When data company MMIT sought to revolutionize its data collection landscape and web scraping processes, it called on Concept to Cloud. We didn’t just help them modernize their operations; we laid the foundation for autonomous, scalable data extraction systems.

Seeking to push the boundaries of what MMIT could bring to the data industry — particularly regarding efficiency and reliability—required us to look beyond the company’s current processes and far into the future.

Scope

• MVP Development
• Prototype Building
• Data Processing
• Cloud Integration
• Crawler Enhancement
• Pipeline DesignTeam Training

MMIT faced a dual challenge

As a company heavily reliant on web-scraped data, their existing processes were increasingly costly and problematic. MMIT’s data collection methods ranged from automated scraping across various platforms to manual extraction by teams in India. As the company grew, so did the complexity of maintaining and optimizing its technology across multiple platforms.

MMIT also faced scalability issues. Adding new data sources was time-consuming and required extensive testing and validation. Quality assurance was labor-intensive, and the occasional blocking of their crawlers led to inconsistent access to crucial datasets.

Charting a New Course

MMIT decided to tackle these challenges head-on, relying on Concept to Cloud to shift the core of their operations from manual to automated, from fragmented to unified. What MMIT came to understand was that the future would likely involve autonomous, scalable data extraction systems. The need for more data sources refreshed more frequently and the desire for reduced manual intervention made eliminating—or, in the short term, assisting—the human element an attractive proposition. But, shifting from their existing processes to pioneering autonomous data extraction was the most significant change in the company’s history, and they needed partners to make it happen. To deliver the advanced data extraction aspect, MMIT decided that leveraging Databricks was a smart first step. They also explored various cutting-edge technologies, including DARPA-inspired crawler technology. Invited to build an MVP as a precursor to scaling the solution, we deployed a team to work closely with MMIT to deliver a prototype that captured the required interactions. This was a dramatic first step for a company developing a solution in a rapidly evolving data landscape.

Data, Tech, Teams

Because they endeavored to overhaul their organization and become a modern data company, cross-team unification was critical, refining the vision and immersing themselves in the new processes. The idea was that while we were helping them transform their technological capabilities and data operations overall, we would also help them transform their development and data management approach. When it came to tech, Databricks was the natural choice. This powerful platform enables the processing of large-scale data in a cloud environment. In other words, this project had MMIT and our team working on the cutting edge of data processing and transformation.

Most importantly, we came away with the single, focused goal of creating a state-of-the-art system that leverages cutting-edge technology, including Databricks, custom libraries, and cloud-based hosting to drive autonomous data extraction. It was to be a system to ingest web-based content and transform it into tabular data – all managed through a unified platform. And we were doing it on an accelerated schedule, driving toward a robust solution to revolutionize MMIT’s data operations.

For the data extraction, we enhanced our crawler with additional custom libraries we wrote to add more functionality. We designed reusable pipelines for HTML data processing and integrated the crawler and crawl process into Databricks. Lastly, the entire system was built with scalability and future expansion in mind. All this teaming and tech created an

amazingly effective solution to put autonomous data extraction capabilities in the hands of MMIT.

A Data-Driven Success

Though the industry seems light years (or at least just plain years) from fully-automated data operations, this project is already an achievement. Currently, web-based content is ingested into the system, transformed into tabular data, and delivered to MMIT’s data warehouse—all while feeding the machine learning engine in the background for continual improvement.

Additionally, we formed an early vision of autonomy and created a data extraction solution delivered in a remarkably short time frame. In turn, MMIT has a workable solution much faster than anticipated. Plus, in the same timeline, they were able to pivot from a partially manual, fragmented data collection organization into an automated, unified data company, allowing for continuous innovation at velocity and scale.