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

Dieser Kurs ist Teil von Spezialisierung „PySpark for Data Science“

Dozent: Edureka

Bei enthalten

Mehr erfahren

5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

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

Werkzeuge, die Sie lernen werden

Kategorie: PySpark
Kategorie: Dashboard
Kategorie: Apache Spark

Wichtige Details

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In diesem Kurs gibt es 5 Module

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.

Das ist alles enthalten

16 Videos4 Lektüren4 Aufgaben1 Diskussionsthema

16 VideosInsgesamt 87 Minuten

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

4 LektürenInsgesamt 55 Minuten

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

4 AufgabenInsgesamt 38 Minuten

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

1 DiskussionsthemaInsgesamt 10 Minuten

Introduce Yourself10 Minuten

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.

Das ist alles enthalten

8 Videos4 Lektüren3 Aufgaben1 Diskussionsthema

8 VideosInsgesamt 38 Minuten

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

4 LektürenInsgesamt 50 Minuten

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

3 AufgabenInsgesamt 32 Minuten

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

1 DiskussionsthemaInsgesamt 10 Minuten

Spark Streaming Migration10 Minuten

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.

Das ist alles enthalten

33 Videos4 Lektüren4 Aufgaben1 Diskussionsthema

33 VideosInsgesamt 172 Minuten

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

4 LektürenInsgesamt 55 Minuten

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

4 AufgabenInsgesamt 38 Minuten

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

1 DiskussionsthemaInsgesamt 10 Minuten

Spark Structured Streaming10 Minuten

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.

Das ist alles enthalten

29 Videos5 Lektüren4 Aufgaben2 Diskussionsthemen

29 VideosInsgesamt 140 Minuten

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

5 LektürenInsgesamt 65 Minuten

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

4 AufgabenInsgesamt 38 Minuten

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

2 DiskussionsthemenInsgesamt 20 Minuten

Challenges with Deep Learning10 Minuten
Spark NLP Applications10 Minuten

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.

Das ist alles enthalten

1 Video1 Lektüre1 Aufgabe1 Diskussionsthema

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Dozent

Edureka

185 Kurse173.405 Lernende

von

Edureka

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Häufig gestellte Fragen

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.