While many are familiar with the foundational AWS services such as EC2, S3, and RDS, there are several advanced AWS services that provide powerful capabilities for specialized use cases. This blog will explore some of the advanced AWS services you should know to leverage the full potential of the AWS cloud. AWS Training in Pune


1. AWS Lambda: Serverless Computing
AWS Lambda is a serverless computing service that lets you run code without provisioning or managing servers. Lambda automatically scales your application by running code in response to triggers such as changes in data, shifts in system state, or user actions.

Key Features:
Automatically scales applications
Integrated with AWS services such as S3, DynamoDB, and Kinesis
Supports multiple programming languages including Node.js, Python, Java, and Go
Pay only for the compute time you consume
Use Cases:
Real-time file processing
Data transformation and ETL tasks
Building RESTful APIs
Event-driven applications
2. Amazon SageMaker: Machine Learning
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

Key Features:
Provides tools for every step of the ML lifecycle including data labeling, model training, and deployment
Pre-built algorithms and frameworks
Managed Jupyter notebooks for easy experimentation
One-click training and tuning with hyperparameter optimization
Integration with other AWS services such as S3, EC2, and Lambda
Use Cases:
Predictive analytics
Recommendation systems
Fraud detection
Image and video analysis
3. AWS Fargate: Container Management
AWS Fargate is a serverless compute engine for containers that works with both Amazon ECS and Amazon EKS. It allows you to run containers without having to manage servers or clusters. AWS Classes in Pune


Key Features:
Eliminates the need to provision and manage servers
Scales automatically with the demand of the container applications
Integrated with AWS security services such as IAM and VPC
Pay only for the resources you use
Use Cases:
Microservices
Batch processing
Continuous integration and deployment
Application migration to containers
4. Amazon Aurora: High-Performance Databases
Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, combining the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases.

Key Features:
High performance and availability
Automated backups, continuous backup to S3, and point-in-time recovery
Global database capabilities
Multi-master and serverless options
Use Cases:
Enterprise applications
SaaS applications
E-commerce platforms
Data warehousing
5. Amazon Redshift: Data Warehousing
Amazon Redshift is a fully managed data warehouse service that makes it simple and cost-effective to analyze all your data using standard SQL and existing business intelligence tools.

Key Features:
Columnar storage and data compression for high performance
Massively parallel processing
Integrated with AWS data lake and other analytics services
Redshift Spectrum for querying data directly in S3
Use Cases:
Business intelligence
Big data analytics
Data warehousing
ETL operations
6. AWS Glue: Data Integration
AWS Glue is a fully managed ETL (extract, transform, load) service that makes it easy to prepare and load your data for analytics.

Key Features:
Automatic schema discovery and data cataloging
Scalable ETL jobs with a serverless architecture
Integration with data sources such as S3, RDS, Redshift, and more
Support for Python and Scala
Use Cases:
Data preparation and transformation
Data integration and migration
Building data lakes
Real-time analytics
7. Amazon Kinesis: Real-Time Data Streaming
Amazon Kinesis is a platform on AWS to collect, process, and analyze real-time, streaming data, allowing you to get timely insights and react quickly to new information.
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Key Features:
Kinesis Data Streams for real-time data streaming
Kinesis Data Firehose for loading streaming data into data lakes and warehouses
Kinesis Data Analytics for real-time analytics
Fully managed and scalable
Use Cases:
Real-time data analytics
Log and event monitoring
IoT data processing
Clickstream analytics
8. AWS Elastic Beanstalk: Simplified Application Deployment
AWS Elastic Beanstalk is an easy-to-use service for deploying and scaling web applications and services developed with Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, Nginx, Passenger, and IIS.

Key Features:
Automatic scaling and load balancing
Integrated with other AWS services such as RDS, S3, and CloudWatch
Supports multiple programming languages and platforms
Provides a web-based interface and CLI for deployment and management
Use Cases:
Web applications
Microservices
APIs
Backend services
9. AWS Step Functions: Workflow Automation
AWS Step Functions is a serverless orchestration service that lets you coordinate multiple AWS services into serverless workflows so you can build and update apps quickly.

Key Features:
Visual workflow design and monitoring
Integration with AWS Lambda, ECS, SNS, and other AWS services
Built-in error handling and retry capabilities
Scalable and highly available
Use Cases:
Microservice orchestration
Data processing pipelines
ETL workflows
IoT backend workflows