Data Streaming and NLP with PySpark
Master PySpark for real-time data and natural language processing. Learn distributed computing to build scalable applications for streaming analytics.
Master PySpark for real-time data and natural language processing. Learn distributed computing to build scalable applications for streaming analytics.
This course explores data streaming and Natural Language Processing (NLP) using the power of PySpark distributed computing. Through comprehensive modules, you'll gain practical skills to build scalable data-streaming applications and perform advanced NLP tasks on large datasets. You'll learn to analyze real-time data streams, implement Spark's Structured Streaming for fault-tolerant processing, and apply sophisticated NLP techniques for text analysis. The course covers stream processing fundamentals, Spark Streaming architecture, Structured Streaming operations, deep learning integration, and optimization strategies. With hands-on assignments and projects, you'll develop expertise in designing data pipelines, processing streaming data efficiently, and communicating insights through visualizations. This knowledge is essential for modern data professionals working with big data and real-time analytics applications.
Instructors:
English
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What you'll learn
Analyze streaming data to extract insights and trends in real-time applications
Design and implement data pipelines for real-time streaming sources
Implement advanced data processing techniques with PySpark for large-scale datasets
Evaluate different NLP techniques for data processing and sentiment analysis
Create interactive visualizations to communicate insights from streaming data
Apply fault-tolerant processing using Spark's Structured Streaming
Skills you'll gain
This course includes:
7.3 Hours PreRecorded video
16 assignments
Access on Mobile, Tablet, Desktop
Batch access
Shareable certificate
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There are 5 modules in this course
Data Streaming and NLP with PySpark equips learners with skills to build scalable data pipelines and perform advanced natural language processing on large datasets. The course covers stream processing fundamentals, Spark Streaming architecture and evolution, Structured Streaming programming models, input sources, transformations, and output sinks. Students also explore deep learning integration with PySpark, NLP techniques including tokenization, lemmatization, and TF-IDF, and optimization strategies for performance tuning. Through hands-on labs and practical assignments, learners gain experience in processing real-time data streams, implementing window operations, handling failures with checkpointing, and creating interactive visualizations to communicate insights effectively.
Stream Processing with Apache Spark
Module 1 · 3 Hours to complete
Spark Streaming
Module 2 · 2 Hours to complete
Foundations of Structured Streaming
Module 3 · 4 Hours to complete
Spark NLP
Module 4 · 4 Hours to complete
Course-Wrap up and Assessment
Module 5 · 1 Hours to complete
Instructor
Inspiring the Next Generation of Tech Professionals
Edureka is dedicated to providing high-quality, instructor-led online training, empowering professionals to enhance their skills in various domains. The platform features a diverse team of experienced instructors who are passionate about teaching and possess extensive industry knowledge. These instructors facilitate a wide range of courses covering topics such as data science, artificial intelligence, machine learning, and cloud computing. Edureka's commitment to education is reflected in its innovative approach to learning, which includes interactive sessions, real-world projects, and 24/7 support for students. By fostering a collaborative learning environment, Edureka ensures that learners not only acquire technical skills but also develop critical thinking and problem-solving abilities essential for success in today's fast-paced job market.
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