Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By : Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei
Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By: Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei

Overview of this book

Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: • Mastering Apache Spark 2.x by Romeo Kienzler • Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla • Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook
Table of Contents (23 chapters)
Title Page
About Packt


About the Authors

Romeo Keinzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics. His current research focus is on scalable machine learning on Apache Spark. He is a contributor to various open source projects and works as an associate professor for artificial intelligence at Swiss University of Applied Sciences, Berne. He is a member of the IBM Technical Expert Council and the IBM Academy of Technology, IBM's leading brains trust.


Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Aachen, Germany. He holds a BSc and an MSc degree in Computer Science. Before joining Fraunhofer FIT, he worked as a Researcher at Insight Centre for Data Analytics, Ireland. Before this, he worked as a Lead Engineer at Samsung Electronics' distributed R&D Institutes in Korea, India, Turkey, and Bangladesh. Previously, he worked as a Research Assistant at the database lab, Kyung Hee University, Korea. He also worked as an R&D engineer with BMTech21 Worldwide, Korea. Before this, he worked as a Software Engineer with i2SoftTechnology, Dhaka, Bangladesh. He has more than 8 years' experience in the area of research and development with a solid understanding of algorithms and data structures in C, C++, Java, Scala, R, and Python. He has published several books, articles, and research papers concerning big data and virtualization technologies, such as Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce. He is also equally competent with deep learning technologies such as TensorFlow, DeepLearning4j, and H2O. His research interests include machine learning, deep learning, the semantic web, linked data, big data, and bioinformatics. Also he is the author of the following book titles: Large-Scale Machine Learning with Spark (Packt Publishing Ltd.) Deep Learning with TensorFlow (Packt Publishing Ltd.) Scala and Spark for Big Data Analytics (Packt Publishing Ltd.)

Sridhar Alla is a big data expert helping companies solve complex problems in distributed computing, large-scale data science and analytics practice. He presents regularly at several prestigious conferences and provides training and consulting to companies. He holds a bachelor's in computer science from JNTU, India. He loves writing code in Python, Scala, and Java. He also has extensive hands-on knowledge of several Hadoop-based technologies, TensorFlow, NoSQL, IoT, and deep learning.

 Siamak Amirghodsi (Sammy) is a world-class senior technology executive leader with an entrepreneurial track record of overseeing big data strategies, cloud transformation, quantitative risk management, advanced analytics, large-scale regulatory data platforming, enterprise architecture, technology road mapping, multi-project execution, and organizational streamlining in Fortune 20 environments in a global setting. Siamak is a hands-on big data, cloud, machine learning, and AI expert, and is currently overseeing the large-scale cloud data platforming and advanced risk analytics build out for a tier-1 financial institution in the United States. Siamak's interests include 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.


Meenakshi Rajendran is a hands-on big data analytics and data governance manager with expertise in large-scale data platforming and machine learning program execution on a global scale. She 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. She not only understands the underpinnings of big data and data science technology but also has a solid understanding of the human side of the equation as well.Meenakshi’s favorite languages are Python, R, Julia, and Scala. Her areas of research and interest are Apache Spark, cloud, regulatory data governance, machine learning, Cassandra, and managing global data teams at scale. In her free time, she dabbles in software engineering management literature, cognitive psychology, and chess for relaxation.


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 has an extensive experience in designing and building real-time financial applications for some of the largest financial institutions and exchanges in USA. He is a deep learning early adopter and is currently working on a large-scale cloud-based data platform with deep learning net augmentation.Shuen Mei is a big data analytic platforms expert with 15+ years of experience in the financial services industry. He is experienced 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.Shuen is also a certified AWS solutions architect with emphasis on peta-byte range real-time data platform systems. Shuen is a skilled software engineer with extensive experience in delivering infrastructure, code, data architecture, and performance tuning solutions in trading and finance for Fortune 100 companies.




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