Databricks, Spark and BDAS
Discussion of BDAS (Berkeley Data Analytics Systems), especially Spark and related projects, and also of Databricks, the company commercializing Spark.
“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||1 Comment|
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.
data Artisans and Flink basics start:
- Flink is an Apache project sponsored by the Berlin-based company data Artisans.
- Flink has been viewed in a few different ways, all of which are similar to how Spark is seen. In particular, per co-founder Kostas Tzoumas:
- Flink’s original goal was “Hadoop done right”.
- Now Flink is focused on streaming analytics, as an alternative to Spark Streaming, Samza, et al.
- Kostas seems to see Flink as a batch-plus-streaming engine that’s streaming-first.
Like many open source projects, Flink seems to have been partly inspired by a Google paper.
To this point, data Artisans and Flink have less maturity and traction than Databricks and Spark. For example: Read more
|Categories: Cloudera, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, Hortonworks, Intel, Market share and customer counts, Open source, Streaming and complex event processing (CEP)||3 Comments|
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 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.
In a companion introduction to Kafka post, I observed that Kafka at its core is remarkably simple. Confluent offers a marchitecture diagram that illustrates what else is on offer, about which I’ll note:
- The red boxes — “Ops Dashboard” and “Data Flow Audit” — are the initial closed-source part. No surprise that they sound like management tools; that’s the traditional place for closed source add-ons to start.
- “Schema Management”
- Is used to define fields and so on.
- Is not equivalent to what is ordinarily meant by schema validation, in that …
- … it allows schemas to change, but puts constraints on which changes are allowed.
- Is done in plug-ins that live with the producer or consumer of data.
- Is based on the Hadoop-oriented file format Avro.
Kafka offers little in the way of analytic data transformation and the like. Hence, it’s commonly used with companion products. Read more
|Categories: Data integration and middleware, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, Kafka and Confluent, Market share and customer counts, Streaming and complex event processing (CEP)||3 Comments|
Cloudera released Version 2 of Cloudera Director, which is a companion product to Cloudera Manager focused specifically on the cloud. This led to a discussion about — you guessed it! — Cloudera and the cloud.
Making Cloudera run in the cloud has three major aspects:
- Cloudera’s usual software, ported to run on the cloud platform(s).
- Cloudera Director, which for example launches cloud instances.
- Points of integration, e.g. taking information about security-oriented roles from the platform and feeding then to the role-based security that is specific to Cloudera Enterprise.
Features new in this week’s release of Cloudera Director include:
- An API for job submission.
- Support for spot and preemptable instances.
- High availability.
- Some cluster repair.
- Some cluster cloning.
I.e., we’re talking about some pretty basic/checklist kinds of things. Cloudera Director is evidently working for Amazon AWS and Google GCP, and planned for Windows Azure, VMware and OpenStack.
As for porting, let me start by noting: Read more
I’m on two overlapping posting kicks, namely “lessons from the past” and “stuff I keep saying so might as well also write down”. My recent piece on Oracle as the new IBM is an example of both themes. In this post, another example, I’d like to memorialize some points I keep making about business intelligence and other analytics. In particular:
- BI relies on strong data access capabilities. This is always true. Duh.
- Therefore, BI and other analytics vendors commonly reinvent the data management wheel. This trend ebbs and flows with technology cycles.
Similarly, BI has often been tied to data integration/ETL (Extract/Transform/Load) functionality.* But I won’t address that subject further at this time.
*In the Hadoop/Spark era, that’s even truer of other analytics than it is of BI.
My top historical examples include:
- The 1970s analytic fourth-generation languages (RAMIS, NOMAD, FOCUS, et al.) commonly combined reporting and data management.
- The best BI visualization technology of the 1980s, Executive Information Systems (EIS), was generally unsuccessful. The core reason was a lack of what we’d now call drilldown. Not coincidentally, EIS vendors — notably leader Comshare — didn’t do well at DBMS-like technology.
- Business Objects, one of the pioneers of the modern BI product category, rose in large part on the strength of its “semantic layer” technology. (If you don’t know what that is, you can imagine it as a kind of virtual data warehouse modest enough in its ambitions to actually be workable.)
- Cognos, the other pioneer of modern BI, depending on capabilities for which it needed a bundled MOLAP (Multidimensional OnLine Analytic Processing) engine.
- But Cognos later stopped needing that engine, which underscores my point about technology ebbing and flowing.
|Categories: Business intelligence, Business Objects, Cognos, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, Information Builders, MicroStrategy, Software as a Service (SaaS), Teradata||5 Comments|