Reactive Applications with AWS Lambda

Sometimes you may find yourself requiring a CRON script to clean a file, or maybe you need to watch a directory of images to create preview thumbnails when they arrive on the server. Processes like these suffer from the same limitation; they require you to poll a script until you get a “successful” result.

This is problematic because it forces the developer to write redundancy checks in the code instead of just focusing on the core problem. Moreover, file watching utilities generally notify once the file is created, not when the file is finished writing. All of these problems must be accounted for, and result in more complexity, overhead, and development time.

This is where event driven programming can greatly reduce your development overhead. Part of that is maintaining a centralized data lake for all of your raw files. Data lakes generally maintain an event API for easy management and access of files within the lake. In our case, Amazon S3 is the data lake of choice and thanks to AWS Lambda we can hook into the S3 event API with minimal effort for simple use cases like cleaning files.

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Moving One Billion Rows in MySQL (Amazon RDS)

So you may remember from our article in November of 2014 about our switch to Redshift, that Koddi uses Amazon Web Services (AWS) to power our platform. While we have moved some of our data to Redshift, we still have quite a bit in MySQL (RDS), and at the beginning of this year we needed to move our main database from one AWS account to another. The normal process when creating a copy of a database in RDS is to take a snapshot and spin up a new database from this snapshot. However, Amazon doesn’t allow you to share snapshots between accounts. This posed the question, how do we efficiently migrate over a billion rows of data?

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