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Short Name

Clickstream analysis using Apache Spark and Apache Kafka

Short Description

Use Apache Spark and Apache Kafka to demonstrate how to detect real-time trending topics on the Wikipedia web site. Apache Kafka will act as a message queue, and the Apache Spark structured streaming engine will be used to perform the analytics.

Offering Type

Cognitive

Introduction

Author

by Prashant Sharma

Code

• https://github.com/IBM/kafka-streaming

Demo

N/A

Video

TBD

Overview

When the reader has completed this journey, they will understand how to:

• Use Jupyter Notebooks to load, visualize, and analyze data
• Run Notebooks in IBM Data Science Experience
• Perform clickstream analysis using Apache Spark Structured Streaming.
• Build a low-latency processing stream utilizing Apache Kafka.

Flow

1. User connects with Apache Kafka service and sets up a running instance of a clickstream.
2. Run a Jupyter Notebook in IBM's Data Science Experience that interacts with the underlying Apack Spark service. Alternatively, this can be done locally by running the Spark Shell.
3. The Spark service reads and processes data from the Kafka service.
4. Processed Kafka data is relayed back to the user via the Jupyter Nodebook (or console sink if running locally).

Included Components

• Apache Spark: An open-source distributed computing framework that allows you to perform large-scale data processing.
• Apache Kafka: Kafka is used for building real-time data pipelines and streaming apps. It is designed to be horizontally scalable, fault-tolerant and fast.
• IBM Data Science Experience: Analyze data using RStudio, Jupyter, and Python in a configured, collaborative environment that includes IBM value-adds, such as managed Spark.

Featured Technologies

• Jupyter Notebooks: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.

Links

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