Amazon Web Services

Cloud computing is the on-demand delivery of IT resources over the internet, typically with a pay-as-you-go pricing model. Instead of purchasing, owning, and maintaining physical data centers and servers, businesses can access computing services—such as computing power, storage, and databases—on an as-needed basis from cloud providers like Amazon Web Services (AWS).

This curriculum is designed to help you learn about Elastic Load Balancing (ELB) in AWS, with a focus on Application Load Balancers (ALB) and Network Load Balancers (NLB), as well as how they integrate with Amazon ECS (Elastic Container Service). The curriculum will cover the concepts, configurations, and implementation details required for using these load balancers effectively.

This curriculum is designed to provide an in-depth understanding of containerization technologies—AWS Containers, Docker, Kubernetes, and Helm charts—and their integration with AWS services for both application development and cloud migration. It will guide you through using containers to modernize legacy applications, migrate workloads to AWS, and deploy containerized applications in scalable environments.

This curriculum aims to provide a comprehensive understanding of AWS Q Business, AWS Bedrock, and AWS Titan Embeddings, highlighting their roles in business solutions, generative AI applications, and machine learning. You will learn how to implement these services for various business needs, from AI-powered decision-making and automation to leveraging foundational models and embeddings for custom applications.

The AWS Big Data Curriculum is designed to help individuals and teams master the key AWS services required for efficient big data processing, analysis, and visualization. By gaining expertise in these services, users will be equipped to handle large-scale data analytics, perform real-time data processing, and implement machine learning models at scale.

To design a comprehensive big data storage and warehousing solution, we can use a combination of AWS services. Below is a breakdown of each component in the architecture, configuration steps, and resources for implementation.

To design an efficient and scalable data processing and analytics pipeline, we can use a combination of AWS services that enable batch processing, real-time data analytics, and machine learning. Below is an in-depth overview of the tools and services for building a comprehensive data pipeline on AWS.

Big data visualization and reporting tools are essential for turning complex data into actionable insights. AWS provides a comprehensive suite of services and tools that enable users to visualize and report on large datasets efficiently. These tools include Amazon QuickSight, AWS Glue DataBrew, Kibana with Amazon OpenSearch, and Tableau Integration with AWS. Below is an in-depth look at these tools, their features, implementation steps, and resources.

In today’s world, where data is one of the most valuable assets, ensuring robust security and governance for big data is essential. AWS provides a variety of security services and best practices that help ensure data protection, access control, threat detection, and compliance. The services include AWS IAM for identity management, AWS KMS for encryption, AWS WAF for web application security, Amazon GuardDuty for threat detection, and AWS Config for compliance.

Machine Learning (ML) and Artificial Intelligence (AI) play a critical role in transforming data into actionable insights. AWS offers a suite of services that allow you to easily integrate ML and AI into your big data workflows. By combining Amazon SageMaker for model training, AWS Lambda for inference, Amazon S3 for model storage, and AWS Step Functions for workflow orchestration, you can build scalable and efficient ML workflows. Below, we'll break down the architecture and provide detailed steps for implementation.

Real-time data processing is critical for applications that require timely analysis and quick decision-making. AWS provides a robust set of services for ingesting, processing, and analyzing streaming data. Key services for this use case include Amazon Kinesis Data Streams, Amazon Kinesis Data Analytics, AWS IoT Analytics, and Apache Kafka on AWS (MSK). These services work together to form a powerful streaming and real-time analytics pipeline.

Effective monitoring and logging are essential for maintaining the health, security, and performance of your AWS infrastructure. AWS provides a robust suite of tools for monitoring, tracking, and logging application and infrastructure metrics, as well as detecting and troubleshooting issues. These tools include Amazon CloudWatch, AWS X-Ray, AWS CloudTrail, and AWS Trusted Advisor. Let's delve into each of these tools with implementation steps and resources.

Designing a scalable Big Data architecture for FinTech on AWS requires careful planning and a deep understanding of AWS services. By focusing on the end-to-end data pipeline, fault tolerance, and cost optimization, while following best practices for data lake architecture, real-time streaming, and security, you can build a robust and efficient Big Data system for financial applications.

By leveraging AWS tools and services like Kinesis, Glue, S3, and Redshift, you can build a scalable, fault-tolerant, and cost-effective solution to handle the massive volume and velocity of financial data.

Overview

This curriculum is designed to help FinTech professionals master AWS services for big data processing, analysis, and visualization. It covers data storage, ETL processes, data processing, analytics, security, and AI/ML integration.

AWS provides a variety of tools and services to help manage data efficiently. These include storage solutions like Amazon S3, Data Lakes on AWS, and Amazon Redshift, which help organizations manage structured and unstructured data, maintain security, and scale their data architecture as needed. Below, we will dive deeper into these services and walk through the implementation steps.

In this guide, we will elaborate on three essential AWS services used for ETL (Extract, Transform, Load) and data processing: AWS Glue, Amazon EMR, and Amazon Kinesis. We will explore how to use these services to handle data efficiently, from data discovery and transformation to real-time analytics.