Data Science at Scale by Sarah Guido

The majority of a data scientist’s time is spent cleaning and organizing data before insights can be derived. Frequently, with large datasets, a lack of integration with visualization tools makes it hard to know what’s most interesting in the data and also creates challenges for validating numerical insights from models. Given the vast number of tools available in the ecosystem, it is hard to experiment with different tools to pick the most suitable one, especially given the complexity involved in integrating them with one’s solution. The speakers will present an easy to use workflow that solves this integration challenge by combining various open source libraries, databases (e.g. Hive, Postgres, MySQL, HBase etc.) and visualization with distributed analytics. Intel developed a highly scalable library built over Apache Spark with novel graph, statistical and machine learning algorithms that also enhances the user experience of Apache Spark via easier to use APIs. This session will showcase how to address the above mentioned issues for a drug similarity use case. We’ll go from ETL operations on raw drug data to deriving relevant features from the drug’s chemical structure using statistical and graph algorithms, using techniques to identify best model and parameters for this data to derive insights, and then demonstrating the ease of connectivity to different databases and visualization tools.

Dados e análise

Data Science at Scale:
Using Apache Spark for Data Science
at Bitly
Sarah Guido
Spark Summit Europe 2015

Overview
• About me/Bitly
• Spark overview
• Using Spark for data science
• When it works, it’s great! When it works…

About me
• Data Scientist at Bitly
• NYC Python/PyGotham co-organizer
• O’Reilly Media author
• @sarah_guido

About this talk
• This talk is:
– Description of my workflow
– Exploration of within-Spark tools
• This talk is not:
– In-depth exploration of algorithms
– Building new tools on top of Spark
– Any sort of ground truth for how you should be
using Spark

A bit of background
• Need for big data analysis tools
• MapReduce for exploratory data analysis == 
• Iterate/prototype quickly
• Overall goal: understand how people use not
only our app, but the Internet!

Bitly data!
• Legit big data
• 1 hour of decodes is 10 GB
• 1 day is 240 GB
• 1 month is ~7 TB

Why Spark?
• Fast. Really fast.
• Distributed scientific tools
• Python! (Sometimes.)
• Cutting edge technology
• AWS/EMR/S3

Setting up the workflow
• Spark journey
– Hadoop server: 1.2 – Python
– EMR: 1.3 – Python
– EMR: 1.4 – Python/Scala
– EMR: 1.5 – Scala

Let’s set the stage…
• Understanding user behavior
• How do I extract, explore, and model a subset
of our data using Spark?

$Data {"a": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_2) AppleWebKit/600.4.10 (KHTML, like Gecko) Version/8.0.4 Safari/600.4.10", "c": "US", "nk": 0, "tz": "America/Los_Angeles", "g": "1HfTjh8", "h": "1HfTjh7", "u": "http://www.nytimes.com/2015/03/22/opinion/sunday/why- health-care-tech-is-still-so-bad.html?smid=tw-share", "t": 1427288425, "cy": "Seattle"}$

Data processing
• Problem: I want to retrieve NYT decodes
• Solution: well, there are two…
• Spark 1.3

Data processing
• SparkSQL: 8 minutes
• Pure Spark: 4 minutes!!!

Topic modeling
• Problem: we have so many links but no way to
classify them into certain kinds of content
• Solution: LDA (latent Dirichlet allocation)
– Sort of – compare to other solutions
• Spark 1.4

Topic modeling
• LDA in Spark
– Generative model
– Several different methods
– Term frequency vector as input
• “Note: LDA is still an experimental feature
under active development...”

Topic modeling
• Term frequency vector
TERM
DOCUMENT
python data hot dogs baseball zoo
doc_1 1 3 0 0 0
doc_2 0 0 4 1 0
doc_3 4 0 0 0 5

Trend Detection
• Tell our clients when a particular piece of
content is trending
• Transition to Scala
• Workflow improvement
• EMR + Spark 1.5 + Jupyter + Scala!

Architecture
• Right now: not in production
– Buy-in
• Streaming applications for parts of the app
• Python or Scala?
– Scala by force

Some issues
• Hadoop servers
• JVM
• gzip
• 1.4/resource allocation/EMR
• Lack of documentation

Where to go next?
• Spark in production!
• Use for various parts of our app
• Use for R&D and prototyping purposes, with
the potential to expand into the product

Resources/Source Material
• spark.apache.org - documentation
• Databricks blog
• Cloudera blog
• Other Spark users!

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Building accurate machine learning models has been an art of data scientists, i.e., algorithm selection, hyper parameter tuning, feature selection and so on. Recently, challenges to breakthrough this “black-arts” have got started. In cooperation with our partner, NEC Laboratories America, we have developed a Spark-based automatic predictive modeling system. The system automatically searches the best algorithm, parameters and features without any manual work. In this talk, we will share how the automation system is designed to exploit attractive advantages of Spark. The evaluation with real open data demonstrates that our system can explore hundreds of predictive models and discovers the most accurate ones in minutes on a Ultra High Density Server, which employs 272 CPU cores, 2TB memory and 17TB SSD in 3U chassis. We will also share open challenges to learn such a massive amount of models on Spark, particularly from reliability and stability standpoints. This talk will cover the presentation already shown on Spark Summit SF’17 (#SFds5) but from more technical perspective.

No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...

In Sweden, from the Rise ICE Data Center at www.hops.site, we are providing to reseachers both Spark-as-a-Service and, more recently, Tensorflow-as-a-Service as part of the Hops platform. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows, from batch to streaming to structured streaming applications. We will analyse the different frameworks for integrating Spark with Tensorflow, from Tensorframes to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We introduce the different programming models supported and highlight the importance of cluster support for managing different versions of python libraries on behalf of users. We will also present cluster management support for sharing GPUs, including Mesos and YARN (in Hops Hadoop). Finally, we will perform a live demonstration of training and inference for a TensorflowOnSpark application written on Jupyter that can read data from either HDFS or Kafka, transform the data in Spark, and train a deep neural network on Tensorflow. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training.

Apache Spark and Tensorflow as a Service with Jim Dowling

Apache Spark and Tensorflow as a Service with Jim Dowling

With the rapid growth of available datasets, it is imperative to have good tools for extracting insight from big data. The Spark ML library has excellent support for performing at-scale data processing and machine learning experiments, but more often than not, Data Scientists find themselves struggling with issues such as: low level data manipulation, lack of support for image processing, text analytics and deep learning, as well as the inability to use Spark alongside other popular machine learning libraries. To address these pain points, Microsoft recently released The Microsoft Machine Learning Library for Apache Spark (MMLSpark), an open-source machine learning library built on top of SparkML that seeks to simplify the data science process and integrate SparkML Pipelines with deep learning and computer vision libraries such as the Microsoft Cognitive Toolkit (CNTK) and OpenCV. With MMLSpark, Data Scientists can build models with 1/10th of the code through Pipeline objects that compose seamlessly with other parts of the SparkML ecosystem. In this session, we explore some of the main lessons learned from building MMLSpark. Join us if you would like to know how to extend Pipelines to ensure seamless integration with SparkML, how to auto-generate Python and R wrappers from Scala Transformers and Estimators, how to integrate and use previously non-distributed libraries in a distributed manner and how to efficiently deploy a Spark library across multiple platforms.

MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...

The Next Accelerator Logging Service (NXCALS) is a new Big Data project at CERN aiming to replace the existing Oracle-based service. The main purpose of the system is to store and present Controls/Infrastructure related data gathered from thousands of devices in the whole accelerator complex. The data is used to operate the machines, improve their performance and conduct studies for new beam types or future experiments. During this talk, Jakub will speak about NXCALS requirements and design choices that lead to the selected architecture based on Hadoop and Spark. He will present the Ingestion API, the abstractions behind the Meta-data Service and the Spark-based Extraction API where simple changes to the schema handling greatly improved the overall usability of the system. The system itself is not CERN specific and can be of interest to other companies or institutes confronted with similar Big Data problems.

Next CERN Accelerator Logging Service with Jakub Wozniak

In Between (A mobile App for couples, downloaded 20M in Global), from daily batch for extracting metrics, analysis and dashboard. Spark is widely used by engineers and data analysts in Between, thanks to the performance and expendability of Spark, data operating has become extremely efficient. Entire team including Biz Dev, Global Operation, Designers are enjoying data results so Spark is empowering entire company for data driven operation and thinking. Kevin, Co-founder and Data Team leader of Between will be presenting how things are going in Between. Listeners will know how small and agile team is living with data (how we build organization, culture and technical base) after this presentation.

Powering a Startup with Apache Spark with Kevin Kim

Improving Traffic Prediction Using Weather Datawith Ramya Raghavendra

In many cases, Big Data becomes just another buzzword because of the lack of tools that can support both the technological requirements for developing and deploying of the projects and/or the fluency of communication between the different profiles of people involved in the projects. In this talk, we will present Moriarty, a set of tools for fast prototyping of Big Data applications that can be deployed in an Apache Spark environment. These tools support the creation of Big Data workflows using the already existing functional blocks or supporting the creation of new functional blocks. The created workflow can then be deployed in a Spark infrastructure and used through a REST API. For better understanding of Moriarty, the prototyping process and the way it hides the Spark environment to the Big Data users and developers, we will present it together with a couple of examples based on a Industry 4.0 success cases and other on a logistic success case.

Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...

Large-scale testing of new data products or enhancements to existing products in a research and development environment can be a technical challenge for data scientists. In some cases, tools available to data scientists lack production-level capacity, whereas other tools do not provide the algorithms needed to run the methodology. At Nielsen, the Databricks platform provided a solution to both of these challenges. This breakout session will cover a specific Nielsen business case where two methodology enhancements were developed and tested at large-scale using the Databricks platform. Development and large-scale testing of these enhancements would not have been possible using standard database tools.

How Nielsen Utilized Databricks for Large-Scale Research and Development with...

Spline: Apache Spark Lineage not Only for the Banking Industry with Marek Nov...

Since the invention of SQL and relational databases, data production has been about specifying how data is transformed through queries. While Apache Spark can certainly be used as a general distributed query engine, the power and granularity of Spark’s APIs enables a revolutionary increase in data engineering productivity: goal-based data production. Goal-based data production concerns itself with specifying WHAT the desired result is, leaving the details of HOW the result is achieved to a smart data warehouse running on top of Spark. That not only substantially increases productivity, but also significantly expands the audience that can work directly with Spark: from developers and data scientists to technical business users. With specific data and architecture patterns spanning the range from ETL to machine learning data prep and with live demos, this session will demonstrate how Spark users can gain the benefits of goal-based data production.

Goal Based Data Production with Sim Simeonov

Have you imagined a simple machine learning solution able to prevent revenue leakage and monitor your distributed application? To answer this question, we offer a practical and a simple machine learning solution to create an intelligent monitoring application based on simple data analysis using Apache Spark MLlib. Our application uses linear regression models to make predictions and check if the platform is experiencing any operational problems that can impact in revenue losses. The application monitor distributed systems and provides notifications stating the problem detected, that way users can operate quickly to avoid serious problems which directly impact the company’s revenue and reduce the time for action. We will present an architecture for not only a monitoring system, but also an active actor for our outages recoveries. At the end of the presentation you will have access to our training program source code and you will be able to adapt and implement in your company. This solution already helped to prevent about US$3mi in losses last year.

Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...

Getting Ready to use Redis with Apache Spark is a technical tutorial designed to address integrating Redis with an Apache Spark deployment to increase the performance of serving complex decision models. To set the context for the session, we start with a quick introduction to Redis and the capabilities Redis provides. We cover the basic data types provided by Redis and cover the module system. Using an ad serving use-case, we look at how Redis can improve the performance and reduce the cost of using complex ML-models in production. Attendees will be guided through the key steps of setting up and integrating Redis with Spark, including how to train a model using Spark then load and serve it using Redis, as well as how to work with the Spark Redis module. The capabilities of the Redis Machine Learning Module (redis-ml) will be discussed focusing primarily on decision trees and regression (linear and logistic) with code examples to demonstrate how to use these feature. At the end of the session, developers should feel confident building a prototype/proof-of-concept application using Redis and Spark. Attendees will understand how Redis complements Spark and how to use Redis to serve complex, ML-models with high performance.

Getting Ready to Use Redis with Apache Spark with Dvir Volk

The use of large-scale machine learning and data mining methods is becoming ubiquitous in many application domains ranging from business intelligence and bioinformatics to self-driving cars. These methods heavily rely on matrix computations, and it is hence critical to make these computations scalable and efficient. These matrix computations are often complex and involve multiple steps that need to be optimized and sequenced properly for efficient execution. This work presents new efficient and scalable matrix processing and optimization techniques based on Spark. The proposed techniques estimate the sparsity of intermediate matrix-computation results and optimize communication costs. An evaluation plan generator for complex matrix computations is introduced as well as a distributed plan optimizer that exploits dynamic cost-based analysis and rule-based heuristics The result of a matrix operation will often serve as an input to another matrix operation, thus defining the matrix data dependencies within a matrix program. The matrix query plan generator produces query execution plans that minimize memory usage and communication overhead by partitioning the matrix based on the data dependencies in the execution plan. We implemented the proposed matrix techniques inside the Spark SQL, and optimize the matrix execution plan based on Spark SQL Catalyst. We conduct case studies on a series of ML models and matrix computations with special features on different datasets. These are PageRank, GNMF, BFGS, sparse matrix chain multiplications, and a biological data analysis. The open-source library ScaLAPACK and the array-based database SciDB are used for performance evaluation. Our experiments are performed on six real-world datasets are: social network data ( e.g., soc-pokec, cit-Patents, LiveJournal), Twitter2010, Netflix recommendation data, and 1000 Genomes Project sample. Experiments demonstrate that our proposed techniques achieve up to an order-of-magnitude performance.

MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...

Kapil Malik and Arvind Heda will discuss a solution for interactive querying of large scale structured data, stored in a distributed file system (HDFS / S3), in a scalable and reliable manner using a unique combination of Spark SQL, Apache Zeppelin and Spark Job-server (SJS) on Yarn. The solution is production tested and can cater to thousands of queries processing terabytes of data every day. It contains following components – 1. Zeppelin server : A custom interpreter is deployed, which de-couples spark context from the user notebooks. It connects to the remote spark context on Spark Job-server. A rich set of APIs are exposed for the users. The user input is parsed, validated and executed remotely on SJS. 2. Spark job-server : A custom application is deployed, which implements the set of APIs exposed on Zeppelin custom interpreter, as one or more spark jobs. 3. Context router : It routes different user queries from custom interpreter to one of many Spark Job-servers / contexts. The solution has following characteristics – * Multi-tenancy There are hundreds of users, each having one or more Zeppelin notebooks. All these notebooks connect to same set of Spark contexts for running a job. * Fault tolerance The notebooks do not use Spark interpreter, but a custom interpreter, connecting to a remote context. If one spark context fails, the context router sends user queries to another context. * Load balancing Context router identifies which contexts are under heavy load / responding slowly, and selects the most optimal context for serving a user query. * Efficiency We use Alluxio for caching common datasets. * Elastic resource usage We use spark dynamic allocation for the contexts. This ensures that cluster resources are blocked by this application only when it’s doing some actual work.

Indicium: Interactive Querying at Scale Using Apache Spark, Zeppelin, and Spa...

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Mais de Spark Summit (20)

FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang

VEGAS: The Missing Matplotlib for Scala/Apache Spark with DB Tsai and Roger M...

Apache Spark Structured Streaming Helps Smart Manufacturing with Xiaochang Wu

Improving Traffic Prediction Using Weather Data with Ramya Raghavendra

A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP Artem...

No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...

Apache Spark and Tensorflow as a Service with Jim Dowling

MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...

Next CERN Accelerator Logging Service with Jakub Wozniak

Powering a Startup with Apache Spark with Kevin Kim

Improving Traffic Prediction Using Weather Datawith Ramya Raghavendra

Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...

How Nielsen Utilized Databricks for Large-Scale Research and Development with...

Spline: Apache Spark Lineage not Only for the Banking Industry with Marek Nov...

Goal Based Data Production with Sim Simeonov

Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...

Getting Ready to Use Redis with Apache Spark with Dvir Volk

MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...

Indicium: Interactive Querying at Scale Using Apache Spark, Zeppelin, and Spa...

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