Big Data Things

Of late, a lot of people have asked me for tips on how to crack Data Engineering interviews at FAANG (Facebook, Amazon, Apple, Netflix, Google) or similar companies. I have not worked at all of these companies so I can't share tips which will necessarily apply for all of them but I will share tips which can be generalized for most of the big companies. First of all, the field of Data Engineering has expanded a lot in the last few years and has become one of the core functions of any big technology company. The obvious reason for this expansion is the amount of data being generated by devices and data-centric economy of the internet age. Each company is focussed on making the best use of data owned by them by making data driven decisions. If you compare this to the Data Engineering roles which used to exist a decade back, you will see a huge change. In the past, Data Engineering was invariably focussed on Databases and SQL. Even now, these two form some part of most Data Engin...

In this post, I will try to share some actual questions asked by top companies for Data Engineer positions. A lot of these companies will cover data modelling as one of the rounds and will use the data model for the next round based on SQL queries. Q1: Find the number of drivers available for rides in any area at any given point of time. Q2: Do you consider Driver and Rider as separate entities? Why or why not? Q3: Give me all passenger names who used the app for only airport rides. Q4: How will you decide where to apply surge pricing? Q5: How will you calculate wait times for rides? Q6: A driver can ride multiple cars, how will you find out who is driving which car at any moment? Q7: Find out Rank without using any function. Q8: How will you delete duplicates from a table? Q9: How will you find percentile? Q10:  You have 3 tables, user_dim (user_id, account_id), account_dim (account_id, paying_customer), and dload_facts (date, user_id, and downloads), fin...

In this post, I will try to cover the data architecture Uber has built to support their big data applications. This should be applicable for other ride hailing apps as well. There are multiple modules at play here so I will try to give a brief overview along with a detailed discussion on the data architecture.  An aeroplane view of the problem tells us that effectively we are trying to solve a demand vs. supply problem. All the Drivers active at any point of time constitute the supply while all the cab requesters (Riders) form the demand. What Uber tries to do is to have the best matching between demand and supply "at any point at any moment". I have highlighted the location and temporal conditions as they are critical for the success of what Uber tries to do. Let's get started to see how Uber comes around these challenges by creating a scalable data architecture. Every Driver active on Uber keeps sending his location data to the server (e.g. every 5 seconds). T...