Computerlogy is a tech-driven company focused on marketing and data analytics.

Launched in 2009, and now a business of Future Stream Network (FSN), Computerlogy provides social media management and social media listening and analytics products to businesses in sectors such as telecommunications, banking and finance. Its products and services enable companies to undertake market research, understand user preferences and behaviors, and execute marketing campaigns and strategies.

Over its growth from six employees to about 75 employees, Computerlogy has maintained a strong technical focus—about 45 of its workers are developers. Based in Thailand, the company has customers throughout the Asia Pacific region.

The challenge for Computerlogy is to ingest and make sense of huge amounts of diverse, social media data. To support business growth and profitability, it must do so in a manner that is efficient and scalable.

MongoDB Atlas has been a key part of the way Computerlogy has transformed its approach to managing data. It has helped accelerate development speeds, reduced downtime, and enabled the business to cope with constant changes to data formats.

Computerlogy provides end-to-end social media and marketing analytics to customers across the Asia Pacific region. Its services help companies understand their users’ behaviours and preferences, enabling smarter marketing campaigns. Its clients include some of the region’s biggest banks and telcos.

From its early days as a start-up in Sriracha, Chonburi, Computerlogy recognized that the cloud would allow the scale and flexibility to drive international expansion. This led to selection of Google Cloud however, it still needed a versatile platform to manage, store and make use of its data.

“Our business is focused on social media data management and collection. This means we deal with enormous volumes of complex data and need the ability to scale quickly,” explains Vachara Aemavat, Co-founder, Computerlogy. “We then need to pull meaningful insights from that data.”

Computerlogy went to market to find a trusted database that had a number of important attributes.

• Firstly, it needed the flexibility to adapt to data sizes and unstructured data formats because Computerlogy’s applications gather data from multiple social media platforms which are constantly changing.

• Second, with a smaller team the database had to support fast and efficient development.

• Third, the database had to have enterprise grade security and management features to ensure customer data was protected and in compliance at all times.

“Our business faces new challenges each day and our database infrastructure must support those changes,” continues Aemavat. “There is also the challenge of designing applications that are based on the data from various social media platforms, data volumes that can be unpredictable. It's important we use technology that is highly flexible.”

To give its developers the agility to innovate quickly, Computerlogy needed a database that could manage a massive volume of unstructured data yet remain flexible and customizable. That's why the team chose MongoDB Atlas, the most advanced cloud database service on the market.

“Our developer community recommended MongoDB,” says Aemavat. “We initially deployed a community version, but quickly recognized we could do even more than we’d hoped if we used the managed service. We brought in MongoDB’s Professional Services team to plan a migration to the enterprise version.”

MongoDB Atlas provides unmatched data distribution and mobility across all major cloud providers (including Google Cloud) as well as built-in automation for resource and workload optimization. It also includes features such as native sharding – the process of splitting larger datasets across multiple distributed collections, or “shards" – and autoscaling, the ability to automatically scale capacity in response to usage.

“From our prior experiences with databases, the difference with MongoDB Atlas is the flexibility. It can handle all types of unstructured data storage,” adds Aemavat. “Being able to deal with constantly changing data means we can develop new applications that are consistent with our business. MongoDB gets rid of our previous limitations.”

Vachara Aemavat, Co-founder, Computerlogy

Computerlogy worked closely with MongoDB’s Professional Services team to create the new database environment, which consists of nine nodes, three shards and two replica sets. There is a total of 6TB of data stored across 1.5bn documents. The system averages around 8,000 reads per second and 35,000 writes per second but could scale to significantly more than that if required.

“We’ve enlarged our infrastructure and data collection capabilities. This means we can query data faster, and achieve bigger results. It makes real-time insights realistic for our customers,” explained Jakkris Kietpermsak, one of the co-founders and the CTO of Computerlogy.

Moving to MongoDB Atlas helped the team in multiple ways including resolving performance issues and giving access to powerful monitoring tools. Most importantly, explained Kietpermsak, it also meant the development team had more of its most precious resource: time. “We could focus on developing features in the application layer, rather than spending our time managing the database layer,” he added.

Computerlogy now enjoys a more robust and versatile data platform, which ensures zero downtime, freeing up internal resources to focus on more important tasks, such as building new features and responding to customer requests. Furthermore, the flexibility of the MongoDB architecture encourages the company to find new revenue opportunities.

Aemavat says faster database recovery results in a 20 percent reduction in downtime. Rather than directing efforts into fixing problems, Computerlogy is now better able to focus on developing new opportunities: “We are now able to focus on building valuable new features that actually serve our customers and improve revenue.,” he adds. “This is only possible because we’ve freed up the time of our developers from spending time on monitoring and maintaining our infrastructure.”

With MongoDB Atlas in place, Computerlogy is well placed to continue its evolution and build stable applications with a focus on low maintenance resources, such as serverless architecture. It also intends to enhance the in-depth analytics of data by using machine learning via Google Cloud Platform.

“MongoDB’s future-ready technology is ideal for creating new applications for social media because it accommodates various types of data, and is optimized for rapid change,” concludes Aemavat. “It is probably the best cloud database for any social application on Google Cloud, which suits a data analytic company like ours.”

Vachara Aemavat, Co-founder, Computerlogy