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AKTU Stream Processing and Analytics (BCAM062) Syllabus PDF – B.Tech 3rd Year
Stream Processing and Analytics BCAM062
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Official AKTU syllabus of Stream Processing and Analytics (BCAM062) for B.Tech 3rd Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Stream Processing and Analytics
Download PDFBCAM062 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course, the student will be able to:
| CO 1 | Explain the need for stream processing | K , K 1 2 |
| CO 2 | Comprehend the architectures of stream processing. | K , K 2 3 |
| CO 3 | Explain and run Distributed Processing and Resilience Model | K , K 1 2 |
| CO 4 | Design effective streaming solutions using Structured Streaming | K , K 5 6 |
| CO 5 | Design effective streaming solutions using Spark Streaming | K , K 5 6 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Fundamentals of Stream Processing: What Is Stream Processing? Examples of Stream Processing- Scaling Up Data Processing- Distributed Stream Processing- Introducing Apache Spark. Stream-Processing Model: Sources and Sinks- Immutable Streams Defined from One Another- Transformations and Aggregations- Window Aggregations – Stateless and Stateful Processing- The Effect of Time. Practical Component: a. Installing and configuring Apache Spark b. Installing and configuring the Scala IDE c. Installing and configuring JDK | 08 |
| II | Components of a Data Platform- Architectural Models- The Use of a Batch-Processing Component in a Streaming Application- Referential Streaming Architectures- Streaming Versus Batch Algorithms. Apache Spark as a Stream-Processing Engine: Spark’s Memory Usage- Understanding Latency- Throughput- Oriented Processing- Fast Implementation of Data Analysis. Practical Component: a. Write your own Spark Streaming program, to count the number of words in text data received from data server listening on a TCP socket b. Write a simple Spark Streaming program that prints a sample of the tweets it receives from Twitter every second. | 08 |
| III | Spark’s Distributed Processing Model: Running Apache Spark with a Cluster Manager- Spark’s Own Cluster Manager – Resilience and Fault Tolerance in a Distributed System- Data Delivery Semantics- Micro batching and One-Element-at-a-Time – Bringing Micro batch and One- Record-at a- Time Closer Together- Dynamic Batch Interval- Structured Streaming Processing Model. Spark’s Resilience Model: Resilient Distributed Datasets in Spark – Spark Components – Spark’s Fault-Tolerance Guarantees. Practical Component: a. Create Spark RDD using parallelize with sparkContext.parallelize() method and using Spark shell b. Write a script in Spark to Read all text files from a directory into a single RDD c. Write a spark program to load a CSV file into Spark RDD using a Scala d. Write a Spark Streaming program for adding 1 to the stream of integers in a reliable, fault tolerant manner, and then visualize them. | 08 |
| IV | Introducing Structured Streaming- The Structured Streaming Programming Model – Structured Streaming in Action – Structured Streaming Sources – Structured Streaming Sinks – Event Time– Based Stream Processing. Practical Component: a. Develop a streaming application by- Connecting to a Stream, Preparing the Data in the Stream, Performing Operations on Streaming Dataset, creating a Query, Starting the Stream Processing and Exploring the data. b. Create a Structured streaming job by Initializing Spark, acquiring streaming data from sources, declaring the operations we want to apply to the streaming data and outputting the resulting data using Sinks. c. Create a small but complete Internet of Things (IoT)-inspired streaming program. d. Define the schema in Structured Streaming to handle the data at different levels. e. Create custom sinks to write data to systems not supported by the default implementations | 08 |
| V | Introducing Spark Streaming – The Spark Streaming Programming Model – The Spark Streaming Execution Model – Spark Streaming Sources – Spark Streaming Sinks – Time-Based Stream Processing- Working with Spark SQL – Checkpointing – Monitoring Spark Streaming- Performance Tuning. Practical Component: Develop any Spark Streaming application and do the following: a) Create a Spark Streaming Context, b) Define one or several Dstreams from data sources or other Dstreams c) Define one or more output operations to materialize the results of these | 08 |
Text Books
- Gerard Maas and Francois Garillot , “Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming”, O’Reilly, 2019.
- Henrique C. M. Andrade, Buǧra Gedik and Deepak S. Turaga, “Fundamentals of Stream Processing: Application Design, Systems, and Analytics”, Cambridge University Press, 2014.
- Bryon Ellis, “Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data”, Wiley, 1st edition, 2014.
- Anindita Basak, Krishna Venkataraman, Ryan Murphy, Manpreet Singh, “Stream Analytics with Microsoft Azure”, Packt Publishing, December 2017.
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