Discussion of technologies related to information query and analysis. Related subjects include:
“Real-time” technology excites people, and has for decades. Yet the actual, useful technology to meet “real-time” requirements remains immature, especially in cases which call for rapid human decision-making. Here are some notes on that conundrum.
1. I recently posted that “real-time” is getting real. But there are multiple technology challenges involved, including:
- General streaming. Some of my posts on that subject are linked at the bottom of my August post on Flink.
- Low-latency ingest of data into structures from which it can be immediately analyzed. That helps drive the (re)integration of operational data stores, analytic data stores, and other analytic support — e.g. via Spark.
- Business intelligence that can be used quickly enough. This is a major ongoing challenge. My clients at Zoomdata may be thinking about this area more clearly than most, but even they are still in the early stages of providing what users need.
- Advanced analytics that can be done quickly enough. Answers there may come through developments in anomaly management, but that area is still in its super-early days.
- Alerting, which has been under-addressed for decades. Perhaps the anomaly management vendors will finally solve it.
|Categories: Business intelligence, Databricks, Spark and BDAS, In-memory DBMS, Investment research and trading, Log analysis, Streaming and complex event processing (CEP), Text, Web analytics, Zoomdata||Leave a Comment|
Then felt I like some watcher of the skies
When a new planet swims into his ken
— John Keats, “On First Looking Into Chapman’s Homer”
1. In June I wrote about why anomaly management is hard. Well, not only is it hard to do; it’s hard to talk about as well. One reason, I think, is that it’s hard to define what an anomaly is. And that’s a structural problem, not just a semantic one — if something is well enough understood to be easily described, then how much of an anomaly is it after all?
Artificial intelligence is famously hard to define for similar reasons.
“Anomaly management” and similar terms are not yet in the software marketing mainstream, and may never be. But naming aside, the actual subject matter is important.
2. Anomaly analysis is clearly at the heart of several sectors, including:
- IT operations
- Factory and other physical-plant operations
Each of those areas features one or both of the frameworks:
- Surprises are likely to be bad.
- Coincidences are likely to be suspicious.
So if you want to identify, understand, avert and/or remediate bad stuff, data anomalies are the first place to look.
3. The “insights” promised by many analytics vendors — especially those who sell to marketing departments — are also often heralded by anomalies. Already in the 1970s, Walmart observed that red clothing sold particularly well in Omaha, while orange flew off the shelves in Syracuse. And so, in large college towns, they stocked their stores to the gills with clothing in the colors of the local football team. They also noticed that fancy dresses for little girls sold especially well in Hispanic communities … specifically for girls at the age of First Communion.
|Categories: Business intelligence, Log analysis, Predictive modeling and advanced analytics, Web analytics||Leave a Comment|
1. The cloud is super-hot. Duh. And so, like any hot buzzword, “cloud” means different things to different marketers. Four of the biggest things that have been called “cloud” are:
- The Amazon cloud, Microsoft Azure, and their competitors, aka public cloud.
- Software as a service, aka SaaS.
- Co-location in off-premises data centers, aka colo.
- On-premises clusters (truly on-prem or colo as the case may be) designed to run a broad variety of applications, aka private cloud.
Further, there’s always the idea of hybrid cloud, in which a vendor peddles private cloud systems (usually appliances) running similar technology stacks to what they run in their proprietary public clouds. A number of vendors have backed away from such stories, but a few are still pushing it, including Oracle and Microsoft.
This is a good example of Monash’s Laws of Commercial Semantics.
2. Due to economies of scale, only a few companies should operate their own data centers, aka true on-prem(ises). The rest should use some combination of colo, SaaS, and public cloud.
This fact now seems to be widely understood.
I’ve been an analyst for 35 years, and debates about “real-time” technology have run through my whole career. Some of those debates are by now pretty much settled. In particular:
- Yes, interactive computer response is crucial.
- Into the 1980s, many apps were batch-only. Demand for such apps dried up.
- Business intelligence should occur at interactive speeds, which is a major reason that there’s a market for high-performance analytic RDBMS.
- Theoretical arguments about “true” real-time vs. near-real-time are often pointless.
- What matters in most cases is human users’ perceptions of speed.
- Most of the exceptions to that rule occur when machines race other machines, for example in automated bidding (high frequency trading or otherwise) or in network security.
A big issue that does remain open is: How fresh does data need to be? My preferred summary answer is: As fresh as is needed to support the best decision-making. I think that formulation starts with several advantages:
- It respects the obvious point that different use cases require different levels of data freshness.
- It cautions against people who think they need fresh information but aren’t in a position to use it. (Such users have driven much bogus “real-time” demand in the past.)
- It covers cases of both human and automated decision-making.
Straightforward applications of this principle include: Read more
I used to spend most of my time — blogging and consulting alike — on data warehouse appliances and analytic DBMS. Now I’m barely involved with them. The most obvious reason is that there have been drastic changes in industry structure:
- Many of the independent vendors were swooped up by acquisition.
- None of those acquisitions was a big success.
- Microsoft did little with DATAllegro.
- Netezza struggled with R&D after being bought by IBM. An IBMer recently told me that their main analytic RDBMS engine was BLU.
- I hear about Vertica more as a technology to be replaced than as a significant ongoing market player.
- Pivotal open-sourced Greenplum. I have detected few people who care.
- Ditto for Actian’s offerings.
- Teradata claimed a few large Aster accounts, but I never hear of Aster as something to compete or partner with.
- Smaller vendors fizzled too. Hadapt and Kickfire went to Teradata as more-or-less acquihires. InfiniDB folded. Etc.
- Impala and other Hadoop-based alternatives are technology options.
- Oracle, Microsoft, IBM and to some extent SAP/Sybase are still pedaling along … but I rarely talk with companies that big.
Simply reciting all that, however, begs the question of whether one should still care about analytic RDBMS at all.
My answer, in a nutshell, is:
Analytic RDBMS — whether on premises in software, in the form of data warehouse appliances, or in the cloud – are still great for hard-core business intelligence, where “hard-core” can refer to ad-hoc query complexity, reporting/dashboard concurrency, or both. But they aren’t good for much else.
Databricks CEO Ali Ghodsi checked in because he disagreed with part of my recent post about Databricks. Ali’s take on Databricks’ position in the Spark world includes:
- What I called Databricks’ “secondary business” of “licensing stuff to Spark distributors” was really about second/third tier support. Fair enough. But distributors of stacks including Spark, for whatever combination of on-premise and cloud as the case may be, may in many cases be viewed as competitors to Databricks cloud-only service. So why should Databricks help them?
- Databricks’ investment in Spark Summit and similar evangelism is larger than I realized.
- Ali suggests that the fraction of Databricks’ engineering devoted to open source Spark is greater than I understood during my recent visit.
Ali also walked me through customer use cases and adoption in wonderful detail. In general:
- A large majority of Databricks customers have machine learning use cases.
- Predicting and preventing user/customer churn is a huge issue across multiple market sectors.
The story on those sectors, per Ali, is: Read more
During my recent visit to Databricks, I of course talked a lot about technology — largely with Reynold Xin, but a bit with Ion Stoica as well. Spark 2.0 is just coming out now, and of course has a lot of enhancements. At a high level:
- Using the new terminology, Spark originally assumed users had data engineering skills, but Spark 2.0 is designed to be friendly to data scientists.
- A lot of this is via a focus on simplified APIs, based on
- Unlike similarly named APIs in R and Python, Spark DataFrames work with nested data.
- Machine learning and Spark Streaming both work with Spark DataFrames.
- There are lots of performance improvements as well, some substantial. Spark is still young enough that Bottleneck Whack-A-Mole yields huge benefits, especially in the SparkSQL area.
- SQL coverage is of course improved. For example, SparkSQL can now perform all TPC-S queries.
The majority of Databricks’ development efforts, however, are specific to its cloud service, rather than being donated to Apache for the Spark project. Some of the details are NDA, but it seems fair to mention at least:
- Databricks’ notebooks feature for organizing and launching machine learning processes and so on is a biggie. Jupyter is an open source analog.
- Databricks has been working on security, and even on the associated certifications.
Two of the technical initiatives Reynold told me about seemed particularly cool. Read more
|Categories: Benchmarks and POCs, Cloud computing, Databricks, Spark and BDAS, Predictive modeling and advanced analytics, Streaming and complex event processing (CEP)||3 Comments|
I visited Databricks in early July to chat with Ion Stoica and Reynold Xin. Spark also comes up in a large fraction of the conversations I have. So let’s do some catch-up on Databricks and Spark. In a nutshell:
- Spark is indeed the replacement for Hadoop MapReduce.
- Spark is becoming the default platform for machine learning.
- SparkSQL (nee’ Shark) is puttering along predictably.
- Databricks reports good success in its core business of cloud-based machine learning support.
- Spark Streaming has strong adoption, but its position is at risk.
- Databricks, the original authority on Spark, is not keeping a tight grip on that role.
I shall explain below. I also am posting separately about Spark evolution, especially Spark 2.0. I’ll also talk a bit in that post about Databricks’ proprietary/closed-source technology.
Spark is the replacement for Hadoop MapReduce.
This point is so obvious that I don’t know what to say in its support. The trend is happening, as originally decreed by Cloudera (and me), among others. People are rightly fed up with the limitations of MapReduce, and — niches perhaps aside — there are no serious alternatives other than Spark.
The greatest use for Spark seems to be the same as the canonical first use for MapReduce: data transformation. Also in line with the Spark/MapReduce analogy: Read more
|Categories: Cloudera, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, MapReduce, Market share and customer counts, Predictive modeling and advanced analytics||6 Comments|
I learned some newish terms on my recent trip. They’re meant to solve the problem that “data scientists” used to be folks with remarkably broad skill sets, few of whom actually existed in ideal form. So instead now it is increasingly said that:
- “Data engineers” can code, run clusters, and so on, in support of what’s always been called “data science”. Their knowledge of the math of machine learning/predictive modeling and so on may, however, be limited.
- “Data scientists” can write and run scripts on single nodes; anything more on the engineering side might strain them. But they have no-apologies skills in the areas of modeling/machine learning.
- I raised concerns about the “data science” term 4 years ago.
- I spent three weeks in California on a hybrid personal/business trip. I had a bunch of meetings, but not three weeks’ worth.
- The timing was awkward for most companies I wanted to see. No blame accrues to those who didn’t make themselves available.
- I came back with a nasty cough. Follow-up phone calls aren’t an option until next week.
- I’m impatient to start writing. Hence tonight’s posts. But it’s difficult for a man and his cough to be productive at the same time.
A running list of recent posts is:
- As a companion to this post, I’m publishing a very long one on vendor lock-in.
- Spark and Databricks are both prospering, and of course enhancing their technology as well.
- Ditto DataStax.
- Flink is interesting as the streaming technology it’s now positioned to be, rather than the overall Spark alternative it used to be positioned as but which the world didn’t need.
Subjects I’d like to add to that list include:
- MemSQL, Zoomdata, and Neo Technology (also prospering).
- Cloudera (multiple topics, as usual).
- Analytic SQL engines (“traditional” analytic RDBMS aren’t doing well).
- Microsoft’s reinvention (it feels real).
- Metadata (it’s ever more of a thing).
- Machine learning (it’s going to be a big portion of my research going forward).
- Transitions to the cloud — this subject affects almost everything else.