Apache Spark and it's Components

Explore the architecture and components of Apache Spark, including Spark Core, Spark SQL, MLlib, and GraphX. Understand how Spark achieves fast big data processing through Resilient Distributed Datasets and its Master/Slave design to support machine learning and streaming workloads.

We'll cover the following...

- Apache Spark
- Apache Spark Workloads
- How Apache Spark Works?
- - Resilient Distributed Datasets
- Advantages of Apache Spark

Apache Spark

Spark was developed in 2019 at the University of California Berkeley. Apache Spark is an open-source and distributed processing system that is used for processing big data. It holds many advantages over Hadoop and one of them is very fast. It has the ability to utilize in-memory caching and query execution to retrieve results to queries in a quick manner. It is well suited for Machine Learning, Graph Analytics, Batch processing, and real-time processing. It provides API’s for famous programming languages like Java, Scala, Python, and R.

1.What is Data Science ?

2.Applications of Data Science

3.Overview of Libraries

4.Probability and Statistics

5.Machine Learning Part-1

6.Machine Learning Part-2

7.Machine Learning Part-3

8.Deep Learning

9.Machine Learning Tools and Libraries

10.Big Data Tools and Technologies

11.Where to go next ?

Mock Interview

Mock Interview

Apache Spark and it's Components

Apache Spark

Apache Spark Workloads