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If you face challenges on how to analyze real-time data, create real-world streaming processing in Spark, and face some common pitfalls in your Spark code and are looking for a solution to get you out of the development problems providing you with some best practices so that you can code better, efficiently and faster for analyzing a large amount of data, then this learning series is perfect for you!With this well thought out Learning Path, you will first begin by learning the fundamentals of Apache Spark which includes Resilient Distributed Datasets (RDD), HDFS, YARN, create effective Spark application and execute it on Hadoop cluster & much more. Then you will learn to analyze data using machine learning techniques and graphs. Moving further you will focus o some amazing tips & tricks to improve particular aspects of programming & administration in Apache Spark & also speed up your Spark jobs by reducing shuffles. Finally, you will learn some quick & simple solutions to troubleshoot development issues and debugging techniques with Apache Spark.Contents and OverviewThis training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Apache Spark Fundamentals you will begin learning about the Apache Spark programming fundamentals such as Resilient Distributed Datasets (RDD) and See which operations can be used to perform a transformation or action operation on the RDD. We'll show you how to load and save data from various data sources as a different type of files, No-SQL and RDBMS databases, etc.. We'll also explain Spark advanced programming concepts such as managing Key-Value pairs, accumulators, etc. Finally, you'll discover how to create an effective Spark application and execute it on the Hadoop cluster to the data and gain insights to make informed business decisions. By the end of this video, you will be well-versed with all the fundamentals of Apache Spark and implementing them in Spark.The second course, Advanced Analytics, and Real-Time Data Processing in Apache Spark you will learn how to implement the high-velocity streaming operation for data processing in order to perform efficient analytics on your real-time data. You'll analyze data using machine learning techniques and graphs. You'll learn about Spark Streaming and create real-world streaming processing that addresses all the problems that need to be solved. You'll solve problems using Machine Learning techniques and find out about all the tools available in the MLlibtoolkit. You'll find out how to leverage Graphs to solve real-world problems. At the end of this video, you'll also see some useful Machine Learning algorithms with the help of Spark MLlib and will integrate Spark with R. We'll also make sure you're confident and prepared for graph processing, as you'll learn more about the GraphX API. By the end, you'll be well-versed in the aspects of real-time analytics and implement them with Apache Spark.The third course, Apache Spark: Tips, Tricks, & Techniques you'll learn to implement some practical and proven techniques to improve particular aspects of programming and administration in Apache Spark. You will explore 7 sections that will address different aspects of Spark via 5 specific techniques with clear instructions on how to carry out different Apache Spark tasks with hands-on experience. The techniques are demonstrated using practical examples and best practices. By the end of this course, you will have learned some exciting tips, best practices, and techniques with Apache Spark. You will be able to perform tasks and get the best data out of your databases much faster and with ease.The fourth course, Troubleshooting Apache Spark will give you new possibilities and you'll cover many aspects of Apache Spark; some you may know and some you probably never knew existed. If you take a lot of time learning and performing tasks on Spark, you are unable to leverage Apache Spark's full capabilities and features, and face a roadblock in your development journey. You'll face issues and will be unable to optimize your development process due to common problems and bugs; you'll be looking for techniques which can save you from falling into any pitfalls and common errors during development. With this course, you'll learn to implement some practical and proven techniques to improve particular aspects of Apache Spark with proper research. You need to understand the common problems and issues Spark developers face, collate them, and build simple solutions for these problems. One way to understand common issues is to look out for Stack Overflow queries. This course is a high-quality troubleshooting course, highlighting issues faced by developers in different stages of their application development and providing them with simple and practical solutions to these issues. It supplies solutions to some problems and challenges faced by developers; however, this course also focuses on discovering new possibilities with Apache Spark. By the end of this course, you will have solved your Spark problems without any hassle.About the Authors:Nishant Garg has over 16 years of software architecture and development experience in various technologies, such as Java Enterprise Edition, SOA, Spring, Hadoop, Hive, Flume, Sqoop, Oozie, Spark, YARN, Impala, Kafka, Storm, Solr/Lucene, NoSQL databases (such as HBase, Cassandra, and MongoDB), and MPP databases (such as Greenplum). He received his MS in software systems from the Birla Institute of Technology and Science, Pilani, India, and is currently working as a senior technical architect for the Big Data R & D Labs with Impetus Infotech Pvt. Ltd. Previously, Nishant has enjoyed working with some of the most recognizable names in IT services and financial industries, employing full software life cycle methodologies such as Agile and SCRUM. Nishant has also undertaken many speaking engagements on big data technologies and is also the author of Learning Apache Kafka & HBase Essentials, Packt Publishing.Tomasz Lelek is a Software Engineer and Co-Founder of InitLearn. He mostly does programming in Java and Scala. He dedicates his time and effort to get better at everything. He is currently diving into Big Data technologies. Tomasz is very passionate about everything associated with software development. He has been a speaker at a few conferences in Poland-Confitura and JDD, and at the Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference. He was also a speaker at an international event in Dhaka. He is very enthusiastic and loves to share his knowledge. Amazon Keywords: Data processing, data modeling, data analysis, data analytics, graphical processing, data frame operations, R algorithm.

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