What skills will I gain from this course?

<text variant="body1">You will gain skills in real-time data processing, natural language processing techniques, data pipeline design, and the ability to leverage distributed computing with PySpark.

How is model inference handled in streaming mode?

You will learn how to integrate trained ML/NLP models into a streaming workflow using PySpark Structured Streaming. The course demonstrates how to run real-time inference while managing throughput and latency.

Does the certificate carry any weight or recognition?

Yes. You will receive a verifiable certificate of completion, which can be added to your LinkedIn profile, resume, and professional portfolio to showcase PySpark and streaming expertise.

Can I preview a course before enrolling?

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

When will I have access to the lectures and assignments?

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

What will I get when I enroll?

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Data Streaming and NLP with PySpark

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Data Streaming and NLP with PySpark

Ce cours fait partie de Spécialisation "PySpark for Data Science"

Instructeur : Edureka

Inclus avec

5 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

5 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Analyze streaming data to extract insights and trends in real-time applications.
Analyze real-time data streams and apply Spark Streaming techniques for efficient processing.
Develop robust streaming applications using Spark's Structured Streaming for fault-tolerant processing.
Implement NLP techniques to process and analyze textual data efficiently.

Compétences que vous acquerrez

Catégorie : Performance Tuning
Catégorie : Distributed Computing
Catégorie : Natural Language Processing
Catégorie : Data Pipelines
Catégorie : Interactive Data Visualization
Catégorie : Deep Learning
Catégorie : Real Time Data
Catégorie : Dashboard Creation
Catégorie : Data Processing

Outils que vous découvrirez

Catégorie : Dashboard
Catégorie : PySpark
Catégorie : Apache Spark

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

16 devoirs

Enseigné en Anglais

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Ce cours fait partie de la Spécialisation "PySpark for Data Science"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

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Acquérez une compréhension de base d'un sujet ou d'un outil
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Il y a 5 modules dans ce cours

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!

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.

Inclus

16 vidéos4 lectures4 devoirs1 sujet de discussion

16 vidéosTotal 87 minutes

Course Introduction: Data Streaming and NLP with PySpark2 minutes
What is Stream Processing7 minutes
Scaling up data processing7 minutes
Distributed Stream Processing6 minutes
Popular Frameworks for Distributed Processing 4 minutes
Some examples of Stream Processing6 minutes
Sources and Sinks5 minutes
Data Sinks in Stream Processing4 minutes
Transformations and Aggregations7 minutes
Window Aggregations6 minutes
Tumbling Window Aggregations2 minutes
Stateless and Stateful Processing6 minutes
The Effect of time7 minutes
Components of a Data Platforms7 minutes
Architectural Models6 minutes
Referential Streaming6 minutes

4 lecturesTotal 55 minutes

Welcome to Data Streaming and NLP with PySpark10 minutes
Explore Stream Processing Model15 minutes
Streaming Architectures in PySpark20 minutes
Module Summary: Stream Processing with Apache Spark10 minutes

4 devoirsTotal 38 minutes

Practice Quiz : Introduction to Stream Processing6 minutes
Practice Quiz : Stream Processing Model6 minutes
Practice Quiz : Streaming Architectures6 minutes
Knowledge Check: Stream Processing with Apache Spark20 minutes

1 sujet de discussionTotal 10 minutes

Introduce Yourself10 minutes

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.

Inclus

8 vidéos4 lectures3 devoirs1 sujet de discussion

8 vidéosTotal 38 minutes

Introduction to Spark Streaming6 minutes
Dstream Abstraction6 minutes
The structure of a spark streaming application5 minutes
Understanding Dstream Transformations5 minutes
Element centric D stream Transformations4 minutes
RDD centric Dstream Transformations5 minutes
Spark Streaming Deprecation3 minutes
Exploring Spark Structured Streaming4 minutes

4 lecturesTotal 50 minutes

Working of Spark Streaming15 minutes
Understanding Spark Structured Streaming10 minutes
Spark Streaming Deprecation15 minutes
Module Summary: Spark Streaming10 minutes

3 devoirsTotal 32 minutes

Practice Quiz: Introduction to Spark Streaming6 minutes
Practice Quiz: Spark Streaming Evolution6 minutes
Knowledge Check: Spark Streaming20 minutes

1 sujet de discussionTotal 10 minutes

Spark Streaming Migration10 minutes

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.

Inclus

33 vidéos4 lectures4 devoirs1 sujet de discussion

33 vidéosTotal 172 minutes

Introduction to Structured Processing7 minutes
Batch Analytics6 minutes
Key Concepts of Batch Analytics5 minutes
Stream Analytics6 minutes
Programming Model of Structured Streaming6 minutes
State Management 4 minutes
Input Sources6 minutes
Socket and Rate sources4 minutes
Basic Operations of Structured Streaming4 minutes
Window Operations5 minutes
Output Sinks 6 minutes
Console and Memory Sinks2 minutes
Setup for Streaming Content3 minutes
Streaming the Content from Local file7 minutes
Performing Aggregation on Streaming Data7 minutes
Saving the Data to SQL5 minutes
Reading Data from Local Socket5 minutes
Connecting to Rate and Local files5 minutes
Loading Data from Kafka7 minutes
Writing Output to local files7 minutes
Writing Streaming Data to Memory and Kafka Sink5 minutes
Joining Operations6 minutes
Stream-Static Join5 minutes
Inner Join in Structured Streaming5 minutes
Join Conditions and Watermarking3 minutes
Monitoring streaming6 minutes
Reporting Metrics5 minutes
Recovering from Failures with Checkpointing7 minutes
Benefits and Challenges in Checkpointing6 minutes
Strategies for Effective Recovery5 minutes
Reading IOT Data4 minutes
Performing Window Operations5 minutes
Writing Output IOT Data to CSV file.6 minutes

4 lecturesTotal 55 minutes

Introduction to PySpark Structured Streaming15 minutes
Operations in PySpark Structured Streaming15 minutes
Advanced Operations in PySpark Structured Streaming15 minutes
Module Summary: Foundations of Structured Streaming10 minutes

4 devoirsTotal 38 minutes

Practice Quiz : Introduction to Structured Streaming6 minutes
Practice Quiz: Core Operations in Streaming Data6 minutes
Practice Quiz: Management of Streaming Workflows 6 minutes
Knowledge Check: Foundations of Structured Streaming20 minutes

1 sujet de discussionTotal 10 minutes

Spark Structured Streaming10 minutes

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.

Inclus

29 vidéos5 lectures4 devoirs2 sujets de discussion

29 vidéosTotal 140 minutes

Overview of Deep Learning7 minutes
Neural Networks Overview7 minutes
Common Activation Functions3 minutes
Importance of Data Preprocessing7 minutes
Data Transformation Techniques6 minutes
Training Neural Networks7 minutes
Introduction to Distributed Learning5 minutes
Benefits of Distributed Learning3 minutes
PySpark for Distributed Learning5 minutes
Overview of Frame works5 minutes
H2O. ai Framework3 minutes
Demonstration of Image Classification6 minutes
Implementing SParkDL7 minutes
Introduction to Natural Language Processing6 minutes
Types of NLP Tasks4 minutes
Key Concepts of NLP5 minutes
Semantic Analysis3 minutes
Processing Words3 minutes
Information Retrieval7 minutes
Advanced Information Retrieval Techniques 3 minutes
Performing Finisher with Spark NLP5 minutes
Implementing TF-IDF4 minutes
Demonstration of Lemmatizer6 minutes
Demonstration of Tokenizer with Spark NLP4 minutes
Optimizing Data Handling and Storage in PySpark4 minutes
Using Parquet and ORC for Optimized Data Storage4 minutes
Performance Tuning for PySpark Jobs5 minutes
Caching and Persisting Data frames3 minutes
Best Practices for Writing PySpark Code6 minutes

5 lecturesTotal 65 minutes

Deep learning Overview10 minutes
Hyper Parameter Tuning15 minutes
Overview of NLP15 minutes
Overview of PySpark Best Practices15 minutes
Module Summary: Spark NLP10 minutes

4 devoirsTotal 38 minutes

Practice Quiz: PySpark for Deep Learning6 minutes
Practice Quiz: Spark NLP6 minutes
Practice Quiz: Optimizations and Best Practices6 minutes
Knowledge Check: Spark NLP20 minutes

2 sujets de discussionTotal 20 minutes

Challenges with Deep Learning10 minutes
Spark NLP Applications10 minutes

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.

Inclus

1 vidéo1 lecture1 devoir1 sujet de discussion

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Instructeur

Edureka

185 Cours173 947 apprenants

Offert par

Edureka

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Foire Aux Questions

Yes, this course is designed to introduce NLP concepts, making it accessible for beginners. However, a basic understanding of NLP principles will be beneficial.

A basic understanding of Python programming, familiarity with data processing concepts, and some exposure to natural language processing (NLP) will help you succeed in this course.

This course is ideal for data professionals, aspiring data engineers, machine learning enthusiasts, and anyone interested in real-time data processing and NLP applications.

The course explains both. DStreams are the older micro-batch API, while Structured Streaming is the modern, declarative API built on Spark SQL, offering easier syntax, end-to-end fault tolerance, and better integration with DataFrames. Learners will see why Structured Streaming is the industry standard today.