AppLift Tech Blog
Apache Spark ecosystem has revolutionised the Big Data processing world with its unique unified programming model. This has made Spark a must-have technology for companies such as AppLift, which rely heavily on open source technologies for its data engineering and data science needs.
By Chandan Prakash in Development — April 19th, 2017
AppLift specializes in delivering data-driven performance-optimized mobile app install campaigns. Some of the problems that we come across when solving these problems, include click-through-rate prediction (rate at which to expect clicks given that we have shown an impression), conversion-rate-prediction (rate at which to expect conversions or app-installs given that we have a click on the impression shown) and fraud-traffic classification.
By Sunil Nandihalli in Backend — May 26th, 2016
At AppLift, we are using Apache Storm to build Real Time Events Processing Pipeline for analyzing, aggregating, frequency capping and most importantly taking budget decisions in real-time per Ad-campaign with least possible latency. The events data is streamed to Apache Storm in real-time; via in-house built publisher-subscribers mechanism using ZMQ; where events are processed and persisted.
By Servesh Jain in Backend — May 26th, 2016
At AppLift we use Apache Spark for quite a few data pipelines, and we are big fans of both Spark and Scala. That said, recently we were a little bit puzzled after getting exceptions while trying to upgrade to the recently released version 1.5.2 - even more so because the exceptions were stemming not from misusing the Spark API itself but rather something more fundamental: JDBC.
By Bruno Wozniak in Backend — March 11th, 2016
The tech team at PubNative have come up with a nifty new Go MySQL driver that creates less objects in the heap, resulting in a lower Garbage Collection time. It’s really easy to use. Check out the readme, try it out, and give them your feedback!
By Eyal Golan in Backend, News — February 12th, 2016
Here at AppLift we have multiple internal tools for managing our ad campaigns. Our methods don't deviate much from well-known practices, we prefer small autonomous components with explicit interface and we try to re-use as much code as possible. This post will be about these and other ideas that worked well for us.
By Vladimir Feskov in Frontend — January 28th, 2016