Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye!
Key Features
- Learn Scala’s sophisticated type system that combines Functional Programming and object-oriented concepts
- Work on a wide array of applications, from simple batch jobs to stream processing and machine learning
- Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark
Book Description
Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you.
The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment.
You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio.
By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.
What you will learn
- Understand object-oriented & functional programming concepts of Scala
- In-depth understanding of Scala collection APIs
- Work with RDD and DataFrame to learn Spark’s core abstractions
- Analysing structured and unstructured data using SparkSQL and GraphX
- Scalable and fault-tolerant streaming application development using Spark structured streaming
- Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML
- Build clustering models to cluster a vast amount of data
- Understand tuning, debugging, and monitoring Spark applications
- Deploy Spark applications on real clusters in Standalone, Mesos, and YARN
Who this book is for
Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker.
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 in computer science. Before joining Fraunhofer FIT, he had been working as a researcher at the Insight Centre for data analytics, Ireland. Previously, he worked as a lead engineer with Samsung Electronics' distributed R&D centers in Korea, India, Vietnam, Turkey, and Bangladesh. Earlier, he worked as a research assistant in the Database Lab at Kyung Hee University, Korea, and as an R&D engineer with BMTech21 Worldwide, Korea. Even before that, he worked as a software engineer with i2SoftTechnology, Dhaka, Bangladesh.
He has more than 8 years of experience in the area of research and development, with a solid knowledge of algorithms and data structures in C/C++, Java, Scala, R, and Python-focused big data technologies: Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce, and deep learning technologies: TensorFlow, DeepLearning4j, and H2O-Sparking Water. His research interests include machine learning, deep learning, semantic web, linked data, big data, and bioinformatics. He is the author of the following book titles with Packt:
Large-Scale Machine Learning with Spark
Deep Learning with TensorFlow
Sridhar Alla is a big data expert helping small and big companies solve complex problems, such as data warehousing, governance, security, real-time processing, high-frequency trading, and establishing large-scale data science practices. He is an agile practitioner as well as a certified agile DevOps practitioner and implementer. He started his career as a storage software engineer at Network Appliance, Sunnyvale, and then worked as the chief technology officer at a cyber security firm, eIQNetworks, Boston. His job profile includes the role of the director of data science and engineering at Comcast, Philadelphia. He is an avid presenter at numerous Strata, Hadoop World, Spark Summit, and other conferences. He also provides onsite/online training on several technologies. He has several patents filed in the US PTO on large-scale computing and distributed systems. He holds a bachelors degree in computer science from JNTU, Hyderabad, India, and lives with his wife in New Jersey.
Sridhar has over 18 years of experience writing code in Scala, Java, C, C++, Python, R and Go. He also has extensive hands-on knowledge of Spark, Hadoop, Cassandra, HBase, MongoDB, Riak, Redis, Zeppelin, Mesos, Docker, Kafka, ElasticSearch, Solr, H2O, machine learning, text analytics, distributed computing and high performance computing.
