Explore how AWS S3 provides scalable object storage with high availability and security, and its use cases for data lakes, backups, archival, static websites, IoT, and machine learning.

Learn to create an S3 bucket in AWS, upload diverse equipment data, and organize time-series files with year-month-day folder structures for scalable analytics.

Learn about Amazon S3 key features including storage classes, lifecycle management, object lock, replication, pre-signed URLs, event notifications, strong read-after-write consistency, S3 select, batch operations, and static website hosting.

Explore AWS Glue, a fully managed, serverless data processing and ETL tool with a cloud data catalog, enabling scalable batch or near real-time data preparation across sources.

Learn key AWS Glue features for building ETL jobs in the console with notebooks and interactive sessions, including spark streaming, Glue workflows and triggers, data catalog, crawlers, and schema registry.

Explore how AWS glue crawlers populate the data catalog with metadata. Run on-demand or on a schedule, use custom classifiers for complex schemas, and produce databases and tables as metadata.

Create an AWS Glue crawler to scan S3 data, then publish a table in the AWS Glue data catalog for use with Athena.

Learn to build a glue ETL job in an interactive notebook that reads four files via the data catalog and writes the output to S3.

Discover AWS Athena, a serverless, interactive query service that analyzes data in S3 with standard SQL, built on Trino and Presto, enabling analytics across data lakes, warehouses, and on-prem sources.

Learn to query AWS data catalog tables with Athena, run queries on S3 data, and configure the query editor with a dedicated S3 bucket for results in the same region.

Explore AWS QuickSight, a serverless, browser-based cloud business intelligence service that integrates diverse data and delivers fast insights, machine learning-powered anomaly detection and forecasting, with secure, shareable dashboards.

Build Amazon QuickSight dashboard from S3 data by creating a dataset with a manifest file, building visuals (donut chart, bar chart, and pie chart), and sharing dashboard with your organization.

Learn to build a QuickSight dashboard from multiple S3 datasets, including raw and transform data with manifest files, and publish visuals like tables, donut and line charts.

Learn to create calculated fields and calculated columns in a QuickSight data set from S3, including parsing date formats and extracting equipment attribute values with if-else logic.

Learn how to add and configure filters in a QuickSight dashboard, create visuals like pie and donut charts, apply filters to visuals, sheets, or the entire dashboard, and publish.

Create and analyze datasets in AWS QuickSight, build dashboards with charts and tables, use calculated fields and filters, and embed dashboards or visuals on a website.

Upload files to AWS S3 using dotnet eight by creating an S3 client and using the transfer utility to upload a local file to a bucket in AP South S2.

List all objects in an S3 bucket using a dotnet 8 application by fetching metadata in batches, handling continuation tokens, and iterating through results to display prefixes and sizes.

Learn how to delete a file from an AWS S3 bucket using a Microsoft .NET 8 application, including crafting a delete object request, bucket and key, and handling delete markers.

Generate a pre-signed URL for S3 objects with a Microsoft .NET 8 application to securely deliver large files to clients, reducing server CPU and memory usage, with expiry control.

Copy S3 objects from a source bucket to a destination bucket using per-object copy requests, with listing and status checks, and note S3 native copy for large datasets.

Learn how to delete all objects from an S3 bucket using a dotnet application, handling pagination with continuation tokens and verifying deletion in the console.

Learn to check if an S3 bucket or object exists using a dotnet program, by issuing get bucket location and get object metadata requests, and handle not found errors.

Learn how to list all versions of an S3 object using .NET and understand S3 versioning benefits such as restoring deleted or older versions and cheaper storage via lifecycle policies.

Learn to download an S3 object to a local machine using a .NET application by configuring the bucket name and object key, streaming the response to a file.

Discover DynamoDB, a serverless NoSQL database offering millisecond performance at any scale. Create tables with items and attributes, and use partition and sort keys along with secondary indexes and streams.

Explore DynamoDB’s serverless on-demand capacity, zero administration, and auto-scaling to zero, with NoSQL key-value and document models, strong reads, AC transactions, denormalize data models, and point-in-time backups.

Deploy a DynamoDB table using AWS CDK by bootstrapping your account, building the project, and deploying a pay-per-request table named employee detail with a string partition key.