- Gartner produced a database magic quadrant that places all manner of entities in strange comparisons.
- Gartner’s DMSA and ODMS MQs make no sense without $$$.
Gartner produces highly unusual Magic Quadrants. One is called the Data Management System and Analytics (DMSA), and the other the Operational Data Management System Magic Quadrant (DMSA) MQ. In this article, we will review these bizarre MQs and assign an accuracy rating.
Gartner’s DMSA MQ
Because of these facts, it makes it confusing what type of database is being compared. As we will discuss, that is only the beginning of the problem with this MQ.
These are not all of the database types, but they are some of the major ones.
- In Memory
If you look at how DB Engines lists databases, they always declare the database type.
See the Database Model column. DB Engines lists the type of database or declares whether it is a mixed type. Some databases can combine more than one type into one database.
Hmmmmm…..Different Database Types for Different Purposes?
The different database types apply for different purposes. For this reason, they aren’t directly comparable unless they are in the same or a related type. We have gone through a period where the number of database types has increased significantly. This will also mean a less dominant use of RDBMSs in the future. A significant factor that is driving this increase in database types being accessed is the DaaS providers like AWS, Google Cloud, and Azure that allow customers to spin up databases and test them at a very low cost. If the database is found to not be desirable for the purpose, the database can be quickly deactivated, and the charges then cease.
The DMSA MQ
Now let us review how Gartner decided to layout the MQ.
Two problems become apparent immediately. One is that the databases being compared is unclear. Second, why are different database types listed in the same MQ in the first place?
Which Database Is Being Compared?
The MQ includes a jumble of vendors, except in only a few cases the database itself is not mentioned. For example, we can guess that when Gartner states Oracle, they are referring to the Oracle RDMBS DB (Oracle 18, Oracle 12, or Oracle 11). Oracle also has several other databases, but Oracle is only dominant in one database type, which is the relational/RDBMS. There is just a stunning lack of specificity in this MQ.
The Databases in the Magic Quadrant
The most desirable quadrant is the upper right. This is the so-called “magic” quadrant.
If we stay up where Oracle is, we can see Teradata. Teradata also is categorized as an RDBMS, but it is highly specialized. Teradata’s specialty is called massively parallel processing, and if a customer is not looking for a data warehouse, Teradata is not going to be used. Oracle’s RDBMS, on the other hand, is also used for data warehouses, but it is a more general RDBMS. Therefore, it makes sense to compare Oracle RDBMS to Teradata as it has an RBDMS, but not without mentioning the context or the application the database is being put towards.
Microsoft’s primary database is SQL Server. This is another RDBMS, and like Oracle RDBMS is highly generalized in its use. Then we move to IBM, which has DB2 and Informix, which are both RDBMSs. DB2 is far more widely used than Informix, so is Gartner discussing DB2 here or Informix? We will assume its DB2.
The next two closest companies are AWS and SAP. AWS is a particularly problematic comparison. AWS has its databases like DynamoDB, Aurora, and Redshift, but then it provides services for a long list of databases. Here the contrast created by Gartner ultimately falls apart. Not only does AWS provide its databases, but it hosts the other databases in the MQ!
- So if the Oracle database is brought up on AWS, then what is AWS’s rating?
- Oracle also tries to get companies to place the Oracle DB on Oracle Cloud. But the Oracle Cloud is abysmal, and there are more Oracle DB instances on AWS than Oracle Cloud.
- So if AWS is being included in the analysis, and AWS is primarily a database service provider, then what is Gartner comparing exactly?
The top right “magic” quadrant is rounded out by SAP that offers HANA and Adaptive Server (which SAP barely markets versus HANA but which is more widely used than HANA). Both of these are RBDMS, with HANA being a hybrid of RDBMS + column store/in-memory. They also offer..
- SQL Anywhere
- Advantage Database Server. (Adaptive Server, SQL Anywhere, IQ, and Advantage Database Server were all Sybase DBs.)
The last three of which have a niche market share.
So what can be taken from looking at what Gartner values?
Well, if we exclude AWS as an outlier to the group, Gartner thinks that RDBMSs are the best type of database for “data management and analytics.” How do we know this? This can be surmised from the fact that most of the RDBMSs in the MQ is in the top right quadrant, and 100% of the entities listed in the top right “magic” quadrant are RDBMS vendors, with Teradata being a particular case a provides a specialized data warehouse database.
The Bottom Right Quadrant
Let us look at the bottom right quadrant.
The only entity in the bottom right is MarkLogic. MarkLogic a very niche database, and as there are only 20 entries in the MQ and therefore it seems odd that it would be included in the MQ.
The Top Left Quadrant
Now let us look at the companies in the top left of the MQ.
MemSQL is not a frequently discussed database. This should be unsurprising as it is the 73rd most popular DB, according to DB Engines. Like 1010data, which we will discuss in a moment, it is a database with a very low profile. Hewlett Packard Enterprise is simply a consulting company. Yet, according to Gartner, they are the 9th most essential or magic “database” that should be in an MQ? HPE’s website shows their products as servers, data storage, software, and applications. Under software, they offer..
- Hybrid cloud management software
- Infrastructure management software
- Network management software
- Server management software
They also claim to have the most SAP HANA customers at 2,400. They offer hardware for the Oracle DB, and they offer hardware and database as a service for Azure. That is all very nice, but why is HPE in this MQ? It offers Stonebraker’s Vertica that they bought in 2011, which according to DB engines, is the 27th most widely used database. Interestingly Vertica is not prominently mentioned on the HPE website.
Cloudera one of several vendors that add on to the Hadoop software ecosystem (Hadoop is not one thing or a database, but rather a constellation of database things). Google is like AWS, a database as a service that also offers proprietary databases like BigQuery and BigTable. A DaaS that provides a variety of databases cannot be a Magic Quadrant that is designed to compare different databases. Again, the question of what database being compared comes up. What database is Google? What database is Google Cloud is the correct question, as Google is a search engine, Google Cloud is what offers database as a service to customers.
The Bottom Left Quadrant
Now let us look at the companies in the bottom left or the undesirable quadrant of the MQ. We will call this the WQ or worst quadrant.
Snowflake only does data warehouses and competes with Teradata. And Snowflake is the 119th most widely used database according to DB Engines. So should they be profiled in an MQ that has only 20 entries? Well, let us take a look into that, shall we?
Snowflake has raised hundreds of millions of dollars, including $480 million in just 2018, and with that money, you can buy full coverage. Furthermore, Snowflake is rumored to be preparing for an IPO, and the Gartner rating will be used to promote Snowflake with potential investors. This makes Snowflake “highly motivated.” I was recently reading some complimentary articles about Snowflake in Forbes and know that Forbes takes paid placements. It got me thinking about whether are articles were paid placements.
Pivotal is a Hadoop+ provider. MongoDB is an open-source document NoSQL database, but it is also a traded public company.
According to DB Engines, 1010data is the 142nd most popular database in general use, and we had never heard of 1010data before reading this MQ. 1010data “smells” very much like a company that was included because they brought enough cash to the table. 1010data is below HAWQ and above Infobright in DB Engines’ listing, but neither Infobright nor HAWQ nor any of the other databases around them are in the MQ. None of the databases down this low are in the MQ.
Well, HAWQ is an open-source database. Infobright is private, but who knows if they were willing to pay. What can explain the selective inclusion of a database like 1010data? Again, 1010data is a very, very lightly used database. How do they rate a ranking in the top 20 of the MQ?
Hortonworks and MapR Technologies are Hadoop+, EDB is a PostgreSQL DaaS, and Huawei and Transwarp Technologies are both consulting companies that are not known to have any offering or addition to the databases they consult with. And the question arises again, why are consulting companies that are a) not databases, and b) not known even to offer add-ons to databases in the top 20 most important database MQ?
The Descriptive Statistics to the DMSA MQ
The following statistics apply to the MQ.
- 5/8 RDBMS or 62% of the RDBMSs in the MQ are in the upper right quadrant, or the magic and most desirable quadrant.
- Two entries that have RDBMS scored in the worst of the bottom left quadrant. One was an open-source DaaS. And the other is 1010data, which is barely discussed as a database.
- 6/8 NoSQL or 75% of the non-RDBMSs (which includes NoSQL, Hadoop, and Document) are in the lower right or least desirable quadrant.
- 2/20 or 10% of the entries (AWS and Google Cloud) are DaaS providers that have a large number of databases and cannot be placed into an MQ like this.
- 1/20 or 5% of the entries are a single database DaaS (EDB), but the comparison should have been PostgreSQL, not EDB. EDB is not a database, it is an access provider, and value-added add on provider PostgreSQL.
- 14/18 entries (AWS and Google Cloud are removed from this calculation) or 77% of the entries are either associated with an RDBMS or Hadoop. This understates the variety of database types being used.
- Only 0/20 or 0% of the entries, is a straight open-source database project. Other open-source entries are not the project itself, but one of the providers of value-added services for PostgreSQL.
Why Are Different Database Types in the Same MQ?
No distinction is drawn in database type in this MQ. There are many different types of databases, and they can’t be compared to one another as they don’t do the same thing. In this MQ, we have MongoDB, which is a NoSQL DB with SAP, whose primary database is HANA, which is a mixture of column-oriented and row-oriented design. (But here again, SAP still sells ex-Sybase databases like SAP IQ and the Adaptive Server. Adaptive Server is more popular, so perhaps we should state Adaptive Server is the database.). How can MongoDB, or Cloudera is a distribution of Hadoop, be compared in an MQ against HANA? These databases have different uses and designs.
What the DMSA MQ Tells Us
From this MQ, it is clear that…
- Gartner does not like non-RDBMS entries.
- Gartner does not like open source. Open-source databases are becoming increasingly popular, but Gartner does not seem to notice. No money
- Gartner has no problem placing highly illogical entries like MemSQL, 1010data, Huawei, and Transwarp into their MQ if those entities are willing to pay.
- Very few databases are specifically mentioned. We categorized the entries as Database Vendor, Database as a Service, Consulting Company, and Open Source Database.
The Method of Gartner’s Madness
In one were to analyze this MQ without understanding how Gartner works, the impression is that the overall MQ is entirely insane. But looked at from another way, it makes perfect sense. Gartner receives around 1/3 of their income from vendors. Some of the most commonly used databases are now open source. However, an open-source project cannot pay Gartner anything. This is why Gartner cannot follow where databases are going. The growth is in non-RDBMS and open source. Therefore instead of rating PostgreSQL (that can’t pay), Gartner rates EBS (which can pay). The same applies to the Hadoop entries. Hadoop is managed as an open-source project. Therefore, Gartner will not rate Hadoop as a database or grouping of database components, but instead evaluate companies that charge money on top of Hadoop like Cloudera, MapR Technologies, and Pivotal. To Gartner, even entities that are purely consulting companies like Huawei, Transwarp, and HPE (Vertica) warrant inclusion over highly popular databases Redis or Elasticsearch, because the former entities are multinationals who can pay. In contrast, the later entities are open source projects — but actual databases.
Gartner’s ODMS Magic Quadrant
Gartner has another database-oriented MQ called the ODMS MQ. This uses the term operational data management system. Reviewing the 2015 version versus the 2017 version of this MQ is instructive.
One could go through the same exercise we just went through with the DMSA MQ that we just did with the OMDS MQ.
Notice the listing of exceptionally lightly used databases like Altibase (they just went open source in 2018, but were closed source at the time of this publication) and Clustrix, which is ranked 181st by DB Engines. Again, the preference for listing companies rather than databases continues.
Now let us review the same MQ but for 2017.
The 2015 version of the ODMS MQ has 31 entries. But by 2017 it drops to 11. The open-source entries are all gone, even though three of them performed very well in the 2015 version of the MQ.
This brings up an interesting question.
How can an MQ have so 2/3rds of its entrants disappear in just two years? Three entrants that were in the upper right quadrant in 2015 are not anywhere in the MQ in 2017.
Advice on Enjoying the Quiz
To see the full screen just select the lower right-hand corner and expand. Trust us, expanding makes the experience a whole lot more fun.
There is a serious problem when such MQs with so many glaring inconsistencies can be published without invoking widespread ridicule. When one researches databases, one is constantly confronted with how well an entity performed in the Gartner MQ listing, but without any questioning, whether the Gartner MQ is anything more than an ability to pay.
Gartner has a self-explanatory method on how to scores entities in its MQ.
- Gartner figures out how much each entity paid.
- Gartner then moves the dots around to make it look like it is not organizing the dots based on this.
- Gartner throws in occasional misdirection entries like MariaDB or MongoDB, that did not pay Gartner, or perhaps paid little. Without some entries that are known to be good but will not or cannot pay, Gartner could not maintain the illusion that the MQ has any legitimacy.
- Gartner then places generalized company profiles below the MQ, which does not give little insight as to why the entries score as they did in the MQ. As Gartner says, “90% of the analysis is not written down.” This gives the incentive to customers to sign up for one on one briefings with the analyst. Therefore the objective of the MQ is to make them impenetrable or not to make sense. That way, the customer/buyer has to reach out. Academic research or any research works in the opposite manner of this. When I read academic research (or any research for that matter), it is not required to reach out to the researcher to ask them, “what are you talking about.” But with Gartner, that is how they make their money. Therefore, the more impossible the MQ, in a way, the better.
The DMSA and ODMS MQ by Gartner makes absolutely no sense from the database perspective and can only be understood from the standpoint of how Gartner receives its funding, where everything falls into line, and it makes perfect sense.
These MQs receive an accuracy rating of 1 out of 10. It is useless for decision making concerning DBs. But it does tell us who is paying Gartner to be included in this MQ.
The Problem: Thinking that Gartner is Focused on What is True
Gartner is hired by companies who fundamentally don’t understand how Gartner functions. Gartner has virtually no first-hand experience in the technologies that they evaluate and get most of their information from speaking with executives at buyers or executives at vendors and consulting firms. Gartner is also not a research entity. They compare very poorly to real research entities once you dig into the details as we did in the article How Gartner’s Research Compares to Real Research Entities. Gartner serves to direct IT spending to the most expensive solutions as these are the companies that can afford to pay Gartner the most money. Gartner has enormously aggressive internal sales goals that place accuracy far below revenue growth in importance.
Financial Bias Disclosure
Neither this article nor any other article on the Brightwork website is paid for by a software vendor, including Oracle, SAP or their competitors. As part of our commitment to publishing independent, unbiased research; no paid media placements, commissions or incentives of any nature are allowed.
Advice on Enjoying the Multimedia Presentations
To see the full screen just select the lower right-hand corner and expand. Trust us, expanding makes the experience a whole lot more fun.
Gartner is the most influential IT analyst firm in the world. Their approval can make or break a vendor in an application category, or at the very least control their growth. Gartner has been behind most of the major IT trends for decades. However, many people read Gartner reports without understanding how Gartner works, how it comes to its information, its orientation, or even the details of the methods it uses for its analytical products. All of this and more is explained in this book.
Table of Contents
- Chapter 1: Introduction
- Chapter 2: An Overview of Gartner
- Chapter 3: How Gartner Makes Money
- Chapter 4: Comparing Gartner to the RAND Corporation, and Academic Research
- Chapter 5: The Magic Quadrant
- Chapter 6: Other Analytical Products Offered by Gartner
- Chapter 7: Gartner’s Future and Cloud Computing
- Chapter 8: Adjusting the Magic Quadrant
- Chapter 9: Is Gartner Worth the Investment?
- Chapter 10: Conclusion
- Appendix a: How to Use Independent Consultants for Software Selection
- Appendix b: What Does the History of Media Tell Us About This Topic
- Appendix c: Disclosure Statements and Code of Ethics