Before diving into Apache Spark, it's beneficial to understand some foundational technologies. One of the first areas to familiarise yourself with is data store interaction, given that Spark can leverage a multitude of different data stores.
Additionally, a basic understanding of Hadoop is beneficial, as this popular distributed data infrastructure often complements Spark in executing big data tasks. Alongside this, having a grasp on SQL enables you to interact with and retrieve data from databases efficiently, should you plan to use these as a data source within Spark.
A basic familiarity with distributed database systems, such as Hbase or Cassandra, can be advantageous as well. Lastly, to effectively interact with Spark, proficiency in a programming language that Spark recognises- like Python, Java, R, or Scala programming languages- is essential. With these skills under your belt, you'll be well-equipped to make the most of what Apache Spark has to offer.
What Will You Learn?
This tutorial offers a swift yet comprehensive learning experience, ensuring mastery through efficient instruction and practical examples.
- module 1: SPARK FUNDAMENTALS
- module 2:STARTING WITH SPARK
- module 3:DELVING INTO RDDS
- module 4:DATA AGGREGATION USING PAIR RDDS
- module 5:CRAFTING AND LAUNCHING SPARK APPLICATIONS
- module 6:MASTERY IN PARALLEL PROCESSING
- module 7:PERSISTENCE IN SPARK RDD
- module 8:INTRODUCTION TO SPARK'S MLlib
- module 9:APACHE FLUME AND APACHE KAFKA SYNERGY
- module 10:REAL-TIME DATA WITH SPARK STREAMING
- module 11:ENHANCING SPARK'S EFFICIENCY
- module 12:ENGAGING WITH SPARK SQL AND DATA FRAMES
- module 13:SPARK'S SCHEDULING AND PARTITION TECHNIQUES