Data warehousing
Analysis of issues in data warehousing, with extensive coverage of database management systems and data warehouse appliances that are optimized to query large volumes of data. Related subjects include:
Metamarkets’ back-end technology
This is part of a three-post series:
- Introduction to Metamarkets and Druid
- Druid overview
- Metamarkets’ back-end technology (this post)
The canonical Metamarkets batch ingest pipeline is a bit complicated.
- Data lands on Amazon S3 (uploaded or because it was there all along).
- Metamarkets processes it, primarily via Hadoop and Pig, to summarize and denormalize it, and then puts it back into S3.
- Metamarkets then pulls the data into Hadoop a second time, to get it ready to be put into Druid.
- Druid is notified, and pulls the data from Hadoop at its convenience.
By “get data read to be put into Druid” I mean:
- Build the data segments (recall that Druid manages data in rather large segments).
- Note metadata about the segments.
That metadata is what goes into the MySQL database, which also retains data about shards that have been invalidated. (That part is needed because of the MVCC.)
By “build the data segments” I mean:
- Make the sharding decisions.
- Arrange data columnarly within shard.
- Build a compressed bitmap for each shard.
When things are being done that way, Druid may be regarded as comprising three kinds of servers: Read more
Metamarkets Druid overview
This is part of a three-post series:
- Introduction to Metamarkets and Druid
- Druid overview (this post)
- Metamarkets’ back-end technology
My clients at Metamarkets are planning to open source part of their technology, called Druid, which is described in the Druid section of Metamarkets’ blog. The timing of when this will happen is a bit unclear; I know the target date under NDA, but it’s not set in stone. But if you care, you can probably contact the company to get involved earlier than the official unveiling.
I imagine that open-source Druid will be pretty bare-bones in its early days. Code was first checked in early in 2011, and Druid seems to have averaged around 1 full-time developer since then. What’s more, it’s not obvious that all the features I’m citing here will be open-sourced; indeed, some of the ones I’m describing probably won’t be.
In essence, Druid is a distributed analytic DBMS. Druid’s design choices are best understood when you recall that it was invented to support Metamarkets’ large-scale, RAM-speed, internet marketing/personalization SaaS (Software as a Service) offering. In particular:
- Druid tries to use RAM well.
- Druid tries to stay up all the time.
- Druid has multi-valued fields. (Numeric, but of course you can use encoding tricks to be effectively more general.)
- Druid’s big limitation is to assume that there’s literally only one (denormalized) table per query; you can’t even join to dimension tables.
- SQL is a bit of an afterthought; I would expect Druid’s SQL functionality to be pretty stripped-down out of the gate.
Interestingly, the single-table/multi-valued choice is echoed at WibiData, which deals with similar data sets. However, WibiData’s use cases are different from Metamarkets’, and in most respects the WibiData architecture is quite different from that of Metamarkets/Druid.
Introduction to Metamarkets and Druid
I previously dropped a few hints about my clients at Metamarkets, mentioning that they:
- Have built vertical-market analytic platform technology.
- Use a lot of Hadoop.
- Throw good parties. (That’s where the background photo on my Twitter page comes from.)
But while they’re a joy to talk with, writing about Metamarkets has been frustrating, with many hours and pages of wasted of effort. Even so, I’m trying again, in a three-post series:
- Introduction to Metamarkets and Druid (this post)
- Druid overview
- Metamarkets’ back-end technology
Much like Workday, Inc., Metamarkets is a SaaS (Software as a Service) company, with numerous tiers of servers and an affinity for doing things in RAM. That’s where most of the similarities end, however, as Metamarkets is a much smaller company than Workday, doing very different things.
Metamarkets’ business is SaaS (Software as a Service) business intelligence, on large data sets, with low latency in both senses (fresh data can be queried on, and the queries happen at RAM speed). As you might imagine, Metamarkets is used by digital marketers and other kinds of internet companies, whose data typically wants to be in the cloud anyway. Approximate metrics for Metamarkets (and it may well have exceeded these by now) include 10 customers, 100,000 queries/day, 80 billion 100-byte events/month (before summarization), 20 employees, 1 popular CEO, and a metric ton of venture capital.
To understand how Metamarkets’ technology works, it probably helps to start by realizing: Read more
Kognitio’s story today
I had dinner tonight with the Kognitio folks. So far as I can tell:
- Branding has been mercifully simplified. Everything is now called “Kognitio” (as opposed to, for example, “WX2”).
- Notwithstanding its long history of selling disk-based DBMS and denigrating memory-only configurations, Kognitio now says that in fact it’s always been an in-memory DBMS vendor.
- Notwithstanding its long history of selling (or attempting to sell) analytic DBMS, Kognitio wants to be viewed as an accelerator to your existing DBMS. This is apparently inspired in part by SAP HANA, notwithstanding that HANA’s direction is to evolve into a hybrid OLTP/analytic general-purpose DBMS.
- Notwithstanding its lack of analytic platform features, Kognitio wants to be viewed as selling an analytic platform.
- Notwithstanding its memory-centric focus, Kognitio doesn’t want to compress data. Kognitio’s opinion — which to my knowledge is shared by few people outside Kognitio — seems to be that the CPU cost of compression/decompression isn’t justified by the RAM savings from compression.
- Kognitio still is pushing a cloud/SaaS (Software as a Service) story. Even if you want to use Kognitio (the product) on-premises, Kognitio (the company) calls that “private cloud” and offers to let you pay annually.
Kognitio believes that this story is appealing, especially to smaller venture-capital-backed companies, and backs that up with some frieNDA pipeline figures.
Between that success claim and SAP’s HANA figures, it seems that the idea of using an in-memory DBMS to accelerate analytics has legs. This makes sense, as the BI vendors — Qlik Tech excepted — don’t seem to be accomplishing much with their proprietary in-memory alternatives. But I’m not sure that Kognitio would be my first choice to fill that role. Rather, if I wanted to buy an unsuccessful analytic RDBMS to use as an in-memory accelerator, I might consider ParAccel, which is columnar, has an associated compression story, has always had a hybrid memory-centric flavor much as Kognitio has, and is well ahead of Kognitio in the analytic platform derby. That said, I’ll confess to not having talked with or heard much about ParAccel for a while, so I don’t know if they’ve been able maintain technical momentum any more than Kognitio has.
| Categories: Cloud computing, Data warehousing, Database compression, Kognitio, Memory-centric data management, ParAccel, Software as a Service (SaaS) | 2 Comments |
Thoughts on “data science”
Teradata is paying me to join a panel on “data science” in downtown Boston, Tuesday May 22, at 3:00 pm. A planning phone call led me to jot down a few notes on the subject, which I’m herewith adapting into a blog post.
For starters, I have some concerns about the concepts of data science and data scientist. Too often, the term “data scientist” is used to suggest that one person needs to have strong skills both in analytics and in data management. But in reality, splitting those roles makes perfect sense. Further:
- It may or may not make sense to say that a computer scientist is doing “science”; the term “data scientist” inherits that ambiguity.
- It may or may not make sense to say that a corporate scientist is doing “science”; for example, a petroleum geologist might do very valuable work without making any scientific discoveries. The term “data scientist” inherits that ambiguity too.
- Too often, people use the term big data as if it were something radically new, rather than a continuation of what has been done in large-scale analytic data management for decades. “Data science” has a similar problem.
- The term “data science” sounds as if you need specialized academic training to do it, which isn’t really true.
The leader in raising these issues is probably Neil Raden.
But there’s one respect in which I think the term “data science” is highly appropriate. In conventional science, gathering data is just as much of an accomplishment as analyzing it. Indeed, most Nobel Prizes are given for experimental results. Similarly, if you’re doing data science, you should be thinking hard about how to corral ever more useful data. Techniques include but are not limited to:
- Keeping data you used to throw away. This has driven a lot of growth in relational data warehouses and big bit buckets alike.
- Bribing customers and prospects. Loyalty cards are the paradigmatic example.
- Split testing. The more internet-based users you have, the more tests you can do.
- Storing derived data. That can be as simple as pre-computing the scores from your predictive analytics model, or it can be as complex as running a 50-step sequence of Hadoop jobs.
- Getting data from third parties, for example:
- Supply chain partners (right now this rarely amounts to more than simple BI, but that could change in the future).
- Data vendors of various kinds (e.g. credit bureaus).
- Social media/the internet in general, which also usually involves some kind of service provider.
| Categories: Analytic technologies, Data warehousing, Predictive modeling and advanced analytics, Teradata | 4 Comments |
Big Data hype?
A reporter wrote in to ask whether investor interest in “Big Data” was justified or hype. (More precisely, that’s how I reinterpreted his questions. 🙂 ) His examples were Splunk’s IPO, Teradata’s stock price increase, and Birst’s financing. In a nutshell:
- My comments, lightly edited, are in plain text below.
- Further thoughts are in italics.
- Of course I also linked him to my post “Big Data” has jumped the shark.
- Overall, my responses boil down to “Of course there’s some hype.”
1. A great example of hype is that anybody is calling Birst a “Big Data” or “Big Data analytics” company. If anything, Birst is a “little data” analytics company that claims, as a differentiating feature, that it can handle ordinary-sized data sets as well. Read more
| Categories: Business intelligence, Data warehousing, IBM and DB2, Microsoft and SQL*Server, Oracle, Splunk | 14 Comments |
Many kinds of memory-centric data management
I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:
- The desire for human real-time interactive response naturally leads to keeping data in RAM.
- Many databases will be ever cheaper to put into RAM over time, thanks to Moore’s Law. (Most) traditional databases will eventually wind up in RAM.
- However, there will be exceptions, mainly on the machine-generated side. Where data creation and RAM data storage are getting cheaper at similar rates … well, the overall cost of RAM storage may not significantly decline.
Getting more specific than that is hard, however, because:
- The possibilities for in-memory data storage are as numerous and varied as those for disk.
- The individual technologies and products for in-memory storage are much less mature than those for disk.
- Solid-state options such as flash just confuse things further.
Consider, for example, some of the in-memory data management ideas kicking around. Read more
Human real-time
I first became an analyst in 1981. And so I was around for the early days of the movement from batch to interactive computing, as exemplified by:
- The rise of minicomputers as mainframe alternatives (first VAXen, then the ‘nix systems that did largely supplant mainframes).
- The move from batch to interactive computing even on mainframes, a key theme of 1980s application software industry competition.
Of course, wherever there is interactive computing, there is a desire for interaction so fast that users don’t notice any wait time. Dan Fylstra, when he was pitching me the early windowing system VisiOn, characterized this as response so fast that the user didn’t tap his fingers waiting.* And so, with the move to any kind of interactive computing at all came a desire that the interaction be quick-response/low-latency. Read more
IBM DB2 10
Shortly before Tuesday’s launch of DB2 10, IBM’s Conor O’Mahony checked in for a relatively non-technical briefing.* More precisely, this is about DB2 for “distributed” systems, aka LUW (Linux/Unix/Windows); some of the features have already been in the mainframe version of DB2 for a while. IBM is graciously permitting me to post the associated DB2 10 announcement slide deck.
*I hope any errors in interpretation are minor.
Major aspects of DB2 10 include new or improved capabilities in the areas of:
- Compression.
- Analytic query performance.
- Data ingest.
- Multi-temperature data management.
- Workload management.
- Graph management/relationship analytics.
- Time-travel, bitemporal features, and bitemporal time-travel.
Of course, there are various other enhancements too, including to security (fine-grained access control), Oracle compatibility, and DB2 pureScale. Everything except the pureScale part is also reflected in IBM InfoSphere Warehouse, which is a near-superset of DB2.*
*Also, the data ingest part isn’t in base DB2.
| Categories: Data warehousing, Database compression, IBM and DB2, RDF and graphs, Solid-state memory, Workload management | 6 Comments |
Juggling analytic databases
I’d like to survey a few related ideas:
- Enterprises should each have a variety of different analytic data stores.
- Vendors — especially but not only IBM and Teradata — are acknowledging and marketing around the point that enterprises should each have a number of different analytic data stores.
- In addition to having multiple analytic data management technology stacks, it is also desirable to have an agile way to spin out multiple virtual or physical relational data marts using a single RDBMS. Vendors are addressing that need.
- Some observers think that the real essence of analytic data management will be in data integration, not the actual data management.
Here goes. Read more