The popular graph analytics framework extends its coverage of the data science workflow
GraphLab’s SFrame, an interesting and somewhat under-the-radar tool was unveiled1 at Strata Santa Clara. It is a disk-based, flat table representation that extends GraphLab to tabular data. With the addition of SFrame, users can leverage GraphLab’s many algorithms on data stored as either graphs or tables. More importantly SFrame increases GraphLab’s coverage of the data science workflow: it allows users with terabyte-sized datasets to clean their data and create new features directly within GraphLab (SFrame performance can scale linearly with the number of available cores).
The beta version of SFrame can read data from local disk, HDFS, S3 or a URL, and save to a human-readable .csv or a more efficient native format. Once an SFrame is created and saved to disk no reprocessing of the data is needed. Below is Python code that illustrates how to read a .csv file into SFrame, create a new data feature and save it to disk on S3:
Hardcore Data Science speakers provided many practical suggestions and tips
One of the most popular offerings at Strata Santa Clara was Hardcore Data Science day. Over the next few weeks we hope to profile some of the speakers who presented, and make the video of the talks available as a bundle. In the meantime here are some notes and highlights from a day packed with great talks.
We’ve come to think of analytics as being comprised primarily of data and algorithms. Once data has been collected, “wrangled”, and stored, algorithms are unleashed to unlock its value. Longtime machine-learning researcher Alice Zheng of GraphLab, reminded attendees that data structures are critical to scaling machine-learning algorithms. Unfortunately there is a disconnect between machine-learning research and implementation (so much so, that some recent advances in large-scale ML are “rediscoveries” of known data structures):
While there are many data structures that arise in computer science, Alice devoted her talk to two data structures1 that are widely used in machine-learning:
Applications get easier to build as packaged combinations of open source tools become available
As a user who tends to mix-and-match many different tools, not having to deal with configuring and assembling a suite of tools is a big win. So I’m really liking the recent trend towards more integrated and packaged solutions. A recent example is the relaunch of Cloudera’s Enterprise Data hub, to include Spark1 and Spark Streaming. Users benefit by gaining automatic access to analytic engines that come with Spark2. Besides simplifying things for data scientists and data engineers, easy access to analytic engines is critical for streamlining the creation of big data applications.
Another recent example is Dendrite3 – an interesting new graph analysis solution from Lab41. It combines Titan (a distributed graph database), GraphLab (for graph analytics), and a front-end that leverages AngularJS, into a Graph exploration and analysis tool for business analysts:
Business users are starting to tackle problems that require machine-learning and statistics
I talk with many new companies who build tools for business analysts and other non-technical users. These new tools streamline and simplify important data tasks including interactive analysis (e.g., pivot tables and cohort analysis), interactive visual analysis (as popularized by Tableau and Qlikview), and more recently data preparation. Some of the newer tools scale to large data sets, while others explicitly target small to medium-sized data.
As I noted in a recent post, companies are beginning to build data analysis tools1 that target non-experts. Companies are betting that as business users start interacting with data, they will want to tackle some problems that require advanced analytics. With business analysts far outnumbering data scientists, it makes sense to offload some problems to non-experts2.
Moreover data seems to support the notion that business users are interested in more complex problems. I recently looked at data3 from 11 large Meetups (in NYC and the SF Bay Area) that target business analysts and business intelligence users. Altogether these Meetups had close to 5,000 active4 members. As you can see in the chart below, business users are interested in topics like machine learning (1 in 5), predictive analytics (1 in 4), and data mining (1 in 4):
Depending on the nature of the problem, data size, and deliverable, I still draw upon an array of tools for data visualization. As I survey the Design track at next month’s Strata conference, I see creators and power users of visualization tools that many data scientists have come to rely on. Several pioneers will lead sessions on (new) tools for creating static and interactive charts, against small and massive data sets.
The Grammar of Graphics
To this day, I find R (specifically ggplot2) to be a tool I turn to for producing static visualizations. Even the simplest charts allow me to quickly spot data problems and anomalies, and a tool like ggplot2 can accomplish a lot in very few lines of code. Charts produced by ggplot2 look much nicer than simple R plots and once you get past the initial learning curve, they are easy to fine-tune and customize.
Hadley Wickham1, the creator of ggplot2, is speaking on two new domain specific languages (ggvis and dplyr) that make it easy for R users to declaratively create interactive web graphics. As Hadley describes it, ggvis is interactive Grammar of Graphics for R. As more data scientists turn to interactive visualizations that can be shared through web browsers, ggvis is the natural next tool for ggplot2 users.
Leland Wilkinson, the primary author of The Grammar of Graphics2, will also be at Strata to lead a tutorial on an interesting expert system that lets machine-learning techniques be accessible to business users. Leland’s work has influenced many other visualization tools including Polaris (from the Stanford team that founded Tableau), Bokeh, and ggbio (for genomics data). Effective visualization techniques will be an important component of his Strata tutorial.
It has roots in academic scientific computing, but has features that appeal to many data scientists
As I noted in a recent post on reproducing data projects, notebooks have become popular tools for maintaining, sharing, and replicating long data science workflows. Much of that is due to the popularity of IPython1. In development since 2001, IPython grew out of the scientific computing community and has slowly added features that appeal to data scientists.
Roots in academic scientific computing
As IPython creator Fernando Perez noted in his “historical retrospective”, exploratory analysis in a scientific setting requires a solid interactive environment. After years of development IPython has become a great tool for interacting with data. IPython also addresses other important pain points for scientists – reproducibility and collaboration – issues that are equally important to data scientists working in industry.
IPython is more than just Python
With an interactive widget architecture that’s 100% language-agnostic, these days IPython is used by many other programming language communities2, including Julia, Haskell, F#, Ruby, Go, and Scala. If you’re a data scientist who likes to mix-and-match languages, you can create, maintain, and share multi-language data projects in IPython:
Open source, distributed computing tools speedup an important processing pipeline for genomics data
As open source, big data tools enter the early stages of maturation, data engineers and data scientists will have many opportunities to use them to “work on stuff that matters”. Along those lines, computational biology and medicine are areas where skilled data professionals are already beginning to make an impact. I recently came across a compelling open source project from UC Berkeley’s AMPLab: ADAM is a processing engine and set of formats for genomics data.
Second-generation sequencing machines produce more detailed and thus much larger files for analysis (250+ GB file for each person). Existing data formats and tools are optimized for single-server processing and do not easily scale out. ADAM uses distributed computing tools and techniques to speedup key stages of the variant processing pipeline (including sorting and deduping):
Very early on the designers of ADAM realized that a well-designed data schema (that specifies the representation of data when it is accessed) was key to having a system that could leverage existing big data tools. The ADAM format uses the Apache Avro data serialization system and comes with a human-readable schema that can be accessed using many programming languages (including C/C++/C#, Java/Scala, php, Python, Ruby). ADAM also includes a data format/access API implemented on top of Apache Avro and Parquet, and a data transformation API implemented on top of Apache Spark. Because it’s built with widely adopted tools, ADAM users can leverage components of the Hadoop (Impala, Hive, MapReduce) and BDAS (Shark, Spark, GraphX, MLbase) stacks for interactive and advanced analytics.
The Delite framework has produced high-performance languages that target data scientists
An important reason why pydata tools and Spark appeal to data scientists is that they both cover many data science tasks and workloads (Spark users can move seamlessly between batch and streaming). Being able to use the same programming style and syntax for workflows that span a variety of tasks is a huge productivity boost. In the case of Spark (and Hadoop), the emergence of a variety of scalable analytic engines have made distributed computing applications much easier to build.
Delite: a framework for embedded, parallel, and high-performance DSLs
Another way to boost productivity is to use a family of high-performance languages that cover many data science tasks. Ideally you want languages that allow programmers to focus on applications (not on low-level details of parallel programming) and that can run efficiently on different machines and architectures1 (CPU, GPU). And just like pydata and Spark, syntax and context-switching shouldn’t get in the way of tackling complex data science workflows.
The Delite framework from Stanford’s Pervasive Parallelism Lab (PPL) has been used to produce a family of high-performance domain specific languages (DSLs) that target different data analysis tasks. DSLs are programming languages2 with restricted expressiveness (for a particular domain) and tend to be high-level in nature (they are often declarative and deterministic). Delite is a compiler and runtime infrastructure that allows language designers to use aggressive, domain-specific optimizations to deliver high-performance DSLs. Using Delite, the team at Stanford produced DSLs embedded in a functional language (Scala) with performance results comparable to hand-optimized implementations (e.g. MATLAB, LINQ) across different domains.
It's an extensive, well-documented, and accessible, curated library of machine-learning models
I use a variety of tools for advanced analytics, most recently I’ve been using Spark (and MLlib), R, scikit-learn, and GraphLab. When I need to get something done quickly, I’ve been turning to scikit-learn for my first pass analysis. For access to high-quality, easy-to-use, implementations1 of popular algorithms, scikit-learn is a great place to start. So much so that I often encourage new and seasoned data scientists to try it whenever they’re faced with analytics projects that have short deadlines.
I recently spent a few hours with one of scikit-learn’s core contributors Olivier Grisel. We had a free flowing discussion were we talked about machine-learning, data science, programming languages, big data, Paris, and … scikit-learn! Along the way, I was reminded by why I’ve come to use (and admire) the scikit-learn project.
Commitment to documentation and usability
One of the reasons I started2 using scikit-learn was because of its nice documentation (which I hold up as an example for other communities and projects to emulate). Contributions to scikit-learn are required to include narrative examples along with sample scripts that run on small data sets. Besides good documentation there are other core tenets that guide the community’s overall commitment to quality and usability: the global API is safeguarded, all public API’s are well documented, and when appropriate contributors are encouraged to expand the coverage of unit tests.
Models are chosen and implemented by a dedicated team of experts
scikit-learn’s stable of contributors includes experts in machine-learning and software development. A few of them (including Olivier) are able to devote a portion of their professional working hours to the project.
Covers most machine-learning tasks
Scan the list of things available in scikit-learn and you quickly realize that it includes tools for many of the standard machine-learning tasks (such as clustering, classification, regression, etc.). And since scikit-learn is developed by a large community of developers and machine-learning experts, promising new techniques tend to be included in fairly short order.
As a curated library, users don’t have to choose from multiple competing implementations of the same algorithm (a problem that R users often face). In order to assist users who struggle to choose between different models, Andreas Muller created a simple flowchart for users:
Analytic services are tailoring their solutions for specific problems and domains
In relatively short order Amazon’s internal computing services has become the world’s most successful cloud computing platform. Conceived in 2003 and launched in 2006, AWS grew quickly and is now the largest web hosting company in the world. With the recent addition of Kinesis (for stream processing), AWS continues to add services and features that make it an attractive platform for many enterprises.
A few other companies have followed a similar playbook: technology investments that benefit a firm’s core business, is leased out to other companies, some of whom may operate in the same industry. An important (but not well-known) example comes from finance. A widely used service provides users with clean, curated data sets and sophisticated algorithms with which to analyze them. It turns out that the world’s largest asset manager makes its investment and risk management systems available to over 150 pension funds, banks, and other institutions. In addition to the $4 trillion managed by BlackRock, the company’s Aladdin Investment Management system is used to manage1 $11 trillion in additional assets from external managers.