Apache-Spark

Ever had a Spark job that keeps re-reading the same data over and over? You might need caching. But cache at the wrong time, and you’ll actually slow things down. Here’s when caching helps, when it doesn’t, and how to use it right. This blog will be short but sweet! 🔍 What is Caching? Think of caching like keeping your frequently used files on your desk instead of walking to the filing cabinet every time you need them. ...

The Problem: Union function isn’t as Simple as it Seems Picture this: You have a large dataset that you need to process in different ways, so you: Split it into smaller pieces Transform each piece differently Put them back together using union Sounds straightforward, right? Well, there’s a catch that most developers don’t know about. The Hidden Performance Killer 🐌 Here’s what’s actually happening behind the scenes when you use union: ...

Introduction When building event-driven architectures with AWS Lambda and Apache Kafka, one of the most critical yet often misunderstood components is offset management especially for event source mapping when you use lambda functions. Many developers wonder: Do I need to manage Kafka offsets manually? or What happens when my consumer group’s offsets expire? In this blog post, we’ll demystify how AWS Lambda’s Event Source Mapping handles Kafka offsets automatically and what you actually need to know as a developer. ...

Introduction In this blog post, we’ll zoom into the details of how column ordering and data types can cause issues when using the union function in Apache Spark to combine two dataframes. We’ll explore real-world examples that illustrate the problem and provide practical solutions to overcome these challenges. By the end of this post, you’ll have a better understanding of how to use union effectively and avoid common pitfalls that can lead to job failures. ...

Introduction Hi there 👋! In this blog post, we will explore why JSON is not suitable as a big data file format. We’ll compare it to the widely used Parquet format and dig deep to demonstrate, through examples, how the JSON format can significantly degrade the performance of your data processing jobs. JSON (JavaScript Object Notation) is a popular and versatile data format, but it has limitations when dealing with large-scale data operations. On the other hand, Parquet, an open-source columnar storage format, has become the go-to choice for big data applications. ...

Introduction Hi, welcome to my first blog post. This post is the first one in a series of many that will follow. Who is Tortoise and who is Hare? Well, In many books about Apache Spark that I was reading, I didn’t found a clear idea of the performance of dataframes compared to the datasets. In this blog post, we will debunk that mystery and show some concrete results and insights regarding this matter. ...