Web analytics
Discussion of how data warehousing and analytic technologies are applied to clickstream analysis and other web analytics challenges. Related subjects include:
- The use of analytic technologies for logfile analysis
- (in Text Technologies) Online marketing
Netezza Q1 earning call transcript
I finally read the Netezza Q1 earnings call transcript, put out by Seeking Alpha. Highlights included:
- Netezza got 14 new-name accounts and 21 follow-on deals. Average sale in both groups was right around $1 million.
- The economy is tough, deals are slipping, and nobody knows for sure what will happen.
- Netezza’s main head-to-head competitors are Oracle and Teradata. Netezza claims good but not perfect win rates against each, but concedes that those vendors (especially Oracle) of course get other deals Netezza never sees.
- Netezza characterizes Teradata as offering its multiple product lines, trying to upsell many customers from cheaper to more expensive product lines, and being selectively aggressive about pricing. None of this is surprising to me.
- 80% of Netezza’s Q1 revenue, and perhaps even a higher fraction of new-name accounts, was in four vertical markets: “Digital media,” telecom, government, and financial services.
- Some time over the next few months, Netezza will give at least some more clarity about future products.
One tip for the Netezza folks, by the way, from this former stock analyst — you should never use the word “certainly” about a deal you haven’t closed yet. “Almost surely” could be OK, but “certainly” — well, it certainly was not the thing to say.
More on Fox Interactive Media’s use of Greenplum
Greenplum’s most important reference is probably its energetic advocate Fox Interactive Media, even ahead of much larger user Greenplum user eBay, and notwithstanding Aster Data’s large presence in Fox subsidiary MySpace. I just ran across a “review” of Greenplum by FIM’s Brian Dolan, neatly summarizing his views about Greenplum’s strengths, weaknesses, and uses inside Fox. Highlights include: Read more
| Categories: Data warehousing, Fox and MySpace, Greenplum, Web analytics | 2 Comments |
Sneakernet to the cloud
Recently, Amazon CTO Werner Vogels put up a blog post which suggested that, now and in the future, the best way to get large databases into the cloud is via sneakernet. In some circumstances, he is surely right. Possible implications include:
- When sending data to the cloud, you probably want to compress it to the max before sending. Clearpace’s new RainStor structured-data archiving service emphasizes that idea. RainStor marketing says cloud, cloud, cloud — but Clearpace thinks you really should have a bit of its software onsite too, to compress the data before sending it across the wire.
- Getting data from one cloud to another cloud could be problematic. I’m fond of saying that weblog data naturally lives in the cloud at your hosting company’s location, so you should analyze it there too. But this makes the most sense if you analyze it or at least filter/reduce it in place. (That said, the really, really big web companies have lots of different data centers, and presumably do move huge amounts of log data from place to place.)
But for one-time moves of data sets — sure, sneaker net/snail mail should work just fine.
| Categories: Amazon and its cloud, Cloud computing, Database compression, EAI, EII, ETL, ELT, ETLT, Web analytics | 2 Comments |
Facebook, Hadoop, and Hive
I few weeks ago, I posted about a conversation I had with Jeff Hammerbacher of Cloudera, in which he discussed a Hadoop-based effort at Facebook he previously directed. Subsequently, Ashish Thusoo and Joydeep Sarma of Facebook contacted me to expand upon and in a couple of instances correct what Jeff had said. They also filled me in on Hive, a data-manipulation add-on to Hadoop that they developed and subsequently open-sourced.
Updating the metrics in my Cloudera post,
- Facebook has 400 terabytes of disk managed by Hadoop/Hive, with a slightly better than 6:1 overall compression ratio. So the 2 1/2 petabytes figure for user data is reasonable.
- Facebook’s Hadoop/Hive system ingests 15 terabytes of new data per day now, not 10.
- Hadoop/Hive cycle times aren’t as fast as I thought I heard from Jeff. Ad targeting queries are the most frequent, and they’re run hourly. Dashboards are repopulated daily.
Nothing else in my Cloudera post was called out as being wrong.
In a new-to-me metric, Facebook has 610 Hadoop nodes, running in a single cluster, due to be increased to 1000 soon. Facebook thinks this is the second-largest* Hadoop installation, or else close to it. What’s more, Facebook believes it is unusual in spreading all its apps across a single huge cluster, rather than doing different kinds of work on different, smaller sub-clusters. Read more
| Categories: Data warehousing, EAI, EII, ETL, ELT, ETLT, Facebook, Hadoop, MapReduce, Parallelization, Petabyte-scale data management, Specific users, Web analytics, Yahoo | 48 Comments |
37 Ways To Get More From Analytics, Version 2.0
As I hoped, there were some very helpful responses to my post listing ways to improve analytic effectiveness. Here’s a second draft incorporating them. Comments continue to be very welcome. I need to finalize this soon. Read more
| Categories: Analytic technologies, Business intelligence, Data warehousing, Presentations, Web analytics | 4 Comments |
eBay’s two enormous data warehouses
A few weeks ago, I had the chance to visit eBay, meet briefly with Oliver Ratzesberger and his team, and then catch up later with Oliver for dinner. I’ve already alluded to those discussions in a couple of posts, specifically on MapReduce (which eBay doesn’t like) and the astonishingly great difference between high- and low-end disk drives (to which eBay clued me in). Now I’m finally getting around to writing about the core of what we discussed, which is two of the very largest data warehouses in the world.
Metrics on eBay’s main Teradata data warehouse include:
- >2 petabytes of user data
- 10s of 1000s of users
- Millions of queries per day
- 72 nodes
- >140 GB/sec of I/O, or 2 GB/node/sec, or maybe that’s a peak when the workload is scan-heavy
- 100s of production databases being fed in
Metrics on eBay’s Greenplum data warehouse (or, if you like, data mart) include:
- 6 1/2 petabytes of user data
- 17 trillion records
- 150 billion new records/day, which seems to suggest an ingest rate well over 50 terabytes/day
- 96 nodes
- 200 MB/node/sec of I/O (that’s the order of magnitude difference that triggered my post on disk drives)
- 4.5 petabytes of storage
- 70% compression
- A small number of concurrent users
| Categories: Analytic technologies, Data warehouse appliances, Data warehousing, eBay, Greenplum, Petabyte-scale data management, Teradata, Web analytics | 48 Comments |
Introduction to Tokutek
Tokutek has a paradoxical pitch: Tokutek writes data particularly quickly, and therefore you’re supposed to buy Tokutek for query-oriented uses. Highlights of the Tokutek story include:
- Tokutek is a MySQL storage engine.
- MySQL/Tokutek writes indexed data a lot faster than B-tree-based alternatives. (The claim is 10s of 1000s of rows per second on a single server.)
- MySQL/Tokutek reads data at B-tree speeds. (But not, I presume, at the speed of specialized analytic DBMS.)
- Tokutek is not yet ACID-compliant. They’re working on that, but we don’t know what the performance implications will be when they achieve it. ACID compliance won’t come as soon as the May release (Tokutek Version 2.0).
- Tokutek has made one sale. Others are in the pipeline.
Tokutek’s initial target market is the usual combination of clickstream/personalization/other network management. The idea is that many data warehouse technologies have trouble getting latency below, say, 15 seconds to 5 minutes, at least at very high update volumes. So if immediacy is more important than raw complex query performance, Tokutek’s performance profile could be attractive. Read more
| Categories: Data warehousing, MySQL, Tokutek and TokuDB, Web analytics | 14 Comments |
Cloudera presents the MapReduce bull case
Monday was fire-drill day regarding MapReduce vs. MPP relational DBMS. The upshot was that I was quoted in Computerworld and paraphrased in GigaOm as being a little more negative on MapReduce than I really am, in line with my comment
Frankly, my views on MapReduce are more balanced than [my] weary negativity would seem to imply.
Tuesday afternoon the dial turned a couple notches more positive yet, when I talked with Michael Olson and Jeff Hammerbacher of Cloudera. Cloudera is a new company, built around the open source MapReduce implementation Hadoop. So far Cloudera gives away its Hadoop distribution, without charging for any sort of maintenance or subscription, and just gets revenue from professional services. Presumably, Cloudera plans for this business model to change down the road.
Much of our discussion revolved around Facebook, where Jeff directed a huge and diverse Hadoop effort. Apparently, Hadoop played much of the role of an enterprise data warehouse at Facebook — at least for clickstream/network data — including:
- 2 1/2 petabytes of data managed via Hadoop
- 10 terabytes/day of data ingested via Hadoop (Edit: Some of these metrics have been updated in a subsequent post about Facebook.)
- Ad targeting queries run every 15 minutes in Hadoop
- Dashboard roll-up queries run every hour in Hadoop
- Ad-hoc research/analytic Hadoop queries run whenever
- Anti-fraud analysis done in Hadoop
- Text mining (e.g., of things written on people’s “walls”) done in Hadoop
- 100s or 1000s of simultaneous Hadoop queries
- JSON-based social network analysis in Hadoop
Some Facebook data, however, was put into an Oracle RAC cluster for business intelligence. And Jeff does concede that query execution is slower in Hadoop than in a relational DBMS. Hadoop was also used to build the index for Facebook’s custom text search engine.
Jeff’s reasons for liking Hadoop over relational DBMS at Facebook included: Read more
Twitter is considering using MapReduce
From a Twitter job listing (formatting mine). The most interesting section is “Additional preferred experience.” Read more
| Categories: Analytic technologies, Data warehousing, MapReduce, Specific users, Web analytics | 6 Comments |
Aleri update
My skeptical remarks on the Aleri/Coral8 merger generated some pushback. Today I actually got around to talking with John Morell, who was marketing chief at Coral8 and has remained with the combined company. First, some quick metrics:
- The combined Aleri has around 100 employees, 60-40 from Aleri vs. Coral8.
- The combined Aleri has around 80 customers. All of Aleri’s, with one sort-of exception at Banks.com, were in financial services. A large minority of Coral8’s were in financial services too.
- However, half of Aleri’s marketing spend going forward is budgeted outside the financial services markets. Not unreasonably, John presents this as a proof point Aleri is serious about selling to other markets.
- Aleri had 12-14 people in the UK pre-merger. Coral8 had none in Europe.
- Coral8 had 15 OEMs pre-merger, some actually generating revenue. Aleri had substantially none.
- Coral8 had been closing a “couple” of customers/quarter in online commerce. But recently, that rate ramped up to a “few.”
- Aleri’s engine is used to handle “many” hundreds of thousands of messages per second. Coral8’s highest-throughput user processes 100-150,000 messages/second.
John is sticking by the company line that there will be an integrated Aleri/Coral8 engine in around 12 months, with all the performance optimization of Aleri and flexibility of Coral8, that compiles and runs code from any of the development tools either Aleri or Coral8 now has. While this is a lot faster than, say, the Informix/Illustra or Oracle/IRI Express integrations, John insists that integrating CEP engines is a lot easier. We’ll see.
I focused most of the conversation on Aleri’s forthcoming efforts outside the financial services market. John sees these as being focused around Coral8’s old “Continuous (Business) Intelligence” message, enhanced by Aleri’s Live OLAP. Aleri Live OLAP is an in-memory OLAP engine, real-time/event-driven, fed by CEP. Queries can be submitted via ODBO/MDX today. XMLA is coming. John reports that quite a few Coral8 customers are interested in Live OLAP, and positions the capability as one Coral8 would have had to develop had the company remained independent. Read more