Real-Time Analytics 222891-D
Laboratorium (LAB) Semestr letni 2024/25

From Flat Files to Data Mash: Data Processing Models in Big Data.

ETL and Batch (Offline Learning) and Incremental (Online Learning) Modeling. Map-Reduce.

Data Streams, Events, and Time and Time Window Concepts in Real-time Data Processing.

Microservices and Communication via REST API.

Contemporary Architectures for Stream Data Processing Applications - Lambda, Kappa, Pub/Sub.

Processing Structured and Unstructured Data. Programming Environment for Python.

Utilizing Python Object-Oriented Elements in the Modeling Process with Scikit-Learn and Keras.

Python Object-Oriented Programming Basics. Building Classes for Random Walk, Perceptron, and Adeline Algorithms.

Preparing a Microservice with an ML Model for Production.

Streaming Data Using RDDs with Apache Spark. Introduction to the DataFrame Object.

Methods for Creating Data Streams Using the DataFrame Object in Apache Spark. Setting Output and Input.

Streaming Data Using Apache Kafka. Producer and Consumer Objects.

Capturing Data Streams from Apache Kafka Sources and Transforming Them in Apache Spark.

Anomaly Detection for Streaming Data Using Apache Kafka, Apache Spark, and the Isolation Forest Model.

Review of Projects.