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:
I hear much discussion of shortfalls in analytic technology, especially from companies that want to fill in the gaps. But how much do these gaps actually matter? In many cases, that depends on what the analytic technology is being used for. So let’s think about some different kinds of analytic task, and where they each might most stress today’s available technology.
In separating out the task areas, I’ll focus first on the spectrum “To what extent is this supposed to produce novel insights?” and second on the dimension “To what extent is this supposed to be integrated into a production/operational system?” Issues of latency, algorithmic novelty, etc. can follow after those. In particular, let’s consider the tasks: Read more
|Categories: Business intelligence, Data warehousing, Databricks, Spark and BDAS, Hadoop, Netezza, NoSQL, Predictive modeling and advanced analytics, Tableau Software||1 Comment|
I’m skeptical of data federation. I’m skeptical of all-things-to-all-people claims about logical data layers, and in particular of Gartner’s years-premature “Logical Data Warehouse” buzzphrase. Still, a reasonable number of my clients are stealthily trying to do some kind of data layer middleware, as are other vendors more openly, and I don’t think they’re all crazy.
Here are some thoughts as to why, and also as to challenges that need to be overcome.
There are many things a logical data layer might be trying to facilitate — writing, querying, batch data integration, real-time data integration and more. That said:
- When you’re writing data, you want it to be banged into a sufficiently-durable-to-acknowledge condition fast. If acknowledgements are slow, performance nightmares can ensue. So writing is the last place you want an extra layer, perhaps unless you’re content with the durability provided by an in-memory data grid.
- Queries are important. Also, they formally are present in other tasks, such as data transformation and movement. That’s why data manipulation packages (originally Pig, now Hive and fuller SQL) are so central to Hadoop.
7-10 years ago, I repeatedly argued the viewpoints:
- Relational DBMS were the right choice in most cases.
- Multiple kinds of relational DBMS were needed, optimized for different kinds of use case.
- There were a variety of specialized use cases in which non-relational data models were best.
Since then, however:
- Hadoop has flourished.
- NoSQL has flourished.
- Graph DBMS have matured somewhat.
- Much of the action has shifted to machine-generated data, of which there are many kinds.
So it’s probably best to revisit all that in a somewhat organized way.
I’m taking a few weeks defocused from work, as a kind of grandpaternity leave. That said, the venue for my Dances of Infant Calming is a small-but-nice apartment in San Francisco, so a certain amount of thinking about tech industries is inevitable. I even found time last Tuesday to meet or speak with my clients at WibiData, MemSQL, Cloudera, Citus Data, and MongoDB. And thus:
1. I’ve been sloppy in my terminology around “geo-distribution”, in that I don’t always make it easy to distinguish between:
- Storing different parts of a database in different geographies, often for reasons of data privacy regulatory compliance.
- Replicating an entire database into different geographies, often for reasons of latency and/or availability/ disaster recovery,
The latter case can be subdivided further depending on whether multiple copies of the data can accept first writes (aka active-active, multi-master, or multi-active), or whether there’s a clear single master for each part of the database.
What made me think of this was a phone call with MongoDB in which I learned that the limit on number of replicas had been raised from 12 to 50, to support the full-replication/latency-reduction use case.
2. Three years ago I posted about agile (predictive) analytics. One of the points was:
… if you change your offers, prices, ad placement, ad text, ad appearance, call center scripts, or anything else, you immediately gain new information that isn’t well-reflected in your previous models.
Subsequently I’ve been hearing more about predictive experimentation such as bandit testing. WibiData, whose views are influenced by a couple of Very Famous Department Store clients (one of which is Macy’s), thinks experimentation is quite important. And it could be argued that experimentation is one of the simplest and most direct ways to increase the value of your data.
3. I’d further say that a number of developments, trends or possibilities I’m seeing are or could be connected. These include agile and experimental predictive analytics in general, as noted in the previous point, along with: Read more
I commonly write about real or apparent technical differentiation, in a broad variety of domains. But actually, computers only do a couple of kinds of things:
- Accept instructions.
- Execute them.
And hence almost all IT product differentiation fits into two buckets:
- Easier instruction-giving, whether that’s in the form of a user interface, a language, or an API.
- Better execution, where “better” usually boils down to “faster”, “more reliable” or “more reliably fast”.
As examples of this reductionism, please consider:
- Application development is of course a matter of giving instructions to a computer.
- Database management systems accept and execute data manipulation instructions.
- Data integration tools accept and execute data integration instructions.
- System management software accepts and executes system management instructions.
- Business intelligence tools accept and execute instructions for data retrieval, navigation, aggregation and display.
Similar stories are true about application software, or about anything that has an API (Application Programming Interface) or SDK (Software Development Kit).
Yes, all my examples are in software. That’s what I focus on. If I wanted to be more balanced in including hardware or data centers, I might phrase the discussion a little differently — but the core points would still remain true.
What I’ve said so far should make more sense if we combine it with the observation that differentiation is usually restricted to particular domains. Read more
It seems reasonable to wonder whether analytic data management is headed for the cloud. In no particular order:
- Amazon Redshift appears to be prospering.
- So are some SaaS (Software as a Service) business intelligence vendors.
- Amazon Elastic MapReduce is still around.
- Snowflake Computing launched with a cloud strategy.
- Cazena, with vague intentions for cloud data warehousing, destealthed.*
- Cloudera made various cloud-related announcements.
- Data is increasingly machine-generated, and machine-generated data commonly originates off-premises.
- The general argument for cloud-or-at-least-colocation has compelling aspects.
- Analytic workloads can be “bursty”, and so could benefit from true cloud elasticity.
I talked with the Snowflake Computing guys Friday. For starters:
- Snowflake is offering an analytic DBMS on a SaaS (Software as a Service) basis.
- The Snowflake DBMS is built from scratch (as opposed, to for example, being based on PostgreSQL or Hadoop).
- The Snowflake DBMS is columnar and append-only, as has become common for analytic RDBMS.
- Snowflake claims excellent SQL coverage for a 1.0 product.
- Snowflake, the company, has:
- 50 people.
- A similar number of current or past users.
- 5 referenceable customers.
- 2 techie founders out of Oracle, plus Marcin Zukowski.
- Bob Muglia as CEO.
Much of the Snowflake story can be summarized as cloud/elastic/simple/cheap.*
*Excuse me — inexpensive. Companies rarely like their products to be labeled as “cheap”.
In addition to its purely relational functionality, Snowflake accepts poly-structured data. Notes on that start:
- Ingest formats are JSON, XML or AVRO for now.
- I gather that the system automagically decides which fields/attributes are sufficiently repeated to be broken out as separate columns; also, there’s a column for the documents themselves.
I don’t know enough details to judge whether I’d call that an example of schema-on-need.
A key element of Snowflake’s poly-structured data story seems to be lateral views. I’m not too clear on that concept, but I gather: Read more
|Categories: Amazon and its cloud, Cloud computing, Data mart outsourcing, Data models and architecture, Data warehousing, Market share and customer counts, Parallelization, Pricing, Software as a Service (SaaS), Structured documents||2 Comments|
Hadoop World/Strata is this week, so of course my clients at Cloudera will have a bunch of announcements. Without front-running those, I think it might be interesting to review the current state of the Cloudera product line. Details may be found on the Cloudera product comparison page. Examining those details helps, I think, with understanding where Cloudera does and doesn’t place sales and marketing focus, which given Cloudera’s Hadoop market stature is in my opinion an interesting thing to analyze.
So far as I can tell (and there may be some errors in this, as Cloudera is not always accurate in explaining the fine details):
- CDH (Cloudera Distribution … Hadoop) contains a lot of Apache open source code.
- Cloudera has a much longer list of Apache projects that it thinks comprise “Core Hadoop” than, say, Hortonworks does.
- Specifically, that list currently is: Hadoop, Flume, HCatalog, Hive, Hue, Mahout, Oozie, Pig, Sentry, Sqoop, Whirr, ZooKeeper.
- In addition to those projects, CDH also includes HBase, Impala, Spark and Cloudera Search.
- Cloudera Manager is closed-source code, much of which is free to use. (I.e., “free like beer” but not “free like speech”.)
- Cloudera Navigator is closed-source code that you have to pay for (free trials and the like excepted).
- Cloudera Express is Cloudera’s favorite free subscription offering. It combines CDH with the free part of Cloudera Manager. Note: Cloudera Express was previously called Cloudera Standard, and that terminology is still reflected in parts of Cloudera’s website.
- Cloudera Enterprise is the umbrella name for Cloudera’s three favorite paid offerings.
- Cloudera Enterprise Basic Edition contains:
- All the code in CDH and Cloudera Manager, and I guess Accumulo code as well.
- Commercial licenses for all that code.
- A license key to use the entirety of Cloudera Manager, not just the free part.
- Support for the “Core Hadoop” part of CDH.
- Support for Cloudera Manager. Note: Cloudera is lazy about saying this explicitly, but it seems obvious.
- The code for Cloudera Navigator, but that’s moot, as the corresponding license key for Cloudera Navigator is not part of the package.
- Cloudera Enterprise Data Hub Edition contains:
- Everything in Cloudera Basic Edition.
- A license key for Cloudera Navigator.
- Support for all of HBase, Accumulo, Impala, Spark, Cloudera Search and Cloudera Navigator.
- Cloudera Enterprise Flex Edition contains everything in Cloudera Basic Edition, plus support for one of the extras in Data Hub Edition.
In analyzing all this, I’m focused on two particular aspects:
- The “zero, one, many” system for defining the editions of Cloudera Enterprise.
- The use of “Data Hub” as a general marketing term.
|Categories: Cloudera, Data warehousing, Databricks, Spark and BDAS, Hadoop, HBase, Hortonworks, Open source, Pricing||2 Comments|
I’m on record as noting and agreeing with an industry near-consensus that Spark, rather than Tez, will be the replacement for Hadoop MapReduce. I presumed that Hortonworks, which is pushing Tez, disagreed. But Shaun Connolly of Hortonworks suggested a more nuanced view. Specifically, Shaun tweeted thoughts including:
Tez vs Spark = Apples vs Oranges.
Spark is general-purpose engine with elegant APIs for app devs creating modern data-driven apps, analytics, and ML algos.
Tez is a framework for expressing purpose-built YARN-based DAGs; its APIs are for ISVs & engine/tool builders who embed it
[For example], Hive embeds Tez to convert its SQL needs into purpose-built DAGs expressed optimally and leveraging YARN
That said, I haven’t yet had a chance to understand what advantages Tez might have over Spark in the use cases that Shaun relegates it to.
- The Twitter discussion with Shaun was a spin-out from my research around streaming for Hadoop.
|Categories: Data warehousing, Databricks, Spark and BDAS, Hadoop, Hortonworks, Predictive modeling and advanced analytics||6 Comments|
The genesis of this post is that:
- Hortonworks is trying to revitalize the Apache Storm project, after Storm lost momentum; indeed, Hortonworks is referring to Storm as a component of Hadoop.
- Cloudera is talking up what I would call its human real-time strategy, which includes but is not limited to Flume, Kafka, and Spark Streaming. Cloudera also sees a few use cases for Storm.
- This all fits with my view that the Current Hot Subject is human real-time data freshness — for analytics, of course, since we’ve always had low latencies in short-request processing.
- This also all fits with the importance I place on log analysis.
- Cloudera reached out to talk to me about all this.
Of course, we should hardly assume that what the Hadoop distro vendors favor will be the be-all and end-all of streaming. But they are likely to at least be influential players in the area.
In the parts of the problem that Cloudera emphasizes, the main tasks that need to be addressed are: Read more
|Categories: Cloudera, Complex event processing (CEP), Data warehousing, EAI, EII, ETL, ELT, ETLT, Hadoop, Health care, Hortonworks, Kafka and Confluent, Log analysis, Specific users, Splunk, Web analytics||9 Comments|