Descrição do Curso

This comprehensive tutorial will acquaint you with all the aspects of real-time analytics with Apache Spark, one of the trending Big Data processing frameworks on the market today. It will show you how to leverage the features of various components of the Spark framework to efficiently process, analyze, and visualize your data.

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 address 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.

About the Author

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 modelling, data analysis, data analytics, graphical processing, data frame operations, R algorithm

Detalhes do Curso
en
en
Packt Publishing
Rítmo flexível
Avançado
4 horas
Detalhes do Curso
en
en
Packt Publishing
Rítmo flexível
Avançado
4 horas
Descrição do Curso

This comprehensive tutorial will acquaint you with all the aspects of real-time analytics with Apache Spark, one of the trending Big Data processing frameworks on the market today. It will show you how to leverage the features of various components of the Spark framework to efficiently process, analyze, and visualize your data.

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 address 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.

About the Author

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 modelling, data analysis, data analytics, graphical processing, data frame operations, R algorithm