Notes about Cutting-Edge Technologies and Everything

Lyft was a late entrant to the ride-sharing business model, at least compared to its competitor Uber, which pioneered the concept and remains the largest provider. That delay in starting out actually gave Lyft a bit of an advantage in terms of architecting its big data infrastructure in the cloud, as it was able to sidestep some of the challenges that Uber faced in building out its on-prem system. Lyft and Uber, like many of the young Silicon Valley companies shaking up established business models, aren’t shy about sharing information about their computer infrastructure. They both share an ethos of openness in regards to using and developing technology. That openness is also pervasive at Google, Facebook, Twitter, and other Valley outfits that created much of the big data ecosystem, most of which is, of course, open source. So when the folks at Lyft were blueprinting how to construct a system that could do all the things that a ride-sharing app has to do – tracking and connectin...

Introduction One of the most popular and effective enterprise case-studies which leverage analytics today is log analytics. Almost every small and big organization today have multiple systems and infrastructure running day in and day out. To effectively keep their business running, organizations need to know if their infrastructure is performing to its maximum potential. This involves analyzing system and application logs and maybe even apply predictive analytics on log data. The amount of log data is typically massive, depending on the type of organizational infrastructure and applications running on it. Gone are the days when we were limited by just trying to analyze a sample of data on a single machine due to compute constraints. Powered by big data, better and distributed computing, big data processing and open-source analytics frameworks like Spark, we can perform scalable log analytics on potentially millions and billions of log messages daily. The i...