What is Apache Spark?
According to IBM, Apache Spark (Spark) is an open-source data-processing engine for large data sets. It is designed to deliver the computational speed, scalability, and programmability required for Big Data—specifically for streaming data, graph data, machine learning, and artificial intelligence (AI) applications.
Spark’s analytics engine processes data 10 to 100 times faster than alternatives. It scales by distributing processing work across large clusters of computers, with built-in parallelism and fault tolerance. It even includes APIs for popular programming languages among data analysts and data scientists, including Scala, Java, Python, and R.
What is Apache Hadoop?
The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
What is the Cloudera Developer Training for Spark & Hadoop course about?
This four-day hands-on training course delivers developers’ key concepts and expertise to use Apache Spark to develop high-performance parallel applications. Participants will learn how to use Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. Developers will also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The course covers how to work with “big data” stored in a distributed file system and execute Spark applications on a Hadoop cluster. After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries.
- How the Apache Hadoop ecosystem fits in with the data processing lifecycle
- How data is distributed, stored, and processed in a Hadoop cluster
- How to write, configure, and deploy Apache Spark applications on a Hadoop cluster
- How to use the Spark shell and Spark applications to explore, process, and analyze distributed data • How to query data using Spark SQL, DataFrames, and Datasets
- How to use Spark Streaming to process a live data stream
This course is designed for developers and engineers who have programming experience, but prior knowledge of Spark and Hadoop is not required. Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required. Basic familiarity with the Linux command line is assumed. Basic knowledge of SQL is helpful.
Upon completion of the course, attendees are encouraged to continue their studies and register for the CCA Spark and Hadoop Developer exam. Certification is a great differentiator. It helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills and expertise.
- Introduction to Apache Hadoop and the Hadoop Ecosystem
- Apache Hadoop Overview
- Apache Hadoop File Storage
- Distributed Processing on an Apache Hadoop Cluster
- Apache Spark Basics
- Working with DataFrames and Schemas
- Analyzing Data with DataFrame Queries
- RDD Overview
- Transforming Data with RDDs
Apache Spark and Apache Hadoop are two of the most promising and prominent distributed systems for processing big data in the machine learning world today.
To get a good understanding and learn the difference between the two systems, opt for this comprehensive course that sheds light on how to work with big data.
To enroll, contact P2L today!