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

Ce cours n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues.

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 : Data Transformation
Catégorie : Data Visualization
Catégorie : Data Processing
Catégorie : Natural Language Processing
Catégorie : Real Time Data
Catégorie : Data Pipelines
Catégorie : Distributed Computing
Catégorie : Deep Learning

Outils que vous découvrirez

Catégorie : Apache Spark
Catégorie : Dashboard
Catégorie : PySpark

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 vidéos Total 87 minutes

Course Introduction: Data Streaming and NLP with PySpark 2 minutes
What is Stream Processing 7 minutes
Scaling up data processing 7 minutes
Distributed Stream Processing 6 minutes
Popular Frameworks for Distributed Processing 4 minutes
Some examples of Stream Processing 6 minutes
Sources and Sinks 5 minutes
Data Sinks in Stream Processing 4 minutes
Transformations and Aggregations 7 minutes
Window Aggregations 6 minutes
Tumbling Window Aggregations 2 minutes
Stateless and Stateful Processing 6 minutes
The Effect of time 7 minutes
Components of a Data Platforms 7 minutes
Architectural Models 6 minutes
Referential Streaming 6 minutes

4 lectures Total 55 minutes

Welcome to Data Streaming and NLP with PySpark 10 minutes
Explore Stream Processing Model 15 minutes
Streaming Architectures in PySpark 20 minutes
Module Summary: Stream Processing with Apache Spark 10 minutes

4 devoirs Total 38 minutes

Knowledge Check: Stream Processing with Apache Spark 20 minutes
Practice Quiz : Introduction to Stream Processing 6 minutes
Practice Quiz : Stream Processing Model 6 minutes
Practice Quiz : Streaming Architectures 6 minutes

1 sujet de discussion Total 10 minutes

Introduce Yourself 10 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 vidéos Total 38 minutes

Introduction to Spark Streaming 6 minutes
Dstream Abstraction 6 minutes
The structure of a spark streaming application 5 minutes
Understanding Dstream Transformations 5 minutes
Element centric D stream Transformations 4 minutes
RDD centric Dstream Transformations 5 minutes
Spark Streaming Deprecation 3 minutes
Exploring Spark Structured Streaming 4 minutes

4 lectures Total 50 minutes

Working of Spark Streaming 15 minutes
Understanding Spark Structured Streaming 10 minutes
Spark Streaming Deprecation 15 minutes
Module Summary: Spark Streaming 10 minutes

3 devoirs Total 32 minutes

Knowledge Check: Spark Streaming 20 minutes
Practice Quiz: Introduction to Spark Streaming 6 minutes
Practice Quiz: Spark Streaming Evolution 6 minutes

1 sujet de discussion Total 10 minutes

Spark Streaming Migration 10 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 vidéos Total 172 minutes

Introduction to Structured Processing 7 minutes
Batch Analytics 6 minutes
Key Concepts of Batch Analytics 5 minutes
Stream Analytics 6 minutes
Programming Model of Structured Streaming 6 minutes
State Management 4 minutes
Input Sources 6 minutes
Socket and Rate sources 4 minutes
Basic Operations of Structured Streaming 4 minutes
Window Operations 5 minutes
Output Sinks 6 minutes
Console and Memory Sinks 2 minutes
Setup for Streaming Content 3 minutes
Streaming the Content from Local file 7 minutes
Performing Aggregation on Streaming Data 7 minutes
Saving the Data to SQL 5 minutes
Reading Data from Local Socket 5 minutes
Connecting to Rate and Local files 5 minutes
Loading Data from Kafka 7 minutes
Writing Output to local files 7 minutes
Writing Streaming Data to Memory and Kafka Sink 5 minutes
Joining Operations 6 minutes
Stream-Static Join 5 minutes
Inner Join in Structured Streaming 5 minutes
Join Conditions and Watermarking 3 minutes
Monitoring streaming 6 minutes
Reporting Metrics 5 minutes
Recovering from Failures with Checkpointing 7 minutes
Benefits and Challenges in Checkpointing 6 minutes
Strategies for Effective Recovery 5 minutes
Reading IOT Data 4 minutes
Performing Window Operations 5 minutes
Writing Output IOT Data to CSV file. 6 minutes

4 lectures Total 55 minutes

Introduction to PySpark Structured Streaming 15 minutes
Operations in PySpark Structured Streaming 15 minutes
Advanced Operations in PySpark Structured Streaming 15 minutes
Module Summary: Foundations of Structured Streaming 10 minutes

4 devoirs Total 38 minutes

Knowledge Check: Foundations of Structured Streaming 20 minutes
Practice Quiz : Introduction to Structured Streaming 6 minutes
Practice Quiz: Core Operations in Streaming Data 6 minutes
Practice Quiz: Management of Streaming Workflows 6 minutes

1 sujet de discussion Total 10 minutes

Spark Structured Streaming 10 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 vidéos Total 140 minutes

Overview of Deep Learning 7 minutes
Neural Networks Overview 7 minutes
Common Activation Functions 3 minutes
Importance of Data Preprocessing 7 minutes
Data Transformation Techniques 6 minutes
Training Neural Networks 7 minutes
Introduction to Distributed Learning 5 minutes
Benefits of Distributed Learning 3 minutes
PySpark for Distributed Learning 5 minutes
Overview of Frame works 5 minutes
H2O. ai Framework 3 minutes
Demonstration of Image Classification 6 minutes
Implementing SParkDL 7 minutes
Introduction to Natural Language Processing 6 minutes
Types of NLP Tasks 4 minutes
Key Concepts of NLP 5 minutes
Semantic Analysis 3 minutes
Processing Words 3 minutes
Information Retrieval 7 minutes
Advanced Information Retrieval Techniques 3 minutes
Performing Finisher with Spark NLP 5 minutes
Implementing TF-IDF 4 minutes
Demonstration of Lemmatizer 6 minutes
Demonstration of Tokenizer with Spark NLP 4 minutes
Optimizing Data Handling and Storage in PySpark 4 minutes
Using Parquet and ORC for Optimized Data Storage 4 minutes
Performance Tuning for PySpark Jobs 5 minutes
Caching and Persisting Data frames 3 minutes
Best Practices for Writing PySpark Code 6 minutes

5 lectures Total 65 minutes

Deep learning Overview 10 minutes
Hyper Parameter Tuning 15 minutes
Overview of NLP 15 minutes
Overview of PySpark Best Practices 15 minutes
Module Summary: Spark NLP 10 minutes

4 devoirs Total 38 minutes

Knowledge Check: Spark NLP 20 minutes
Practice Quiz: PySpark for Deep Learning 6 minutes
Practice Quiz: Spark NLP 6 minutes
Practice Quiz: Optimizations and Best Practices 6 minutes

2 sujets de discussion Total 20 minutes

Challenges with Deep Learning 10 minutes
Spark NLP Applications 10 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

Obtenez un certificat professionnel

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Instructeur

Edureka

157 Cours 146 905 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.