Recently, I observed that Big Data terminology is seriously broken. It is reasonable to reduce the subject to two quasi-dimensions:
- Bigness — Volume, Velocity, size
- Structure — Variety, Variability, Complexity
- High-velocity “big data” problems are usually high-volume as well.*
- Variety, variability, and complexity all relate to the simply-structured/poly-structured distinction.
But the conflation should stop there.
*Low-volume/high-velocity problems are commonly referred to as “event processing” and/or “streaming”.
When people claim that bigness and structure are the same issue, they oversimplify into mush. So I think we need four pieces of terminology, reflective of a 2×2 matrix of possibilities. For want of better alternatives, my suggestions are:
- Relational big data is data of high volume that fits well into a relational DBMS.
- Multi-structured big data is data of high volume that doesn’t fit well into a relational DBMS. Alternative: Poly-structured big data.
- Conventional relational data is data of not-so-high volume that fits well into a relational DBMS. Alternatives: Ordinary/normal/smaller relational data.
- Smaller poly-structured data is data for which dynamic schema capabilities are important, but which doesn’t rise to “big data” volume.
|Categories: Cassandra, Data models and architecture, Data warehousing, Exadata, Facebook, Google, Hadoop, HBase, Log analysis, Market share and customer counts, MarkLogic, NewSQL, NoSQL, Oracle, Splunk, Yahoo||10 Comments|
This is Part 1 of a three post series. The posts cover:
- Confusion about text data management.
- Choices for text data management (general and short-request).
- Choices for text data management (analytic).
There’s much confusion about the management of text data, among technology users, vendors, and investors alike. Reasons seems to include:
- The terminology around text data is inaccurate.
- Data volume estimates for text are misleading.
- Multiple different technologies are in the mix, including:
- Enterprise text search.
- Text analytics — text mining, sentiment analysis, etc.
- Document stores — e.g. document-oriented NoSQL, or MarkLogic.
- Log management and parsing — e.g. Splunk.
- Text archiving — e.g., various specialty email archiving products I couldn’t even name.
- Public web search — Google et al.
- Text search vendors have disappointed, especially technically.
- Text analytics vendors have disappointed, especially financially.
- Other analytic technology vendors ignore what the text analytic vendors actually have accomplished, and reinvent inferior wheels rather than OEM the state of the art.
Above all: The use cases for text data vary greatly, just as the use cases for simply-structured databases do.
There are probably fewer people now than there were six years ago who need to be told that text and relational database management are very different things. Other misconceptions, however, appear to be on the rise. Specific points that are commonly overlooked include: Read more
|Categories: Analytic technologies, Archiving and information preservation, Google, Log analysis, MarkLogic, NoSQL, Oracle, Splunk, Text||2 Comments|
I spoke with Eliot Horowitz and Max Schierson of 10gen last month about MongoDB users and use cases. The biggest clusters they came up with weren’t much over 100 nodes, but clusters an order of magnitude bigger were under development. The 100 node one we talked the most about had 33 replica sets, each with about 100 gigabytes of data, so that’s in the 3-4 terabyte range total. In general, the largest MongoDB databases are 20-30 TB; I’d guess those really do use the bulk of available disk space. Read more
|Categories: Data models and architecture, Games and virtual worlds, Log analysis, MongoDB and 10gen, NoSQL, Solid-state memory, Specific users, Splunk, Telecommunications, Web analytics||13 Comments|
I refer often to machine-generated data, which is commonly generated inexpensively and in log-like formats, and is often best aggregated in a big bit bucket before you try to do much analysis on it. The term has caught on, to the point that perhaps it’s time to distinguish more carefully among different kinds of machine-generated data. In particular, I think it may be useful to distinguish between:
- Log-stream machine-generated data, when what you’re looking at — at least initially — is the entire output of verbose logging systems.
- Remote machine-generated data.
Here’s what I’m thinking of for the second category. I rather frequently hear of cases in which data is generated by large numbers of remote machines, which occasionally send messages home. For example: Read more
|Categories: Analytic technologies, Cloud computing, Log analysis, MySQL, Netezza, Splunk, Truviso||2 Comments|
A major driver of Hadoop adoption is the “big bit bucket” use case. Users take a whole lot of data, often machine-generated data in logs of different kinds, and dump it into one place, managed by Hadoop, at open-source pricing. Hadoop hardware doesn’t need to be that costly either. And once you get that data into Hadoop, there are a whole lot of things you can do with it.
Of course, there are various outfits who’d like to sell you not-so-cheap bit buckets. Contending technologies include Hadoop appliances (which I don’t believe in), Splunk (which in many use cases I do), and MarkLogic (ditto, but often the cases are different from Splunk’s). Cloudera and IBM, among other vendors, would also like to sell you some proprietary software to go with your standard Apache Hadoop code.
So the question arises — why would you want to spend serious money to look after your low-value data? The answer, of course, is that maybe your log data isn’t so low-value. Read more
We hear much these days about unstructured or semi-structured (as opposed to) structured data. Those are misnomers, however, for at least two reasons. First, it’s not really the data that people think is un-, semi-, or fully structured; it’s databases.* Relational databases are highly structured, but the data within them is unstructured — just lists of numbers or character strings, whose only significance derives from the structure that the database imposes.
*Here I’m using the term “database” literally, rather than as a concise synonym for “database management system”. But see below.
Second, a more accurate distinction is not whether a database has one structure or none – it’s whether a database has one structure or many. The easiest way to see this is for databases that have clearly-defined schemas. A relational database has one schema (even if it is just the union of various unrelated sub-schemas); an XML database, however, can have as many schemas as it contains documents.
One small terminological problem is easily handled, namely that people don’t talk about true databases very often, at least when they’re discussing generalities; rather, they talk about data and DBMS.* So let’s talk of DBMS being “structured” singly or multiply or whatever, just as the databases they’re designed to manage are.
*And they refer to the DBMS as “databases,” because they don’t have much other use for the word.
All that said — I think that single vs. multiple database structures isn’t a bright-line binary distinction; rather, it’s a spectrum. For example: Read more
|Categories: Cassandra, Couchbase, Data models and architecture, HBase, IBM and DB2, MarkLogic, MongoDB and 10gen, NoSQL, Splunk, Theory and architecture||18 Comments|
Edit: This disclosure has been superseded by a March, 2012 version.
From time to time, I disclose our vendor client lists. Another iteration is below. To be clear:
- This is a list of Monash Advantage members.
- All our vendor clients are Monash Advantage members, unless …
- … we work with them primarily in their capacity as technology users. (A large fraction of our user clients happen to be SaaS vendors.)
- We do not usually disclose our user clients.
- We do not usually disclose our venture capital clients, nor those who invest in publicly-traded securities.
- Included in the list below are two expired Monash Advantage members who haven’t said they will renew, as mentioned in my recent post on analyst bias. (You can probably imagine a couple of reasons for that obfuscation.)
With that said, our vendor client disclosures at this time are:
- Aster Data
- SAND Technology
- Schooner Information Technology
|Categories: Analytic technologies, Aster Data, Cloudera, Data warehousing, Google, Hadoop, MapReduce, SenSage, Splunk||8 Comments|
As noted in my other introductory post, Splunk sells software called Splunk, which is used for log analysis. These can be logs of various kinds, but for the purpose of understanding Splunk technology, it’s probably OK to assume they’re clickstream/network event logs. In addition, Splunk seems to have some aspirations of having its software used for general schema-free analytics, but that’s in early days at best.
Splunk’s core technology indexes text and XML files or streams, especially log files. Technical highlights of that part include: Read more
|Categories: Analytic technologies, Log analysis, MapReduce, Splunk, Structured documents, Text, Web analytics||14 Comments|
I dropped by log analysis software vendor Splunk a few weeks ago for a chat with Marketing VP Steve Sommer (who some you may know from Cognos and/or Informix), Product Management VP Christina Noren, and above all co-founder/CTO Erik Swan. Splunk turns out to be a pretty interesting company, from both business and technical standpoints. For one thing, Splunk seems highly regarded by most people I mention it to.
Splunk’s technical stories include:
- Text search over log files.
- Business intelligence over text search. (That part sounds a lot like Attivio.)
- MapReduce with schema flexibility and smart multi-stage execution plans. (That part sounds a lot like Aster Data.)
More on those in a separate post.
Less technical Splunk highlights include: Read more
|Categories: Analytic technologies, Fox and MySpace, Investment research and trading, Log analysis, Splunk, Telecommunications, Text, Web analytics||1 Comment|