Oct
15
2019
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Databricks brings its Delta Lake project to the Linux Foundation

Databricks, the big data analytics service founded by the original developers of Apache Spark, today announced that it is bringing its Delta Lake open-source project for building data lakes to the Linux Foundation under an open governance model. The company announced the launch of Delta Lake earlier this year, and, even though it’s still a relatively new project, it has already been adopted by many organizations and has found backing from companies like Intel, Alibaba and Booz Allen Hamilton.

“In 2013, we had a small project where we added SQL to Spark at Databricks […] and donated it to the Apache Foundation,” Databricks CEO and co-founder Ali Ghodsi told me. “Over the years, slowly people have changed how they actually leverage Spark and only in the last year or so it really started to dawn upon us that there’s a new pattern that’s emerging and Spark is being used in a completely different way than maybe we had planned initially.”

This pattern, he said, is that companies are taking all of their data and putting it into data lakes and then doing a couple of things with this data, machine learning and data science being the obvious ones. But they are also doing things that are more traditionally associated with data warehouses, like business intelligence and reporting. The term Ghodsi uses for this kind of usage is “Lake House.” More and more, Databricks is seeing that Spark is being used for this purpose and not just to replace Hadoop and doing ETL (extract, transform, load). “This kind of Lake House patterns we’ve seen emerge more and more and we wanted to double down on it.”

Spark 3.0, which is launching today soon, enables more of these use cases and speeds them up significantly, in addition to the launch of a new feature that enables you to add a pluggable data catalog to Spark.

Delta Lake, Ghodsi said, is essentially the data layer of the Lake House pattern. It brings support for ACID transactions to data lakes, scalable metadata handling and data versioning, for example. All the data is stored in the Apache Parquet format and users can enforce schemas (and change them with relative ease if necessary).

It’s interesting to see Databricks choose the Linux Foundation for this project, given that its roots are in the Apache Foundation. “We’re super excited to partner with them,” Ghodsi said about why the company chose the Linux Foundation. “They run the biggest projects on the planet, including the Linux project but also a lot of cloud projects. The cloud-native stuff is all in the Linux Foundation.”

“Bringing Delta Lake under the neutral home of the Linux Foundation will help the open-source community dependent on the project develop the technology addressing how big data is stored and processed, both on-prem and in the cloud,” said Michael Dolan, VP of Strategic Programs at the Linux Foundation. “The Linux Foundation helps open-source communities leverage an open governance model to enable broad industry contribution and consensus building, which will improve the state of the art for data storage and reliability.”

Oct
08
2019
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Satya Nadella looks to the future with edge computing

Speaking today at the Microsoft Government Leaders Summit in Washington, DC, Microsoft CEO Satya Nadella made the case for edge computing, even while pushing the Azure cloud as what he called “the world’s computer.”

While Amazon, Google and other competitors may have something to say about that, marketing hype aside, many companies are still in the midst of transitioning to the cloud. Nadella says the future of computing could actually be at the edge, where computing is done locally before data is then transferred to the cloud for AI and machine learning purposes. What goes around, comes around.

But as Nadella sees it, this is not going to be about either edge or cloud. It’s going to be the two technologies working in tandem. “Now, all this is being driven by this new tech paradigm that we describe as the intelligent cloud and the intelligent edge,” he said today.

He said that to truly understand the impact the edge is going to have on computing, you have to look at research, which predicts there will be 50 billion connected devices in the world by 2030, a number even he finds astonishing. “I mean this is pretty stunning. We think about a billion Windows machines or a couple of billion smartphones. This is 50 billion [devices], and that’s the scope,” he said.

The key here is that these 50 billion devices, whether you call them edge devices or the Internet of Things, will be generating tons of data. That means you will have to develop entirely new ways of thinking about how all this flows together. “The capacity at the edge, that ubiquity is going to be transformative in how we think about computation in any business process of ours,” he said. As we generate ever-increasing amounts of data, whether we are talking about public sector kinds of use case, or any business need, it’s going to be the fuel for artificial intelligence, and he sees the sheer amount of that data driving new AI use cases.

“Of course when you have that rich computational fabric, one of the things that you can do is create this new asset, which is data and AI. There is not going to be a single application, a single experience that you are going to build, that is not going to be driven by AI, and that means you have to really have the ability to reason over large amounts of data to create that AI,” he said.

Nadella would be more than happy to have his audience take care of all that using Microsoft products, whether Azure compute, database, AI tools or edge computers like the Data Box Edge it introduced in 2018. While Nadella is probably right about the future of computing, all of this could apply to any cloud, not just Microsoft.

As computing shifts to the edge, it’s going to have a profound impact on the way we think about technology in general, but it’s probably not going to involve being tied to a single vendor, regardless of how comprehensive their offerings may be.

Sep
17
2019
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Data storage company Cloudian launches a new edge analytics subsidiary called Edgematrix

Cloudian, a company that enables businesses to store and manage massive amounts of data, announced today the launch of Edgematrix, a new unit focused on edge analytics for large data sets. Edgematrix, a majority-owned subsidiary of Cloudian, will first be available in Japan, where both companies are based. It has raised a $9 million Series A from strategic investors NTT Docomo, Shimizu Corporation and Japan Post Capital, as well as Cloudian co-founder and CEO Michael Tso and board director Jonathan Epstein. The funding will be used on product development, deployment and sales and marketing.

Cloudian itself has raised a total of $174 million, including a $94 million Series E round announced last year. Its products include the Hyperstore platform, which allows businesses to store hundreds of petrabytes of data on premise, and software for data analytics and machine learning. Edgematrix uses Hyperstore for storing large-scale data sets and its own AI software and hardware for data processing at the “edge” of networks, closer to where data is collected from IoT devices like sensors.

The company’s solutions were created for situations where real-time analytics is necessary. For example, it can be used to detect the make, model and year of cars on highways so targeted billboard ads can be displayed to their drivers.

Tso told TechCrunch in an email that Edgematrix was launched after Cloudian co-founder and president Hiroshi Ohta and a team spent two years working on technology to help Cloudian customers process and analyze their data more efficiently.

“With more and more data being created at the edge, including IoT data, there’s a growing need for being able to apply real-time data analysis and decision-making at or near the edge, minimizing the transmission costs and latencies involved in moving the data elsewhere,” said Tso. “Based on the initial success of a small Cloudian team developing AI software solutions and attracting a number of top-tier customers, we decided that the best way to build on this success was establishing a subsidiary with strategic investors.”

Edgematrix is launching in Japan first because spending on AI systems there is expected to grow faster than in any other market, at a compound annual growth rate of 45.3% from 2018 to 2023, according to IDC.

“Japan has been ahead of the curve as an early adopter of AI technology, with both the governmetn and private sector viewing it as essential to boosting productivity,” said Tso. “Edgematrix will focus on the Japanese market for at least the next year, and assuming that all goes well, it would then expand to North America and Europe.”

Jul
29
2019
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Microsoft acquires data privacy and governance service BlueTalon

Microsoft today announced that it has acquired BlueTalon, a data privacy and governance service that helps enterprises set policies for how their employees can access their data. The service then enforces those policies across most popular data environments and provides tools for auditing policies and access, too.

Neither Microsoft nor BlueTalon disclosed the financial details of the transaction. Ahead of today’s acquisition, BlueTalon had raised about $27.4 million, according to Crunchbase. Investors include Bloomberg Beta, Maverick Ventures, Signia Venture Partners and Stanford’s StartX fund.

BlueTalon Policy Engine How it works

“The IP and talent acquired through BlueTalon brings a unique expertise at the apex of big data, security and governance,” writes Rohan Kumar, Microsoft’s corporate VP for Azure Data. “This acquisition will enhance our ability to empower enterprises across industries to digitally transform while ensuring right use of data with centralized data governance at scale through Azure.”

Unsurprisingly, the BlueTalon team will become part of the Azure Data Governance group, where the team will work on enhancing Microsoft’s capabilities around data privacy and governance. Microsoft already offers access and governance control tools for Azure, of course. As virtually all businesses become more data-centric, though, the need for centralized access controls that work across systems is only going to increase and new data privacy laws aren’t making this process easier.

“As we began exploring partnership opportunities with various hyperscale cloud providers to better serve our customers, Microsoft deeply impressed us,” BlueTalon CEO Eric Tilenius, who has clearly read his share of “our incredible journey” blog posts, explains in today’s announcement. “The Azure Data team was uniquely thoughtful and visionary when it came to data governance. We found them to be the perfect fit for us in both mission and culture. So when Microsoft asked us to join forces, we jumped at the opportunity.”

Jun
10
2019
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With Tableau and Mulesoft, Salesforce gains full view of enterprise data

Back in the 2010 timeframe, it was common to say that content was king, but after watching Google buy Looker for $2.6 billion last week and Salesforce nab Tableau for $15.7 billion this morning, it’s clear that data has ascended to the throne in a business context.

We have been hearing about Big Data for years, but we’ve probably reached a point in 2019 where the data onslaught is really having an impact on business. If you can find the key data nuggets in the big data pile, it can clearly be a competitive advantage, and companies like Google and Salesforce are pulling out their checkbooks to make sure they are in a position to help you out.

While Google, as a cloud infrastructure vendor, is trying to help companies on its platform and across the cloud understand and visualize all that data, Salesforce as a SaaS vendor might have a different reason — one that might surprise you — given that Salesforce was born in the cloud. But perhaps it recognizes something fundamental. If it truly wants to own the enterprise, it has to have a hybrid story, and with Mulesoft and Tableau, that’s precisely what it has — and why it was willing to spend around $23 billion to get it.

Making connections

Certainly, Salesforce chairman Marc Benioff has no trouble seeing the connections between his two big purchases over the last year. He sees the combination of Mulesoft connecting to the data sources and Tableau providing a way to visualize as a “beautiful thing.”

May
15
2019
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Tealium, a big data platform for structuring disparate customer information, raises $55M at $850M valuation

The average enterprise today uses about 90 different software packages, with between 30-40 of them touching customers directly or indirectly. The data that comes out of those systems can prove to be very useful — to help other systems and employees work more intelligently, to help companies make better business decisions — but only if it’s put in order: now, a startup called Tealium, which has built a system precisely to do just that and works with the likes of Facebook and IBM to help manage their customer data, has raised a big round of funding to continue building out the services it provides.

Today, it is announcing a $55 million round of funding — a Series F led by Silver Lake Waterman, the firm’s late-stage capital growth fund; with ABN AMRO, Bain Capital, Declaration Partners, Georgian Partners, Industry Ventures, Parkwood and Presidio Ventures also participating.

Jeff Lunsford, Tealium’s CEO, said the company is not disclosing valuation, but he did say that it was “substantially” higher than when the company was last priced three years ago. That valuation was $305 million in 2016, according to PitchBook — a figure Lunsford didn’t dispute when I spoke with him about it, and a source close to the company says it is “more than double” this last valuation, and actually around $850 million.

He added that the company is close to profitability and is projected to make $100 million in revenues this year, and that this is being considered the company’s “final round” — presumably a sign that it will either no longer need external funding and that if it does, the next step might be either getting acquired or going public.

This brings the total raised by Tealium to $160 million.

The company’s rise over the last eight years has dovetailed with the rapid growth of big data. The movement of services to digital platforms has resulted in a sea of information. Much of that largely sits untapped, but those who are able to bring it to order can reap the rewards by gaining better insights into their organizations.

Tealium had its beginnings in amassing and ordering tags from internet traffic to help optimise marketing and so on — a business where it competes with the likes of Google and Adobe.

Over time, it has expanded and capitalised to a much wider set of data sources that range well beyond web and commerce, and one use of the funding will be to continue expanding those data sources, and also how they are used, with an emphasis on using more AI, Lunsford said.

“There are new areas that touch customers like smart home and smart office hardware, and each requires a step up in integration for a company like us,” he said. “Then once you have it all centralised you could feed machine learning algorithms to have tighter predictions.”

That vast potential is one reason for the investor interest.

“Tealium enables enterprises to solve the customer data fragmentation problem by integrating and enriching data across sources, in real-time, to create audiences while providing data governance and fidelity,” said Shawn O’Neill, managing director of Silver Lake Waterman, in a statement. “Jeff and his team have built a great platform and we are excited to support the company’s continued growth and investment in innovation.”

The rapid growth of digital services has already seen the company getting a big boost in terms of the data that is passing through its cloud-based platform: it has had a 300% year-over-year increase in visitor profiles created, with current tech customers including the likes of Facebook, IBM, Visa and others from across a variety of sectors, such as healthcare, finance and more.

“You’d be surprised how many big tech companies use Tealium,” Lunsford said. “Even they have a limited amount of bandwidth when it comes to developing their internal platforms.”

People like to say that “data is the new oil,” but these days that expression has taken on perhaps an unintended meaning: just like the overconsumption of oil and fossil fuels in general is viewed as detrimental to the long-term health of our planet, the overconsumption of data has also become a very problematic spectre in our very pervasive world of tech.

Governments — the European Union being one notable example — are taking up the challenge of that latter issue with new regulations, specifically GDPR. Interestingly, Lunsford says this has been a good thing rather than a bad thing for his company, as it gives a much clearer directive to companies about what they can use, and how it can be used.

“They want to follow the law,” he said of their clients, “and we give them the data freedom and control to do that.” It’s not the only company tackling the business opportunity of being a big-data repository at a time when data misuse is being scrutinised more than ever: InCountry, which launched weeks ago, is also banking on this gap in the market.

I’d argue that this could potentially be one more reason why Tealium is keen on expanding to areas like IoT and other sources of customer information: just like the sea, the pool of data that’s there for the tapping is nearly limitless.

Feb
20
2019
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Why Daimler moved its big data platform to the cloud

Like virtually every big enterprise company, a few years ago, the German auto giant Daimler decided to invest in its own on-premises data centers. And while those aren’t going away anytime soon, the company today announced that it has successfully moved its on-premises big data platform to Microsoft’s Azure cloud. This new platform, which the company calls eXtollo, is Daimler’s first major service to run outside of its own data centers, though it’ll probably not be the last.

As Daimler’s head of its corporate center of excellence for advanced analytics and big data Guido Vetter told me, the company started getting interested in big data about five years ago. “We invested in technology — the classical way, on-premise — and got a couple of people on it. And we were investigating what we could do with data because data is transforming our whole business as well,” he said.

By 2016, the size of the organization had grown to the point where a more formal structure was needed to enable the company to handle its data at a global scale. At the time, the buzz phrase was “data lakes” and the company started building its own in order to build out its analytics capacities.

Electric lineup, Daimler AG

“Sooner or later, we hit the limits as it’s not our core business to run these big environments,” Vetter said. “Flexibility and scalability are what you need for AI and advanced analytics and our whole operations are not set up for that. Our backend operations are set up for keeping a plant running and keeping everything safe and secure.” But in this new world of enterprise IT, companies need to be able to be flexible and experiment — and, if necessary, throw out failed experiments quickly.

So about a year and a half ago, Vetter’s team started the eXtollo project to bring all the company’s activities around advanced analytics, big data and artificial intelligence into the Azure Cloud, and just over two weeks ago, the team shut down its last on-premises servers after slowly turning on its solutions in Microsoft’s data centers in Europe, the U.S. and Asia. All in all, the actual transition between the on-premises data centers and the Azure cloud took about nine months. That may not seem fast, but for an enterprise project like this, that’s about as fast as it gets (and for a while, it fed all new data into both its on-premises data lake and Azure).

If you work for a startup, then all of this probably doesn’t seem like a big deal, but for a more traditional enterprise like Daimler, even just giving up control over the physical hardware where your data resides was a major culture change and something that took quite a bit of convincing. In the end, the solution came down to encryption.

“We needed the means to secure the data in the Microsoft data center with our own means that ensure that only we have access to the raw data and work with the data,” explained Vetter. In the end, the company decided to use the Azure Key Vault to manage and rotate its encryption keys. Indeed, Vetter noted that knowing that the company had full control over its own data was what allowed this project to move forward.

Vetter tells me the company obviously looked at Microsoft’s competitors as well, but he noted that his team didn’t find a compelling offer from other vendors in terms of functionality and the security features that it needed.

Today, Daimler’s big data unit uses tools like HD Insights and Azure Databricks, which covers more than 90 percents of the company’s current use cases. In the future, Vetter also wants to make it easier for less experienced users to use self-service tools to launch AI and analytics services.

While cost is often a factor that counts against the cloud, because renting server capacity isn’t cheap, Vetter argues that this move will actually save the company money and that storage costs, especially, are going to be cheaper in the cloud than in its on-premises data center (and chances are that Daimler, given its size and prestige as a customer, isn’t exactly paying the same rack rate that others are paying for the Azure services).

As with so many big data AI projects, predictions are the focus of much of what Daimler is doing. That may mean looking at a car’s data and error code and helping the technician diagnose an issue or doing predictive maintenance on a commercial vehicle. Interestingly, the company isn’t currently bringing to the cloud any of its own IoT data from its plants. That’s all managed in the company’s on-premises data centers because it wants to avoid the risk of having to shut down a plant because its tools lost the connection to a data center, for example.

Feb
07
2019
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Microsoft Azure sets its sights on more analytics workloads

Enterprises now amass huge amounts of data, both from their own tools and applications, as well as from the SaaS applications they use. For a long time, that data was basically exhaust. Maybe it was stored for a while to fulfill some legal requirements, but then it was discarded. Now, data is what drives machine learning models, and the more data you have, the better. It’s maybe no surprise, then, that the big cloud vendors started investing in data warehouses and lakes early on. But that’s just a first step. After that, you also need the analytics tools to make all of this data useful.

Today, it’s Microsoft turn to shine the spotlight on its data analytics services. The actual news here is pretty straightforward. Two of these are services that are moving into general availability: the second generation of Azure Data Lake Storage for big data analytics workloads and Azure Data Explorer, a managed service that makes easier ad-hoc analysis of massive data volumes. Microsoft is also previewing a new feature in Azure Data Factory, its graphical no-code service for building data transformation. Data Factory now features the ability to map data flows.

Those individual news pieces are interesting if you are a user or are considering Azure for your big data workloads, but what’s maybe more important here is that Microsoft is trying to offer a comprehensive set of tools for managing and storing this data — and then using it for building analytics and AI services.

(Photo credit:Josh Edelson/AFP/Getty Images)

“AI is a top priority for every company around the globe,” Julia White, Microsoft’s corporate VP for Azure, told me. “And as we are working with our customers on AI, it becomes clear that their analytics often aren’t good enough for building an AI platform.” These companies are generating plenty of data, which then has to be pulled into analytics systems. She stressed that she couldn’t remember a customer conversation in recent months that didn’t focus on AI. “There is urgency to get to the AI dream,” White said, but the growth and variety of data presents a major challenge for many enterprises. “They thought this was a technology that was separate from their core systems. Now it’s expected for both customer-facing and line-of-business applications.”

Data Lake Storage helps with managing this variety of data since it can handle both structured and unstructured data (and is optimized for the Spark and Hadoop analytics engines). The service can ingest any kind of data — yet Microsoft still promises that it will be very fast. “The world of analytics tended to be defined by having to decide upfront and then building rigid structures around it to get the performance you wanted,” explained White. Data Lake Storage, on the other hand, wants to offer the best of both worlds.

Likewise, White argued that while many enterprises used to keep these services on their on-premises servers, many of them are still appliance-based. But she believes the cloud has now reached the point where the price/performance calculations are in its favor. It took a while to get to this point, though, and to convince enterprises. White noted that for the longest time, enterprises that looked at their analytics projects thought $300 million projects took forever, tied up lots of people and were frankly a bit scary. “But also, what we had to offer in the cloud hasn’t been amazing until some of the recent work,” she said. “We’ve been on a journey — as well as the other cloud vendors — and the price performance is now compelling.” And it sure helps that if enterprises want to meet their AI goals, they’ll now have to tackle these workloads, too.

Feb
06
2019
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Big companies are not becoming data-driven fast enough

I remember watching MIT professor Andrew McAfee years ago telling stories about the importance of data over gut feeling, whether it was predicting successful wines or making sound business decisions. We have been hearing about big data and data-driven decision making for so long, you would think it has become hardened into our largest organizations by now. As it turns out, new research by NewVantage Partners finds that most large companies are having problems implementing an organization-wide, data-driven strategy.

McAfee was fond of saying that before the data deluge we have today, the way most large organizations made decisions was via the HiPPO — the highest paid person’s opinion. Then he would chide the audience that this was not the proper way to run your business. Data, not gut feelings, even those based on experience, should drive important organizational decisions.

While companies haven’t failed to recognize McAfee’s advice, the NVP report suggests they are having problems implementing data-driven decision making across organizations. There are plenty of technological solutions out there today to help them, from startups all the way to the largest enterprise vendors, but the data (see, you always need to go back to the data) suggests that it’s not a technology problem, it’s a people problem.

Executives can have farsighted vision that their organizations need to be data-driven. They can acquire all of the latest solutions to bring data to the forefront, but unless they combine that with a broad cultural shift and a deep understanding of how to use that data inside business processes, they will continue to struggle.

The study’s authors, Randy Bean and Thomas H. Davenport, wrote about the people problem in their study’s executive summary. “We hear little about initiatives devoted to changing human attitudes and behaviors around data. Unless the focus shifts to these types of activities, we are likely to see the same problem areas in the future that we’ve observed year after year in this survey.”

The survey found that 72 percent of respondents have failed in this regard, reporting they haven’t been able to create a data-driven culture, whatever that means to individual respondents. Meanwhile, 69 percent reported they had failed to create a data-driven organization, although it would seem that these two metrics would be closely aligned.

Perhaps most discouraging of all is that the data is trending the wrong way. Over the last several years, the report’s authors say that those organizations calling themselves data-driven has actually dropped each year from 37.1 percent in 2017 to 32.4 percent in 2018 to 31.0 percent in the latest survey.

This matters on so many levels, but consider that as companies shift to artificial intelligence and machine learning, these technologies rely on abundant amounts of data to work effectively. What’s more, every organization, regardless of its size, is generating vast amounts of data, simply as part of being a digital business in the 21st century. They need to find a way to control this data to make better decisions and understand their customers better. It’s essential.

There is so much talk about innovation and disruption, and understanding and affecting company culture, but so much of all this is linked. You need to be more agile. You need to be more digital. You need to be transformational. You need to be all of these things — and data is at the center of all of it.

Data has been called the new oil often enough to be cliché, but these results reveal that the lesson is failing to get through. Companies need to be data-driven now, this instant. This isn’t something to be working toward at this point. This is something you need to be doing, unless your ultimate goal is to become irrelevant.

Feb
05
2019
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Databricks raises $250M at a $2.75B valuation for its analytics platform

Databricks, the company founded by the original team behind the Apache Spark big data analytics engine, today announced that it has raised a $250 million Series E round led by Andreessen Horowitz. Coatue Management, Green Bay Ventures, Microsoft and NEA, also participated in this round, which brings the company’s total funding to $498.5 million. Microsoft’s involvement here is probably a bit of a surprise, but it’s worth noting that it also worked with Databricks on the launch of Azure Databricks as a first-party service on the platform, something that’s still a rarity in the Azure cloud.

As Databricks also today announced, its annual recurring revenue now exceeds $100 million. The company didn’t share whether it’s cash flow-positive at this point, but Databricks CEO and co-founder Ali Ghodsi shared that the company’s valuation is now $2.75 billion.

Current customers, which the company says number around 2,000, include the likes of Nielsen, Hotels.com, Overstock, Bechtel, Shell and HP.

“What Ali and the Databricks team have built is truly phenomenal,” Green Bay Ventures co-founder Anthony Schiller told me. “Their success is a testament to product innovation at the highest level. Databricks is without question best-in-class and their impact on the industry proves it. We were thrilled to participate in this round.”

While Databricks is obviously known for its contributions to Apache Spark, the company itself monetizes that work by offering its Unified Analytics platform on top of it. This platform allows enterprises to build their data pipelines across data storage systems and prepare data sets for data scientists and engineers. To do this, Databricks offers shared notebooks and tools for building, managing and monitoring data pipelines, and then uses that data to build machine learning models, for example. Indeed, training and deploying these models is one of the company’s focus areas these days, which makes sense, given that this is one of the main use cases for big data, after all.

On top of that, Databricks also offers a fully managed service for hosting all of these tools.

“Databricks is the clear winner in the big data platform race,” said Ben Horowitz, co-founder and general partner at Andreessen Horowitz, in today’s announcement. “In addition, they have created a new category atop their world-beating Apache Spark platform called Unified Analytics that is growing even faster. As a result, we are thrilled to invest in this round.”

Ghodsi told me that Horowitz was also instrumental in getting the company to re-focus on growth. The company was already growing fast, of course, but Horowitz asked him why Databricks wasn’t growing faster. Unsurprisingly, given that it’s an enterprise company, that means aggressively hiring a larger sales force — and that’s costly. Hence the company’s need to raise at this point.

As Ghodsi told me, one of the areas the company wants to focus on is the Asia Pacific region, where overall cloud usage is growing fast. The other area the company is focusing on is support for more verticals like mass media and entertainment, federal agencies and fintech firms, which also comes with its own cost, given that the experts there don’t come cheap.

Ghodsi likes to call this “boring AI,” since it’s not as exciting as self-driving cars. In his view, though, the enterprise companies that don’t start using machine learning now will inevitably be left behind in the long run. “If you don’t get there, there’ll be no place for you in the next 20 years,” he said.

Engineering, of course, will also get a chunk of this new funding, with an emphasis on relatively new products like MLFlow and Delta, two tools Databricks recently developed and that make it easier to manage the life cycle of machine learning models and build the necessary data pipelines to feed them.

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