1. Censorship worries me, a lot. A classic example is Vietnam, which basically has outlawed online political discussion.
And such laws can have teeth. It’s hard to conceal your internet usage from an inquisitive government.
2. Software and software related patents are back in the news. Google, which said it was paying $5.5 billion or so for a bunch of Motorola patents, turns out to really have paid $7 billion or more. Twitter and IBM did a patent deal as well. Big numbers, and good for certain shareholders. But this all benefits the wider world — how?
The purpose of legal intellectual property protections, simply put, is to help make it a good decision to create something. …
Why does “securing … exclusive Right[s]” to the creators of things that are patented, copyrighted, or trademarked help make it a good decision for them to create stuff? Because it averts competition from copiers, thus making the creator a monopolist in what s/he has created, allowing her to at least somewhat value-price her creation.
I.e., the core point of intellectual property rights is to prevent copying-based competition. By way of contrast, any other kind of intellectual property “right” should be viewed with great suspicion.
That Constitutionally-based principle makes as much sense to me now as it did then. By way of contrast, “Let’s give more intellectual property rights to big corporations to protect middle-managers’ jobs” is — well, it’s an argument I view with great suspicion.
But I find it extremely hard to think of a technology industry example in which development was stimulated by the possibility of patent protection. Yes, the situation may be different in pharmaceuticals, or for gadgeteering home inventors, but I can think of no case in which technology has been better, or faster to come to market, because of the possibility of a patent-law monopoly. So if software and business-method patents were abolished entirely – even the ones that I think could be realistically adjudicated — I’d be pleased.
3. In November, 2008 I offered IT policy suggestions for the incoming Obama Administration, especially: Read more
|Categories: Buying processes, Google, IBM and DB2, Public policy, Surveillance and privacy||1 Comment|
Generalizing about SaaS (Software as a Service) is hard. To prune some of the confusion, let’s start by noting:
- SaaS has been around for over half a century, and at times has been the dominant mode of application delivery.
- The term multi-tenancy is being used in several different ways.
- Multi-tenancy, in the purest sense, is inessential to SaaS. It’s simply an implementation choice that has certain benefits for the SaaS provider. And by the way, …
- … salesforce.com, the chief proponent of the theory that true multi-tenancy is the hallmark of true SaaS, abandoned that position this week.
- Internet-based services are commonly, if you squint a little, SaaS. Examples include but are hardly limited to Google, Twitter, Dropbox, Intuit, Amazon Web Services, and the company that hosts this blog (KnownHost).
- Some of the core arguments for SaaS’ rise, namely the various efficiencies of data center outsourcing and scale, apply equally to the public cloud, to SaaS, and to AEaaS (Anything Else as a Service).
- These benefits are particularly strong for inherently networked use cases. For example, you really don’t want to be hosting your website yourself. And salesforce.com got its start supporting salespeople who worked out of remote offices.
- In theory and occasionally in practice, certain SaaS benefits, namely the outsourcing of software maintenance and updates, could be enjoyed on-premises as well. Whether I think that could be a bigger deal going forward will be explored in future posts.
For smaller enterprises, the core outsourcing argument is compelling. How small? Well:
- What’s the minimum level of IT operations headcount needed for mission-critical systems? Let’s just say “several”.
- What does that cost? Fully burdened, somewhere in the six figures.
- What fraction of the IT budget should such headcount be? As low a double digit percentage as possible.
- What fraction of revenues should be spent on IT? Some single-digit percentage.
So except for special cases, an enterprise with less than $100 million or so in revenue may have trouble affording on-site data processing, at least at a mission-critical level of robustness. It may well be better to use NetSuite or something like that, assuming needed features are available in SaaS form.*
|Categories: Amazon and its cloud, Buying processes, Cloud computing, Data mart outsourcing, Data warehouse appliances, Data warehousing, Infobright, Netezza, Pricing, salesforce.com, Software as a Service (SaaS), Workday||5 Comments|
Some subjects just keep coming up. And so I keep saying things like:
Most generalizations about “Big Data” are false. “Big Data” is a horrific catch-all term, with many different meanings.
Most generalizations about Hadoop are false. Reasons include:
- Hadoop is a collection of disparate things, most particularly data storage and application execution systems.
- The transition from Hadoop 1 to Hadoop 2 will be drastic.
- For key aspects of Hadoop — especially file format and execution engine — there are or will be widely varied options.
Hadoop won’t soon replace relational data warehouses, if indeed it ever does. SQL-on-Hadoop is still very immature. And you can’t replace data warehouses unless you have the power of SQL.
Note: SQL isn’t the only way to provide “the power of SQL”, but alternative approaches are just as immature.
Most generalizations about NoSQL are false. Different NoSQL products are … different. It’s not even accurate to say that all NoSQL systems lack SQL interfaces. (For example, SQL-on-Hadoop often includes SQL-on-HBase.)
A few days ago I posted Daniel Abadi’s thoughts in a discussion of Hadapt, Microsoft PDW (Parallel Data Warehouse)/PolyBase, Pivotal/Greenplum Hawq, and other SQL-Hadoop combinations. This is Dave DeWitt’s response. Emphasis mine.
|Categories: Benchmarks and POCs, Cloudera, Clustering, Data warehousing, Greenplum, Hadapt, Hadoop, MapReduce, Microsoft and SQL*Server, PostgreSQL, SQL/Hadoop integration||6 Comments|
Hmm. I probably should have broken this out as three posts rather than one after all. Sorry about that.
Discussions of DBMS performance are always odd, for starters because:
- Workloads and use cases vary greatly.
- In particular, benchmarks such as the YCSB or TPC-H aren’t very helpful.
- It’s common for databases or at least working sets to be entirely in RAM — but it’s not always required.
- Consistency and durability models vary. What’s more, in some systems — e.g. MongoDB — there’s considerable flexibility as to which model you use.
- In particular, there’s an increasingly common choice in which data is written synchronously to RAM on 2 or more servers, then asynchronously to disk on each of them. Performance in these cases can be quite different from when all writes need to be committed to disk. Of course, you need sufficient disk I/O to keep up, so SSDs (Solid-State Drives) can come in handy.
- Many workloads are inherently single node (replication aside). Others are not.
MongoDB and 10gen
I caught up with Ron Avnur at 10gen. Technical highlights included: Read more
I recently complained that the Gartner Magic Quadrant for Data Warehouse DBMS conflates many use cases into one set of rankings. So perhaps now would be a good time to offer some thoughts on how to tell use cases apart. Assuming you know that you really want to manage your analytic database with a relational DBMS, the first questions you ask yourself could be:
- How big is your database? How big is your budget?
- How do you feel about appliances?
- How do you feel about the cloud?
- What are the size and shape of your workload?
- How fresh does the data need to be?
Let’s drill down. Read more
Two different vendors recently tried to inflict benchmarks on me. Both were YCSBs, so I decided to look up what the YCSB (Yahoo! Cloud Serving Benchmark) actually is. It turns out that the YCSB:
- Was developed by — you guessed it! — Yahoo.
- Is meant to simulate workloads that fetch web pages, including the writing portions of those workloads.
- Was developed with NoSQL data managers in mind.
- Bakes in one kind of sensitivity analysis — latency vs. throughput.
- Is implemented in extensible open source code.
That actually sounds pretty good, especially the extensibility part;* it’s likely that the YCSB can be useful in a variety of product selection scenarios. Still, as recent examples show, benchmark marketing is an annoying blight upon the database industry.
*With extensibility you can test your own workloads and do your own sensitivity analyses.
At the highest level, Oracle and IBM have similar strategic priorities, in line with the Innovator’s Dilemma/Innovator’s Solution issues I keep mentioning. That is:
- Oracle and IBM sell mainly to large enterprises with complex IT needs.
- Oracle and IBM sell mainly to their respective existing customers.
- Oracle and IBM are looking to preserve and expand revenue, margins, and share-of-wallet at those large existing customers.
- Oracle and IBM rely on and encourage customers’ desire to consolidate purchasing among as few vendors as possible.
- Technical implications include:
- Oracle and IBM invest in features that only large, complex enterprises care about.
- Oracle and IBM offer many kinds of technology and services, which they strive to make work fairly well together.
Of course, there are major differences in the two companies’ product and service portfolios. Some of the biggest are: Read more
In my recent series of Hadoop posts, there were several cases where I had to choose between recommending that enterprises:
- Go with the most advanced features any vendor was credibly advocating.
- Be more cautious, and only adopt features that have been solidly proven in the field.
I favored the more advanced features each time. Here’s why.
To a first approximation, I divide Hadoop use cases into two major buckets, only one of which I was addressing with my comments:
1. Analytic data management.* Here I favored features over reliability because they are more important, for Hadoop as for analytic RDBMS before it. When somebody complains about an analytic data store not being ready for prime time, never really working, or causing them to tear their hair out, what they usually mean is that:
- It couldn’t do the work that needed doing …
- … with reasonable performance and turnaround time …
- … without undue effort in administration and/or programming.
Those complaints are much, much, more frequent than “It crashed”. So it was for Netezza, DATAllegro, Greenplum, Aster Data, Vertica, Infobright, et al. So it also is for Hadoop. And how does one address those complaints? By performance and feature enhancements, of the kind that the Hadoop community is introducing at high speed. Read more
|Categories: Buying processes, Data warehousing, EAI, EII, ETL, ELT, ETLT, Hadoop, HBase, Hortonworks, Open source||1 Comment|
This is part of a four-post series, covering:
- Annoying Hadoop marketing themes that should be ignored.
- Hadoop versions and distributions, and their readiness or lack thereof for production.
- In general, how “enterprise-ready” is Hadoop?
- HBase 0.92 (this post)
As part of my recent round of Hadoop research, I talked with Cloudera’s Todd Lipcon. Naturally, one of the subjects was HBase, and specifically HBase 0.92. I gather that the major themes to HBase 0.92 are:
- Performance, scalability, and so on.
- “Coprocessors”, which are like triggers or stored procedures.
- Security, as the first major application of co-processors.
HBase coprocessors are Java code that links straight into HBase. As with other DBMS extensions of the “links straight into the DBMS code” kind,* HBase coprocessors seem best suited for very sophisticated users and third parties.** Evidently, coprocessors have already been used to make HBase security more granular — role-based, per-column-family/per-table, etc. Further, Todd thinks coprocessors could serve as a good basis for future HBase enhancements in areas such as aggregation or secondary indexing. Read more
|Categories: Benchmarks and POCs, Cloudera, Hadoop, HBase, MapReduce, NoSQL, Open source, Storage, Theory and architecture||2 Comments|