The Era of Microsoft on Windows-Only Is Over – OMG

Written by Donald Feinberg and Merv Adrian

On 25-Sep-2017 at Ignite, Microsoft announced general availability of SQL Server 2017, now supporting both Windows and Linux platforms, as well as support for containers. It can now book revenue for a product already widely used by early release customers.

What does this imply for the $34.4 billion database management system (DBMS) Market? Over the years, Microsoft has grown SQL Server revenue substantially, capturing over 20 percent of the DBMS market without a Linux offering. Few thought we would see the day where a major Microsoft software product would run on anything other than Windows.

Microsoft SQL Server started life as Sybase SQL Server. In 1988, Microsoft acquired joint rights on x86 and called it SQL Server. In 1993, the partnership was dissolved and Microsoft retained SQL Server and developed it independently of Sybase, running on x86 and Windows OS. SAP ASE, formerly Sybase ASE, (Sybase was acquired by SAP in 2010) shares the procedural language Transact-SQL (T-SQL) with SQL Server.

Linux support has been a long time in coming. Both of us were in (separate) meetings at Microsoft 10 or 12 years ago, where we suggested that SQL Server be ported to Linux. The notion was met by the senior management of the then Server & Tools Group (STG) with strong disagreement (and several “expletives deleted.”). Our premise then – and still – was that this would position SQL Server as a portable DBMS, boosting sales, offering more addressable market to compete in. Customers would know they could move to Linux if desired, removing the notion of lock-in to the Windows Server OS.

Today, SQL Server runs on Windows and Linux – and containers (Docker and Kubernetes), putting it on an equal footing with other DBMS products. It supports Availability Groups that span both OSs, enhancing cross-OS testing and migration projects. Microsoft claims over 2 million Docker pulls of SQL Server 2017 for Linux since November 2016. With the generally lower pricing of SQL Server, including availability on-premises with a subscription instead of a license + maintenance, as well as pricing and discount programs including a joint marketing program with Red Hat (see Microsoft’s press release), we expect increased competition with other relational DBMS players, like IBM Db2Oracle and SAP ASE.

The momentum is clear. Gartner Software Market numbers show that Microsoft passed IBM in total DBMS revenues in 2014 and is now second only to Oracle. In 2016 overall DBMS revenues grew at 7.7 percent and Microsoft grew at 10.3 percent, strengthening its #2 position, while Oracle grew 3.3 percent – off a much larger base that includes the Linux workloads Microsoft did not compete for. With a competitively priced product that is now portable across more than one operating system, Microsoft SQL Server is positioned to gain even more market share. To further support this, SQL Server on-premises is now fully compatible to Azure SQL Database, allowing customers full flexibility in choosing the desired platform, using on-premises SQL Server licenses for Azure deployments. Its on-premises subscription pricing positions it competitively with open-source RDBMS products, with no upfront license fees. In the year ahead, competition will be more heated than is has been for years.

Perspectives on Hadoop Part Two: Pausing Plans

By Merv Adrian and Nick Heudecker 

In the first post in this series , I looked at the size of revenue streams for RDBMS software and maintenance/support and noted that they amount to $33B, pointing out that pure play Hadoop vendors had a high hill to climb. (I didn’t say so specifically, but in 2014, Gartner estimates that the three leading vendors generated less than $150M.)

In this post, Nick and I turn from Procurement to Plans and examine the buying intentions uncovered in Gartner surveys.


–more in Gartner blog–

Perspectives on Hadoop: Procurement, Plans, and Positioning

I have the privilege of working for the world’s leading information technology research and advisory company, covering information management with a strong focus for the past few years on an emerging software stack called Hadoop. In the early part of 2015, that particular technology is moving from early adopter status to early majority in its marketplace adoption. The discussions and published work around it have been exciting and controversial, so in this post (and a couple to follow) I describe three interlocking research perspectives on Hadoop: procurement (counting real money actually spent); plans (surveys of intentions to invest) and positioning (subjective interpretations of what the first two mean.)

Procurement Perspective: Hadoop is a (Very) Small Market Today

–more on Gartner blog–



Hadoop Investments Continue: Teradata, HP Jockey For Position

Interest from the leading players continues to drive investment in the Hadoop marketplace. This week Teradata made two acquisitions – Revelytix and Hadapt – that enrich its already sophisticated big data portfolio, while HP made a $50M investment in, and joined the board of, Hortonworks. These moves continue the ongoing effort by leading players. 4 of the top 5 DBMS players (Oracle, Microsoft, IBM, SAP and Teradata) and 3 of the top 7 IT companies (Samsung, Apple, Foxconn, HP, IBM, Hitachi, Microsoft) have now made direct moves into the Hadoop space. Oracle’s recent Big Data Appliance and Big Data SQL, and Microsoft’s HDInsight represent substantial moves to target Hadoop opportunities, and these Teradata and HP moves mean they don’t want to be left behind.



Aspirational Marketing and Enterprise Data Hubs

In the Hadoop community there is a great deal of talk of late about its positioning as an Enterprise Data Hub. My description of this is “aspirational marketing;” it addresses the ambition its advocates have for how Hadoop will be used, when it realizes the vision of capabilities currently in early development. There’s nothing wrong with this, but it does need to be kept in perspective. It’s a long way off.


AAA is Not Enough Security in the Big Data Era

Talk to security folks, especially network ones, and AAA will likely come up. It stands for authentication, authorization and accounting (sometimes audit). There are even protocols such as Radius (Remote Authentication Dial In User Service, much evolved from its first uses) and Diameter, its significantly expanded (and punnily named) newer cousin, implemented in commercial and open source versions, included in hardware for networks and storage. AAA is and will remain a key foundation of security in the big data era, but as a longtime information management person, I believe it’s time to acknowledge that it’s not enough, and we need a new A – anonymization.


BYOH – Hadoop’s a Platform. Get Used To It.

When is a technology offering a platform? Arguably, when people build products assuming it will be there. Or extend their existing products to support it, or add versions designed to run on it. Hadoop is there. The age of Bring Your Own Hadoop (BYOH) is clearly upon us.  Specific support for components such as Pig and Hive vary, as do capabilities and levels of partnership in development, integration and co-marketing. Some vendors are in many categories – for example, Pentaho and IBM at opposite ends of the size spectrum interact with Hadoop in development tools, data integration, BI, and other ways. A few category examples, by no means exhaustive:


Hadoop 2013 – Part One: Performance

It’s no surprise that we’ve been treated to many year-end lists and predictions for Hadoop (and everything else IT) in 2013. I’ve never been that much of a fan of those exercises, but I’ve been asked so much lately that I’ve succumbed. Herewith, the first of a series of posts on what I see as the 4 Ps of Hsdoop in the year ahead: performance, projects, platforms and players.

— more —

2013 Data Resolution: Avoid Architectural Cul-de-Sacs

I had an inquiry today from a client using packaged software for a business system that is built on a proprietary, non-relational datastore (in this case an object-oriented DBMS.) They have an older version of the product – having “failed” with a recent upgrade attempt.

The client contacted me to ask about ways to integrate this OODBMS-based system with others in their environment. They said the vendor-provided utilities were not very good and hard to use, and the vendor has not given them any confidence it will improve. The few staff programmers who have learned enough internals have already built a number of one-off connections using multiple methods, and were looking for a more generalizable way to create a layer for other systems to use when they need data from the underlying database. They expect more such requests, and foresee chaos, challenges hiring and retaining people with the right skills, and cycles of increasing cost and operational complexity.
My reply: “you’re absolutely right.”

Amazon Redshift Disrupts DW Economics – But Nothing Comes Without Costs

At its first re:Invent conference in Late November, Amazon announced Redshift, a new managed service for data warehousing. Amazon also offered details and customer examples that made AWS’  steady inroads toward enterprise, mainstream application acceptance very visible.

Redshift is made available via MPP nodes of 2TB (XL) or 16TB (8XL), running Paraccel’s high-performance columnar, compressed DBMS, scaling to 100 8XL nodes, or 1.6PB of compressed data. XL nodes have 2 virtual cores, with 15GB of memory, while 8XL nodes have 16 virtual cores and 120 GB of memory and operate on 10Gigabit ethernet.

Reserved pricing (the more likely scenario, involving a commitment of 1 year or 3 years) is set at “under $1000 per TB per year” for a 3 year commitment, combining upfront and hourly charges. Continuous, automated backup for up to 100% of the provisioned storage is free. Amazon does not charge for data transfer into or out of the data clusters. Network connections, of course, are not free  – see Doug Henschen’s Information Week story for details.

This is a dramatic thrust in pricing, but it does not come without giving up some things.