There could be more to the Salesforce+ video streaming service than meets the eye

When Salesforce announced its new business video streaming service called Salesforce+ this week, everyone had a reaction. While not all of it was positive, some company watchers also wondered if there was more to this announcement than meets the eye.

If you look closely, the new initiative suggests that Salesforce wants to take a bite out of LinkedIn and other SaaS content platforms and publishers. The video streaming service could be a launch point for a broader content platform, where its partners are producing their own content and using Salesforce+ infrastructure to help them advertise to and cultivate their own customers.

The video streaming service could be a launch point for a broader content platform, where its partners are producing their own content and using Salesforce+ infrastructure to help them advertise to and cultivate their own customers.

The company has, after all, done exactly this sort of thing with its online marketplaces and industry events to great success. Salesforce generated almost $6 billion in its most recent quarterly earnings report. That mostly comes from selling its sales, marketing and service software, not any kind of content production, but it has lots of experience putting on Dreamforce, its massive annual customer event, as well as smaller events throughout the year around the world.

On its face, Salesforce+ is a giant, ambitious and quite expensive content marketing play. The company reportedly has hired a large professional staff to produce and manage the content, and built a broadcasting and production studio designed to produce quality shows in-house. It believes that by launching with content from Dreamforce, its highly successful customer conference, attended by tens of thousands people every year pre-pandemic, it can prime the viewing pump and build audience momentum that way, perhaps even using celebrities as it often does at its events to drive audience. It is less clear about the long-term business goals.


VCs are betting big on Kubernetes: Here are 5 reasons why

I worked at Google for six years. Internally, you have no choice — you must use Kubernetes if you are deploying microservices and containers (it’s actually not called Kubernetes inside of Google; it’s called Borg). But what was once solely an internal project at Google has since been open-sourced and has become one of the most talked about technologies in software development and operations.

For good reason. One person with a laptop can now accomplish what used to take a large team of engineers. At times, Kubernetes can feel like a superpower, but with all of the benefits of scalability and agility comes immense complexity. The truth is, very few software developers truly understand how Kubernetes works under the hood.

I like to use the analogy of a watch. From the user’s perspective, it’s very straightforward until it breaks. To actually fix a broken watch requires expertise most people simply do not have — and I promise you, Kubernetes is much more complex than your watch.

How are most teams solving this problem? The truth is, many of them aren’t. They often adopt Kubernetes as part of their digital transformation only to find out it’s much more complex than they expected. Then they have to hire more engineers and experts to manage it, which in a way defeats its purpose.

Where you see containers, you see Kubernetes to help with orchestration. According to Datadog’s most recent report about container adoption, nearly 90% of all containers are orchestrated.

All of this means there is a great opportunity for DevOps startups to come in and address the different pain points within the Kubernetes ecosystem. This technology isn’t going anywhere, so any platform or tooling that helps make it more secure, simple to use and easy to troubleshoot will be well appreciated by the software development community.

In that sense, there’s never been a better time for VCs to invest in this ecosystem. It’s my belief that Kubernetes is becoming the new Linux: 96.4% of the top million web servers’ operating systems are Linux. Similarly, Kubernetes is trending to become the de facto operating system for modern, cloud-native applications. It is already the most popular open-source project within the Cloud Native Computing Foundation (CNCF), with 91% of respondents using it — a steady increase from 78% in 2019 and 58% in 2018.

While the technology is proven and adoption is skyrocketing, there are still some fundamental challenges that will undoubtedly be solved by third-party solutions. Let’s go deeper and look at five reasons why we’ll see a surge of startups in this space.


Containers are the go-to method for building modern apps

Docker revolutionized how developers build and ship applications. Container technology has made it easier to move applications and workloads between clouds. It also provides as much resource isolation as a traditional hypervisor, but with considerable opportunities to improve agility, efficiency and speed.


Enterprise AI 2.0: The acceleration of B2B AI innovation has begun

Two decades after businesses first started deploying AI solutions, one can argue that they’ve made little progress in achieving significant gains in efficiency and profitability relative to the hype that drove initial expectations.

On the surface, recent data supports AI skeptics. Almost 90% of data science projects never make it to production; only 20% of analytics insights through 2022 will achieve business outcomes; and even companies that have developed an enterprisewide AI strategy are seeing failure rates of up to 50%.

But the past 25 years have only been the first phase in the evolution of enterprise AI — or what we might call Enterprise AI 1.0. That’s where many businesses remain today. However, companies on the leading edge of AI innovation have advanced to the next generation, which will define the coming decade of big data, analytics and automation — Enterprise AI 2.0.

The difference between these two generations of enterprise AI is not academic. For executives across the business spectrum — from healthcare and retail to media and finance — the evolution from 1.0 to 2.0 is a chance to learn and adapt from past failures, create concrete expectations for future uses and justify the rising investment in AI that we see across industries.

Two decades from now, when business leaders look back to the 2020s, the companies who achieved Enterprise AI 2.0 first will have come to be big winners in the economy, having differentiated their services, scooped up market share and positioned themselves for ongoing innovation.

Framing the digital transformations of the future as an evolution from Enterprise AI 1.0 to 2.0 provides a conceptual model for business leaders developing strategies to compete in the age of automation and advanced analytics.

Enterprise AI 1.0 (the status quo)

Starting in the mid-1990s, AI was a sector marked by speculative testing, experimental interest and exploration. These activities occurred almost exclusively in the domain of data scientists. As Gartner wrote in a recent report, these efforts were “alchemy … run by wizards whose talents will not scale in the organization.”


4 key areas SaaS startups must address to scale infrastructure for the enterprise

Startups and SMBs are usually the first to adopt many SaaS products. But as these customers grow in size and complexity — and as you rope in larger organizations — scaling your infrastructure for the enterprise becomes critical for success.

Below are four tips on how to advance your company’s infrastructure to support and grow with your largest customers.

Address your customers’ security and reliability needs

If you’re building SaaS, odds are you’re holding very important customer data. Regardless of what you build, that makes you a threat vector for attacks on your customers. While security is important for all customers, the stakes certainly get higher the larger they grow.

Given the stakes, it’s paramount to build infrastructure, products and processes that address your customers’ growing security and reliability needs. That includes the ethical and moral obligation you have to make sure your systems and practices meet and exceed any claim you make about security and reliability to your customers.

Here are security and reliability requirements large customers typically ask for:

Formal SLAs around uptime: If you’re building SaaS, customers expect it to be available all the time. Large customers using your software for mission-critical applications will expect to see formal SLAs in contracts committing to 99.9% uptime or higher. As you build infrastructure and product layers, you need to be confident in your uptime and be able to measure uptime on a per customer basis so you know if you’re meeting your contractual obligations.

While it’s hard to prioritize asks from your largest customers, you’ll find that their collective feedback will pull your product roadmap in a specific direction.

Real-time status of your platform: Most larger customers will expect to see your platform’s historical uptime and have real-time visibility into events and incidents as they happen. As you mature and specialize, creating this visibility for customers also drives more collaboration between your customer operations and infrastructure teams. This collaboration is valuable to invest in, as it provides insights into how customers are experiencing a particular degradation in your service and allows for you to communicate back what you found so far and what your ETA is.

Backups: As your customers grow, be prepared for expectations around backups — not just in terms of how long it takes to recover the whole application, but also around backup periodicity, location of your backups and data retention (e.g., are you holding on to the data too long?). If you’re building your backup strategy, thinking about future flexibility around backup management will help you stay ahead of these asks.


The CockroachDB EC-1

Every application is a palimpsest of technologies, each layer forming a base that enables the next layer to function. Web front ends rely on JavaScript and browser DOM, which rely on back-end APIs, which themselves rely on databases.

As one goes deeper down the stack, engineering decisions become ever more conservative — changing the location of a button in a web app is an inconvenience; changing a database engine can radically upend an entire project.

It’s little surprise then that database technologies are among the longest-lasting engineering projects in the modern software developer toolkit. MySQL, which remains one of the most popular database engines in the world, was first released in the mid-1990s, and Oracle Database, launched more than four decades ago, is still widely used in high-performance corporate environments.

Database technology can change the world, but the world in these parts changes very, very slowly. That’s made building a startup in the sector a tough equation: Sales cycles can be painfully slow, even when new features can dramatically expand a developer’s capabilities. Competition is stiff and comes from some of the largest and most entrenched tech companies in the world. Exits have also been few and far between.

That challenge — and opportunity — is what makes studying Cockroach Labs so interesting. The company behind CockroachDB attempts to solve a long-standing problem in large-scale, distributed database architecture: How to make it so that data created in one place on the planet is always available for consumption by applications that are thousands of miles away, immediately and accurately. Making global data always available immediately and accurately might sound like a simple use case, but in reality it’s quite the herculean task. Cockroach Labs’ story is one of an uphill struggle, but one that saw it turn into a next-generation, $2-billion-valued database contender.

The lead writer of this EC-1 is Bob Reselman. Reselman has been writing about the enterprise software market for more than two decades, with a particular emphasis on teaching and educating engineers on technology. The lead editor for this package was Danny Crichton, the assistant editor was Ram Iyer, the copy editor was Richard Dal Porto, figures were designed by Bob Reselman and stylized by Bryce Durbin, and illustrations were drawn by Nigel Sussman.

CockroachDB had no say in the content of this analysis and did not get advance access to it. Reselman has no financial ties to CockroachDB or other conflicts of interest to disclose.

The CockroachDB EC-1 comprises four main articles numbering 9,100 words and a reading time of 37 minutes. Here’s what we’ll be crawling over:

We’re always iterating on the EC-1 format. If you have questions, comments or ideas, please send an email to TechCrunch Managing Editor Danny Crichton at


CockroachDB, the database that just won’t die

There is an art to engineering, and sometimes engineering can transform art. For Spencer Kimball and Peter Mattis, those two worlds collided when they created the widely successful open-source graphics program, GIMP, as college students at Berkeley.

That project was so successful that when the two joined Google in 2002, Sergey Brin and Larry Page personally stopped by to tell the new hires how much they liked it and explained how they used the program to create the first Google logo.

Cockroach Labs was started by developers and stays true to its roots to this day.

In terms of good fortune in the corporate hierarchy, when you get this type of recognition in a company such as Google, there’s only one way you can go — up. They went from rising stars to stars at Google, becoming the go-to guys on the Infrastructure Team. They could easily have looked forward to a lifetime of lucrative employment.

But Kimball, Mattis and another Google employee, Ben Darnell, wanted more — a company of their own. To realize their ambitions, they created Cockroach Labs, the business entity behind their ambitious open-source database CockroachDB. Can some of the smartest former engineers in Google’s arsenal upend the world of databases in a market spotted with the gravesites of storage dreams past? That’s what we are here to find out.

Berkeley software distribution

Mattis and Kimball were roommates at Berkeley majoring in computer science in the early-to-mid-1990s. In addition to their usual studies, they also became involved with the eXperimental Computing Facility (XCF), an organization of undergraduates who have a keen, almost obsessive interest in CS.


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:

  1. What makes reading and writing data over a global geography so hard?
  2. How does CockroachDB address the problem?
  3. 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.


“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.


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.


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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