Theory and architecture
Analysis of design choices in databases and database management systems. Related subjects include:
Interana has an interesting story, in technology and business model alike. For starters:
- Interana does ad-hoc event series analytics, which they call “interactive behavioral analytics solutions”.
- Interana has a full-stack analytic offering, include:
- Its own columnar DBMS …
- … which has a non-SQL DML (Data Manipulation Language) meant to handle event series a lot more fluently than SQL does, but which the user is never expected to learn because …
- … there also are BI-like visual analytics tools that support plenty of drilldown.
- Interana sells all this to “product” departments rather than marketing, because marketing doesn’t sufficiently value Interana’s ad-hoc query flexibility.
- Interana boasts >40 customers, with annual subscription fees ranging from high 5 figures to low 7 digits.
And to be clear — if we leave aside any questions of marketing-name sizzle, this really is business intelligence. The closest Interana comes to helping with predictive modeling is giving its ad-hoc users inspiration as to where they should focus their modeling attention.
Interana also has an interesting twist in its business model, which I hope can be used successfully by other enterprise software startups as well. Read more
For starters, let me say:
- SequoiaDB, the company, is my client.
- SequoiaDB, the product, is the main product of SequoiaDB, the company.
- SequoiaDB, the company, has another product line SequoiaCM, which subsumes SequoiaDB in content management use cases.
- SequoiaDB, the product, is fundamentally a JSON data store. But it has a relational front end …
- … and is usually sold for RDBMS-like use cases …
- … except when it is sold as part of SequoiaCM, which adds in a large object/block store and a content-management-oriented library.
- SequoiaDB’s products are open source.
- SequoiaDB’s largest installation seems to be 2 PB across 100 nodes; that includes block storage.
- Figures for DBMS-only database sizes aren’t as clear, but the sweet spot of the cluster-size range for such use cases seems to be 6-30 nodes.
- SequoiaDB, the company, was founded in Toronto, by former IBM DB2 folks.
- Even so, it’s fairly accurate to view SequoiaDB as a Chinese company. Specifically:
- SequoiaDB’s founders were Chinese nationals.
- Most of them went back to China.
- Other employees to date have been entirely Chinese.
- Sales to date have been entirely in China, but SequoiaDB has international aspirations
- SequoiaDB has >100 employees, a large majority of which are split fairly evenly between “engineering” and “implementation and technical support”.
- SequoiaDB’s marketing (as opposed to sales) department is astonishingly tiny.
- SequoiaDB cites >100 subscription customers, including 10 in the global Fortune 500, a large fraction of which are in the banking sector. (Other sectors mentioned repeatedly are government and telecom.)
Unfortunately, SequoiaDB has not captured a lot of detailed information about unpaid open source production usage.
|Categories: Application areas, Business intelligence, Data models and architecture, Data warehousing, Databricks, Spark and BDAS, Market share and customer counts, NoSQL, OLTP, Open source, PostgreSQL, SequoiaDB, Structured documents||4 Comments|
Crate.io and CrateDB basics include:
- Crate.io makes CrateDB.
- CrateDB is a quasi-RDBMS designed to receive sensor data and similar IoT (Internet of Things) inputs.
- CrateDB’s creators were perhaps a little slow to realize that the “R” part was needed, but are playing catch-up in that regard.
- Crate.io is an outfit founded by Austrian guys, headquartered in Berlin, that is turning into a San Francisco company.
- Crate.io says it has 22 employees and 5 paying customers.
- Crate.io cites bigger numbers than that for confirmed production users, clearly active clusters, and overall product downloads.
In essence, CrateDB is an open source and less mature alternative to MemSQL. The opportunity for MemSQL and CrateDB alike exists in part because analytic RDBMS vendors didn’t close it off.
CrateDB’s not-just-relational story starts:
- A column can contain ordinary values (of usual-suspect datatypes) or “objects”, …
- … where “objects” presumably are the kind of nested/hierarchical structures that are common in the NoSQL/internet-backend world, …
- … except when they’re just BLOBs (Binary Large OBjects).
- There’s a way to manually define “strict schemas” on the structured objects, and a syntax for navigating their structure in WHERE clauses.
- There’s also a way to automagically infer “dynamic schemas”, but it’s simplistic enough to be more suitable for development/prototyping than for serious production.
|Categories: Columnar database management, Data models and architecture, Databricks, Spark and BDAS, GIS and geospatial, MemSQL, NoSQL, Open source, Structured documents||3 Comments|
After a July visit to DataStax, I wrote
The idea that NoSQL does away with DBAs (DataBase Administrators) is common. It also turns out to be wrong. DBAs basically do two things.
- Handle the database design part of application development. In NoSQL environments, this part of the job is indeed largely refactored away. More precisely, it is integrated into the general app developer/architect role.
- Manage production databases. This part of the DBA job is, if anything, a bigger deal in the NoSQL world than in more mature and automated relational environments. It’s likely to be called part of “devops” rather than “DBA”, but by whatever name it’s very much a thing.
That turns out to understate the core point, which is that DBAs still matter in non-RDBMS environments. Specifically, it’s too narrow in two ways.
- First, it’s generally too narrow as to what DBAs do; people with DBA-like skills are also involved in other areas such as “data governance”, “information lifecycle management”, storage, or what I like to call data mustering.
- Second — and more narrowly — the first bullet point of the quote is actually incorrect. In fact, the database design part of application development can be done by a specialized person up front in the NoSQL world, just as it commonly is for RDBMS apps.
My wake-up call for that latter bit was a recent MongoDB 3.4 briefing. MongoDB certainly has various efforts in administrative tools, which I won’t recapitulate here. But to my surprise, MongoDB also found a role for something resembling relational database design. The idea is simple: A database administrator defines a view against a MongoDB database, where views: Read more
|Categories: Databricks, Spark and BDAS, Hadoop, MongoDB, NoSQL, Streaming and complex event processing (CEP)||Leave a Comment|
“Multimodel” database management is a hot new concept these days, notwithstanding that it’s been around since at least the 1990s. My clients at MongoDB of course had to join the train as well, but they’ve taken a clear and interesting stance:
- A query layer with multiple ways to query and analyze data.
- A separate data storage layer in which you have a choice of data storage engines …
- … each of which has the same logical (JSON-based) data structure.
When I pointed out that it would make sense to call this “multimodel query” — because the storage isn’t “multimodel” at all — they quickly agreed.
To be clear: While there are multiple ways to read data in MongoDB, there’s still only one way to write it. Letting that sink in helps clear up confusion as to what about MongoDB is or isn’t “multimodel”. To spell that out a bit further: Read more
|Categories: Database diversity, Emulation, transparency, portability, MongoDB, MySQL, NoSQL, Open source, RDF and graphs, Structured documents, Text||3 Comments|
1. The cloud is super-hot. Duh. And so, like any hot buzzword, “cloud” means different things to different marketers. Four of the biggest things that have been called “cloud” are:
- The Amazon cloud, Microsoft Azure, and their competitors, aka public cloud.
- Software as a service, aka SaaS.
- Co-location in off-premises data centers, aka colo.
- On-premises clusters (truly on-prem or colo as the case may be) designed to run a broad variety of applications, aka private cloud.
Further, there’s always the idea of hybrid cloud, in which a vendor peddles private cloud systems (usually appliances) running similar technology stacks to what they run in their proprietary public clouds. A number of vendors have backed away from such stories, but a few are still pushing it, including Oracle and Microsoft.
This is a good example of Monash’s Laws of Commercial Semantics.
2. Due to economies of scale, only a few companies should operate their own data centers, aka true on-prem(ises). The rest should use some combination of colo, SaaS, and public cloud.
This fact now seems to be widely understood.
I’ve been an analyst for 35 years, and debates about “real-time” technology have run through my whole career. Some of those debates are by now pretty much settled. In particular:
- Yes, interactive computer response is crucial.
- Into the 1980s, many apps were batch-only. Demand for such apps dried up.
- Business intelligence should occur at interactive speeds, which is a major reason that there’s a market for high-performance analytic RDBMS.
- Theoretical arguments about “true” real-time vs. near-real-time are often pointless.
- What matters in most cases is human users’ perceptions of speed.
- Most of the exceptions to that rule occur when machines race other machines, for example in automated bidding (high frequency trading or otherwise) or in network security.
A big issue that does remain open is: How fresh does data need to be? My preferred summary answer is: As fresh as is needed to support the best decision-making. I think that formulation starts with several advantages:
- It respects the obvious point that different use cases require different levels of data freshness.
- It cautions against people who think they need fresh information but aren’t in a position to use it. (Such users have driven much bogus “real-time” demand in the past.)
- It covers cases of both human and automated decision-making.
Straightforward applications of this principle include: Read more
I used to spend most of my time — blogging and consulting alike — on data warehouse appliances and analytic DBMS. Now I’m barely involved with them. The most obvious reason is that there have been drastic changes in industry structure:
- Many of the independent vendors were swooped up by acquisition.
- None of those acquisitions was a big success.
- Microsoft did little with DATAllegro.
- Netezza struggled with R&D after being bought by IBM. An IBMer recently told me that their main analytic RDBMS engine was BLU.
- I hear about Vertica more as a technology to be replaced than as a significant ongoing market player.
- Pivotal open-sourced Greenplum. I have detected few people who care.
- Ditto for Actian’s offerings.
- Teradata claimed a few large Aster accounts, but I never hear of Aster as something to compete or partner with.
- Smaller vendors fizzled too. Hadapt and Kickfire went to Teradata as more-or-less acquihires. InfiniDB folded. Etc.
- Impala and other Hadoop-based alternatives are technology options.
- Oracle, Microsoft, IBM and to some extent SAP/Sybase are still pedaling along … but I rarely talk with companies that big.
Simply reciting all that, however, begs the question of whether one should still care about analytic RDBMS at all.
My answer, in a nutshell, is:
Analytic RDBMS — whether on premises in software, in the form of data warehouse appliances, or in the cloud – are still great for hard-core business intelligence, where “hard-core” can refer to ad-hoc query complexity, reporting/dashboard concurrency, or both. But they aren’t good for much else.
I visited DataStax on my recent trip. That was a tipping point leading to my recent discussions of NoSQL DBAs and misplaced fear of vendor lock-in. But of course I also learned some things about DataStax and Cassandra themselves.
On the customer side:
- DataStax customers still overwhelmingly use Cassandra for internet back-ends — web, mobile or otherwise as the case might be.
- This includes — and “includes” might be understating the point — traditional enterprises worried about competition from internet-only ventures.
Customers in large numbers want cloud capabilities, as a potential future if not a current need.
One customer example was a large retailer, who in the past was awful at providing accurate inventory information online, but now uses Cassandra for that. DataStax brags that its queries come back in 20 milliseconds, but that strikes me as a bit beside the point; what really matters is that data accuracy has gone from “batch” to some version of real-time. Also, Microsoft is a DataStax customer, using Cassandra (and Spark) for the Office 365 backend, or at least for the associated analytics.
Per Patrick McFadin, the four biggest things in DataStax Enterprise 5 are: Read more
- I spent three weeks in California on a hybrid personal/business trip. I had a bunch of meetings, but not three weeks’ worth.
- The timing was awkward for most companies I wanted to see. No blame accrues to those who didn’t make themselves available.
- I came back with a nasty cough. Follow-up phone calls aren’t an option until next week.
- I’m impatient to start writing. Hence tonight’s posts. But it’s difficult for a man and his cough to be productive at the same time.
A running list of recent posts is:
- As a companion to this post, I’m publishing a very long one on vendor lock-in.
- Spark and Databricks are both prospering, and of course enhancing their technology as well.
- Ditto DataStax.
- Flink is interesting as the streaming technology it’s now positioned to be, rather than the overall Spark alternative it used to be positioned as but which the world didn’t need.
Subjects I’d like to add to that list include:
- MemSQL, Zoomdata, and Neo Technology (also prospering).
- Cloudera (multiple topics, as usual).
- Analytic SQL engines (“traditional” analytic RDBMS aren’t doing well).
- Microsoft’s reinvention (it feels real).
- Metadata (it’s ever more of a thing).
- Machine learning (it’s going to be a big portion of my research going forward).
- Transitions to the cloud — this subject affects almost everything else.