Add to ORKGFast Data Processing with Spark - Second Edition is for software developers who want to learn how to write distributed programs with Spark. It will help developers who have had problems that were too big to be dealt with on a single computer. No previous experience with distributed programming is necessary. This book assumes knowledge of either Java, Scala, or Python
Distributed data processing systems are the standard means for large-scale data analysis in the Big ...
Modern Data-Intensive Scalable Computing (DISC) systems are designed to process data through batch j...
While cluster computing frameworks are continuously evolving to provide real-time data analysis capa...
Today's data deluge calls for novel, scalable data handling and processing solutions. Spark has emer...
Processing big data in real-time is challenging due to scalability, information consistency, and fau...
This is an introductory book on PySpark. This book is about PySpark: Python API for Spark.Apache Spa...
Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements...
The Hadoop ecosystem is the leading opensource platform for distributed storing and processing big d...
The area of Big Data is commonly characterized by situations where the volumes of data are such that...
The digital era's requirements pose many challenges related to deployment, implementation and effici...
This master's thesis deals with Big data processing in distributed system Apache Spark using tools, ...
If you are a data engineer, an application developer, or a data scientist who would like to leverage...
Distribution as a concept means that a task (for example, data storage or code execution) is paralle...
Spark SQL is a new module in Apache Spark that integrates rela-tional processing with Spark’s functi...
Master's thesis in Computer scienceIt is now commonly realized that the energy consumption in our wo...
Distributed data processing systems are the standard means for large-scale data analysis in the Big ...
Modern Data-Intensive Scalable Computing (DISC) systems are designed to process data through batch j...
While cluster computing frameworks are continuously evolving to provide real-time data analysis capa...
Today's data deluge calls for novel, scalable data handling and processing solutions. Spark has emer...
Processing big data in real-time is challenging due to scalability, information consistency, and fau...
This is an introductory book on PySpark. This book is about PySpark: Python API for Spark.Apache Spa...
Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements...
The Hadoop ecosystem is the leading opensource platform for distributed storing and processing big d...
The area of Big Data is commonly characterized by situations where the volumes of data are such that...
The digital era's requirements pose many challenges related to deployment, implementation and effici...
This master's thesis deals with Big data processing in distributed system Apache Spark using tools, ...
If you are a data engineer, an application developer, or a data scientist who would like to leverage...
Distribution as a concept means that a task (for example, data storage or code execution) is paralle...
Spark SQL is a new module in Apache Spark that integrates rela-tional processing with Spark’s functi...
Master's thesis in Computer scienceIt is now commonly realized that the energy consumption in our wo...
Distributed data processing systems are the standard means for large-scale data analysis in the Big ...
Modern Data-Intensive Scalable Computing (DISC) systems are designed to process data through batch j...
While cluster computing frameworks are continuously evolving to provide real-time data analysis capa...