Learn Big-Data-IoT with PySpark, Spark Streaming and Kafka

Learn Big-Data-IoT with PySpark, Spark Streaming and Kafka

Learning Python Spark and Spark Streaming  without the working knowledge of IoT frameworks and Cloud is a job half learned. Organizations are moving to the cloud in their digital transformation journey, and deployment of BigData Framework on the cloud under the clustered environment is the hottest Big-Data skill set.

This is a unique course where you will not only learn PySpark - the best Big-Data Framework - but also Cluster-based cloud computing with Azure HDInsight, AWS EMR, and GCP DataProc under one course. You will learn Spark Streaming with IoT data pipelines using Kafka.

Great Visualization will help you understand the implied concepts better.

You will learn PySpark with more than 40+ Spark Transformation and Action methods of  Resilient Distributed Datasets (RDD) and Spark Data-frames with hand-on demos.

You will learn about Spark Architecture, Dataframe, and RDD concepts.

You will learn how to install the necessary libraries of Pyspark.

You will learn how to perform cloud-based cluster computing.

You will learn about Spark Streaming and Spark Structured Streaming.

You will learn about Tumbling Window, Sliding Window and Watermarking in Spark Structured Streaming.

We will have more than 8 different examples of Spark Streaming to work through concepts.

You will learn about Big Data Ingestion and Pre-processing examples You will learn SQL in Pyspark.

You can prepare for CCA 175 certification taking this course.

This course is a must and precursor to learning SparkML - the most versatile, scalable BigData ML framework.

Learn BigData and IoT with PySpark, Spark Streaming, Kafka, IoT Architecture, 40+ RDDs & Dataframes Functions- Hands-on

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What you will learn

• Complete Pyspark, Spark Streaming, Kafka, Cluster Computing using Spark Jobs, AWS, Azure, GCP Clusters, 40+ data transformation and actions, Great projects

Rating: 3.95

Level: All Levels

Duration: 3.5 hours

Instructor: Implied Concepts AI