EAI, EII, ETL, ELT, ETLT
Analysis of data integration products and technologies, especially ones related to data warehousing, such as ELT (Extract/Transform/Load). Related subjects include:
1. I wish I had some good, practical ideas about how to make a political difference around privacy and surveillance. Nothing else we discuss here is remotely as important. I presumably can contribute an opinion piece to, more or less, the technology publication(s) of my choice; that can have a small bit of impact. But I’d love to do better than that. Ideas, anybody?
2. A few thoughts on cloud, colocation, etc.:
- The economies of scale of colocation-or-cloud over operating your own data center are compelling. Most of the reasons you outsource hardware manufacture to Asia also apply to outsourcing data center operation within the United States. (The one exception I can think of is supply chain.)
- The arguments for cloud specifically over colocation are less persuasive. Colo providers can even match cloud deployments in rapid provisioning and elastic pricing, if they so choose.
- Surely not coincidentally, I am told that Rackspace is deemphasizing cloud, reemphasizing colocation, and making a big deal out of Open Compute. In connection with that, Rackspace has pulled back from its leadership role in OpenStack.
- I’m hearing much more mention of Amazon Redshift than I used to. It seems to have a lot of traction as a simple and low-cost option.
- I’m hearing less about Elastic MapReduce than I used to, although I imagine usage is still large and growing.
- In general, I get the impression that progress is being made in overcoming the inherent difficulties in cloud (and even colo) parallel analytic processing. But it all still seems pretty vague, except for the specific claims being made for traction of Redshift, EMR, and so on.
- Teradata recently told me that in colocation pricing, it is common for floor space to be everything, with power not separately metered. But I don’t think that trend is a big deal, as it is not necessarily permanent.
- Cloud hype is of course still with us.
- Other than the above, I stand by my previous thoughts on appliances, clusters and clouds.
3. As for the analytic DBMS industry: Read more
Many of the companies I talk with boast of freeing business analysts from reliance on IT. This, to put it mildly, is not a unique value proposition. As I wrote in 2012, when I went on a history of analytics posting kick,
- Most interesting analytic software has been adopted first and foremost at the departmental level.
- People seem to be forgetting that fact.
In particular, I would argue that the following analytic technologies started and prospered largely through departmental adoption:
- Fourth-generation languages (the analytically-focused ones, which in fact started out being consumed on a remote/time-sharing basis)
- Electronic spreadsheets
- 1990s-era business intelligence
- Fancy-visualization business intelligence
- Predictive analytics
- Text analytics
- Rules engines
What brings me back to the topic is conversations I had this week with Paxata and Metanautix. The Paxata story starts:
- Paxata is offering easy — and hopefully in the future comprehensive — “data preparation” tools …
- … that are meant to be used by business analysts rather than ETL (Extract/Transform/Load) specialists or other IT professionals …
- … where what Paxata means by “data preparation” is not specifically what a statistician would mean by the term, but rather generally refers to getting data ready for business intelligence or other analytics.
Metanautix seems to aspire to a more complete full-analytic-stack-without-IT kind of story, but clearly sees the data preparation part as a big part of its value.
If there’s anything new about such stories, it has to be on the transformation side; BI tools have been helping with data extraction since — well, since the dawn of BI. Read more
|Categories: Business intelligence, Datameer, EAI, EII, ETL, ELT, ETLT, Predictive modeling and advanced analytics, Progress, Apama, and DataDirect||11 Comments|
A significant fraction of IT professional services industry revenue comes from data integration. But as a software business, data integration has been more problematic. Informatica, the largest independent data integration software vendor, does $1 billion in revenue. INFA’s enterprise value (market capitalization after adjusting for cash and debt) is $3 billion, which puts it way short of other category leaders such as VMware, and even sits behind Tableau.* When I talk with data integration startups, I ask questions such as “What fraction of Informatica’s revenue are you shooting for?” and, as a follow-up, “Why would that be grounds for excitement?”
*If you believe that Splunk is a data integration company, that changes these observations only a little.
On the other hand, several successful software categories have, at particular points in their history, been focused on data integration. One of the major benefits of 1990s business intelligence was “Combines data from multiple sources on the same screen” and, in some cases, even “Joins data from multiple sources in a single view”. The last few years before application servers were commoditized, data integration was one of their chief benefits. Data warehousing and Hadoop both of course have a “collect all your data in one place” part to their stories — which I call data mustering — and Hadoop is a data transformation tool as well.
The name of this blog comes from an August, 2005 column. 8 1/2 years later, that analysis holds up pretty well. Indeed, I’d keep the first two precepts exactly as I proposed back then:
- Task-appropriate data managers. Much of this blog is about task-appropriate data stores, so I won’t say more about them in this post.
- Drastic limitations on relational schema complexity. I think I’ve been vindicated on that one by, for example:
- NoSQL and dynamic schemas.
- Schema-on-read, and its smarter younger brother schema-on-need.
- Limitations on the performance and/or allowed functionality of joins in scale-out short-request RDBMS, and the relative lack of complaints about same.
- Funky database design from major Software as a Service (SaaS) vendors such as Workday and Salesforce.com.
- A whole lot of logs.
I’d also keep the general sense of the third precept, namely appropriately-capable data integration, but for that one the specifics do need some serious rework.
For starters, let me say: Read more
|Categories: About this blog, Business intelligence, Database diversity, EAI, EII, ETL, ELT, ETLT, Investment research and trading, NoSQL, Schema on need||2 Comments|
Ever more products try to integrate SQL with Hadoop, and discussions of them seem confused, in line with Monash’s First Law of Commercial Semantics. So let’s draw some distinctions, starting with (and these overlap):
- Are the SQL engine and Hadoop:
- Necessarily on the same cluster?
- Necessarily or at least most naturally on different clusters?
- How, if at all, is Hadoop invoked by the SQL engine? Specifically, what is the role of:
- HDFS (Hadoop Distributed File System)?
- Hadoop MapReduce?
- How, if at all, is the SQL engine invoked by Hadoop?
- If something is called a “connector”, then Hadoop and the SQL engine are most likely on separate clusters. Good features include (but these can partially contradict each other):
- A way of making data transfer maximally parallel.
- Query planning that is smart about when to process on the SQL engine and when to use Hadoop’s native SQL (Hive or otherwise).
- If something is called “SQL-on-Hadoop”, then Hadoop and the SQL engine are or should be on the same cluster, using the same nodes to store and process data. But while that’s a necessary condition, I’d prefer that it not be sufficient.
Let’s go to some examples. Read more
|Categories: Cloudera, Data integration and middleware, EAI, EII, ETL, ELT, ETLT, Hadapt, Hadoop, HBase, Hortonworks, MapReduce, Microsoft and SQL*Server, NewSQL, PostgreSQL, SQL/Hadoop integration, Teradata||36 Comments|
From time to time I like to do “what I’m working on” posts. From my recent blogging, you probably already know that includes:
- Hadoop (always, and please see below).
- Analytic RDBMS (ditto).
- NoSQL and NewSQL.
- Specifically, SQL-on-Hadoop
- Spark and other memory-centric technology, including streaming.
- Public policy, mainly but not only in the area of surveillance/privacy.
- General strategic advice for all sizes of tech company.
Other stuff on my mind includes but is not limited to:
1. Certain categories of buying organizations are inherently leading-edge.
- Internet companies have adopted Hadoop, NoSQL, NewSQL and all that en masse. Often, they won’t even look at things that are conventional or expensive.
- US telecom companies have been buying 1 each of every DBMS on the market since pre-relational days.
- Financial services firms — specifically algorithmic traders and broker-dealers — have been in their own technical world for decades …
- … as have national-security agencies …
- … as have pharmaceutical research departments.
Fine. But what really intrigues me is when more ordinary enterprises also put leading-edge technologies into production. I pester everybody for examples of that.
I’ve heard a lot of buzz recently around Spark. So I caught up with Ion Stoica and Mike Franklin for a call. Let me start by acknowledging some sources of confusion.
- Spark is very new. All Spark adoption is recent.
- Databricks was founded to commercialize Spark. It is very much in stealth mode …
- … except insofar as Databricks folks are going out and trying to drum up Spark adoption.
- Ion Stoica is running Databricks, but you couldn’t tell that from his UC Berkeley bio page. Edit: After I posted this, Ion’s bio was quickly updated.
- Spark creator and Databricks CTO Matei Zaharia is an MIT professor, but actually went on leave there before he ever showed up.
- Cloudera is perhaps Spark’s most visible supporter. But Cloudera’s views of Spark’s role in the world is different from the Spark team’s.
The “What is Spark?” question may soon be just as difficult as the ever-popular “What is Hadoop?” That said — and referring back to my original technical post about Spark and also to a discussion of prominent Spark user ClearStory — my try at “What is Spark?” goes something like this:
- Spark is a distributed execution engine for analytic processes …
- … which works well with Hadoop.
- Spark is distinguished by a flexible in-memory data model …
- … and farms out persistence to HDFS (Hadoop Distributed File System) or other existing data stores.
- Intended analytic use cases for Spark include:
- SQL data manipulation.
- ETL-like data manipulation.
- Streaming-like data manipulation.
- Machine learning.
- Graph analytics.
It took me a bit of time, and an extra call with Vertica’s long-time R&D chief Shilpa Lawande, but I think I have a decent handle now on Vertica 7, code-named Crane. The two aspects of Vertica 7 I find most interesting are:
- Flex Zone, a schema-on-need technology very much like Hadapt’s (but of course with access to Vertica performance).
- What sounds like an alternate query execution capability for short-request queries, the big point of which is that it saves them from being broadcast across the whole cluster, hence improving scalability. (Adding nodes of course doesn’t buy you much for the portion of a workload that’s broadcast.)
Other Vertica 7 enhancements include:
- A lot of Bottleneck Whack-A-Mole.
- “Significant” improvements to the Vertica management console.
- Security enhancements (Kerberos), Hadoop integration enhancements (HCatalog), and enhanced integration with Hadoop security (Kerberos again).
- Some availability hardening. (“Fault groups”, which for example let you ensure that data is replicated not just to 2+ nodes, but also that the nodes aren’t all on the same rack.)
- Java as an option to do in-database analytics. (Who knew that feature was still missing?)
- Some analytic functionality. (Approximate COUNT DISTINCT, but not yet Approximate MEDIAN.)
Overall, two recurring themes in our discussion were:
- Load and ETL (Extract/Transform/Load) performance, and/or obviating ETL.
- Short-request performance, in the form of more scalable short-request concurrency.
Glassbeam checked in recently, and they turn out to exemplify quite a few of the themes I’ve been writing about. For starters:
- Glassbeam has an analytic technology stack focused on poly-structured machine-generated data.
- Glassbeam partially organizes that data into event series …
- … in a schema that is modified as needed.
Glassbeam basics include:
- Founded in 2009.
- Based in Santa Clara. Back-end engineering in Bangalore.
- $6 million in angel money; no other VC.
- High single-digit customer count, …
- … plus another high single-digit number of end customers for an OEM offering a limited version of their product.
All Glassbeam customers except one are SaaS/cloud (Software as a Service), and even that one was only offered a subscription (as oppose to perpetual license) price.
So what does Glassbeam’s technology do? Glassbeam says it is focused on “machine data analytics,” specifically for the “Internet of Things”, which it distinguishes from IT logs.* Specifically, Glassbeam sells to manufacturers of complex devices — IT (most of its sales so far ), medical, automotive (aspirational to date), etc. — and helps them analyze “phone home” data, for both support/customer service and marketing kinds of use cases. As of a recent release, the Glassbeam stack can: Read more
Much of modern analytic technology deals with what might be called an entity-centric sequence of events. For example:
- You receive and open various emails.
- You click on and look at various web sites and pages.
- Specific elements are displayed on those pages.
- You study various products, and even buy some.
Analytic questions are asked along the lines “Which sequences of events are most productive in terms of leading to the events we really desire?”, such as product sales. Another major area is sessionization, along with data preparation tasks that boil down to arranging data into meaningful event sequences in the first place.
A number of my clients are focused on such scenarios, including WibiData, Teradata Aster (e.g. via nPath), Platfora (in the imminent Platfora 3), and others. And so I get involved in naming exercises. The term entity-centric came along a while ago, because “user-centric” is too limiting. (E.g., the data may not be about a person, but rather specifically about the actions taken on her mobile device.) Now I’m adding the term event series to cover the whole scenario, rather than the “event sequence(s)” I might appear to have been hinting at above.
I decided on “event series” earlier this week, after noting that: Read more
|Categories: Aster Data, Business intelligence, Data warehousing, EAI, EII, ETL, ELT, ETLT, Platfora, Predictive modeling and advanced analytics, Teradata, Vertica Systems, Web analytics, WibiData||19 Comments|