May
27
2020
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RudderStack raises $5M seed round for its open-source Segment competitor

RudderStack, a startup that offers an open-source alternative to customer data management platforms like Segment, today announced that it has raised a $5 million seed round led by S28 Capital. Salil Deshpande of Uncorrelated Ventures and Mesosphere/D2iQ co-founder Florian Leibert (through 468 Capital) also participated in this round.

In addition, the company also today announced that it has acquired Blendo, an integration platform that helps businesses transform and move data from their data sources to databases.

Like its larger competitors, RudderStack helps businesses consolidate all of their customer data, which is now typically generated and managed in multiple places — and then extract value from this more holistic view. The company was founded by Soumyadeb Mitra, who has a Ph.D. in database systems and worked on similar problems previously when he was at 8×8 after his previous startup, MairinaIQ, was acquired by that company.

Mitra argues that RudderStack is different from its competitors thanks to its focus on developers, its privacy and security options and its focus on being a data warehouse first, without creating yet another data silo.

“Our competitors provide tools for analytics, audience segmentation, etc. on top of the data they keep,” he said. “That works well if you are a small startup, but larger enterprises have a ton of other data sources — at 8×8 we had our own internal billing system, for example — and you want to combine this internal data with the event stream data — that you collect via RudderStack or competitors — to create a 360-degree view of the customer and act on that. This becomes very difficult with the SaaS-hosted data model of our competitors — you won’t be sending all your internal data to these cloud vendors.”

Part of its appeal, of course, is the open-source nature of RudderStack, whose GitHub repository now has more than 1,700 stars for the main RudderStack server. Mitra credits getting on the front page of HackerNews for its first sale. On that day, it received over 500 GitHub stars, a few thousand clones and a lot of signups for its hosted app. “One of those signups turned out to be our first paid customer. They were already a competitor’s customer, but it wasn’t scaling up so were looking to build something in-house. That’s when they found us and started working with us,” he said.

Because it is open source, companies can run RudderStack anyway they want, but like most similar open-source companies, RudderStack offers multiple hosting options itself, too, that include cloud hosting, starting at $2,000 per month, with unlimited sources and destination.

Current users include IFTTT, Mattermost, MarineTraffic, Torpedo and Wynn Las Vegas.

As for the Blendo acquisition, it’s worth noting that the company only raised a small amount of money in its seed round. The two companies did not disclose the price of the acquisition.

“With Blendo, I had the opportunity to be part of a great team that executed on the vision of turning any company into a data-driven organization,” said Blendo founder Kostas Pardalis, who has joined RudderStack as head of Growth. “We’ve combined the talented Blendo and RudderStack teams together with the technology that both companies have created, at a time when the customer data market is ripe for the next wave of innovation. I’m excited to help drive RudderStack forward.”

Mitra tells me that RudderStack acquired Blendo instead of building its own version of this technology because “it is not a trivial technology to build — cloud sources are really complicated and have weird schemas and API challenges and it would have taken us a lot of time to figure it out. There are independent large companies doing the ETL piece.”

Apr
22
2020
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Fishtown Analytics raises $12.9M Series A for its open-source analytics engineering tool

Philadelphia-based Fishtown Analytics, the company behind the popular open-source data engineering tool dbt, today announced that it has raised a $12.9 million Series A round led by Andreessen Horowitz, with the firm’s general partner Martin Casado joining the company’s board.

“I wrote this blog post in early 2016, essentially saying that analysts needed to work in a fundamentally different way,” Fishtown founder and CEO Tristan Handy told me, when I asked him about how the product came to be. “They needed to work in a way that much more closely mirrored the way the software engineers work and software engineers have been figuring this shit out for years and data analysts are still like sending each other Microsoft Excel docs over email.”

The dbt open-source project forms the basis of this. It allows anyone who can write SQL queries to transform data and then load it into their preferred analytics tools. As such, it sits in-between data warehouses and the tools that load data into them on one end, and specialized analytics tools on the other.

As Casado noted when I talked to him about the investment, data warehouses have now made it affordable for businesses to store all of their data before it is transformed. So what was traditionally “extract, transform, load” (ETL) has now become “extract, load, transform” (ELT). Andreessen Horowitz is already invested in Fivetran, which helps businesses move their data into their warehouses, so it makes sense for the firm to also tackle the other side of this business.

“Dbt is, as far as we can tell, the leading community for transformation and it’s a company we’ve been tracking for at least a year,” Casado said. He also argued that data analysts — unlike data scientists — are not really catered to as a group.

Before this round, Fishtown hadn’t raised a lot of money, even though it has been around for a few years now, except for a small SAFE round from Amplify.

But Handy argued that the company needed this time to prove that it was on to something and build a community. That community now consists of more than 1,700 companies that use the dbt project in some form and over 5,000 people in the dbt Slack community. Fishtown also now has over 250 dbt Cloud customers and the company signed up a number of big enterprise clients earlier this year. With that, the company needed to raise money to expand and also better service its current list of customers.

“We live in Philadelphia. The cost of living is low here and none of us really care to make a quadro-billion dollars, but we do want to answer the question of how do we best serve the community,” Handy said. “And for the first time, in the early part of the year, we were like, holy shit, we can’t keep up with all of the stuff that people need from us.”

The company plans to expand the team from 25 to 50 employees in 2020 and with those, the team plans to improve and expand the product, especially its IDE for data analysts, which Handy admitted could use a bit more polish.

Mar
06
2020
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Oribi brings its web analytics platform to the US

Oribi, an Israeli startup promising to democratize web analytics, is now launching in the United States.

While we’ve written about a wide range of new or new-ish analytics companies, founder and CEO Iris Shoor said that most of them aren’t built for Oribi’s customers.

“A lot of companies are more focused on the high end,” Shoor told me. “Usually these solutions are very much based on a lot of technical resources and integrations — these are the Mixpanels and Heap Analytics and Adobe Marketing Clouds.”

She said that Oribi, on the other hand, is designed for small and medium businesses that don’t have large technical teams: “They have digital marketing strategies that are worth a few hundred thousand dollars a month, they have very large activity, but they don’t have a team for it. And I would say that all of them are using Google Analytics.”

Shoor described Oribi as designed specifically “to compete with Google Analytics” by allowing everyone on the team to get the data they need without requiring developers to write new code for every event they want to track.

Event Correlations

In fact, if you use Oribi’s plugins for platforms like WordPress and Shopify, there’s no coding at all involved in the process. Apparently, that’s because Oribi is already tracking every major event in the customer journey. It also allows the team to define the conversion goals that they want to focus on — again, with no coding required.

Shoor contrasted Oribi with analytics platforms that simply provide “more and more data” but don’t help customers understand what to do with that data.

“We’ve created something that is much more clean,” she said. “We give them insights of what’s working; in the background, we create all these different queries and correlations about which part of the funnels are broken and where they can optimize.”

There are big businesses using Oribi already — including Audi, Sony and Crowne Plaza — but the company is now turning its attention to U.S. customers. Shoor said Oribi isn’t opening an office in the United States right away, but there are plans to do so in the next year.

Sep
18
2019
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Tableau update uses AI to increase speed to insight

Tableau was acquired by Salesforce earlier this year for $15.7 billion, but long before that, the company had been working on its fall update, and today it announced several new tools, including a new feature called “Explain Data” that uses AI to get to insight quickly.

“What Explain Data does is it moves users from understanding what happened to why it might have happened by automatically uncovering and explaining what’s going on in your data. So what we’ve done is we’ve embedded a sophisticated statistical engine in Tableau, that when launched automatically analyzes all the data on behalf of the user, and brings up possible explanations of the most relevant factors that are driving a particular data point,” Tableau chief product officer, Francois Ajenstat explained.

He added that what this really means is that it saves users time by automatically doing the analysis for them, and It should help them do better analysis by removing biases and helping them dive deep into the data in an automated fashion.

Explain Data Superstore extreme value

Image: Tableau

Ajenstat says this is a major improvement, in that, previously users would have do all of this work manually. “So a human would have to go through every possible combination, and people would find incredible insights, but it was manually driven. Now with this engine, they are able to essentially drive automation to find those insights automatically for the users,” he said.

He says this has two major advantages. First of all, because it’s AI-driven it can deliver meaningful insight much faster, but also it gives a more rigorous perspective of the data.

In addition, the company announced a new Catalog feature, which provides data bread crumbs with the source of the data, so users can know where the data came from, and whether it’s relevant or trustworthy.

Finally, the company announced a new server management tool that helps companies with broad Tableau deployment across a large organization to manage those deployments in a more centralized way.

All of these features are available starting today for Tableau customers.

Jul
01
2019
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Video platform Kaltura adds advanced analytics

You may not be familiar with Kaltura‘s name, but chances are you’ve used the company’s video platform at some point or another, given that it offers a variety of video services for enterprises, educational institutions and video-on-demand platforms, including HBO, Phillips, SAP, Stanford and others. Today, the company announced the launch of an advanced analytics platform for its enterprise and educational users.

This new platform, dubbed Kaltura Analytics for Admins, will provide its users with features like user-level reports. This may sound like a minor feature, because you probably don’t care about the exact details of a given user’s interactions with your video, but it will allow businesses to link this kind of behavior to other metrics. With this, you could measure the ROI of a given video by linking video watch time and sales, for example. This kind of granularity wasn’t possible with the company’s existing analytics systems. Companies and schools using the product will also get access to time-period comparisons to help admins identify trends, deeper technology and geolocation reports, as well as real-time analytics for live events.

eCDN QoS dashboard

“Video is a unique data type in that it has deep engagement indicators for measurement, both around video creation — what types of content are being created by whom, as well as around video consumption and engagement with content — what languages were selected for subtitles, what hot-spots were clicked upon in video,” said Michal Tsur, president and general manager of Enterprise and Learning at Kaltura. “Analytics is a very strategic area for our customers. Both for tech companies who are building on our VPaaS, as well as for large organizations and universities that use our video products for learning, communication, collaboration, knowledge management, marketing and sales.”

Tsur also tells me the company is looking at how to best use machine learning to give its customers even deeper insights into how people watch videos — and potentially even offer predictive analytics in the long run.

Jun
07
2019
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Google continues to preach multi-cloud approach with Looker acquisition

When Google announced it was buying Looker yesterday morning for $2.6 billion, you couldn’t blame some of the company’s 1,600 customers if they worried a bit if Looker would continue its multi-cloud approach. But Google Cloud chief Thomas Kurian made clear the company will continue to support an open approach to its latest purchase when it joins the fold later this year.

It’s consistent with the messaging from Google Next, the company’s cloud conference in April. It was looking to portray itself as the more open cloud. It was going to be friendlier to open-source projects, running them directly on Google Cloud. It was going to provide a way to manage your workloads wherever they live, with Anthos.

Ray Wang, founder and principal analyst at Constellation Research, says that in a multi-cloud world, Looker represented one of the best choices, and that could be why Google went after it. “Looker’s strengths include its centralized data-modeling and governance, which promotes consistency and reuse. It runs on top of modern cloud databases including Google BigQuery, AWS Redshift and Snowflake,” Wang told TechCrunch. He added, “They wanted to acquire a tool that is as easy to use as Microsoft Power BI and as deep as Tableau.”

Patrick Moorhead, founder and principal analyst at Moor Insights & Strategy, also sees this deal as part of a consistent multi-cloud message from Google. “I do think it is in alignment with its latest strategy outlined at Google Next. It has talked about rich analytics tools that could pull data from disparate sources,” he said.

Kurian pushing the multi-cloud message

Google Cloud CEO Thomas Kurian, who took over from Diane Greene at the end of last year, was careful to emphasize the company’s commitment to multi-cloud and multi-database support in comments to media and analysts yesterday. “We first want to reiterate, we’re very committed to maintaining local support for other clouds, as well as to serve data from multiple databases because customers want a single analytics foundation for their organization, and they want to be able to in the analytics foundation, look at data from multiple data sources. So we’re very committed to that,” Kurian said yesterday.

From a broader customer perspective, Kurian sees Looker providing customers with a single way to access and visualize data. “One of the things that is challenging for organizations in operationalizing business intelligence, that we feel that Looker has done really well, is it gives you a single place to model your data, define your data definitions — like what’s revenue, who’s a gold customer or how many servers tickets are open — and allows you then to blend data across individual data silos, so that as an organization, you’re working off a consistent set of metrics,” Kurian explained.

In a blog post announcing the deal, Looker CEO Frank Bien sought to ease concerns that the company might move away from the multi-cloud, multi-database support. “For customers and partners, it’s important to know that today’s announcement solidifies ours as well as Google Cloud’s commitment to multi-cloud. Looker customers can expect continuing support of all cloud databases like Amazon Redshift, Azure SQL, Snowflake, Oracle, Microsoft SQL Server, Teradata and more,” Bien wrote in the post.

No antitrust concerns

Kurian also emphasized that this deal shouldn’t attract the attention of antitrust regulators, who have been sniffing around the big tech companies like Google/Alphabet, Apple and Amazon as of late. “We’re not buying any data along with this transaction. So it does not introduce any concentration risk in terms of concentrating data. Secondly, there are a large number of analytic tools in the market. So by just acquiring Looker, we’re not further concentrating the market in any sense. And lastly, all the other cloud players also have their own analytic tools. So it represents a further strengthening of our competitive position relative to the other players in the market,” he explained. Not to mention its pledge to uphold the multi-cloud and multi-database support, which should show it is not doing this strictly to benefit Google or to draw customers specifically to GCP.

Just this week, the company announced a partnership with Snowflake, the cloud data warehouse startup that has raised almost a billion dollars, to run on Google Cloud Platform. It already runs AWS and Microsoft Azure. In fact, Wang suggested that Snowflake could be next on Google’s radar as it tries to build a multi-cloud soup-to-nuts analytics offering.

Regardless, with Looker the company has a data analytics tool to complement its data processing tools, and together the two companies should provide a fairly comprehensive data solution. If they truly keep it multi-cloud, that should keep current customers happy, especially those who work with tools outside of the Google Cloud ecosystem or simply want to maintain their flexibility.

Jun
06
2019
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Google to acquire analytics startup Looker for $2.6 billion

Google made a big splash this morning when it announced it’s going to acquire Looker, a hot analytics startup that’s raised more than $280 million. It’s paying $2.6 billion for the privilege and adding the company to Google Cloud.

Thomas Kurian, the man who was handed the reins to Google Cloud at the end of last year, sees the two companies bringing together a complete data analytics solution for customers. “The combination provides an end-to-end analytics platform to connect, collect, analyze and visualize data across Google Cloud, Azure, AWS, on-premises databases and ISV applications,” Kurian explained at a media event this morning.

Google Cloud has been mired in third place in the cloud infrastructure market, and grabbing Looker gives it an analytics company with a solid track record. The last time I spoke to Looker, it was announcing a hefty $103 million in funding on a $1.6 billion valuation. Today’s price is a nice even billion over that.

As I wrote at the time, Looker’s CEO Frank Bien wasn’t all that interested in bragging about valuations; he wanted to talk about what he considered more important numbers:

He reported that the company has 1,600 customers now and just crossed the $100 million revenue run rate, a significant milestone for any enterprise SaaS company. What’s more, Bien reports revenue is still growing 70 percent year over year, so there’s plenty of room to keep this going.

Today, in a media briefing on the deal, he said that from the start, his company was really trying to disrupt the business intelligence and analytics market. “What we wanted to do was disrupt this pretty staid ecosystem of data visualization tools and data prep tools that companies were being forced to build solutions. We thought it was time to rationalize a new platform for data, a single place where we could really reconstitute a single view of information and make it available in the enterprise for business purposes,” he said.

Diagram: Google & Looker

Slide: Google & Looker

Bien saw today’s deal as a chance to gain the scale of the Google cloud platform, and as successful as the company has been, it’s never going to have the reach of Google Cloud. “What we’re really leveraging here, and I think the synergy with Google Cloud, is that this data infrastructure revolution and what really emerged out of the Big Data trend was very fast, scalable — and now in the cloud — easy to deploy data infrastructure,” he said.

 

Kurian also emphasized that the company will intend to support multiple databases and multiple deployment strategies, whether multi-cloud, hybrid or on premises.

Perhaps, it’s not a coincidence that Google went after Looker as the two companies had a strong existing partnership and 350 common customers, according to Google. “We have many common customers we’ve worked with. One of the great things about this acquisition is that the two companies have known each other for a long time, we share very common culture,” Kurian said.

This is a huge deal for Google Cloud, easily topping the $625 million it paid for Apigee in 2016. It marks the first major deal in the Kurian era as Google tries to beef up its market share. While the two companies share common customers, the addition of Looker should bring a net gain that could help them upsell to other parts of the Looker customer base.

Per usual, this deal is going to be subject to regulatory approval, but it is expected to close later this year if all goes well.

May
08
2019
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Sumo Logic announces $110M Series G investment on valuation over $1B

Sumo Logic, a cloud data analytics and log analysis company, announced a $110 million Series G investment today. The company indicated that its valuation was “north of a billion dollars,” but wouldn’t give an exact figure.

Today’s round was led by Battery Ventures with participation from new investors Tiger Global Management and Franklin Templeton. Other unnamed existing investors also participated, according to the company. Today’s investment brings the total raised to $345 million.

When we spoke to Sumo Logic CEO Ramin Sayar at the time of its $75 million Series F in 2017, he indicated the company was on its way to becoming a public company. While that hasn’t happened yet, he says it is still the goal for the company, and investors wanted in on that before it happened.

“We don’t need to raise capital. We had plenty of capital already, but when you bring on crossover investors and others in this stage of a company, they have minimum check sizes and they have a lot of appetite to help you as you get ready to address a lot of the challenges and opportunities as you become a public company,” he said.

He says the company will be investing the money in continuing to develop the platform, whether that’s through acquisitions, which of course the money would help with, or through the company’s own engineering efforts.

The IPO idea remains a goal, but Sayar was not willing or able to commit to when that might happen. The company clearly has plenty of runway now to last for quite some time.

“We could go out now if we wanted to, but we made a decision that that’s not what we’re going to do, and we’re going to continue to double down and invest, and therefore bring some more capital in to give us more optionality for strategic tuck-ins and product IP expansion, international expansion — and then look to the public markets [after] we do that,” he said.

Dharmesh Thakker, general partner at investor Battery Ventures, says his firm likes Sumo Logic’s approach and sees a big opportunity ahead with this investment. “We have been tracking the Sumo Logic team for some time, and admire the company’s early understanding of the massive cloud-native opportunity and the rise of new, modern application architectures,” he said in a statement.

The company crossed the $100 million revenue mark last year and has 2,000 customers, including Airbnb, Anheuser-Busch and Samsung. It competes with companies like Splunk, Scalyr and Loggly.

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.

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