Gain insights into PySpark within big data. Learn about data ingestion, distributed computing, data processing, and performance optimization to solve real-world problems and apply machine learning.

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This course explores the big data ecosystem, focusing on hands-on utilization of PySpark—the Python API for Apache Spark. 

In this course, you’ll experience a balanced blend of theory and practice. You’ll learn about data ingestion, storage, distributed computing, PySpark’s intricacies, data processing, data analysis, performance optimization, tool integration, and practical applications like machine learning. 

This course, suited for beginners to intermediate learners, will give you an understanding of big data tools and techniques. After completing this course, you’ll be fully equipped with effective problem-solving capabilities in real-world scenarios.

Mastering Big Data with PySpark



- **High throughput:** Kafka can deliver messages at network-limited throughput using a cluster with minimal latency.
- **Scalability:** The platform scales smoothly to accommodate clusters with up to a thousand brokers, managing trillions of messages daily and petabytes of data.
- **Storage:** Kafka securely stores streams of data in a distributed, durable, and fault-tolerant cluster.
- **Availability:** It ensures robust connectivity across separate clusters, spanning various geographic locations.

# Kafka architecture

Kafka operates as an event streaming platform, integrating three critical capabilities for end-to-end event streaming solutions:

- **Publishing and subscribing:** It allows writing (publishing) and reading (subscribing) streams of events, facilitating continuous data import/export from different systems.
- **Persistent storage:** Kafka stores streams of events reliably and durably, allowing data retention for as long as needed.
- **Event processing:** It processes streams of events in real-time and retrospectively.

## How Kafka works

Kafka's core components involve **brokers** (servers) that manage **topics**, which are streams of events. Topics are partitioned and replicated across multiple brokers for fault tolerance and scalability. Each event record encapsulates key, value, timestamp, and optional metadata.

- **Producers:** These client applications publish events to Kafka topics.
- **Consumers:** Applications retrieve data from these topics. Consumers can manage their progress via offsets, while Kafka organizes consumers into logical groups called consumer groups for parallel data processing.

The communication within Kafka occurs through a high-performance TCP network protocol, allowing seamless interaction between producers, consumers, brokers, and topics. Understanding these fundamental concepts is crucial for effectively utilizing Kafka in handling real-time data streams and distributed event processing.

Learn to integrate PySpark with Kafka Streams.

Introduction to the Course

Introduction to Big Data

Exploring PySpark Core and RDDs

PySpark DataFrames and SQL

Customer Churn Analysis Using PySpark

Machine Learning with PySpark

Modeling with PySpark MLlib

Predicting Diabetes in Patients Using PySpark MLlib

Performance Optimization in PySpark

PySpark Optimization: Analyzing NYC Restaurants Data

Integrating PySpark with Other Big Data Tools

Wrap Up

Apriori Algorithm for Finding Frequent Itemsets with PySpark

PySpark Integration with Apache Kafka