Big Data with Apache Spark and AWS: A Powerful Combination

Big Data with Apache Spark and AWS offers a powerful solution for processing massive datasets efficiently. Apache Spark is a fast, open-source, distributed computing engine known for in-memory processing, making it ideal for real-time analytics, batch processing, and machine learning. When integrated with Amazon Web Services (AWS), Spark becomes highly scalable and cost-effective.

AWS provides services like Amazon EMR for deploying Spark clusters, Amazon S3 for storing big data, and AWS Glue for ETL jobs. This combination enables businesses to analyze vast amounts of structured and unstructured data with speed and flexibility.

Together, Spark and AWS empower organizations to build data pipelines, analyze streaming data, and train machine learning models with ease. Their seamless integration supports high availability, rapid deployment, and reduced infrastructure costs, making them a go-to choice for modern big data solutions in the cloud.

This approach accelerates insights, innovation, and decision-making at scale.

### **What You Will Learn:**
1. **Core Spark Concepts**:
* Understand the fundamental **data abstractions** in Spark like **DataFrames**, **RDDs**, and **Datasets** for efficient distributed data processing.
2. **Cluster Management**:
* Learn how to create, manage, and terminate Spark clusters, both on **Amazon EMR** and **Databricks**, to run distributed jobs at scale.
3. **Big Data Storage**:
* Gain insights into how **HDFS** and **Amazon S3** can be leveraged to store and manage large volumes of data. Learn how Spark interacts with these systems to read and write data efficiently.
4. **Data Manipulation**:
* Master **data transformations** and operations such as **filtering**, **grouping**, and **joining** datasets in Spark to clean and process data before analysis.
5. **SQL and DataFrames**:
* Learn how to use **SparkSQL** for querying structured data with SQL commands and how to manipulate data using **DataFrames** and **SparkSQL** APIs.
6. **Machine Learning**:
* Understand how to implement **machine learning models** such as **Naive Bayes**, **K-Means**, and **Logistic Regression** for classification, clustering, and regression tasks using **MLlib**.
7. **Working with Cloud Platforms**:
* Explore the use of **AWS S3** and **Databricks Community Edition** for data storage and big data analytics. Learn how these platforms integrate with Spark for large-scale data processing.
By the end of the course, you'll be equipped with the skills to efficiently handle and analyze large datasets, implement machine learning models, and perform data processing tasks using **Apache Spark** and **AWS** cloud services.

Course Content

Creating Clusters
### 02 - Creating Clusters: Summary In this section, you'll learn how to create and configure clusters for running big data applications using Apache Spark on AWS. A cluster is a collection of virtual servers (nodes) that work together to process large datasets efficiently. Using Amazon EMR (Elastic MapReduce), you can easily launch and manage Spark clusters without handling complex infrastructure. You’ll define key parameters such as: * Cluster name and region * Instance types and count (Master and Core nodes) * Applications to install (e.g., Apache Spark, Hadoop) * Bootstrap actions for custom setup * Permissions and security groups You’ll also learn how to optimize cost with Spot Instances and how to enable logging and monitoring using CloudWatch. By the end of this module, you’ll have a fully functional Spark cluster running on AWS, ready for data processing tasks. This step is foundational for executing scalable, distributed big data jobs in the cloud

Class 1:Creating an AWS Instance

00:00
class 2:Connecting to AWS Instance with SSH

00:00
Class 3:Connecting to AWS Instance with PuTTY

00:00
class 4:Spark Clusters

00:00
class 5:Spark Clusters in depth

00:00
class 6:Learn How to Terminate Your Clusters

00:00

Data and Modeling Basics
This section introduces the fundamental concepts of data processing and modeling in Spark. It covers how to load, clean, and transform raw data into usable formats, as well as how to work with key data structures like RDDs and DataFrames. The module also explores basic modeling techniques, including training models using MLlib, Spark's machine learning library. You'll learn how to apply these techniques for predictive analytics and data insights.

Data Sources and Data Manipulation
This section focuses on integrating and manipulating data from various sources in Apache Spark. You'll learn how to load data from different formats such as CSV, JSON, Parquet, and Hive tables, along with external sources like Amazon S3 or HDFS. The module also covers how to transform and clean data using Spark's built-in functions, including filtering, aggregating, and joining data to prepare it for analysis or modeling. Techniques for handling missing values, type conversions, and optimizing performance using caching and partitioning are also explored. Mastering these data manipulation techniques is essential for efficiently working with large datasets and performing complex analytics in Spark.

Various
Various: Summary This section covers a variety of advanced topics related to working with big data using Apache Spark. It delves into data manipulation techniques, the use of SparkSQL for querying structured data, and the integration of Spark with cloud services like AWS S3 for data storage. You'll also learn how to optimize DataFrame operations and apply advanced processing techniques to handle large datasets efficiently. Additionally, the section highlights the importance of cluster management, modeling techniques like Naive Bayes and K-Means, and working with different data sources**. By mastering these concepts, you'll be able to scale your big data applications and perform advanced analytics on large, distributed datasets with Spark's powerful tools.

Summary
### Databricks Community Edition: Summary Databricks Community Edition is a free, cloud-based platform that provides users with a collaborative environment to explore Apache Spark, Delta Lake, and machine learning. It offers a user-friendly notebook interface for running code in Python, SQL, R, and Scala, making it ideal for individuals looking to learn or experiment with big data and analytics. Although it offers limited resources compared to the full version, it gives access to Spark clusters for distributed data processing, making it an excellent starting point for newcomers to data science and big data technologies. The Community Edition is perfect for those seeking a low-cost, easy way to get hands-on experience with Spark and machine learning.

A course by

Farhan

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Original price was: $30,000.00.Current price is: $3,000.00.
Instructor:Farhan
Lessons:17
Students:0
Level:Intermediate
Certifications:No

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Big Data with Apache Spark and AWS: A Powerful Combination

What Will You Learn?

Course Content

Class 1:Creating an AWS Instance

class 2:Connecting to AWS Instance with SSH

Class 3:Connecting to AWS Instance with PuTTY

class 4:Spark Clusters

class 5:Spark Clusters in depth

class 6:Learn How to Terminate Your Clusters

class 1:Data Basics

Class 2:Modeling with Gaussian Generalized Linear Models

class 3:Binomial Generalized Linear Models

class 4:Naive Bayes and K Means Modeling

class 1:Bigger Data and AWS S3

class 2:Accessing S3 Spark Dataframes

class 3:SparkR Dataframe Operations

class 4:Intro to SparkSQL

class 1:Intro to HDFS

class 2:Databricks Community Edition

class 1:Summary

Big Data with Apache Spark and AWS: A Powerful Combination

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