Learning Apache Spark | Master Spark for Big Data Processing

Name: Learning Apache Spark | Master Spark for Big Data Processing
Rating: 4.6 (8 reviews)

Embark on a comprehensive journey to Master Apache Spark from Data Manipulation to Machine Learning!

Last updated 10/2024

English

What you'll learn

Understand the fundamentals of Spark’s architecture and its distributed computing capabilities
Learn to write and optimize Spark SQL queries for efficient data processing
Master the creation and manipulation of DataFrames, a core component of Spark
Learn to read data from different file formats such as CSV and Parquet
Develop skills in filtering, sorting, and aggregating data to extract meaningful insights
Learn to process and analyze streaming data for real-time insights
Explore the capabilities of Spark’s MLlib for machine learning
Learn to create and fine-tune models using pipelines and transformers for predictive analytics

11 sections • 88 lectures • 7h 11m total length

Let’s understand WordCount4:23
Let’s understand Map and Reduce3:07
Programming with Map and Reduce3:24
Let’s understand Hadoop3:54
Apache Hadoop Architecture4:44
Apache Hadoop and Apache Spark5:01
Explore the differences between Apache Hadoop's disk-based, MapReduce approach and Apache Spark's in-memory, RAM-cached computation. Learn why Spark delivers faster processing and how memory vs disk impacts performance across APIs.
Apache Spark Architecture4:06
What is PySpark3:21

Introduction to Pandas3:15
Pandas API on Spark2:57
Reading and Writing Data with Pandas P17:36
Reading and Writing Data with Pandas P25:34
Data Manipulation with PySpark Pandas7:13
Merging and Joining in PySpark Pandas4:57
Grouping and Aggregation with PySpark Pandas4:34
Visualizing Data in PySpark Pandas3:46
Visualize sales data in PySpark pandas using the data frame plot API to create area charts with x and y axes, showing product ID insights.

What is Apache Spark Structure Streaming4:10
How Apache Spark handles Structured Streaming3:32
Handling Programmatically Streaming Data3:26
Programmatic Modes by Apache Spark7:09
DataFrames for Streaming5:41
readStream API8:48
writeStream API8:20
Utilize the write stream API to join sales order and product streams by product ID, then write to a parquet table with a checkpoint location and append mode.
Querying Data2:18
StreamingQuery - stop7:35
Structured Streaming with Kafka and Spark P14:02
Structured Streaming with Kafka and Spark P26:55
Structured Streaming with Kafka and Spark P310:00
Integrate Apache Kafka with Spark Structured Streaming to read from Kafka sources, perform word count on streaming data, and output results to the console.
Terminate the Kafka Environment2:19
Handling Late Data Arrivals and Water Marking P18:29
Handling Late Data Arrivals and Water Marking P28:47

You should know how to write and run Python code
Basic understanding of Python syntax and concepts is necessary
Understanding SQL (Structured Query Language) is important
You should know how to create and manage tables, transform data, and run queries

Unlock the power of big data with Apache Spark!

In this course, you’ll learn how to use Apache Spark with Python to work with data.

We’ll start with the basics and move up to advanced projects and machine learning.

Whether you’re just starting or already know some Python, this course will teach you step-by-step how to process and analyze big data.

What You’ll Learn:

Use PySpark’s DataFrame: Learn to organize and work with data.
Store Data Efficiently: Use formats like Parquet to store data quickly.
Use SQL in PySpark: Work with data using SQL, just like with DataFrames.
Connect PySpark with Python Tools: Dig deeper into data with Python’s data tools.
Machine Learning with PySpark’s MLlib: Work on big projects using machine learning.
Real-World Examples: Learn by doing with practical examples.
Handle Large Data Sets: Understand how to manage big data easily.
Solve Real-World Problems: Apply Spark to real-life data challenges.
Build Confidence in PySpark: Get better at big data processing.
Manage and Analyze Data: Gain skills for both work and personal projects.
Prepare for Data Jobs: Build skills for jobs in tech, finance, and healthcare.

By the end of this course, you’ll have a solid foundation in Spark, ready to tackle real-world data challenges.