AWS Certified Data Engineer Associate DEA-C01 Course 2026

In this video, we will walk you through the essential steps of working with AWS Glue, a fully managed ETL (Extract, Transform, Load) service. Whether you're a beginner or looking to refresh your knowledge, this comprehensive guide will help you get started with AWS Glue.

What You'll Learn:

1. Ingesting Data: Learn how to upload a sample CSV file from your local computer to an S3 bucket, setting it as the source file for your ETL pipeline.

2. Creating an ETL Pipeline: Step-by-step instructions to create an ETL pipeline in AWS Glue to evaluate data quality, including checking row count and making schema changes.

3. Schema Change Module: Discover how to modify the schema using Glue's schema change module, ensuring your data structure meets your requirements.

4. Loading Target Data: Understand how to load the processed data into a different S3 bucket as the target file.

5. Glue Job Configuration: Gain insights into configuring basic Glue job details, such as selecting the worker type and the requested number of workers, to optimize your ETL operations.

Key Highlights:

1. Hands-on demonstration with clear and concise explanations

2. Real-world examples to solidify your understanding

3. Tips and best practices for efficient ETL processing

AWS Services Used:

1. AWS S3

2. AWS Glue

In this comprehensive hands-on tutorial, we dive deep into the world of AWS Data Engineering. Join us as we demonstrate essential data engineering tasks and best practices using AWS services.

Here’s what we’ll cover:

1. Data Quality Checks: Ensure row count is greater than 99 and verify the presence of primary keys in all records.

2. Filter Expression: Apply filter expressions to refine your data.

3. Custom SQL Expression: Fetch only two specific columns from a table using a custom SQL query.

4. Inner Join Using AWS Glue: Learn to perform efficient inner joins with AWS Glue.

5. File Format Conversion: Convert source files from .csv to Parquet using AWS Glue jobs.

6. Schema Inference with AWS Glue Crawler: Automatically infer the schema from S3 source files.

Watch as we demonstrate how to upload input files to an S3 bucket's "source" folder from your local computer and store the transformed files in the "target" folder using Glue jobs. We’ll guide you through every step to ensure your data pipelines are robust, efficient, and cost-effective.

Welcome to our comprehensive guide on AWS S3 Lifecycle Policies! In this video, we dive deep into the theoretical concepts and practical applications of S3 lifecycle management.

Here's what you'll learn:

S3 Storage Classes Explained:

1. S3 Standard: Understand the default storage class, designed for frequently accessed data.

2. S3 Intelligent-Tiering: Learn how this class automatically moves data between two access tiers when access patterns change.

3. S3 Glacier Deep Archive: Discover the most cost-effective storage for data that is rarely accessed and requires long-term retention.

S3 Lifecycle Policy Diagram:

1. Visualize and grasp the lifecycle policies with the help of detailed diagrams.

2. Learn how to automate the transition of objects between different storage classes based on their age and access patterns.

Importance of S3 Lifecycle Management:

1. Explore why lifecycle management is crucial for optimizing storage costs and maintaining efficient data access.

2. See real-world examples of how businesses benefit from implementing effective lifecycle policies.

Cost Demonstration in AWS Console:

1. Step-by-step guide to calculating the costs of different S3 storage tiers using the AWS Pricing Calculator.

2. Compare the costs and make informed decisions about which storage class to use for your data.

Join us for this in-depth tutorial to master S3 lifecycle policies and manage your AWS storage effectively.

Welcome to our comprehensive hands-on tutorial on AWS S3 lifecycle policy! In this video, we'll guide you through the complete process of setting up and managing lifecycle rules for your Amazon S3 bucket.

What you'll learn:

1. Creating an S3 Bucket: Step-by-step instructions to create a new S3 bucket in your AWS account.

2. Uploading Files to S3: Learn how to upload a .txt file from your local computer to the newly created S3 bucket.

3. Setting Up Lifecycle Rules: Discover how to configure object lifecycle rules to manage data efficiently.

Key Concepts:

1. Creating an S3 Bucket - A detailed walkthrough on creating an S3 bucket in AWS.

2. Uploading Files to S3 Bucket - Demonstrating how to upload a .txt file from your local computer to the S3 bucket.

3. Configuring Object Lifecycle Rules:

a) Transition to S3 Standard-IA: After 35 days, the object will transition from S3 Standard to S3 Standard-IA.

b) Transition to S3 Glacier: After 90 days, the object will move to S3 Glacier for long-term storage.

c) Object Deletion: After 120 days, the object will be automatically deleted.

Practical Demonstration:

1. Creating S3 Bucket

2. Uploading a .txt File from Local Computer to S3 bucket

3. Configuring Object Lifecycle Rules:

4. Transition to S3 Standard-IA after 35 days

5. Move to S3 Glacier after 90 days

6. Delete the object after 120 days

By the end of this video, you'll have a thorough understanding of how to set up and manage S3 lifecycle policies to optimize storage costs and data management.

In this comprehensive tutorial, we delve into the powerful concept of AWS S3 object versioning.

What You'll Learn:

1. Theory Concepts: Understand how versioning helps restore objects and protects against accidental deletions or overwrites.

2. Impact of Disabling Versioning: Learn what happens when S3 versioning is disabled and its implications.

3. Hands-On Demonstration: Follow along as we implement S3 object versioning step-by-step.

Practical Steps Covered:

1. Uploading a .txt File: Upload a text file from your local computer to an S3 bucket.

2. Disabling Versioning: Discover the version ID when versioning is disabled.

3. Enabling Versioning: See the version ID once versioning is enabled.

4. Deleting an Object: Demonstrate how versioning preserves deleted objects.

By the end of this video, you'll gain a solid understanding of AWS S3 object versioning and be equipped to implement it in your own projects. Whether you're a beginner or an experienced cloud professional, this tutorial will enhance your skills.

Overview of what will be covered in the video :

Theory Section

1. Permissions: Explanation of the s3:GetObject permission required to query objects

2. Data Formats:

a) Supported data formats: CSV, JSON, Apache Parquet

b) Supported compression formats: GZIP, BZIP2 (for CSV and JSON)

3. Query Limitations: Querying limitations: one object at a time, max SQL expression length of 256 KB

Output Formats:

a) Output result formats: CSV or JSON

b) Record delimitation in the output

Hands-On Demonstration

1. Creating a New S3 Bucket: Step-by-step guide to creating a new S3 bucket

2. Uploading a CSV File: Uploading a .csv file from the local computer to the S3 bucket

3. Explaining S3 Select Parameters:

a) Format: specifying the format of the object (CSV, JSON)

b) Delimiter: choosing a delimiter for CSV files

c) Compression: setting compression options for CSV/JSON files

d) Output Settings: specifying output format (CSV, JSON)

4. Executing Select Queries: a) Running a query to select the top 5 rows from the CSV file

b) Running a COUNT(*) query to count the number of rows in the CSV file

c) Viewing and interpreting the data retrieved by the queries

5. Exporting and Saving Data:

a) Exporting a subset of data from the query results

b) Saving the exported data to the local computer

By covering both the theoretical aspects and practical demonstration, viewers will gain a comprehensive understanding of AWS S3 Select queries. They will not only learn about the necessary permissions, data formats, query limitations, and output formats but also see how to apply these concepts in a hands-on manner, making it an informative and engaging tutorial.

Learn how to query data stored in an S3 bucket using Amazon Athena in this hands-on tutorial ! From setting up an S3 bucket and uploading a CSV file to configuring Amazon Athena from scratch, we cover it all:

1. Create an S3 bucket and upload your CSV file from your local computer.

2. Set up Amazon Athena, including creating a workgroup and specifying the query results location.

3. Use AWS Glue Data Catalog to create a database, add and configure table properties for CSV data with a comma (,) delimiter, and define the schema.

4. Finally, run SQL queries on your data using Amazon Athena.

Perfect for beginners or anyone looking to efficiently query large datasets on AWS. Watch now and follow along!

Unlock the power of DynamoDB with DAX! In this comprehensive tutorial, we’ll walk you through every step of setting up and customizing a DynamoDB DAX Cluster in Amazon cloud. Here’s what you’ll learn:

1. Set Up Amazon EC2 Instances: Utilize Amazon Machine Images and key pairs for seamless SSH authentication.

2. Create a Go Application: Develop an application and establish a sample DynamoDB table.

3. Execute and Measure Commands: Perform GetItem, Query, and Scan operations with DynamoDB and record execution times.

4. Boost Performance with DAX: Experience the speed difference as you repeat the same operations using a DynamoDB Accelerator (DAX) Cluster.

Whether you're a developer or cloud enthusiast, this step-by-step guide will help you optimize your database interactions. Tune in to elevate your DynamoDB expertise!