Data Streaming and NLP with PySpark

Overview

Data Streaming and NLP with PySpark explores streaming data processing and NLP using the power of distributed computing. This course equips learners with the skills to build scalable data-streaming applications and perform advanced NLP tasks on large datasets. Through hands-on labs, you will gain practical experience in processing streaming data and applying NLP techniques using PySpark. By the end of this course, you will be able to: - Analyze the effectiveness of various data streaming frameworks and their applications in real-time analytics. - Design and implement a data pipeline that integrates real-time streaming data sources while ensuring data quality and compliance with security standards. - Implement advanced data processing techniques with PySpark to handle and analyze large-scale streaming datasets efficiently. - Evaluate the impact of different NLP techniques on data processing and sentiment analysis in a streaming context. - Create interactive visualizations and dashboards to communicate insights derived from streaming data effectively. This course is ideal for data professionals, aspiring data engineers, and machine learning enthusiasts who want to leverage PySpark for real-time data processing and NLP applications. Some prior knowledge of Python, data processing concepts, and basic NLP principles is recommended. Join us to enhance your skills in data streaming and natural language processing with PySpark and elevate your expertise in handling real-time data!

Syllabus

Stream Processing with Apache Spark

This module introduces the fundamentals of stream processing with Apache Spark. It covers how data is processed in real-time, the various models for handling data streams, and different architectures used in stream processing systems.

Spark Streaming

In this module, learners will explore Spark Streaming, its core concepts, and the evolution towards Structured Streaming. They will gain an understanding of the DStream abstraction, and the structure of Spark Streaming applications, and explore transformations alongside recent updates.

Foundations of Structured Streaming

In this module, learners are introduced to the fundamental concepts of Structured Streaming in Spark, with a focus on its programming model, core operations, and management of streaming workflows. They will explore structured data processing in both batch and stream contexts.

Spark NLP

This module covers the integration of PySpark with Deep Learning and Natural Language Processing (NLP), followed by optimization strategies for PySpark applications. Learners will explore the foundations of deep learning, NLP techniques, and best practices for performance tuning.

Course-Wrap up and Assessment

This module is meant to test how well you understand the different ideas and lessons you've learned in this course. You will undertake a project based on Spark NLP and complete a comprehensive quiz that will assess your confidence and proficiency in Data Streaming and NLP with PySpark.