
In previous parts of this EC-1, I looked at the origins of CockroachDB, presented an in-depth technical description of its product as well as an analysis of the company’s developer relations and cloud service, CockroachCloud. In this final installment, we’ll look at the future of the company, the competitive landscape within the relational database market, its ability to retain talent as it looks toward a potential IPO or acquisition, and the risks it faces.
CockroachDB’s success is not guaranteed. It has to overcome significant hurdles to secure a profitable place for itself among a set of well-established database technologies that are owned by companies with very deep pockets.
It’s not impossible, though. We’ll first look at MongoDB as an example of how a company can break through the barriers for database startups competing with incumbents.
When life gives you Mongos, make MongoDB
Dev Ittycheria, MongoDB CEO, rings the Nasdaq Stock Market Opening Bell. Image Credits: Nasdaq, Inc
MongoDB is a good example of the risks that come with trying to invent a new database market. The company started out as a purely document-centric database at a time when that approach was the exception rather than the rule.
Web developers like document-centric databases because they address a number of common use cases in their work. For example, a document-centric database works well for storing comments to a blog post or a customer’s entire order history and profile.
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Build a digital ops toolbox to streamline business processes with hyperautomation
Reliance on a single technology as a lifeline is a futile battle now. When simple automation no longer does the trick, delivering end-to-end automation needs a combination of complementary technologies that can give a facelift to business processes: the digital operations toolbox.
According to a McKinsey survey, enterprises that have likely been successful with digital transformation efforts adopted sophisticated technologies such as artificial intelligence, Internet of Things or machine learning. Enterprises can achieve hyperautomation with the digital ops toolbox, the hub for your digital operations.
The hyperautomation market is burgeoning: Analysts predict that by 2025, it will reach around $860 billion.
The toolbox is a synchronous medley of intelligent business process management (iBPM), robotic process automation (RPA), process mining, low code, artificial intelligence (AI), machine learning (ML) and a rules engine. The technologies can be optimally combined to achieve the organization’s key performance indicator (KPI) through hyperautomation.
The hyperautomation market is burgeoning: Analysts predict that by 2025, it will reach around $860 billion. Let’s see why.
The purpose of a digital ops toolbox
The toolbox, the treasure chest of technologies it is, helps with three crucial aspects: process automation, orchestration and intelligence.
Process automation: A hyperautomation mindset introduces the world of “automating anything that can be,” whether that’s a process or a task. If something can be handled by bots or other technologies, it should be.
Orchestration: Hyperautomation, per se, adds an orchestration layer to simple automation. Technologies like intelligent business process management orchestrate the entire process.
Intelligence: Machines can automate repetitive tasks, but they lack the decision-making capabilities of humans. And, to achieve a perfect harmony where machines are made to “think and act,” or attain cognitive skills, we need AI. Combining AI, ML and natural language processing algorithms with analytics propels simple automation to become more cognitive. Instead of just following if-then rules, the technologies help gather insights from the data. The decision-making capabilities enable bots to make decisions.
Simple automation versus hyperautomation
Here’s a story of evolving from simple automation to hyperautomation with an example: an order-to-cash process.
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Q3 IPO cycle starts strong with Couchbase pricing and Kaltura relisting
Today we have new filings from Couchbase and Kaltura: Couchbase set an initial price range for its IPO, something we’ve been waiting for, and Kaltura’s offering is back from hiatus with a new price range and some fresh financial information to boot.
Both bits of news should help us get a handle on how the Q3 2021 IPO cycle is shaping up at the start.
TechCrunch has long expected the third quarter’s IPO haul to prove strong; investors said as 2020 closed that quarters one, three and four would prove very active in terms of public market exits this year. Then the second quarter surpassed expectations, with more companies going public than at least some market observers anticipated.
With that in mind, you can imagine why the newly launched Q3 could prove an active period.
So! Let’s start with a dig into the filing from NoSQL provider Couchbase, working to understand its first price range and what the numbers may say about market demand for technology debuts. Here’s our first look at the company’s value. Then we are taking the Kaltura saga back up, checking into the pricing and second-quarter results from the technology company that provides video-streaming software and services.
Frankly, I’ve been waiting for these filings to drop. So, let’s cut the chat and get into the numbers:
Couchbase’s IPO price range
In its new S-1/A filing, Couchbase reports that it anticipates a $20 to $23 per share IPO price. With a maximum sale of just over 8 million shares, Couchbase could raise as much as $185.15 million in its public offering.
The company will have 40,072,801 shares outstanding after its IPO, not including 1,050,000 shares that are reserved for possible release. The math from here is simple. To calculate Couchbase’s possible simple IPO valuation we can just do a little multiplication:
- Couchbase simple valuation at $20 per share: ~$802 million.
- Couchbase simple valuation at $23 per share: ~$922 million.
If you want to include the company’s reserved shares, add $21 million to the first figure, and $24.2 million to the second. Notably, TechCrunch wrote before it priced that using a historical analog from the Red Hat-IBM sale — both Couchbase and Red Hat work in the OSS space — the company would be worth around $900 million. So, we were pretty close.
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The most important API metric is time to first call
API publishers among Postman’s community of more than 15 million are working toward more seamless and integrated developer experiences for their APIs. Distilled from hundreds of one-on-one discussions, I recently shared a study on increasing adoption of an API with a public workspace in Postman. One of the biggest reasons to use a public workspace is to enhance developer onboarding with a faster time to first call (TTFC), the most important metric you’ll need for a public API.
If you are not investing in TTFC as your most important API metric, you are limiting the size of your potential developer base throughout your remaining adoption funnel.
To understand a developer’s journey, let’s first take a look at factors influencing how much time and energy they are willing to invest in learning your technology and making it work.
- Urgency: Is the developer actively searching for a solution to an existing problem? Or did they hear about your technology in passing and have a mild curiosity?
- Constraints: Is the developer trying to meet a deadline? Or do they have unlimited time and budget to explore the possibilities?
- Alternatives: Is the developer required by their organization to use this solution? Or are they choosing from many providers and considering other ways to solve their problem?
Developer journey to an API
With that context in mind, the following stages describe the developer journey of encountering a new API:
Step 1: Browse
A developer browses your website and documentation to figure out what your API offers. Some people gloss over this step, preferring to learn what your tech offers interactively in the next steps. But judgments are formed at this very early stage, likely while comparing your product among alternatives. For example, if your documentation and onboarding process appears comparatively unorganized and riddled with errors, perhaps it is a reflection of your technology.
Step 2: Signup
Signing up for an account is a developer’s first commitment. It signals their intent to do something with your API. Frequently going hand-in-hand with the next step, signing up is required to generate an API key.
Step 3: First API call
Making the first API call is the first payoff a developer receives and is oftentimes when developers begin more deeply understanding how the API fits into their world. Stripe and Algolia embed interactive guides within their developer documentation to enable first API calls. Stripe and Twitter also use Postman public workspaces for interactive onboarding. Since many developers already use Postman, experiencing an API in familiar territory gets them one step closer to implementation.
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Achieving digital transformation through RPA and process mining
Understanding what you will change is most important to achieve a long-lasting and successful robotic process automation transformation. There are three pillars that will be most impacted by the change: people, process and digital workers (also referred to as robots). The interaction of these three pillars executes workflows and tasks, and if integrated cohesively, determines the success of an enterprisewide digital transformation.
Robots are not coming to replace us, they are coming to take over the repetitive, mundane and monotonous tasks that we’ve never been fond of. They are here to transform the work we do by allowing us to focus on innovation and impactful work. RPA ties decisions and actions together. It is the skeletal structure of a digital process that carries information from point A to point B. However, the decision-making capability to understand and decide what comes next will be fueled by RPA’s integration with AI.
From a strategic standpoint, success measures for automating, optimizing and redesigning work should not be solely centered around metrics like decreasing fully loaded costs or FTE reduction, but should put the people at the center.
We are seeing software vendors adopt vertical technology capabilities and offer a wide range of capabilities to address the three pillars mentioned above. These include powerhouses like UiPath, which recently went public, Microsoft’s Softomotive acquisition, and Celonis, which recently became a unicorn with a $1 billion Series D round. RPA firms call it “intelligent automation,” whereas Celonis targets the execution management system. Both are aiming to be a one-stop shop for all things related to process.
We have seen investments in various product categories for each stage in the intelligent automation journey. Process and task mining for process discovery, centralized business process repositories for CoEs, executives to manage the pipeline and measure cost versus benefit, and artificial intelligence solutions for intelligent document processing.
For your transformation journey to be successful, you need to develop a deep understanding of your goals, people and the process.
Define goals and measurements of success
From a strategic standpoint, success measures for automating, optimizing and redesigning work should not be solely centered around metrics like decreasing fully loaded costs or FTE reduction, but should put the people at the center. To measure improved customer and employee experiences, give special attention to metrics like decreases in throughput time or rework rate, identify vendors that deliver late, and find missed invoice payments or determine loan requests from individuals that are more likely to be paid back late. These provide more targeted success measures for specific business units.
The returns realized with an automation program are not limited to metrics like time or cost savings. The overall performance of an automation program can be more thoroughly measured with the sum of successes of the improved CX/EX metrics in different business units. For each business process you will be redesigning, optimizing or automating, set a definitive problem statement and try to find the right solution to solve it. Do not try to fit predetermined solutions into the problems. Start with the problem and goal first.
Understand the people first
To accomplish enterprise digital transformation via RPA, executives should put people at the heart of their program. Understanding the skill sets and talents of the workforce within the company can yield better knowledge of how well each employee can contribute to the automation economy within the organization. A workforce that is continuously retrained and upskilled learns how to automate and flexibly complete tasks together with robots and is better equipped to achieve transformation at scale.
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To guard against data loss and misuse, the cybersecurity conversation must evolve
Data breaches have become a part of life. They impact hospitals, universities, government agencies, charitable organizations and commercial enterprises. In healthcare alone, 2020 saw 640 breaches, exposing 30 million personal records, a 25% increase over 2019 that equates to roughly two breaches per day, according to the U.S. Department of Health and Human Services. On a global basis, 2.3 billion records were breached in February 2021.
It’s painfully clear that existing data loss prevention (DLP) tools are struggling to deal with the data sprawl, ubiquitous cloud services, device diversity and human behaviors that constitute our virtual world.
Conventional DLP solutions are built on a castle-and-moat framework in which data centers and cloud platforms are the castles holding sensitive data. They’re surrounded by networks, endpoint devices and human beings that serve as moats, defining the defensive security perimeters of every organization. Conventional solutions assign sensitivity ratings to individual data assets and monitor these perimeters to detect the unauthorized movement of sensitive data.
It’s painfully clear that existing data loss prevention (DLP) tools are struggling to deal with the data sprawl, ubiquitous cloud services, device diversity and human behaviors that constitute our virtual world.
Unfortunately, these historical security boundaries are becoming increasingly ambiguous and somewhat irrelevant as bots, APIs and collaboration tools become the primary conduits for sharing and exchanging data.
In reality, data loss is only half the problem confronting a modern enterprise. Corporations are routinely exposed to financial, legal and ethical risks associated with the mishandling or misuse of sensitive information within the corporation itself. The risks associated with the misuse of personally identifiable information have been widely publicized.
However, risks of similar or greater severity can result from the mishandling of intellectual property, material nonpublic information, or any type of data that was obtained through a formal agreement that placed explicit restrictions on its use.
Conventional DLP frameworks are incapable of addressing these challenges. We believe they need to be replaced by a new data misuse protection (DMP) framework that safeguards data from unauthorized or inappropriate use within a corporate environment in addition to its outright theft or inadvertent loss. DMP solutions will provide data assets with more sophisticated self-defense mechanisms instead of relying on the surveillance of traditional security perimeters.
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Dispense with the chasm? No way!
Jeff Bussgang, a co-founder and general partner at Flybridge Capital, recently wrote an Extra Crunch guest post that argued it is time for a refresh when it comes to the technology adoption life cycle and the chasm. His argument went as follows:
- VCs in recent years have drastically underestimated the size of SAMs (serviceable addressable markets) for their startup investments because they were “trained to think only a portion of the SAM is obtainable within any reasonable window of time because of the chasm.”
- The chasm is no longer the barrier it once was because businesses have finally understood that software is eating the world.
- As a result, the early majority has joined up with the innovators and early adopters to create an expanded early market. Effectively, they have defected from the mainstream market to cross the chasm in the other direction, leaving only the late majority and the laggards on the other side.
- That is why we now are seeing multiple instances of very large high-growth markets that appear to have no limit to their upside. There is no chasm to cross until much later in the life cycle, and it isn’t worth much effort to cross it then.
Now, I agree with Jeff that we are seeing remarkable growth in technology adoption at levels that would have astonished investors from prior decades. In particular, I agree with him when he says:
The pandemic helped accelerate a global appreciation that digital innovation was no longer a luxury but a necessity. As such, companies could no longer wait around for new innovations to cross the chasm. Instead, everyone had to embrace change or be exposed to an existential competitive disadvantage.
But this is crossing the chasm! Pragmatic customers are being forced to adopt because they are under duress. It is not that they buy into the vision of software eating the world. It is because their very own lunches are being eaten. The pandemic created a flotilla of chasm-crossings because it unleashed a very real set of existential threats.
The key here is to understand the difference between two buying decision processes, one governed by visionaries and technology enthusiasts (the early adopters and innovators), the other by pragmatists (the early majority).
The key here is to understand the difference between two buying decision processes, one governed by visionaries and technology enthusiasts (the early adopters and innovators), the other by pragmatists (the early majority). The early group makes their decisions based on their own analyses. They do not look to others for corroborative support. Pragmatists do. Indeed, word-of-mouth endorsements are by far the most impactful input not only about what to buy and when but also from whom.
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How to launch a successful RPA initiative
Robotic process automation (RPA) is rapidly moving beyond the early adoption phase across verticals. Automating just basic workflow processes has resulted in such tremendous efficiency improvements and cost savings that businesses are adapting automation at scale and across the enterprise.
While there is a technical component to robotic automation, RPA is not a traditional IT-driven solution. It is, however, still important to align the business and IT processes around RPA. Adapting business automation for the enterprise should be approached as a business solution that happens to require some technical support.
A strong working relationship between the CFO and CIO will go a long way in getting IT behind, and in support of, the initiative rather than in front of it.
A strong working relationship between the CFO and CIO will go a long way in getting IT behind, and in support of, the initiative rather than in front of it.
More important to the success of a large-scale RPA initiative is support from senior business executives across all lines of business and at every step of the project, with clear communications and an advocacy plan all the way down to LOB managers and employees.
As we’ve seen in real-world examples, successful campaigns for deploying automation at scale require a systematic approach to developing a vision, gathering stakeholder and employee buy-in, identifying use cases, building a center of excellence (CoE) and establishing a governance model.
Create an overarching vision
Your strategy should include defining measurable, strategic objectives. Identify strategic areas that benefit most from automation, such as the supply chain, call centers, AP or revenue cycle, and start with obvious areas where business sees delays due to manual workflow processes. Remember, the goal is not to replace employees; you’re aiming to speed up processes, reduce errors, increase efficiencies and let your employees focus on higher value tasks.
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