Building efficient data analytics algorithms
Aegasis Labs partnered up with Clippd to streamline their data pipelines,system design and engineering practices. Clippd were looking for an AI and Cloud solution development partner to help them optimize core components of the system, build two new data analytics modules and improve engineering and automated testing practices.
- Reduce costs and processing times when handling and processing large amounts of data for the users
- Increased the potential reusability of software components by encouraging a modular approach to solution development
- Encourage best practices when it comes to pipeline and software modules and automated testing
Challenge
Developing and deploying data pipelines that process large amounts of data is a complex, iterative process. While the freedom to experiment is vital in any data science workflow, engineering precision is required to get a stable solution out into production.
Successful data engineering pipelines and solutions revolve around addressing these often competing needs: efficient, reproducible, modular design and extremely fast data processing; and rigor in deploying and monitoring pipelines in the cloud.
The engineering team at Clippd were looking to optimize their system architecture and processes and wanted to take full advantage of the best system design and data engineering practices on their core platform:
- Optimize existing data scraping and processing modules to achieve lower executing times and faster delivery of analytics
- Automation to free up data scientists to do the fun modeling work they’d rather be doing
- Performance and scalability
- Implement best engineering practices to achieve modular software design and automated testing
- Clippd were interested in best practices around using Step Functions, Lambda function and Batch operations for their data pipelines.
Our solution
To help address these challenges, Aegasis Labs designed and developed two data analytics and algorithms modules and optimized their existing system and software design to achieve better scalability, performance and reliability. We focused on:
- Designing two new data analysis and analytics algorithms that enabled faster delivery of analytics to the users of the platform
- Increasing the potential reusability of pipeline and software components by encouraging a modular approach to cloud solution development
- Allowing for step-specific software and system optimizations in order to decrease costs and processing times
- Generating informative visualisations to better track experiment results
- Encouraging best practices for pipeline code
- Encouraging best practices for modular software components and automated pipeline testing
At Aegasis Labs we emphasize knowledge sharing with our clients. With these best practices and transferable lessons, the data science and data engineering team at Clippd is well situated to continue delivering innovative solutions that enhance the unique experience of their users.
Results
Over a span of 3 months, Aegasis Labs worked with Clippd and delivered the solution to their challenges. Our solution helped them remove the speed bottleneck issues with their platform.
The two new modules that we developed enabled them to deliver analytics to their platform faster and improve the overall user experience for their users.
They’re now able to take advantage of the data analytics modules, system optimizations and best practices introduced to facilitate development and experimentation, and accelerate the path to production.
“Aegasis Labs successfully developed two analysis and algorithms modules in our pipelines and were delivered to production. They worked closely with the team to ensure seamless collaboration”
Shankar Vasudevan – CTO Clippd