Apache Pig – Thomas Dinsmore's Blog

Spark is the Future of Analytics

At the 2016 Spark Summit, Gartner Research Director Nick Heudecker asked: Is Spark the Future of Data Analysis? It’s an interesting question, and it requires a little parsing. Nobody believes that Spark alone is the future of data analysis, even its most ardent proponents. A better way to frame the question: Does Spark have a role in the future of analytics? What is that role?

Unfortunately, Heudecker didn’t address the question but spent the hour throwing shade at Spark.

Spark is overhyped! He declared. His evidence? This:

One might question an analysis that equates real things like optimization with fake things like “Citizen Data Science.” Gartner’s Hype Cycle by itself proves nothing; it’s a conceptual salad, with neither empirical foundation nor predictive power.

If you want to argue that Spark is overhyped, produce some false or misleading claims by project principals, or documented cases where the software failed to work as claimed. It’s possible that such cases exist. Personally, I don’t know of any, and neither does Nick Heudecker, or he would have included them in his presentation.

Instead, he cited a Gartner survey showing that organizations don’t use Spark and Flink as much as they use other tools for data analysis. From my notes, here are the percentages:

EDW: 57%
Cloud: 44%
Hadoop: 42%
Stat Packages: 32%
Spark or Flink: 9%
Graph Databases: 8%

That 42% figure for Hadoop is interesting. In 2015, Gartner concern-trolled the tech community, trumpeting the finding that “only” 26% of respondents in a survey said they were “deploying, piloting or experimenting with Hadoop.” So — either Hadoop adoption grew from 26% to 42% in a year, or Gartner doesn’t know how to do surveys.

In any event, it’s irrelevant; statistical packages have been available for 40 years, EDWs for 25, Spark for 3. The current rate of adoption for a project in its youth tells you very little about its future. It’s like arguing that a toddler is cognitively challenged because she can’t do integral calculus without checking the Wolfram app on her iPad.

Heudecker closed his presentation with the pronouncement that he had no idea whether or not Spark is the future of data analysis, and bolted the venue faster than a jackrabbit on Ecstasy. Which begs the question: why pay big bucks for analysts who have no opinion about one of the most active projects in the Big Data ecosystem?

Here are eight reasons why Spark has a central role in the future of analytics.

(1) Nearly everyone who uses Hadoop will use Spark.

If you believe that 42% of enterprises use Hadoop, you must believe that 41.9% will use Spark. Every Hadoop distribution includes Spark. Hive and Pig run on Spark. Hadoop early adopters will gradually replace existing MapReduce applications and build most new applications in Spark. Late adopters may never use MapReduce.

The only holdouts for MapReduce will be those who want their analysis the way they want their barbecue: low and slow.

Of course, Hadoop adoption isn’t static. Forrester’s Mike Gualtieri argues that 100% of enterprises will use Hadoop within a few years.

(2) Lots of people who don’t use Hadoop will use Spark.

For Hadoop users, Spark is a fast replacement for MapReduce. But that’s not all it is. Spark is also a general-purpose data processing environment for advanced analytics. Hadoop has baggage that data science teams don’t need, so it’s no surprise to see that most Spark users aren’t using it with Hadoop. One of the key advantages of Spark is that users aren’t tied to a particular storage back end, but can choose from many different options. That’s essential in real-world data science.

(3) For scalable open source data science, Spark is the only game in town.

If you want to argue that Spark has no future, you’re going to have to name an alternative. I’ll give you a minute to think of something.

Time’s up.

You could try to approximate Spark’s capabilities with a collection of other projects: for example, you could use Presto for SQL, H2O for machine learning, Storm for streaming, and Giraph for graph analysis. Good luck pulling those together. H2O.ai was one of the first vendors to build an interface to Spark because even if you want to use H2O for machine learning, you’re still going to use Spark for data wrangling.

“What about Flink?” you ask. Well, what about it? Flink may have a future, too, if anyone ever supports it other than ten guys in a loft on the Tempelhofer Ufer. Flink’s event-based runtime seems well-suited for “pure” streaming applications, but that’s low-value bottom-of-the-stack stuff. Flink’s ML library is still pretty limited, and improving it doesn’t appear to be a high priority for the Flink team.

(4) Data scientists who work exclusively with “small data” still need Spark.

Data scientists satisfy most business requests for insight with small datasets that can fit into memory on a single machine. Even if you measure your largest dataset in gigabytes, however, there are two ways you need Spark: to create your analysis dataset and to parallelize operations.

Your analysis dataset may be small, but it comes from a larger pool of enterprise data. Unless you have servants to pull data for you, at some point you’re going to have to get your hands dirty and deal with data at enterprise scale. If you are lucky, your organization has nice clean data in a well-organized data warehouse that has everything anyone will ever need in a single source of truth.

Ha ha! Just kidding. Single sources of truth don’t exist, except in the wildest fantasies of data warehouse vendors. In reality, you’re going to muck around with many different sources and integrate your analysis data on the fly. Spark excels at that.

For best results, machine learning projects require hundreds of experiments to identify the best algorithm and optimal parameters. If you run those tests serially, it will take forever; distribute them across a Spark cluster, and you can radically reduce the time needed to find that optimal model.

(5) The Spark team isn’t resting on its laurels.

Over time, Spark has evolved from a research project for scalable machine learning to a general purpose data processing framework. Driven by user feedback, Spark has added SQL and streaming capabilities, introduced Python and R APIs, re-engineered the machine learning libraries, and many other enhancements.

Here are some projects under way to improve Spark:

— Project Tungsten, an ongoing effort to optimize CPU and memory utilization.

— A stable serialization format (possibly Apache Arrow) for external code integration.

— Integration with deep learning frameworks, including TensorFlow and Intel’s new BigDL library.

— A cost-based optimizer for Spark SQL.

— Improved interfaces to data sources.

— Continuing improvements to the Python and R APIs.

Performance improvement is an ongoing mission; for selected operations, Spark 2.0 runs 10X faster than Spark 1.6.

(6) More cool stuff is on the way.

Berkeley’s AMPLab, the source of Spark, Mesos, and Tachyon/Alluxio, is now RISELab. There are four projects under way at RISELab that will extend Spark capabilities:

— Clipper is a prediction serving system that brokers between machine learning frameworks and end-user applications. The first Alpha release, planned for mid-April 2017, will serve scikit-learn, Spark ML and Spark MLLib models, and arbitrary Python functions.

— Drizzle, an execution engine for Apache Spark, uses group scheduling to reduce latency in streaming and iterative operations. Lead developer Shivaram Venkataraman has filed a design document to implement this approach in Spark.

— Opaque is a package for Spark SQL that uses Intel SGX trusted hardware to deliver strong security for DataFrames. The project seeks to enable analytics on sensitive data in an untrusted cloud, with data encryption and access pattern hiding.

— Ray is a distributed execution engine for Spark designed for reinforcement learning.

Three Apache projects in the Incubator build on Spark:

— Apache Hivemall is a scalable machine learning library implemented as a collection of Hive UDFs designed to run on Hive, Pig or Spark SQL with MapReduce, Tez or Spark.

— Apache PredictionIO is a machine learning server built on top of an open source stack, including Spark, HBase, Spray, and Elasticsearch.

— Apache SystemML is a library of machine learning algorithms that run on Spark and MapReduce, originally developed by IBM Research.

MIT’s CSAIL lab is working on ModelDB, a system to manage machine learning models. ModelDB extracts and stores model artifacts and metadata, and makes this data available for easy querying and visualization. The current release supports Spark ML and scikit-learn.

(7) Commercial vendors are building on top of Spark.

The future of analytics is a hybrid stack, with open source at the bottom and commercial software for business users at the top. Here is a small sample of vendors who are building easy-to-use interfaces atop Spark.

— Alpine Data provides a collaboration environment for data science and machine learning that runs on Spark (and other platforms.)

— AtScale, an OLAP on Big Data solution, leverages Spark SQL and other SQL engines, including Hive, Impala, and Presto.

— Dataiku markets Data Science Studio, a drag-and-drop data science workflow tool with connectors for many different storage platforms, scikit-learn, Spark ML and XGboost.

— StreamAnalytix, a drag-and-drop platform for real-time analytics, supports Spark SQL and Spark Streaming, Apache Storm, and many different data sources and sinks.

— Zoomdata, an early adopter of Spark, offers an agile visualization tool that works with Spark Streaming and many other platforms.

All of the leading agile BI tools, including Tableau, Qlik, and PowerBI, support Spark. Even stodgy old Oracle’s Big Data Discovery tool runs on Spark in Oracle Cloud.

(8) All of the leading commercial advanced analytics platforms use Spark.

All of them, including SAS, a company that embraces open source the way Sylvester the Cat embraces a skunk. SAS supports Spark in SAS Data Loader for Hadoop, one of SAS’ five different Hadoop architectures. (If you don’t like SAS architecture, wait six months for another.)

screen-shot-2017-02-13-at-12-30-38-pm — Magic Quadrant for Advanced Analytics Platforms, 2016

— IBM embraces Spark like Romeo embraced Juliet, hopefully with a better ending. IBM contributes heavily to the Spark project and has rebuilt many of its software products and cloud services to use Spark.

— KNIME’s Spark Executor enables users of the KNIME Analytics Platform to create and execute Spark applications. Through a combination of visual programming and scripting, users can leverage Spark to access data sources, blend data, train predictive models, score new data, and embed Spark applications in a KNIME workflow.

— RapidMiner’s Radoop module supports visual programming across SparkR, PySpark, Pig, and HiveQL, and machine learning with SparkML and H2O.

— Statistica, which is no longer part of Dell, offers Spark integration in its Expert and Enterprise editions.

— Microsoft supports Spark in AzureHD, and it has rebuilt Microsoft R Server’s Hadoop integration to leverage Spark as well as MapReduce. VentureBeat reports that Databricks will offer its managed service for Spark on Microsoft Azure later this year.

— SAP, another early adopter of Spark, supports Vora, a connector to SAP HANA.

You get the idea. Spark is deeply embedded in the ecosystem, and it’s foolish to argue that it doesn’t play a central role in the future of analytics.

SAS Versus R (Part 1)

Which is better for analytics, SAS or R? One frequently sees discussions on this topic in social media; for examples, see here, here, here, here, here and here. Like many debates in social media, the degree of conviction is often inverse to the quantity of information, and these discussions often produce more heat than light.

The question is serious. Many organizations with a large investment in SAS are actively considering whether to adopt R, either to supplement SAS or to replace it altogether. The trend is especially marked in the analytic services industry, which is particularly sensitive to SAS licensing costs and restrictive conditions.

In this post, I will recap some common myths about SAS and R. In a follow-up post, I will summarize the pros and cons of each as an analytics platform.

Myths About SAS and R

Advocates for SAS and R often support their positions with beliefs that are little more than urban legends; as such, they are not good reasons to choose SAS over R or vice-versa. Let’s review six of these myths.

(1) Regulatory agencies require applicants to use SAS.

This claim is often cited in the context of submissions to the Food and Drug Administration (FDA), apparently by those who have never read the FDA’s regulations governing submissions. The FDA accepts submissions in a range of formats including SAS Transport Files (which an R user can create using the StatTransfer utility.) Nowhere in its regulations does the FDA mandate what software should be used to produce the analysis; like most government agencies, the FDA is legally required to support standards that do not favor single vendors.

Pharmaceutical firms tend to rely heavily on SAS because they trust the software, and not due to any FDA mandate. Among its users, SAS has a deservedly strong reputation for quality; it is a mature product and its statistical techniques are mature, well-tested and completely documented. In short, the software works, which means there is very little incentive for an established user to experiment with something else, just to save on licensing fees.

That trust in SAS isn’t a permanent state of affairs. R is gradually making inroads in the life sciences community; it has already largely displaced SAS in the academic world. Like many other regulatory bodies, the FDA itself uses open source R together with SAS.

(2) R is better than SAS because it is object oriented.

This belief is wrong on two counts: (1) it assumes that object-oriented languages are best for all use cases; and (2) it further assumes that SAS offers no object-oriented capability.

Object-oriented languages are more efficient and easier to use for many analysis tasks. In real-world analytics, however, we often work with messy and complex data; a cursor-based language like the SAS DATA Step offers the user a great deal of flexibility, which is why it is so widely used. Anyone who has ever attempted to translate SAS “first and last” processing into an object-oriented language understands this point. (Yes, it can be done; but it requires a high-level of expertise in the OOL to do it).

In Release 9.3, SAS introduced DS2, an object-oriented language with a defined migration path from SAS DATA Step programming. Hence, for those tasks where object-oriented programming is desirable, DS2 meets this need for the SAS user. (DS2 is included with Base SAS).

(3) You never know what’s inside open source software like R.

Since R is an open programming environment, anyone can develop a package and contribute it to the project. Commercial software vendors like to plant FUD about open source software by suggesting that contributors may be amateurs or worse — in contrast to the “professional” engineering of commercial software.

One of the key virtues of open source software is that you do know what’s inside it because — unlike commercial software — you can inspect the source code. With commercial software, you must have faith in the vendor’s integrity, technical support and willingness to stand by its warranty. For open source software, there is no warranty nor is one required; the code speaks for itself.

When a contributor publishes an enhancement to R, a large community of users evaluates and tests the new feature. This “crowdsourced” testing quickly flags and logs issues with software syntax and semantics, and logged issues are available for anyone to see.

Commercial software vendors like SAS have professional testing and QA departments, but since testing is expensive there is considerable pressure to minimize the expense. Under the pressure of Marketing and Sales deadlines, systematic testing is often the first task to be cut. Bismarck once said that nobody should witness how laws or sausages are made; the same is true for commercial software.

SAS does not disclose the headcount it commits to software testing and QA, but given the size of the R user base, it’s fair to say that the number of people who test and evaluate each R release is far greater than the number of people who evaluate each SAS release.

(4) R is better than SAS because it has thousands of packages.

This is like arguing that Wal-Mart is a better store than Brooks Brothers because it carries more items. Wal-Mart’s breadth of product makes it a great shopping destination for many shoppers, but a Brooks Brothers shopper appreciates the store’s focus on a certain look and personalized service.

By analogy, R’s cornucopia of functionality is both a feature and a bug. Yes, there is a package in R to support every conceivable analytic need; in many cases, there is more than one package. As of this writing, there are 486 packages that support linear regression, which is great unless you only need one and don’t want to sift through 486.

Of course, actual R users don’t check every package to find what they need; they settle on a few trusted packages based on actual experience, word-of-mouth, books, periodicals or other sources of information. In practice, relatively few R packages are actually used; the graph below shows package downloads from RStudio’s popular CRAN mirror in September 2014.

(For the record, the ten most downloaded packages from RStudio’s CRAN mirror in September 2014 were Rcpp, plyr, ggplot2, stringr, digest, reshape2, RColorBrewer, labeling, colorspace and scales.)

For actual users, the relevant measure isn’t the total number of features supported in SAS and R; it’s how those features align with user needs.

N.B. — Some readers may quibble with my use of statistics from a single CRAN mirror as representative of the R community at large. It’s a fair point — there are at least 105 public CRAN mirror sites worldwide — but given RStudio’s strong market presence it’s a reasonable proxy.

(5) Switching from SAS to R is expensive because you have to rewrite all of your code.

It’s true that when switching from SAS to R you have to rewrite programs that you want to keep; there is no engine that will translate SAS code to R code. However, SAS users tend to overestimate the effort and cost to accomplish this task.

Analytic teams that have used SAS for some years typically accumulate a large stock of programs and data; much of this accumulation, however, is junk that will never be re-used. Keep in mind that analytic users don’t work the same way as software developers in IT or a software engineering organization. Production developers tend to work in a collaborative environment that ensures consistent, reliable and stable results. Analytic users, on the other hand, tend to work individually on ad hoc analysis projects; they are often inconsistently trained in software best practices.

When SAS users are pressed to evaluate a library of existing programs and identify the “keepers”, they rarely identify more than 10-20% of the existing library. Hence, the actual effort and expense of program conversion should not be a barrier for most organizations if there is a compelling business case to switch.

It’s also worth noting that sticking with SAS does not free the organization from the cost of code migration, as SAS customers discovered when SAS 9 was released.

The real cost of switching from SAS to R is measured in human capital — in the costs of retraining skilled professionals. For many organizations, this is a deal-breaker at present; but as more R-savvy analysts enter the workforce, the costs of switching will decline.

(6) R is a good choice when working with Big Data.

When working with Big Data, neither “legacy” SAS nor open source R is a good choice, for different reasons.

Open source R runs in memory on a single machine; it can work with data up to available memory, then fails. It is possible to run R in a Hadoop cluster or as table functions inside MPP databases. However, since R runs independently on each node, this is useful only for embarrassingly parallel tasks; for most advanced analytics tasks, you will need to invoke a distributed analytics engine. There are a number of distributed engines you can invoke from R, including H2O, ScaleR and Skytree, but at this point R is simply a client and the actual work is done by the distributed engine.

“Legacy” SAS uses file-swapping to handle out-of-memory problems, but at great cost to performance; when a data set is too large to load into memory, “legacy” SAS slows to a crawl. Through SAS/ACCESS, SAS supports the ability to pass through SQL operations to MPP databases and HiveQL, MapReduce and Pig to Hadoop; however, as is the case with R, “legacy” SAS simply functions as a client and the work is done in the database or Hadoop. The user can accomplish the same tasks using any SQL or Hadoop interface.

To its credit, SAS also offers distributed in-memory software that runs inside Hadoop (the SAS High-Performance Analytics suite and SAS In-Memory Statistics for Hadoop). Of course, these products do not replicate “legacy” SAS; they are entirely new products that support a subset of “legacy” SAS functionality at extra cost. Some migration may be required, since they run DS2 but not the traditional SAS DATA Step. (I cite these points not to denigrate the new SAS software, which appears to be well designed and implemented, but to highlight the discontinuity for SAS users between the “legacy” product and the scalable High Performance products.)

If your organization works with Big Data, your primary focus should be on choosing the right scalable analytics platform, with secondary emphasis on the client or API used to invoke it.