Last June, when Microsoft announced a new infrastructure-as-a-service (IaaS) option on its Windows Azure cloud platform, it became much more competitive with Amazon EC2 as a platform for software-as-a-service (SaaS) solutions. On the NoSQL side, companies like MongoLab and Cloudant added Azure data centers as deployment targets right alongside their EC2 counterparts.
Today that list of data service providers has grown to include Redwood City, CA and Tel Aviv-based SiSense, and its Prism BI and Big Data Analytics technology. This development has impact on BI, big data, Microsoft, and the cloud in general, and they're worth pointing out.
SiSense's "ElastiCube" technology is based on a combination columnar database and data-visualization platform.
In common with in-memory technologies like SAP HANA, Prism uses column-store technology to make short work of data analysis. But since SiSense uses a hybrid disk-and-memory approach, it runs just fine on conventional servers with conventional (and more affordable) amounts of RAM. A memory-only product like HANA needs much beefier infrastructure.
SiSense Prism also competes with the likes of Tableau and QlikView, which offer data visualization and dashboarding alongside columnar data engines. But QlikView, along with Tableau when used with its built-in engine, can hit limits in scale that SiSense is not vulnerable to. Granted, Tableau can query its data sources directly to avoid such limits, but that can make things more complex, especially in the cloud.
SiSense licensing is offered on a subscription basis, featuring costs correlated with the number of users. You can install SiSense yourself, on your own servers, on Windows-based Amazon EC2 instances, and now on Windows Azure persistent Virtual Machines (VMs). SiSense will support you in all three scenarios.
SiSense's combination of subscription-based licensing and do-it-yourself (DIY) installation is somewhat unusual. Typically, SaaS-based products put a black box around the infrastructure and give you a Web-based portal to provision your service and get up and running. You can hire SiSense to make things more turn-key for you if you want them to, but the installation is pretty straightforward, especially for personnel comfortable with provisioning VMs in the cloud.
To the cloud, and back
But on the Windows Azure platform, the DIY approach has some interesting ramifications. Windows Azure VMs are based on the same format and technology as its Hyper-V on-premises virtualization platform. This means that you can set up SiSense in an on-prem virtualized environment, then move it to the cloud. Or you could do the reverse. Or, by cloning a VM image, you could do both. Or you could change your mind and migrate between cloud and data center, repeatedly.
The plot thickens. One issue with BI and big-data analytics in the cloud is that getting the data to the cloud can be slow and difficult. But for SiSense customers who are already on the Windows Azure platform, and who are keeping some or all of their data in Azure SQL Database (Or SQL Server on an Azure VM), SiSense can read that data quickly. And if the SiSense VM is provisioned in the same data center as the source database(s), customers should be able to avoid data egress charges as well. (Note that this is my own conclusion, and not a selling point conveyed by SiSense.)
So SiSense offers in-memory analytics, with on-prem-to-cloud and cloud-to-on-prem portability, and because it's not a black box SaaS offering, it can take advantage of data locality and the performance and economic benefits therein.
What would Redmond do?
But what about Microsoft? Its PowerPivot and Analysis Services Tabular mode engines offer column store, in-memory analytics much like SiSense's and its Power View technology offers interactive data visualization and exploration, which is not unlike SiSense's. But Microsoft doesn't offer PowerPivot and Power View as a service on Azure, and yet here it is partnering with SiSense to offer that company's competing offering. What gives?
If we put aside the fact that SiSense's VP of Marketing, Bruno Aziza, was for many years on the BI team at Microsoft, which I think we should, then there are a couple of important conclusions to draw:
The IaaS push on Windows Azure is all about agnosticism: You can run most major NoSQL databases instead of Azure SQL Database, and you can do so on Linux as well as Windows. You can host applications written in any number of programming languages (including Java, Node.js, Python, and PHP), not just code created in Microsoft's .net development environment. And now you can run competing analytics stacks too. Why not? Windows Azure seeks to be a general-purpose infrastructure environment. To be competitive, it has to do that.
The Microsoft Data Platform Group, which is an outgrowth of the SQL Server team, is embracing interoperability. Use Microsoft analytics products with non-Microsoft data sources, or use non-Microsoft analytics products with SQL Database and SQL Server as data sources. You'll need SQL Server licenses or SQL Database subscriptions or HDInsight/Hadoop services in any case. It's all good.
What does this mean for the big-data analytics world? First, analytics is going mainstream, and can be OpEx funded through subscription offerings. Second, analytics in the cloud is becoming very practical and can be co-located with transactional databases in the cloud, thus mitigating issues of broadband limits and latency.
Microsoft is still entering the big-data arena. SiSense has fewer than 50 employees. The partnership may seem of small significance. But its ramifications are big.