CloudCurls | AWS Cloud, Serverless, DevOps and Automation Tutorials

Introduction Cloud computing is one of the fastest-growing industries in the world. Companies are moving their applications, databases, AI workloads, and infrastructure to the cloud because it provides scalability, security, reliability, and cost savings. From startups to large enterprises, every organization today needs cloud professionals. This creates huge opportunities for freshers who want to build a strong career in technology. If you are confused about: Which cloud platform to start with Whether to learn AWS, Azure, or Google Cloud Which certifications to take Important Tools Cloud Engineers Should Learn What projects to build Common mistakes should avoid Then this guide is for you. This blog is designed specifically for beginners and freshers who want a detailed roadmap to become a cloud engineer in 2026. What is Cloud Computing? Cloud computing means using servers, storage, databases, networking, software, and computing resources over the internet instead of managing everythin...

AWS has introduced a major update to Amazon S3 — you can now mount your S3 bucket like a file system. This new capability, called S3 Files , changes how developers and data teams interact with storage on AWS. For years, S3 was known only as object storage. Now, it behaves much closer to a traditional file system, making it easier to work with existing tools and applications. What is Amazon S3 Files? S3 Files allows you to mount your S3 bucket as if it were a local disk on services like EC2, ECS, EKS, or Lambda. Once mounted, you can use standard file operations such as listing directories, reading files, or writing new data without any special APIs. This means developers no longer need to think in terms of objects and keys. Instead, they can simply work with files and folders, just like they would on a normal Linux system. Why This Matters Earlier, AWS storage worked like this: Amazon S3 → Cheap, scalable object storage Amazon EFS → File system for applications 👉 You had to ...

Kiro is AWS's agentic AI IDE that goes beyond code generation — it plans, designs, executes, and documents your features as a true development partner. Built on Code OSS (the open-source foundation of VS Code), Kiro introduces spec-driven development to transform how software gets built from idea to production. This guide breaks down what Kiro is, what makes it different, and when it delivers the most value for developers. What Is Kiro? Kiro is an agentic AI IDE from AWS that understands the intent behind your prompts and orchestrates entire development workflows — planning, designing, coding, and documenting — without you having to manage any of that manually. What Kiro Handles For You Requirements documentation – Auto-generated in EARS notation from your natural language prompt System design – Analyzes your codebase and proposes architecture that fits your existing stack Task sequencing – An ordered, dependency-aware implementation plan created before a single line...

AWS Lambda is one of the most widely adopted serverless computing services on Amazon Web Services. It allows developers to run code without provisioning or managing servers, revolutionizing how modern applications are built and deployed. While AWS Lambda is powerful and cost-effective for many use cases, it's not suitable for every workload. This comprehensive guide will help you understand when to use AWS Lambda and when to choose alternative AWS compute services. What Is AWS Lambda? AWS Lambda is a serverless, event-driven compute service that automatically runs your code in response to events and scales instantly based on demand. What You Don't Manage Servers – No infrastructure to provision or maintain Operating systems – Automatic patching and updates Capacity planning – Automatic scaling from zero to thousands of concurrent executions Lambda Pricing Model: Pay Only for What You Use Number of invocations – First 1 million requests per month are free Execution dur...

While RNNs are great at handling sequences, they sometimes struggle with long-term dependencies . Imagine trying to remember a detail from 20 steps ago—RNNs often “forget” that information. That’s where  LSTM (Long Short-Term Memory) and later BiLSTM (Bidirectional LSTM) models are being used. These models are game-changers for solar power forecasting. Long Short-Term Memory LSTM is an advanced type of RNN designed to remember information for longer periods. It has special structures called gates: Forget Gate: Decides what information to throw away. Input Gate: Decides what new information to store. Output Gate: Decides what the model should output at each step. LSTMs handle long-term dependencies much better than regular RNNs. Since the same dataset was used here as well, you can read more about it in my earlier RNN blog . Model Results for LSTM The training graph shows that the loss decreases steadily as the number of epochs increases. This means the model learns patterns in th...

Introduction When we think about renewable energy, solar power often comes to mind first. But there’s one big challenge: solar energy is not constant . It changes with weather, seasons, and even time of the day. To make solar power more reliable, predicting its future output becomes very important. That’s where machine learning - especially Recurrent Neural Networks (RNNs)  comes in. What is RNN A Recurrent Neural Network (RNN) is a type of deep learning model that is great at handling sequential data —data that comes in order, like time-series or speech. Unlike regular neural networks, RNNs remember past information by looping their outputs back into the network. This makes them perfect for tasks like predicting solar power output, stock prices, or even natural language processing. How RNN Works RNNs take input data step by step.  They store the output of the previous step and use it along with the new input to make better predictions.  This memory-like ability helps th...

Introduction If you’ve done any data work in Python, chances are you’ve used Pandas —it’s been the go-to library for data analysis and data preparation for years. But as datasets keep getting bigger and performance demands rise, a new player has entered the scene: Polars . Think of it as Pandas’ faster, more modern library. Both are great at handling data, but they differ quite a bit when it comes to speed, scalability, and the way they’re designed In this blog, we’ll dive into the differences between Pandas and Polars, and help you decide which one fits your use case. Pandas vs Polars Both Pandas and Polars can play an important role in data preparation and data analysis. Pandas: Pandas can integrated easily with s cikit-learn , Matplotlib, TensorFlow, and PyTorch. Built on top of  NumPy and designed for in-memory datasets Pandas is ideal for small to medium dataset. Polars: Uses  Apache Arrow memory model for efficient storage Designed to be multi-threaded and more memory...

Introduction When working with large datasets, execution time and efficiency comes into play. Traditional methods of extracting data from the relational databases into Python often involve loading everything into memory, which can be painful and very slow. That’s where connectorX and DuckDB come in handy. Together, they make data extraction and analytics in python very  fast and memory-efficient . What is ConnectorX? ConnectorX is an open-source library built to load data from databases directly into pandas, Polars, or NumPy efficiently. Instead of fetching row by row via  psycopg2  or  sqlalchemy ConnectorX p arallely fetch chunks of data and stream them directly into Python. Supports many databases: MySQL, SQLite, PostgreSQL, SQL Server, BigQuery, Snowflake, and many more. What is DuckDB? DuckDB is an in-process SQL OLAP database. Can query CSV, Parquet, JSON, Arrow datasets, and even pandas/Polars DataFrames. Works directly inside Python and R. Data pro...

Introduction In modern cloud-native applications, managing sensitive information like API keys, database credentials, any third party service credentials and other secrets securely is a top priority. Hardcoding secrets into application code, environmental variables in lambda functions or configuration files can lead to serious security vulnerabilities and operational risks and this is where AWS Secrets Manager comes in—a fully managed service that enables you to store, retrieve, and rotate secrets securely. When combined with AWS Lambda , Secrets Manager allows you to build powerful serverless applications that access secrets dynamically during the runtime, without ever exposing them in your codebase. In this blog, we'll explore how to integrate AWS Secrets Manager with Lambda functions, ensuring your application remains secure, scalable, and maintainable. Whether you're accessing a database, calling a third-party service, or simply avoiding secret sprawl, this guide will wal...

Introduction When working with AWS Lambda functions in Python, especially in database-heavy applications, you often run into deployment package size limits or performance issues due to repeated bundling of common libraries like psycopg2 for PostgreSQL, python-oracledb for Oracle or  mysql-connector-python for MySQL. These database drivers are essential, yet bulky—leading to bloated deployment packages, slower cold starts, and painful debugging across environments. To address this, Lambda Layers offer a powerful solution. Layers allow you to package shared dependencies—such as database drivers—separately and reuse them across multiple functions, simplifying deployment and improving scalability. In this blog, we’ll walk through creating a scalable and reusable Lambda Layer for PostgreSQL or MySQL drivers using Python. You’ll learn not only how to build and deploy these layers, but also best practices to make your architecture more maintainable and efficient in the long run. Whet...

Amazon SNS vs SQS In modern cloud applications, messaging services play a critical role in building scalable, decoupled, and event-driven architectures. On AWS, two of the most commonly used services for this purpose are Amazon Simple Notification Service (SNS) and Amazon Simple Queue Service (SQS) . At first glance, they may appear similar since both handle message delivery between distributed components. However, the way they operate and the problems they are designed to solve are very different. While SNS follows a publish/subscribe model to send messages in real-time, SQS uses a message queue model to ensure reliable, point-to-point communication between producers and consumers. In this blog, we’ll break see the key differences between SQS and SNS, explore common use cases, and look at how they can even be combined to create powerful, event-driven systems. What is Amazon SNS? Simple Notification Service is a fully managed publish/subscribe messaging service. Characteristics Fa...

Introduction Securing APIs is essential for protecting sensitive data and ensuring authorized access to your applications. AWS provides a seamless way to achieve this through the integration of Amazon Cognito and API Gateway. Amazon Cognito simplifies user authentication and identity management, while API Gateway serves as a scalable entry point for your backend services. This blog will guide you through securely connecting Amazon Cognito with API Gateway. You'll learn how to configure user pools, set up authorization mechanisms, and test the setup to ensure only verified users can access your APIs. By following this guide, you'll gain valuable insights into building secure and scalable applications using AWS services. Creation of Cognito User Pool Open the AWS Management Console and search for Amazon Cognito. Navigate to User Pools and select Create User Pool. Begin by defining your application settings. For this tutorial, we'll choose the Traditional Web Application type....

Introduction to AWS Key Management Service (KMS) In today’s digital landscape, safeguarding sensitive data is critical. As organizations increasingly migrate from legacy systems to the cloud, robust encryption and key management solutions become essential to secure data at rest as well as in transit. AWS Key Management Service (KMS) is a fully managed service that helps you create, control, and manage encryption keys securely across AWS services and your applications.  Whether you’re protecting customer information, securing application secrets, or encrypting sensitive documents, AWS KMS provides a scalable and cost-effective solution that integrates seamlessly with other AWS services. In this blog, we will explore how AWS KMS works, its core features, and how it can enhance your security posture in the cloud. Additionally, we will walk through practical use cases and best practices for implementing KMS in your AWS environment. Creating Customer-Managed Keys There are essentially t...

Setting Up Amazon SES destinations Amazon Simple Email Service (SES) allows users to send, receive, and monitor emails at scale. One powerful feature is its ability to send event data (e.g., bounces, complaints, and delivery notifications) to various destinations. In this tutorial, we'll guide you through setting up SES destinations with the following services: Amazon Notification Service SNS Kinesis Data Firehose Amazon Pinpoint SNS as Destination Create an SNS topic Log in to the AWS Console and search for Simple Notification Service (SNS). Select it from the results. Navigate to the Topics section and click Create Topic. Choose Standard as the topic type, optionally add tags, and then click Create Topic to complete the setup. Type of SNS topic Configure the destination in SES Navigate to Simple Email Service (SES) and choose the configuration set where you want to add a destination. Click Add Destination, select the desired event types, and proceed to the next step. Under Destin...