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How to Manage Secrets Securely with AWS Secrets Manager and Lambda

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Using ConnectorX and DuckDB in Python: Step by Step Guide

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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...
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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...
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Integrating Amazon Cognito with API Gateway for Secure API Access

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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....
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Creating a Scalable Lambda Layer for PostgreSQL or MySQL Drivers in Python

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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...
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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...
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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...
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LSTM and BiLSTM Explained: Advanced Deep Learning Techiniques for Time Series Prediction

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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...
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