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Microsoft’s Azure Synapse Analytics bridges the gap between data lakes and warehouses
At its annual Ignite conference in Orlando, Fla., Microsoft today announced a major new Azure service for enterprises: Azure Synapse Analytics, which Microsoft describes as “the next evolution of Azure SQL Data Warehouse.” Like SQL Data Warehouse, it aims to bridge the gap between data warehouses and data lakes, which are often completely separate. Synapse also taps into a wide variety of other Microsoft services, including Power BI and Azure Machine Learning, as well as a partner ecosystem that includes Databricks, Informatica, Accenture, Talend, Attunity, Pragmatic Works and Adatis. It’s also integrated with Apache Spark.
The idea here is that Synapse allows anybody working with data in those disparate places to manage and analyze it from within a single service. It can be used to analyze relational and unstructured data, using standard SQL.
Microsoft also highlights Synapse’s integration with Power BI, its easy to use business intelligence and reporting tool, as well as Azure Machine Learning for building models.
With the Azure Synapse studio, the service provides data professionals with a single workspace for prepping and managing their data, as well as for their big data and AI tasks. There’s also a code-free environment for managing data pipelines.
As Microsoft stresses, businesses that want to adopt Synapse can continue to use their existing workloads in production with Synapse and automatically get all of the benefits of the service. “Businesses can put their data to work much more quickly, productively, and securely, pulling together insights from all data sources, data warehouses, and big data analytics systems,” writes Microsoft CVP of Azure Data, Rohan Kumar.
In a demo at Ignite, Kumar also benchmarked Synapse against Google’s BigQuery. Synapse ran the same query over a petabyte of data in 75% less time. He also noted that Synapse can handle thousands of concurrent users — unlike some of Microsoft’s competitors.
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Microsoft Azure gets into ag tech with the preview of FarmBeats
At its annual Ignite event in Orlando, Fla., Microsoft today announced that Azure FarmBeats, a project that until now was mostly a research effort, will be available as a public preview and in the Azure Marketplace, starting today. FarmBeats is Microsoft’s project that combines IoT sensors, data analysis and machine learning.
“The goal of FarmBeats is to augment farmers’ knowledge and intuition about their own farm with data and data-driven insights,” Microsoft explained in today’s announcement. The idea behind FarmBeats is to take in data from a wide variety of sources, including sensors, satellites, drones and weather stations, and then turn that into actionable intelligence for farmers, using AI and machine learning.
In addition, FarmBeats also wants to be somewhat of a platform for developers who can then build their own applications on top of this data that the platform aggregates and evaluates.
As Microsoft noted during the development process, having satellite imagery is one thing, but that can’t capture all of the data on a farm. For that, you need in-field sensors and other data — yet all of this heterogeneous data then has to be merged and analyzed somehow. Farms also often don’t have great internet connectivity. Because of this, the FarmBeats team was among the first to leverage Microsoft’s efforts in using TV white space for connectivity and, of course, Azure IoT Edge for collecting all of the data.
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Kadena brings free private blockchain service to Azure Marketplace
The hype around blockchain seems to have cooled a bit, but companies like Kadena have been working on enterprise-grade solutions for some time, and continue to push the technology forward. Today, the startup announced that Kadena Scalable Permissioned Blockchain on Azure is available for free in the Azure Marketplace.
Kadena co-founder and CEO Will Martino says today’s announcement builds on the success of last year’s similar endeavor involving AWS. “Our private chain is designed for enterprise use. It’s designed for being high-performance and for integrating with traditional back ends. And by bringing that chain to AWS marketplace, and now to Microsoft Azure, we are servicing almost all of the enterprise blockchain market that takes place in the cloud,” Martino told TechCrunch.
The free product enables companies to get comfortable with the technology and build a Proof of Concept (PoC) without making a significant investment in the tooling. The free tool provides 2,000 transactions a second across four nodes. Once companies figure this out and want to scale, that’s when the company begins making money, but Martino recognizes that the technology is still immature and companies need to get comfortable with it, and that’s what the free versions on the cloud platforms like Azure are encouraging.
Martino says Kadena favors a hybrid approach to enterprise blockchain that combines public and private chains, and in his view, gives customers the best of both worlds. “You can run a smart contract on our public Chainweb protocol that will be launching on October 30th, and that smart contract can be linked to a cluster of private permission chain nodes that are running the other half of the application. This allows you to have all of the market access and openness and transparency and ownerlessness of a public network, while also having the control and the security that you find in a private network,” he said.
Martino and co-founder Stuart Popejoy both worked on early blockchain projects at JPMorgan, but left to start Kadena in 2016. The company has raised $14.9 million to date.
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Microsoft Azure CTO Mark Russinovich will join us for TC Sessions: Enterprise on September 5
Being the CTO for one of the three major hypercloud providers may seem like enough of a job for most people, but Mark Russinovich, the CTO of Microsoft Azure, has a few other talents in his back pocket. Russinovich, who will join us for a fireside chat at our TechCrunch Sessions: Enterprise event in San Francisco on September 5 (p.s. early-bird sale ends Friday), is also an accomplished novelist who has published four novels, all of which center around tech and cybersecurity.
At our event, though, we won’t focus on his literary accomplishments (except for maybe his books about Windows Server) as much as on the trends he’s seeing in enterprise cloud adoption. Microsoft, maybe more so than its competitors, always made enterprise customers and their needs the focus of its cloud initiatives from the outset. Today, as the majority of enterprises is looking to move at least some of their legacy workloads into the cloud, they are often stumped by the sheer complexity of that undertaking.
In our fireside chat, we’ll talk about what Microsoft is doing to reduce this complexity and how enterprises can maximize their current investments into the cloud, both for running new cloud-native applications and for bringing legacy applications into the future. We’ll also talk about new technologies that can make the move to the cloud more attractive to enterprises, including the current buzz around edge computing, IoT, AI and more.
Before joining Microsoft, Russinovich, who has a Ph.D. in computer engineering from Carnegie Mellon, was the co-founder and chief architect of Winternals Software, which Microsoft acquired in 2006. During his time at Winternals, Russinovich discovered the infamous Sony rootkit. Over his 13 years at Microsoft, he moved from Technical Fellow up to the CTO position for Azure, which continues to grow at a rapid clip as it looks to challenge AWS’s leadership in total cloud revenue.
Tomorrow, Friday, August 16 is your last day to save $100 on tickets before prices go up. Book your early-bird tickets now and keep that Benjamin in your pocket.
If you’re an early-stage startup, we only have three demo table packages left! Each demo package comes with four tickets and a great location for your company to get in front of attendees. Book your demo package today before we sell out!
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Microsoft Azure now lets you have a server all to yourself
Microsoft today announced the preview launch of Azure Dedicated Host, a new cloud service that will allow you to run your virtual machines on single-tenant physical services. That means you’re not sharing any resources on that server with anybody else and you’ll get full control over everything that’s running on that machine.
Previously, Azure already offered isolated Virtual Machine sizes for two very large virtual machine types. Those are still available, but their use cases are comparably limited to these new hosts, which offer far more flexibility.
With this move, Microsoft is following in the footsteps of AWS, which also offers Dedicated Hosts with very similar capabilities. Google Cloud, too, offers what it calls “sole-tenant nodes.”
Azure Dedicated Host will support Windows, Linux and SQL Server virtual machines and pricing is per host, independent of the number of virtual machines you end up running on them. You can currently opt for machines with up to 48 physical cores and prices start at $4.039 per hour.
To do this, Microsoft is offering two different processors to power these machines. Type 1 is based on the 2.3 GHz Intel Xeon E5-2673 v4 with up to 3.5 gigahertz of clock speed, while Type 2 features the Intel Xeon® Platinum 8168 with single-core clock speeds of up to 3.7 gigahertz. The available memory ranges from 144GiB to 448GiB. You can find more details here.
As Microsoft notes, these new dedicated hosts can help companies reach their compliance requirements for physical security, data integrity and monitoring. The dedicated hosts still share the same underlying infrastructure as any other host in the Azure data centers, but users have full control over any maintenance window that could impact their servers.
These dedicated hosts can also be grouped into larger host groups in a given Azure region, allowing you to build clusters of your own physical servers inside the Azure data center. Because you’re actually renting a physical machine, any hardware issue on that machine will impact the virtual machines you are running on them, so chances are you’ll want to have multiple dedicated hosts for your failover strategy anyway.
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AWS remains in firm control of the cloud infrastructure market
It has to be a bit depressing to be in the cloud infrastructure business if your name isn’t Amazon. Sure, there’s a huge, growing market, and the companies behind Amazon are growing even faster. Yet it seems no matter how fast they grow, Amazon remains a dot on the horizon.
It seems inconceivable that AWS can continue to hold sway over such a large market for so long, but as we’ve pointed out before, it has been able to maintain its position through true first-mover advantage. The other players didn’t even show up until several years after Amazon launched its first service in 2006, and they are paying the price for their failure to see the way computing would change the way Amazon did.
They certainly see it now, whether it’s IBM, Microsoft or Google, or Tencent and Alibaba, both of which are growing fast in the China/Asia markets. All of these companies are trying to find the formula to help differentiate themselves from AWS and give them some additional market traction.
Cloud market growth
Interestingly, even though companies have begun to move with increasing urgency to the cloud, the pace of growth slowed a bit in the first quarter to a 42 percent rate, according to data from Synergy Research, but that doesn’t mean the end of this growth cycle is anywhere close.
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Microsoft launches a drag-and-drop machine learning tool
Microsoft today announced three new services that all aim to simplify the process of machine learning. These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users.
Getting started with machine learning is hard. Even to run the most basic of experiments takes a good amount of expertise. All of these new tools greatly simplify this process by hiding away the code or giving those who want to write their own code a pre-configured platform for doing so.
The new interface for Azure’s automated machine learning tool makes creating a model as easy as importing a data set and then telling the service which value to predict. Users don’t need to write a single line of code, while in the backend, this updated version now supports a number of new algorithms and optimizations that should result in more accurate models. While most of this is automated, Microsoft stresses that the service provides “complete transparency into algorithms, so developers and data scientists can manually override and control the process.”
For those who want a bit more control from the get-go, Microsoft also today launched into preview a visual interface for its Azure Machine Learning service that will allow developers to build, train and deploy machine learning models without having to touch any code.
This tool, the Azure Machine Learning visual interface, looks suspiciously like the existing Azure ML Studio, Microsoft’s first stab at building a visual machine learning tool. Indeed, the two services look identical. The company never really pushed this service, though, and almost seemed to have forgotten about it despite the fact that it always seemed like a really useful tool for getting started with machine learning.
Microsoft says this new version combines the best of Azure ML Studio with the Azure Machine Learning service. In practice, this means that while the interface is almost identical, the Azure Machine Learning visual interface extends what was possible with ML Studio by running on top of the Azure Machine Learning service and adding that services’ security, deployment and life cycle management capabilities.
The service provides an easy interface for cleaning up your data, training models with the help of different algorithms, evaluating them and, finally, putting them into production.
While these first two services clearly target novices, the new hosted notebooks in Azure Machine Learning are clearly geared toward the more experienced machine learning practitioner. The notebooks come pre-packaged with support for the Azure Machine Learning Python SDK and run in what the company describes as a “secure, enterprise-ready environment.” While using these notebooks isn’t trivial either, this new feature allows developers to quickly get started without the hassle of setting up a new development environment with all the necessary cloud resources.
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Microsoft brings Azure SQL Database to the edge (and Arm)
Microsoft today announced an interesting update to its database lineup with the preview of Azure SQL Database Edge, a new tool that brings the same database engine that powers Azure SQL Database in the cloud to edge computing devices, including, for the first time, Arm-based machines.
Azure SQL Edge, Azure corporate vice president Julia White writes in today’s announcement, “brings to the edge the same performant, secure and easy to manage SQL engine that our customers love in Azure SQL Database and SQL Server.”
The new service, which will also run on x64-based devices and edge gateways, promises to bring low-latency analytics to edge devices as it allows users to work with streaming data and time-series data, combined with the built-in machine learning capabilities of Azure SQL Database. Like its larger brethren, Azure SQL Database Edge will also support graph data and comes with the same security and encryption features that can, for example, protect the data at rest and in motion, something that’s especially important for an edge device.
As White rightly notes, this also ensures that developers only have to write an application once and then deploy it to platforms that feature Azure SQL Database, good old SQL Server on premises and this new edge version.
SQL Database Edge can run in both connected and fully disconnected fashion, something that’s also important for many use cases where connectivity isn’t always a given, yet where users need the kind of data analytics capabilities to keep their businesses (or drilling platforms, or cruise ships) running.
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Much to Oracle’s chagrin, Pentagon names Microsoft and Amazon as $10B JEDI cloud contract finalists
Yesterday, the Pentagon announced two finalists in the $10 billion, decade-long JEDI cloud contract process — and Oracle was not one of them. In spite of lawsuits, official protests and even back-channel complaining to the president, the two finalists are Microsoft and Amazon.
“After evaluating all of the proposals received, the Department of Defense has made a competitive range determination for the Joint Enterprise Defense Infrastructure Cloud request for proposals, in accordance with all applicable laws and regulations. The two companies within the competitive range will participate further in the procurement process,” Elissa Smith, DoD spokesperson for Public Affairs Operations told TechCrunch. She added that those two finalists were in fact Microsoft and Amazon Web Services (AWS, the cloud computing arm of Amazon).
This contract procurement process has caught the attention of the cloud computing market for a number of reasons. For starters, it’s a large amount of money, but perhaps the biggest reason it had cloud companies going nuts was that it is a winner-take-all proposition.
It is important to keep in mind that whether it’s Microsoft or Amazon that is ultimately chosen for this contract, the winner may never see $10 billion, and it may not last 10 years, because there are a number of points where the DoD could back out — but the idea of a single winner has been irksome for participants in the process from the start.
Over the course of the last year, Google dropped out of the running, while IBM and Oracle have been complaining to anyone who will listen that the contract unfairly favored Amazon. Others have questioned the wisdom of even going with a single-vendor approach. Even at $10 billion, an astronomical sum to be sure, we have pointed out that in the scheme of the cloud business, it’s not all that much money — but there is more at stake here than money.
There is a belief here that the winner could have an upper hand in other government contracts, that this is an entrée into a much bigger pot of money. After all, if you are building the cloud for the Department of Defense and preparing it for a modern approach to computing in a highly secure way, you would be in a pretty good position to argue for other contracts with similar requirements.
In the end, in spite of the protests of the other companies involved, the Pentagon probably got this right. The two finalists are the most qualified to carry out the contract’s requirements. They are the top two cloud infrastructure vendors on the market, although Microsoft is far behind with around 13 or 14 percent market share. Amazon is far head, with around 33 percent, according to several companies that track such things.
Microsoft in particular has tools and resources that would be very appealing, especially Azure Stack — a mini private version of Azure, that you can stand up anywhere, an approach that would have great appeal to the military — but both companies have experience with government contracts, and both bring strengths and weaknesses to the table. It will undoubtedly be a tough decision.
In February, the contract drama took yet another turn when the department reported it was investigating new evidence of conflict of interest by a former Amazon employee who was involved in the RFP process for a time before returning to the company. Smith reports that the department found no such conflict, but there could be some ethical violations they are looking into.
“The department’s investigation has determined that there is no adverse impact on the integrity of the acquisition process. However, the investigation also uncovered potential ethical violations, which have been further referred to DOD IG,” Smith explained.
The DoD is supposed to announce the winner this month, but the drama has continued non-stop.
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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.
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