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Proof of Concepts (POCs)

I write a lot of POC projects, especially when I'm learning something new or I need to quickly test if a data pipeline works, or maybe I'm just testing a new integration. I make all these POCs public as Github repositories. I wanted to consolidate the list of POCs in an easy to search fashion. And that's why I have this page here. Below is a list of all the POCs that I've written so far. If a particular POC has an accompanying blog post which explains the code in the POC, I have linked that blog post as well in the list below. Let me know if any of these POCs have helped you in any way.

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

Put data to Amazon Kinesis Firehose delivery stream using Spring Boot

Amazon Kinesis Firehose

If you work with streams of big data which have to be collected, transformed, and analysed, you for sure would have heard of Amazon Kinesis Firehose. It is an AWS service used to load streams of data to data lakes or analytical tools, along with compressing, transforming, or encrypting the data. You can use Firehose to load streaming data to something like S3, or RedShift. From there, you can use a SQL query engine such as Amazon Athena to query this data. You can even connect this data to your BI tool and get real time analytics of the data. This could be very useful in applications where real time analysis of data is necessary. In this post, we'll see

How to Query Athena from a Spring Boot application?

amazon athena

In the last post, we saw how to query data from S3 using Amazon Athena in the AWS Console. But querying from the Console itself if very limited. We can't really do much with the data, and anytime we want to analyse this data, we can't really sit in front of the console the whole day and run queries manually. We need to automate the process. And what better way to do that than writing a piece of code? So in this post, we'll see how we can use the AWS Java SDK in a Spring Boot application and query the same sample data set from the previous post. We'll then log it to the console to make sure we're

Apache Drill vs. Apache Spark – Which SQL query engine is better for you?

Spark

If you are in the big data or data science or BI space, you might have heard about Apache Spark. A few of you might have also heard about Apache Drill, and a tiny bit of you might have actually worked with it. I discovered Apache Drill very recently. But since then, I've come to like what it has to offer. But the first thing that I wondered when I glanced over the capabilities of Apache Drill was, how is this different from Apache Spark? Can I use the two interchangeably? I did some research and found the answers. Here, I'm going to answer these questions for myself and maybe for you guys too. It is very important to understand that

Getting Started with Apache Drill and MongoDB

Not a lot of people have heard of Apache Drill. That is because Drill caters to very specific use cases, it's very niche. But when used, it can make significant differences to the way you interact with data. First, let's see what Apache Drill is, and then how we can connect our MongoDB data source to Drill and easily query data. What is Apache Drill? According to their website, Apache Drill is "Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage." That's pretty much self-explanatory. So, Drill is a tool to query Hadoop, MongoDB, and other NoSQL databases. You can write simple SQL queries that run on the data stored in other databases, and you get the result in a row-column format. The

How you can improve your backend services’ performance using Apache Kafka

apache_kafka_streams

In most real world applications, we have a RESTful API service facing various client applications and a collection of backend services which process the data coming from those clients. Depending on the application, the architecture might have various services spread across multiple clusters of servers, and some form of queue or messaging service gluing them together. Today, we're going to talk about one such messaging service - Apache Kafka - and how it can improve the performance of your services. We're going to assume that we have at least two microservices, one for the APIs that are exposed to the world, and one which processes the requests coming in from the API microservice, but in an async fashion. Because this is

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