"Using my network, I reached out to some FlyData customers and found that FlyData was highly recommended by all of them."
Tradesy enables women to maximize both their personal fashion and their personal finances. The mobile and online clothing resale service launched in October 2012. And has since grown from a nimble, seven-person startup to a 90-person company that is a hip destination for millions of users eager to buy and sell clothes. Tradesy’s innovation lies in its model: by directly connecting women’s closets, Tradesy lets users upgrade their looks and turn clothes they don’t use into cash. As with all user-driven platforms, Tradesy gives control of data input to its users. Though this is a huge benefit to the users, it also makes database management more challenging. Between Tradesy’s rapid company growth and the huge volume of data in its system, it has extremely high standards for how well its developers can navigate its systems.
As Tradesy is a high transaction online resale business, their business intelligence team create reports and run queries on this data on a constant basis. The key business metrics are analyzed and presented to their team where critical decisions are made by multiple stakeholders. The decisions made by the data being reported helps Tradesy get a competitive advantage in the fast-growing marketplace of how people buy and sell fashion.
The complexity of this process looks trivial from the outsider’s perspective. However, Tradesy’s DevOps Manager, Jorge Documet says “Handling the frequent schema changes resulted in mismatched data. Working to resolve this every few months was not ideal.” Running a second MySQL database just for business intelligence was less desirable due to the frequency that they add and remove tables. That’s when Jorge and his team decided to look for a more scalable solution to perform the data migration. Because Tradesy is already hosted on Amazon Web Services and use other AWS applications, it was a no brainer to choose Amazon Redshift as their clear choice for data warehousing. Amazon Redshift allowed them to make sure their production databases are not affected while their BI team is running queries.
Through some searching on the AWS Marketplace for a data replication solution, Jorge came across FlyData as an option. Using his network, he reached out to some FlyData customers and found that FlyData was highly recommended by all of them. Jorge chose to use FlyData SaaS offering as to avoid the need to host any tools or services. The process of setting up the sync was straightforward and Tradesy was up and running with minimal effort. “I look for solutions that requires the least amount of movement. Setting up FlyData was a painless process. Uncommon when it comes to developer tools,” says Jorge. “FlyData’s support staff was very knowledgeable and all issues were resolved in a timely manner.” The simplicity of the setup and reliable service of FlyData was able to free up a lot of precious developer hours that was previously used to monitor the syncing of data. The developers are now more focused on building products. Jorge sleeps better at night.
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