Email functionality is crucial for any web application. Whether it's for sending registration confirmations, password resets, or system alerts, ensuring emails are sent reliably is a top priority. AWS Simple Email Service (SES) provides a powerful, scalable, and cost-effective solution for sending emails in Flask applications. However, many developers run into common pitfalls when setting up AWS SES.
AWS Lambda is a serverless computing service provided by AWS. It is a service that runs your code in response to an event and automatically manages the resources required for running your code. You don't need to worry about any underlying resources which are required.
Implementing real-time data streaming from a server to a client can be challenging, especially when working with APIs that return data in chunks. Let me share a story of how I tackled this problem while using Python Flask for the backend and Vue.js with the Quasar framework for the frontend. It was a journey filled with trials, errors, and some exciting discoveries.
Agentic AI is quickly becoming a buzzword in the world of technology, and for good reason. Imagine AI agents capable of thinking, planning, and executing tasks with minimal human input—this is the promise of Agentic AI. It’s a revolutionary step forward, allowing businesses to operate smarter, faster, and more efficiently.
In the world of big data, efficient management and analysis of large datasets is crucial. Amazon S3 Tables offer a fully managed solution built on Apache Iceberg, a modern table format designed to handle massive-scale analytical workloads with precision and efficiency.
From Pandas to ClickHouse: The Evolution of Our Data Analytics Journey
- Chetan Patel
- Oct 28, 2024
- 4 min read
At Incentius, data has always been at the heart of what we do. We’ve built our business around providing insightful, data-driven solutions to our clients. Over the years, as we scaled our operations, our reliance on tools like Pandas helped us manage and analyze data effectively—until it didn’t.
The turning point came when our data grew faster than our infrastructure could handle. What was once a seamless process started showing cracks. It became clear that the tool we had relied on so heavily for data manipulation—Pandas—was struggling to keep pace. And that’s when the idea of shifting to ClickHouse began to take root.
But this wasn’t just about switching from one tool to another; it was the story of a fundamental transformation in how we approached data analytics at scale.
The Early Days: Pandas, Our First Love
When we first adopted Pandas, it felt like we had unlocked the perfect solution. The flexibility, the powerful data frames, and the ease with which we could manipulate small to mid-sized datasets—it was a game-changer. Our team of data engineers and analysts loved it for its simplicity. And, for a long time, it served us well.
But then something happened. Our datasets began to grow, and so did the complexity of our queries. We went from handling thousands of rows to millions, and then, in what seemed like no time at all, billions. The once seamless operations we had with Pandas turned into long waits for processes to complete, or worse, system crashes.
We found ourselves asking: How can we keep scaling without compromising performance?
The Bottleneck: Pandas in a Big Data World
At first, we tried to optimize Pandas in every way possible. We ran computations on smaller chunks of data, tried parallel processing techniques, and even moved to bigger and more expensive machines to support the growing memory requirements. But these were short-term fixes for a long-term challenge. Pandas was designed to load data into memory, which, for our growing datasets stored on S3, was becoming a major bottleneck.
We realized that as the data we were handling continued to scale, our tools needed to scale with it. Pandas, for all its strengths, wasn’t designed for this new world of distributed, high-performance data analytics. That’s when we started exploring alternatives—and found ClickHouse.
Enter ClickHouse: A New Frontier in Data Analytics
We didn’t immediately jump into using ClickHouse. Like any good story, there was a journey of discovery, a few moments of doubt, and ultimately, a realization that this was the solution we needed.
ClickHouse came onto our radar because of its reputation for handling real-time, high-performance analytics. It was built to thrive in environments like ours—where datasets are huge, queries are complex, and the need for speed is paramount. We started small, running a few test queries on ClickHouse to see how it would perform against Pandas. The results were staggering.
Where Pandas took minutes, sometimes hours, to process data, ClickHouse completed the same tasks in seconds. The first time we ran a complex aggregation on ClickHouse and saw the results in the blink of an eye, we knew we were onto something.
The Turning Point: Scaling Without Limits
Transitioning from Pandas to ClickHouse wasn’t just about better performance; it was about rethinking how we managed our entire data pipeline. Here’s what changed:
- Handling Larger Datasets with Ease: ClickHouse’s columnar storage model meant we could now work with datasets that would’ve been impossible to manage in Pandas. Instead of loading everything into memory, ClickHouse processed data directly from S3, allowing us to scale infinitely without worrying about memory limits.
- Real-Time Insights, Faster Than Ever: One of the biggest challenges we faced with Pandas was the time it took to generate real-time reports. With ClickHouse, real-time analytics became just that—real-time. We could now offer clients up-to-the-minute insights on their data, something that would’ve taken hours in our previous setup.
- Distributed Processing, Maximum Efficiency: ClickHouse’s ability to distribute queries across multiple nodes unlocked a new level of efficiency. We were no longer constrained by the limitations of a single machine. We could now process billions of rows of data across multiple servers, achieving performance that was unimaginable with Pandas.
- Seamless S3 Integration: One of the most powerful features of ClickHouse is its seamless integration with S3. This eliminated the need for us to move data between different storage systems or perform complex ETL processes just to analyze it. We could query data directly where it was stored, saving time, money, and resources.
From Challenge to Opportunity: What This Means for Our Future
Looking back, the decision to transition from Pandas to ClickHouse was more than just a technical upgrade—it was a turning point in how we think about data. The challenges we faced with Pandas forced us to push the boundaries and explore new technologies. ClickHouse wasn’t just a replacement; it became the foundation for a more scalable, robust, and future-proof data infrastructure.
Now, instead of being bogged down by the limitations of in-memory processing, we’re able to take on projects that involve massive datasets with confidence. Our clients benefit from faster insights, more reliable data processing, and a system that’s built to grow with them.
Conclusion: The Evolution Continues
The move to ClickHouse wasn’t the end of our story; it was just the next chapter. As we continue to evolve and scale, we’re constantly looking for ways to push the envelope, to find new tools and technologies that allow us to deliver even greater value to our clients. The lesson we learned from this transition is simple: As the world of data evolves, so must we.
Our journey from Pandas to ClickHouse is a testament to that philosophy—an evolution driven by necessity, but one that has opened the door to endless possibilities.
And with ClickHouse powering our analytics, the possibilities are truly endless.
About Author
