Adobe Target always provided best-in-class experimentation and personalization. Our edge network is geographically distributed and we have points of presence in different parts of the world. This allows us to be closer to our customers' users, but sometimes this is not enough since fundamentally Target always required a network call to retrieve personalized content.
We always knew that this is could be problematic for some of our customers who are looking for near-zero latency for experimentation and personalization. In November 2020 Adobe Target launched NodeJS SDK and Java SDK with On-Device Decisioning capabilities. In a nutshell, On-Device Decisioning allows you to evaluate Target activities on-device avoiding a network roundtrip. For more details please check the official documentation here.
Adobe Target On-Device Decisioning, while great for server-side use cases where you can use one of our SDKs like NodeJS, Java, and soon Python and .NET, can also be used in a serverless setup.
Java and C# are awesome languages, but usually, in a serverless setup, we prefer something a little bit more lightweight like NodeJS or Python. We already mentioned that Adobe Target has an edge network, but it is incomparable to Edge Computing Platforms aka CDNs like AKamai, AWS Cloudfront, or Cloudflare.
This blog is Part 1 In this three-part series that will cover how anyone could use Adobe Target NodeJS SDK to run experimentation and personalization on an edge compute platform. The parts are:
Part 1: Akamai Edge Workers and Adobe Target NodeJS SDK
Part 2: AWS Lambd@Edge and Adobe Target NodeJS SDK
Part 3: Cloudflare Workers and Adobe Target NodeJS SDK