Issues in enterprise application software
1. I think the next decade or so will see much more change in enterprise applications than the last one. Why? Because the unresolved issues are piling up, and something has to give. I intend this post to be a starting point for a lot of interesting discussions ahead.
2. The more technical issues I’m thinking of include:
- How will app vendors handle analytics?
- How will app vendors handle machine-generated data?
- How will app vendors handle dynamic schemas?
- How far will app vendors get with social features?
- What kind of underlying technology stacks will app vendors drag along?
We also always have the usual set of enterprise app business issues, including:
- Will the current leaders — SAP, Oracle and whoever else you want to include — continue to dominate the large-enterprise application market?
- Will the leaders in the large-enterprise market succeed in selling to smaller markets?
- Which new categories of application will be important?
- Which kinds of vendors and distribution channels will succeed in serving small enterprises?
And perhaps the biggest issue of all, intertwined with most of the others, is:
- How will the move to SaaS (Software as a Service) play out?
| Categories: Oracle, SAP AG, Software as a Service (SaaS) | 13 Comments |
Differentiation in business intelligence
Parts of the business intelligence differentiation story resemble the one I just posted for data management. After all:
- Both kinds of products query and aggregate data.
- Both are offered by big “enterprise standard” behemoth companies and also by younger, nimbler specialists.
- You really, really, really don’t want your customer data to leak via a security breach in either kind of product.
That said, insofar as BI’s competitive issues resemble those of DBMS, they are those of DBMS-lite. For example:
- BI is less mission-critical than some other database uses.
- BI has done a lot less than DBMS to deal with multi-structured data.
- Scalability demands on BI are less than those on DBMS — indeed, they’re the ones that are left over after the DBMS has done its data crunching first.
And full-stack analytic systems — perhaps delivered via SaaS (Software as a Service) — can moot the BI/data management distinction anyway.
Of course, there are major differences between how DBMS and BI are differentiated. The biggest are in user experience. I’d say: Read more
| Categories: Business intelligence, Buying processes, ClearStory Data, Data mart outsourcing, Pricing, QlikTech and QlikView, Rocana, Tableau Software | Leave a Comment |
Differentiation in data management
In the previous post I broke product differentiation into 6-8 overlapping categories, which may be abbreviated as:
- Scope
- Accuracy
- (Other) trustworthiness
- Speed
- User experience
- Cost
and sometimes also issues in adoption and administration.
Now let’s use this framework to examine two market categories I cover — data management and, in separate post, business intelligence.
Applying this taxonomy to data management:
Read more
| Categories: Buying processes, Clustering, Data warehousing, Database diversity, Microsoft and SQL*Server, Predictive modeling and advanced analytics, Pricing | 2 Comments |
Cassandra and privacy requirements
For starters:
- I’ve suggested in the past that multi-data-center capabilities are important for “data sovereignty”/geo-compliance.
- The need for geo-compliance just got a lot stronger, with the abolition of the European Union’s Safe Harbour rule for the US. If you collect data in multiple countries, you should be at least thinking about geo-compliance.
- Cassandra is an established leader in multi-data-center operation.
But when I made that connection and checked in accordingly with my client Patrick McFadin at DataStax, I discovered that I’d been a little confused about how multi-data-center Cassandra works. The basic idea holds water, but the details are not quite what I was envisioning.
The story starts:
- Cassandra groups nodes into logical “data centers” (i.e. token rings).
- As a best practice, each physical data center can contain one or more logical data center, but not vice-versa.
- There are two levels of replication — within a single logical data center, and between logical data centers.
- Replication within a single data center is planned in the usual way, with the principal data center holding a database likely to have a replication factor of 3.
- However, copies of the database held elsewhere may have different replication factors …
- … and can indeed have different replication factors for different parts of the database.
In particular, a remote replication factor for Cassandra can = 0. When that happens, then you have data sitting in one geographical location that is absent from another geographical location; i.e., you can be in compliance with laws forbidding the export of certain data. To be clear (and this contradicts what I previously believed and hence also implied in this blog):
- General multi-data-center operation is not what gives you geo-compliance, because the default case is that the whole database is replicated to each data center.
- Instead, you get that effect by tweaking your specific replication settings.
| Categories: Cassandra, Clustering, DataStax, HBase, NoSQL, Open source, Specific users, Surveillance and privacy | 3 Comments |
Basho and Riak
Basho was on my (very short) blacklist of companies with whom I refuse to speak, because they have lied about the contents of previous conversations. But Tony Falco et al. are long gone from the company. So when Basho’s new management team reached out, I took the meeting.
For starters:
- Basho management turned over significantly 1-2 years ago. The main survivors from the old team are 1 each in engineering, sales, and services.
- Basho moved its headquarters to Bellevue, WA. (You get one guess as to where the new CEO lives.) Engineering operations are very distributed geographically.
- Basho claims that it is much better at timely product shipments than it used to be. Its newest product has a planned (or at least hoped-for) 8-week cadence for point releases.
- Basho’s revenue is ~90% subscription.
- Basho claims >200 enterprise clients, vs. 100-120 when new management came in. Unfortunately, I forgot to ask the usual questions about divisions vs. whole organizations, OEM sell-through vs. direct, etc.
- Basho claims an average contract value of >$100K, typically over 2-3 years. $9 million of that (which would be close to half the total, actually), comes from 2 particular deals of >$4 million each.
Basho’s product line has gotten a bit confusing, but as best I understand things the story is:
- There’s something called Riak Core, which isn’t even a revenue-generating product. However, it’s an open source project with some big users (e.g. Goldman Sachs, Visa), and included in pretty much everything else Basho promotes.
- Riak KV is the key-value store previously known as Riak. It generates the lion’s share of Basho’s revenue.
- Riak S2 is an emulation of Amazon S3. Basho thinks that Riak KV loses efficiency when objects get bigger than 1 MB or so, and that’s when you might want to use Riak S2 in addition or instead.
- Riak TS is for time series, and just coming out now.
- Also in the mix are some (extra charge) connectors for Redis and Spark. Presumably, there are more of these to come.
- There’s an umbrella marketing term of “Basho Data Platform”.
Technical notes on some of that include: Read more
Couchbase 4.0 and related subjects
I last wrote about Couchbase in November, 2012, around the time of Couchbase 2.0. One of the many new features I mentioned then was secondary indexing. Ravi Mayuram just checked in to tell me about Couchbase 4.0. One of the important new features he mentioned was what I think he said was Couchbase’s “first version” of secondary indexing. Obviously, I’m confused.
Now that you’re duly warned, let me remind you of aspects of Couchbase timeline.
- 2 corporate name changes ago, Couchbase was organized to commercialize memcached. memcached, of course, was internet companies’ default way to scale out short-request processing before the rise of NoSQL, typically backed by manually sharded MySQL.
- Couchbase’s original value proposition, under the name Membase, was to provide persistence and of course support for memcached. This later grew into a caching-oriented pitch even to customers who weren’t already memcached users.
- A merger with the makers of CouchDB ensued, with the intention of replacing Membase’s SQLite back end with CouchDB at the same time as JSON support was introduced. This went badly.
- By now, however, Couchbase sells for more than distributed cache use cases. Ravi rattled off a variety of big-name customer examples for system-of-record kinds of use cases, especially in session logging (duh) and also in travel reservations.
- Couchbase 4.0 has been in beta for a few months.
Technical notes on Couchbase 4.0 — and related riffs 🙂 — start: Read more
Notes on privacy and surveillance, October 11, 2015
1. European Union data sovereignty laws have long had a “Safe Harbour” rule stating it was OK to ship data to the US. Per the case Maximilian Schrems v Data Protection Commissioner, this rule is now held to be invalid. Angst has ensued, and rightly so.
The core technical issues are roughly:
- Data is usually in one logical database. Data may be replicated locally, for availability and performance. It may be replicated remotely, for availability, disaster recovery, and performance. But it’s still usually logically in one database.
- Now remote geographic partitioning may be required by law. Some technologies (e.g. Cassandra) support that for a single logical database. Some don’t.
- Even under best circumstances, hosting and administrative costs are likely to be higher when a database is split across more geographies (especially when the count is increased from 1 to 2).
Facebook’s estimate of billions of dollars in added costs is not easy to refute.
My next set of technical thoughts starts: Read more
| Categories: Cassandra, DataStax, Surveillance and privacy | 1 Comment |
Notes on packaged applications (including SaaS)
1. The rise of SAP (and later Siebel Systems) was greatly helped by Anderson Consulting, even before it was split off from the accounting firm and renamed as Accenture. My main contact in that group was Rob Kelley, but it’s possible that Brian Sommer was even more central to the industry-watching part of the operation. Brian is still around, and he just leveled a blast at the ERP* industry, which I encourage you to read. I agree with most of it.
*Enterprise Resource Planning
Brian’s argument, as I interpret it, boils down mainly to two points:
- Big ERP companies selling big ERP systems are pathetically slow at adding new functionality. He’s right. My favorite example is the multi-decade slog to integrate useful analytics into operational apps.
- The world of “Big Data” is fundamentally antithetical to the design of current-generation ERP systems. I think he’s right in that as well.
I’d add that SaaS (Software As A Service)/on-premises tensions aren’t helping incumbent vendors either.
But no article addresses all the subjects it ideally should, and I’d like to call out two omissions. First, what Brian said is in many cases applicable just to large and/or internet-first companies. Plenty of smaller, more traditional businesses could get by just fine with no more functionality than is in “Big ERP” today, if we stipulate that it should be:
- Delivered via SaaS.
- Much easier to adopt and use.
| Categories: Database diversity, SAP AG, Software as a Service (SaaS) | 8 Comments |
The potential significance of Cloudera Kudu
This is part of a three-post series on Kudu, a new data storage system from Cloudera.
- Part 1 is an overview of Kudu technology.
- Part 2 is a lengthy dive into how Kudu writes and reads data.
- Part 3 (this post) is a brief speculation as to Kudu’s eventual market significance.
Combined with Impala, Kudu is (among other things) an attempt to build a no-apologies analytic DBMS (DataBase Management System) into Hadoop. My reactions to that start:
- It’s plausible; just not soon. What I mean by that is:
- Success will, at best, be years away. Please keep that in mind as you read this otherwise optimistic post.
- Nothing jumps out at me to say “This will never work!”
- Unlike when it introduced Impala — or when I used to argue with Jeff Hammerbacher pre-Impala 🙂 — this time Cloudera seems to have reasonable expectations as to how hard the project is.
- There’s huge opportunity if it works.
- The analytic RDBMS vendors are beatable. Teradata has a great track record of keeping its product state-of-the-art, but it likes high prices. Most other strong analytic RDBMS products were sold to (or originated by) behemoth companies that seem confused about how to proceed.
- RDBMS-first analytic platforms didn’t do as well as I hoped. That leaves a big gap for Hadoop.
I’ll expand on that last point. Analytics is no longer just about fast queries on raw or simply-aggregated data. Data transformation is getting ever more complex — that’s true in general, and it’s specifically true in the case of transformations that need to happen in human real time. Predictive models now often get rescored on every click. Sometimes, they even get retrained at short intervals. And while data reduction in the sense of “event extraction from high-volume streams” isn’t that a big deal yet in commercial apps featuring machine-generated data — if growth trends continue as much of us expect, it’s only a matter of time before that changes.
Of course, this is all a bullish argument for Spark (or Flink, if I’m wrong to dismiss its chances as a Spark competitor). But it also all requires strong low-latency analytic data underpinnings, and I suspect that several kinds of data subsystem will prosper. I expect Kudu-supported Hadoop/Spark to be a strong contender for that role, along with the best of the old-school analytic RDBMS, Tachyon-supported Spark, one or more contenders from the Hana/MemSQL crowd (i.e., memory-centric RDBMS that purport to be good at analytics and transactions alike), and of course also whatever Cloudera’s strongest competitor(s) choose to back.
| Categories: Cloudera, Databricks, Spark and BDAS, Hadoop, Kafka and Confluent | 6 Comments |
Cloudera Kudu deep dive
This is part of a three-post series on Kudu, a new data storage system from Cloudera.
- Part 1 is an overview of Kudu technology.
- Part 2 (this post) is a lengthy dive into how Kudu writes and reads data.
- Part 3 is a brief speculation as to Kudu’s eventual market significance.
Let’s talk in more detail about how Kudu stores data.
- As previously noted, inserts land in an in-memory row store, which is periodically flushed to the column store on disk. Queries are federated between these two stores. Vertica taught us to call these the WOS (Write-Optimized Store) and ROS (Read-Optimized Store) respectively, and I’ll use that terminology here.
- Part of the ROS is actually another in-memory store, aka the DeltaMemStore, where updates and deletes land before being applied to the DiskRowSets. These stores are managed separately for each DiskRowSet. DeltaMemStores are checked at query time to confirm whether what’s in the persistent store is actually up to date.
- A major design goal for Kudu is that compaction should never block — nor greatly slow — other work. In support of that:
- Compaction is done, server-by-server, via a low-priority but otherwise always-on background process.
- There is a configurable maximum to how big a compaction process can be — more precisely, the limit is to how much data the process can work on at once. The current default figure = 128 MB, which is 4X the size of a DiskRowSet.
- When done, Kudu runs a little optimization to figure out which 128 MB to compact next.
- Every tablet has its own write-ahead log.
- This creates a practical limitation on the number of tablets …
- … because each tablet is causing its own stream of writes to “disk” …
- … but it’s only a limitation if your “disk” really is all spinning disk …
- … because multiple simultaneous streams work great with solid-state memory.
- Log retention is configurable, typically the greater of 5 minutes or 128 MB.
- Metadata is cached in RAM. Therefore:
- ALTER TABLE kinds of operations that can be done by metadata changes only — i.e. adding/dropping/renaming columns — can be instantaneous.
- To keep from being screwed up by this, the WOS maintains a column that labels rows by which schema version they were created under. I immediately called this MSCC — Multi-Schema Concurrency Control 🙂 — and Todd Lipcon agreed.
- Durability, as usual, boils down to “Wait until a quorum has done the writes”, with a configurable option as to what constitutes a “write”.
- Servers write to their respective write-ahead logs, then acknowledge having done so.
- If it isn’t too much of a potential bottleneck — e.g. if persistence is on flash — the acknowledgements may wait until the log has been fsynced to persistent storage.
- There’s a “thick” client library which, among other things, knows enough about the partitioning scheme to go straight to the correct node(s) on a cluster.