Apache Spark 2.x Machine Learning Cookbook

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By Siamak Amirghodsi , Meenakshi Rajendran , Broderick Hall and 1 more
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  1. Practical Machine Learning with Spark Using Scala

About this book

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks.

This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.

Publication date:
September 2017
Publisher
Packt
Pages
666
ISBN
9781783551606

 

Chapter 1. Practical Machine Learning with Spark Using Scala

In this chapter, we will cover:

  • Downloading and installing the JDK
  • Downloading and installing IntelliJ
  • Downloading and installing Spark
  • Configuring IntelliJ to work with Spark and run Spark ML sample codes
  • Running a sample ML code from Spark
  • Identifying data sources for practical machine learning
  • Running your first program using Apache Spark 2.0 with the IntelliJ IDE
  • How to add graphics to your Spark program
 

Introduction


With the recent advancements in cluster computing coupled with the rise of big data, the field of machine learning has been pushed to the forefront of computing. The need for an interactive platform that enables data science at scale has long been a dream that is now a reality.

The following three areas together have enabled and accelerated interactive data science at scale:

  • Apache Spark: A unified technology for data science that combines a fast compute engine and fault-tolerant data structures into a well-designed and integrated offering
  • Machine learning: A field of artificial intelligence enables machines to mimic some of the tasks originally reserved exclusively for the human brain
  • Scala: A modern JVM-based language that on traditional languages, but unites functional and object-oriented concepts without the verboseness of other languages

First, we need to set up the development environment, which will consist of the following components:

  • Spark
  • IntelliJ community edition IDE
  • Scala

The recipes in this chapter will give you detailed instructions for installing and configuring the IntelliJ IDE, Scala plugin, and Spark. After the development environment is set up, we'll proceed to run one of the Spark ML sample codes to test the setup.

Apache Spark

Apache Spark is as the de facto platform and trade for big data analytics and as a complement to the Hadoop paradigm. Spark enables a data scientist to work in the manner that is most conducive to their workflow right out of the box. Spark's approach is to process the workload in a completely distributed manner without the need for MapReduce (MR) or repeated writing of the intermediate results to a disk.

Spark provides an easy-to-use distributed framework in a unified technology stack, which has made it the platform of choice for data science projects, which more often than not require an iterative algorithm that eventually merges toward a solution. These algorithms, due to their inner workings, generate a large amount of intermediate results that need to go from one stage to the next during the intermediate steps. The need for an interactive tool with a robust native distributed machine learning library (MLlib) rules out a disk-based for most of the data science projects.

Spark has a different approach toward cluster computing. It solves the problem as a technology stack rather than as an ecosystem. A large number of centrally managed libraries combined with a lightning-fast compute engine that can support fault-tolerant data structures has poised Spark to take over Hadoop as the preferred big data platform for analytics.

Spark has a modular approach, as depicted in the following diagram:

Machine learning

The aim of learning is to produce machines and devices that can mimic human intelligence and automate some of the tasks that have been traditionally reserved for a human brain. Machine learning algorithms are designed to go through very large data sets in a relatively short time and approximate answers that would have taken a human much longer to process.

The field of machine learning can be classified into many forms and at a high level, it can be classified as supervised and unsupervised learning. Supervised learning algorithms are a class of ML algorithms that use a training set (that is, labeled data) to compute a probabilistic distribution or graphical model that in turn allows them to classify the new data points without further human intervention. Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.

Out of the box, Spark offers a rich set of ML algorithms that can be deployed on large datasets without any further coding. The following figure depicts Spark's MLlib as a mind map. Spark's MLlib is designed to take advantage of parallelism while having fault-tolerant distributed data structures. Spark refers to such data structures as Resilient Distributed Datasets or RDDs:

Scala

Scala is a modern language that is emerging as an alternative to traditional programming languages such as Java and C++. Scala is a JVM-based language not only offers a concise syntax without the traditional boilerplate code, but also incorporates both object-oriented and programming into an extremely crisp and extraordinarily powerful type-safe language.

Scala takes a flexible and expressive approach, which makes it perfect for interacting with Spark's MLlib. The fact that Spark itself is written in Scala provides a strong evidence that the Scala language is a full-service programming language that can be used to create sophisticated system code with heavy performance needs.

Scala builds on Java's tradition by addressing some of its shortcomings, while avoiding an all-or-nothing approach. Scala code compiles into Java bytecode, which in turn makes it possible to coexist with rich Java libraries interchangeably. The ability to use Java libraries with Scala and vice versa provides continuity and a rich environment for software engineers to build modern and complex machine learning systems without being fully disconnected from the Java tradition and code base.

Scala fully supports a feature-rich functional programming paradigm with standard support for lambda, currying, type interface, immutability, lazy evaluation, and a pattern-matching paradigm reminiscent of Perl without the cryptic syntax. Scala is an excellent match for machine learning programming due to its support for algebra-friendly data types, anonymous functions, covariance, contra-variance, and higher-order functions.

Here's a hello world program in Scala:

object HelloWorld extends App { 
   println("Hello World!") 
 } 

Compiling and running HelloWorld in Scala looks like this:

The Apache Spark Machine Learning Cookbook takes a practical approach by offering a multi-disciplinary view with the developer in mind. This book focuses on the interactions and cohesiveness of machine learning, Apache Spark, and Scala. We also take an extra step and teach you how to set up and run a comprehensive development environment familiar to a developer and provide code snippets that you have to run in an interactive shell without the modern facilities that an IDE provides:

Software versions and libraries used in this book

The following table a detailed list of versions and libraries used in this book. If you follow the installation instructions covered in this chapter, it will include most of the items listed here. Any other JAR or library files that may be required for specific recipes are covered via additional installation instructions in the respective recipes:

Core systems

Version

Spark

2.0.0

Java

1.8

IntelliJ IDEA

2016.2.4

Scala-sdk

2.11.8

Miscellaneous JARs that will be required are as follows:

Miscellaneous JARs

Version

bliki-core

3.0.19

breeze-viz

0.12

Cloud9

1.5.0

Hadoop-streaming

2.2.0

JCommon

1.0.23

JFreeChart

1.0.19

lucene-analyzers-common

6.0.0

Lucene-Core

6.0.0

scopt

3.3.0

spark-streaming-flume-assembly

2.0.0

spark-streaming-kafka-0-8-assembly

2.0.0

 

We have additionally tested all the recipes in this book on Spark 2.1.1 and found that the programs executed as expected. It is recommended for learning purposes you use the software versions and libraries listed in these tables.

To stay current with the rapidly changing Spark landscape and documentation, the API links to the Spark documentation mentioned throughout this book point to the latest version of Spark 2.x.x, but the API references in the recipes are explicitly for Spark 2.0.0.

All the documentation links provided in this book will point to the latest documentation on Spark's website. If you prefer to look for documentation for a specific version of Spark (for example, Spark 2.0.0), look for relevant documentation on the Spark website using the following URL:

https://spark.apache.org/documentation.html

We've made the code as simple as possible for clarity purposes rather than demonstrating the advanced features of Scala.

 

Downloading and installing the JDK


The first step is to download the development environment that is required Scala/Spark development.

Getting ready

When you are ready to download and the JDK, access the following link:

http://www.oracle.com/technetwork/java/javase/downloads/index.html

How to do it...

After successful download, follow the on-screen instructions to install the JDK.

 

Downloading and installing IntelliJ


IntelliJ Community Edition is a IDE for Java SE, Groovy, Scala, and Kotlin development. To complete setting up your machine learning with the Spark environment, the IntelliJ IDE needs to be installed.

Getting ready

When you are ready to and install IntelliJ, access the following link:

https://www.jetbrains.com/idea/download/

How to do it...

At the time of writing, we are using IntelliJ version 15.x or later (for example, version 2016.2.4) to test the examples in the book, but feel free to download the latest version. Once the installation file is downloaded, double-click on the downloaded file (.exe) and begin to install the IDE. Leave all the installation options at the default settings if you do not want to make any changes. Follow the on-screen instructions to complete the installation:

 

Downloading and installing Spark


We proceed to and install Spark.

Getting ready

When you are ready to download and install Spark, access the Apache website at this link:

http://spark.apache.org/downloads.html

 

How to do it...

Go to the Apache website and select the required download parameters, as shown in this screenshot:

Make sure to accept the default choices (click on Next) and proceed with the installation.

 

Configuring IntelliJ to work with Spark and run Spark ML sample codes


We need to run some to ensure that the project settings are correct before being able to run the samples that are provided by Spark or any of the listed this book.

Getting ready

We need to be particularly careful when configuring the project structure and global libraries. After we set everything up, we proceed to run the sample ML code provided by the Spark team to verify the setup. Sample code can be found under the Spark directory or can be obtained by downloading the Spark source code with samples.

How to do it...

The following are the steps for configuring IntelliJ to work with Spark MLlib and for running the sample ML code provided by Spark in the examples directory. The examples directory can be found in your home directory for Spark. Use the Scala samples to proceed:

  1. Click on the Project Structure... option, as shown in the following screenshot, to configure project settings:
  1. Verify the settings:
  1. Configure Global Libraries. Select Scala SDK as your global library:
  1. Select the JARs for the new Scala SDK and let the download complete:
  1. Select the project name:
  1. Verify the settings and additional libraries:
  1. Add dependency JARs. Select modules under the Project Settings in the left-hand pane and click on dependencies to choose the required JARs, as shown in the following screenshot:
  1. Select the JAR files provided by Spark. Choose Spark's default installation directory and then select the lib directory:
  1. We then select the JAR files for examples that are provided for Spark out of the box.
  1. Add required JARs by verifying that you selected and imported all the JARs listed under External Libraries in the the left-hand pane:

The next step is to download and install the Flume and Kafka JARs. For the purposes of this book, we have used the Maven repo:

  1. Download and install the Kafka assembly:
  1. Download and install the Flume assembly:
  1. After the download is complete, move the downloaded JAR files to the lib directory of Spark. We used the C drive when we installed Spark:
  1. Open your IDE and verify that all the JARs under the External Libraries folder on the left, as shown in the following screenshot, are present in your setup:
  1. Build the example projects in Spark to verify the setup:
  1. Verify that the build was successful:

There's more...

Prior to Spark 2.0, we needed library from called Guava for facilitating I/O and for providing a set of rich methods of defining tables and then letting Spark broadcast them across the cluster. Due to dependency issues that were hard to work around, Spark 2.0 no longer uses the Guava library. Make sure you use the Guava library if you are using Spark versions prior to 2.0 (required in version 1.5.2). The library can be accessed at the following URL:

https://github.com/google/guava/wiki

You may want to use Guava version 15.0, which can be found here:

https://mvnrepository.com/artifact/com.google.guava/guava/15.0

If you are using installation instructions from previous blogs, make sure to exclude the Guava library from the installation set.

See also

If there are other third-party libraries or JARs required for the completion of the Spark installation, you can find those in the following repository:

https://repo1.maven.org/maven2/org/apache/spark/

 

Running a sample ML code from Spark


We can verify the setup by simply the sample code from the source tree and importing it into IntelliJ to make sure it runs.

Getting ready

We will first run the logistic regression code from the samples to verify installation. In the next section, we proceed to write our own version of the same program and examine the output in order to understand how it works.

How to do it...

  1. Go to the source directory and pick one of the ML sample code files to run. We've selected the logistic regression example.

Note

If you cannot find the source code in your directory, you can always download the Spark source, unzip, and then extract the examples directory accordingly.

  1. After selecting the example, select Edit Configurations..., as shown in the following screenshot:
  1. In the Configurations tab, define the following options:
    • VM options: The choice shown allows you to run a standalone Spark cluster
    • Program arguments: What we are supposed to pass into the program
  1. Run the logistic regression by going to Run 'LogisticRegressionExample', as shown in the following screenshot:
  1. Verify the exit code and make sure it is as shown in the following screenshot:
 

Identifying data sources for practical machine learning


Getting data for machine learning projects a challenge in the past. However, now there is a rich set of public data sources specifically suitable for machine learning.

Getting ready

In addition to the university and government sources, there are many other open sources of data that can be used to learn and code your own examples and projects. We will list the data sources and show you how to best obtain and download data for each chapter.

How to do it...

The following is a list of open source data worth exploring if you would like to develop applications in this field:

  • UCI machine learning repository: This is an extensive library with search functionality. At the time of writing, there were more than 350 datasets. You can click on the https://archive.ics.uci.edu/ml/index.html link to see all the datasets or look for a specific set using a simple search (Ctrl + F).
  • Kaggle datasets: You need to create an account, but you can download any sets for learning as well as for competing in machine learning competitions. The https://www.kaggle.com/competitions link provides details for exploring and learning more about Kaggle, and the inner workings of machine learning competitions.
  • MLdata.org: A public site open to all with a repository of datasets for machine learning enthusiasts.
  • Google Trends: You can find statistics on search volume (as a proportion of total search) for any given term since 2004 on http://www.google.com/trends/explore.
  • The CIA World Factbook: The https://www.cia.gov/library/publications/the-world-factbook/ link provides information on the history, population, economy, government, infrastructure, and military of 267 countries.

See also

Other sources for learning data:

There are some datasets (for example, text analytics in Spanish, and gene and IMF data) that might be of some interest to you:

 

Running your first program using Apache Spark 2.0 with the IntelliJ IDE


The purpose of this is to get you comfortable with compiling and running a recipe using the Spark 2.0 development environment you just set up. We will explore the components and steps in later chapters.

We are going to write our own version of the Spark 2.0.0 program and examine the output so we can understand how it works. To emphasize, this short recipe is only a simple RDD program with Scala sugar syntax to make sure you have set up your environment correctly before starting to work more complicated recipes.

How to do it...

  1. Start a new project in IntelliJ or in an IDE of your choice. Make sure that the necessary JAR files are included.
  2. Download the sample code for the book, find the myFirstSpark20.scala file, and place the code in the following directory.

We installed Spark 2.0 in the C:\spark-2.0.0-bin-hadoop2.7\ directory on a Windows machine.

  1. Place the myFirstSpark20.scala file in the C:\spark-2.0.0-bin-hadoop2.7\examples\src\main\scala\spark\ml\cookbook\chapter1 directory:

Mac users note that we installed Spark 2.0 in the /Users/USERNAME/spark/spark-2.0.0-bin-hadoop2.7/ directory on a Mac machine.

Place the myFirstSpark20.scala file in the /Users/USERNAME/spark/spark-2.0.0-bin-hadoop2.7/examples/src/main/scala/spark/ml/cookbook/chapter1 directory.

  1. Set up the package location where the program will reside:
package spark.ml.cookbook.chapter1 
  1. Import the necessary packages for the Spark session to gain access to the cluster and log4j.Logger to reduce the amount of output produced by Spark:
import org.apache.spark.sql.SparkSession 
import org.apache.log4j.Logger 
import org.apache.log4j.Level 
  1. Set output level to ERROR to reduce Spark's logging output:
Logger.getLogger("org").setLevel(Level.ERROR) 
  1. Initialize a Spark session by specifying configurations with the builder pattern, thus making an entry point available for the Spark cluster:
val spark = SparkSession 
.builder 
.master("local[*]")
 .appName("myFirstSpark20") 
.config("spark.sql.warehouse.dir", ".") 
.getOrCreate() 

The myFirstSpark20 object will run in local mode. The previous code block is a typical way to start creating a SparkSession object.

  1. We then create two array variables:
val x = Array(1.0,5.0,8.0,10.0,15.0,21.0,27.0,30.0,38.0,45.0,50.0,64.0) 
val y = Array(5.0,1.0,4.0,11.0,25.0,18.0,33.0,20.0,30.0,43.0,55.0,57.0) 
  1. We then let Spark create two RDDs based on the array created before:
val xRDD = spark.sparkContext.parallelize(x) 
val yRDD = spark.sparkContext.parallelize(y) 
  1. Next, we let Spark operate on the RDD; the zip() function will create a new RDD from the two RDDs mentioned before:
val zipedRDD = xRDD.zip(yRDD) 
zipedRDD.collect().foreach(println) 

In the console output at runtime (more details on how to run the program in the IntelliJ IDE in the following steps), you will see this:

  1. Now, we sum up the value for xRDD and yRDD and calculate the new zipedRDD sum value. We also calculate the item count for zipedRDD:
val xSum = zipedRDD.map(_._1).sum() 
val ySum = zipedRDD.map(_._2).sum() 
val xySum= zipedRDD.map(c => c._1 * c._2).sum() 
val n= zipedRDD.count() 
  1. We print out the value calculated previously in the console:
println("RDD X Sum: " +xSum) 
println("RDD Y Sum: " +ySum) 
println("RDD X*Y Sum: "+xySum) 
println("Total count: "+n) 

Here's the console output:

  1. We close the program by stopping the Spark session:
spark.stop() 
  1. Once the program is complete, the layout of myFirstSpark20.scala in the IntelliJ project explorer will look like the following:
  1. Make sure there is no compiling error. You can test this by rebuilding the project:

Once the rebuild is complete, there should be a build completed message on the console:

Information: November 18, 2016, 11:46 AM - Compilation completed successfully with 1 warning in 55s 648ms
  1. You can run the previous program by right-clicking on the myFirstSpark20 object in the project explorer and selecting the context menu option (shown in the next screenshot) called Run myFirstSpark20.

Note

You can also use the Run menu from the menu bar to perform the same action.

  1. Once the program is successfully executed, you will see the following message:
Process finished with exit code 0

This is also shown in the following screenshot:

  1. Mac users with IntelliJ will be able to perform this action using the same context menu.

Note

Place the code in the correct path.

How it works...

In this example, we wrote our first Scala program, myFirstSpark20.scala, and displayed the steps to execute the program in IntelliJ. We placed the code in the path described in the steps for both Windows and Mac.

In the myFirstSpark20 code, we saw a typical way to create a SparkSession object and how to configure it to run in local mode using the master() function. We created two RDDs out of the array objects and used a simple zip() function to create a new RDD.

We also did a simple sum calculation on the RDDs that were created and then displayed the result in the console. Finally, we exited and released the resource by calling spark.stop().

There's more...

can be downloaded from http://spark.apache.org/downloads.html.

Documentation for Spark 2.0 related to RDD can be found at http://spark.apache.org/docs/latest/programming-guide.html#rdd-operations.

See also

 

How to add graphics to your Spark program


In this recipe, we discuss to use JFreeChart to add a chart to your Spark 2.0.0 program.

How to do it...

  1. Set up the JFreeChart library. JFreeChart JARs can be downloaded from the https://sourceforge.net/projects/jfreechart/files/ site.

 

  1. The JFreeChart version we have covered in this book is JFreeChart 1.0.19, as can be seen in the following screenshot. It can be downloaded from the https://sourceforge.net/projects/jfreechart/files/1.%20JFreeChart/1.0.19/jfreechart-1.0.19.zip/download site:
  1. Once the ZIP file is downloaded, extract it. We extracted the ZIP file under C:\ for a Windows machine, then proceed to find the lib directory under the extracted destination directory.
  2. We then find the two libraries we need (JFreeChart requires JCommon), JFreeChart-1.0.19.jar and JCommon-1.0.23:
  1. Now we copy the two previously mentioned JARs into the C:\spark-2.0.0-bin-hadoop2.7\examples\jars\ directory.

 

  1. This directory, as mentioned in the previous setup section, is in the classpath for the IntelliJ IDE project setting:

Note

In macOS, you need to place the previous two JARs in the /Users/USERNAME/spark/spark-2.0.0-bin-hadoop2.7/examples\jars\ directory.

  1. Start a new project in IntelliJ or in an IDE of your choice. Make sure that the necessary JAR files are included.
  2. Download the sample code for the book, find MyChart.scala, and place the code in the following directory.
  3. We installed Spark 2.0 in the C:\spark-2.0.0-bin-hadoop2.7\ directory in Windows. Place MyChart.scala in the C:\spark-2.0.0-bin-hadoop2.7\examples\src\main\scala\spark\ml\cookbook\chapter1 directory.
  4. Set up the package location where the program will reside:
  package spark.ml.cookbook.chapter1
  1. Import the necessary packages for the Spark session to gain access to the cluster and log4j.Logger to reduce the amount of output produced by Spark.
  2. Import necessary JFreeChart packages for the graphics:
import java.awt.Color 
import org.apache.log4j.{Level, Logger} 
import org.apache.spark.sql.SparkSession 
import org.jfree.chart.plot.{PlotOrientation, XYPlot} 
import org.jfree.chart.{ChartFactory, ChartFrame, JFreeChart} 
import org.jfree.data.xy.{XYSeries, XYSeriesCollection} 
import scala.util.Random 
  1. Set the output level to ERROR to reduce Spark's logging output:
Logger.getLogger("org").setLevel(Level.ERROR) 
  1. Initialize a Spark session specifying configurations with the builder pattern, thus making an entry point available for the Spark cluster:
val spark = SparkSession 
  .builder 
  .master("local[*]") 
  .appName("myChart") 
  .config("spark.sql.warehouse.dir", ".") 
  .getOrCreate() 
  1. The myChart object will run in local mode. The previous code block is a typical start to creating a SparkSession object.
  2. We then create an RDD using a random number and ZIP the number with its index:
val data = spark.sparkContext.parallelize(Random.shuffle(1 to 15).zipWithIndex) 
  1. We print out the RDD in the console:
data.foreach(println) 

Here is the console output:

  1. We then create a data series for JFreeChart to display:
val xy = new XYSeries("") 
data.collect().foreach{ case (y: Int, x: Int) => xy.add(x,y) } 
val dataset = new XYSeriesCollection(xy) 
  1. Next, we create a chart object from JFreeChart's ChartFactory and set up the basic configurations:
val chart = ChartFactory.createXYLineChart( 
  "MyChart",  // chart title 
  "x",               // x axis label 
  "y",                   // y axis label 
  dataset,                   // data 
  PlotOrientation.VERTICAL, 
  false,                    // include legend 
  true,                     // tooltips 
  false                     // urls 
)
  1. We get the plot object from the chart and prepare it to display graphics:
val plot = chart.getXYPlot() 
  1. We configure the plot first:
configurePlot(plot) 
  1. The configurePlot function is defined as follows; it sets up some basic color schema for the graphical part:
def configurePlot(plot: XYPlot): Unit = { 
  plot.setBackgroundPaint(Color.WHITE) 
  plot.setDomainGridlinePaint(Color.BLACK) 
  plot.setRangeGridlinePaint(Color.BLACK) 
  plot.setOutlineVisible(false) 
} 
  1. We now show the chart:
show(chart) 
  1. The show() function is defined as follows. It is a very standard frame-based graphic-displaying function:
def show(chart: JFreeChart) { 
  val frame = new ChartFrame("plot", chart) 
  frame.pack() 
  frame.setVisible(true) 
}
  1. Once show(chart) is executed successfully, the following frame will pop up:
  1. We close the program by stopping the Spark session:
spark.stop() 

How it works...

In this example, we wrote MyChart.scala and saw the steps for executing the program in IntelliJ. We placed code in the path described in the steps for both Windows and Mac.

In the code, we saw a typical way to create the SparkSession object and how to use the master() function. We created an RDD out of an array of random integers in the range of 1 to 15 and zipped it with the Index.

We then used JFreeChart to compose a basic chart that contains a simple x and y axis, and supplied the chart with the dataset we generated from the original RDD in the previous steps.

We set up the schema for the chart and called the show() function in JFreeChart to show a Frame with the x and y axes displayed as a linear graphical chart.

Finally, we exited and released the resource by calling spark.stop().

See also

Additional examples about the features and capabilities of JFreeChart can be found at the following website:

http://www.jfree.org/jfreechart/samples.html

About the Authors

  • Siamak Amirghodsi

    Siamak Amirghodsi (Sammy) is interested in building advanced technical teams, executive management, Spark, Hadoop, big data analytics, AI, deep learning nets, TensorFlow, cognitive models, swarm algorithms, real-time streaming systems, quantum computing, financial risk management, trading signal discovery, econometrics, long-term financial cycles, IoT, blockchain, probabilistic graphical models, cryptography, and NLP.

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  • Meenakshi Rajendran

    Meenakshi Rajendran is experienced in the end-to-end delivery of data analytics and data science products for leading financial institutions. Meenakshi holds a master's degree in business administration and is a certified PMP with over 13 years of experience in global software delivery environments. Her areas of research and interest are Apache Spark, cloud, regulatory data governance, machine learning, Cassandra, and managing global data teams at scale.

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  • Broderick Hall

    Broderick Hall is a hands-on big data analytics expert and holds a master’s degree in computer science with 20 years of experience in designing and developing complex enterprise-wide software applications with real-time and regulatory requirements at a global scale. He is a deep learning early adopter and is currently working on a large-scale cloud-based data platform with deep learning net augmentation.

    Browse publications by this author
  • Shuen Mei

    Shuen Mei is a big data analytic platforms expert with 15+ years of experience in designing, building, and executing large-scale, enterprise-distributed financial systems with mission-critical low-latency requirements. He is certified in the Apache Spark, Cloudera Big Data platform, including Developer, Admin, and HBase. He is also a certified AWS solutions architect with emphasis on peta-byte range real-time data platform systems.

    Browse publications by this author

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