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Enhancing Data Security and Privacy in the Cloud with AWS Clean Rooms

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Data security and privacy in the cloud is becoming crucial as more organisations are embracing cloud computing and cloud storage. In this post, we’ll see how AWS Clean Rooms can help maintain data security and privacy.

Cleaning and Normalizing Data Using AWS Glue DataBrew

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In this post, we’ll see what is AWS Glue DataBrew and how to use it to clean and transform our data in a data pipeline.

Getting started with Chalice to create AWS Lambdas in Python – Step by Step Tutorial

Using Chalice, you can write a Lambda function, test it locally, and even deploy the Lambda function to your development, test, or production environments. In this post, we’ll see how we can install Chalice on our local machines, write a simple REST API to return the famous “Hello, world!” response, and deploy it to a dev stage on AWS Lambda.

Invoke an AWS Lambda Function from another Lambda Function

I recently discovered that you can't invoke more than one Lambda function in AWS for an S3 event, with the same prefix and suffix (or just with the same suffix, which was the issue in my case). So I wanted a way to invoke one Lambda function from another Lambda function. If you're feeling kind of lost, check out the problem statement in my Github project. That could possibly add some context to the problem. If you don't want to go there, I'll try to explain it here again. The Problem and the Requirement In one of our projects, we have a Lambda function which is invoked whenever a text file is uploaded to a particular S3 bucket. The Lambda function takes

How to automatically trigger AWS Lambda functions using CloudWatch

If you have AWS Lambda functions which need to be triggered periodically, like CRON jobs, there are many ways to achieve this. But I recently discovered a very easy and AWS-way of doing this, which makes life a lot easier. So, there are a lot of ways you can trigger Lambda functions periodically. One of the most common ways I've see people doing this is adding an API Gateway to the Lambda function, and then calling that API periodically as a CRON job from one of the machines in the setup. I actually thought this is how you're supposed do to that. Okay, let me make this clear. I'm not a DevOps guy. I just learn these things as and when

Integrate AWS DynamoDB with Spring Boot

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Here is another POC to add to the growing list of POCs on my Github profile. Today, we’ll see how to integrate AWS DynamoDB with a Spring Boot application. This is going to be super simple, thanks to the AWS Java SDK and the Spring Data DynamoDB package. Let’s get started then. Dependencies First, as usual, we need to create a Spring Boot project, the dependencies of which look like: <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter</artifactId> </dependency> <dependency> <groupId>com.amazonaws</groupId> <artifactId>aws-java-sdk-dynamodb</artifactId> <version>1.11.573</version>

Generative AI in Data Engineering: Transforming the Landscape

Generative AI is revolutionizing data engineering by automating tasks, enhancing data quality, and enabling innovative solutions. This blog explores its fundamentals, practical applications like ETL automation and synthetic data generation, and its transformative impact on modern data workflows. It also offers insights on how data engineers can prepare, recommended learning resources, and comparisons with traditional methods. Embrace generative AI to stay ahead in the rapidly evolving data engineering landscape!

The Road Ahead: Key Data Engineering Trends for 2025

As we step into 2025, the world of data engineering is poised for transformative growth. From the rise of unified data architectures to the integration of AI-driven tools, the landscape is evolving faster than ever. This blog explores the key trends shaping the future—real-time data processing, edge computing, enhanced data governance, and more—while providing actionable insights on how professionals and organizations can adapt. Whether you’re a seasoned data engineer or just starting your journey, this comprehensive guide will help you navigate the challenges and seize the opportunities of 2025 with confidence.

Real-Time Data Processing: Understanding the What, Why, Where, Who, and How

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In today’s data-driven world, businesses and organizations are continuously generating massive amounts of data. While processing data in batch mode remains useful, the need for instant decision-making has led to an increasing focus on real-time data processing. This article delves into what real-time data processing is, why it's essential, its various applications, the tools used to achieve it, trends shaping its evolution, and real-world use cases. What is Real-Time Data Processing? Real-time data processing refers to the capability to continuously ingest, process, and output data as soon as it is generated, with minimal latency. Unlike batch processing, which collects and processes data in large groups at set intervals (e.g., daily or hourly), real-time processing works with data immediately as it becomes available,

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