Abhishek Tiwari

JULY 12, 2018

Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.

Latency 63

Latency Analytics Scalability Engineering 63

The Netflix TechBlog

MAY 26, 2020

And in order to gain visibility into these logs, we need to somehow ingest and enrich this data. It is easier to tune a large Spark job for a consistent volume of data. In other words, we are able to ensure that our Spark app does not “eat” more data than it was tuned to handle. We named this library Sqooby.

Network 150

Network Tuning AWS Big Data 150

The Netflix TechBlog

MARCH 25, 2019

We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management.

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219

Abhishek Tiwari

JULY 20, 2019

As Steve Jobs wisely said, Don’t Be Trapped by Dogma – Which is Living With the Results of Other People’s Thinking In my view, technology executives and engineering leaders are overly obsessed with the Spotify model. Specialisation could be around products, business process, or technologies. And there lies the problem.

Engineering 61

Engineering Retail Airlines Healthcare 61