![select
dateint,
hour,
count(distinct other_properties['CUSTOMER_ID']) signups
from default.start_membership_event
where other_properties['SIGNUP_COUNTRY']='NL'
and other_properties['IS_TESTER']='false'
and ( dateint >= 20130911 OR (dateint = 20130910 AND hour >= 22) )
and ( dateint <= 20130916 OR (dateint = 20130917 AND hour < 22) )
group by
dateint,
hour](https://image.slidesharecdn.com/nflxmstrawspresentationnetflixportion-170825200420/85/2013-Netflix-MicroStrategy-Amazon-AWS-Webinar-16-320.jpg)
2013 Netflix / MicroStrategy / Amazon AWS Webinar
- 1. Albert Wong - Manager Reporting Platform Big Data Visual Analytics Oct 2013 Netflix Case Study Netflix Data Platform
- 6. S3
- 7. Honu
- 16. select dateint, hour, count(distinct other_properties['CUSTOMER_ID']) signups from default.start_membership_event where other_properties['SIGNUP_COUNTRY']='NL' and other_properties['IS_TESTER']='false' and ( dateint >= 20130911 OR (dateint = 20130910 AND hour >= 22) ) and ( dateint <= 20130916 OR (dateint = 20130917 AND hour < 22) ) group by dateint, hour
Editor's Notes
- #2: Hi, my name is Albert Wong I am in charge of the reporting platform at Netflix And today, we'll take a look at how we've set up EMR, Overview where it's used in our data platform, And go over how MicroStrategy plugs into this architecture Lastly, we'll end with a demo, illustrating how we get data from EMR to MSTR
- #3: If you are new to Netflix, we are a tv/movie content streaming business When you log in, we display content you can watch on demand If you're a kid, we provide a section for kids content next to 'watch instantly' at the top And, we also have a DVD service Now, clicking on one of the shows below does 2 things First, you get to watch the show Second, we, at the data platform, get data to analyze
- #4: Simplified view of our of data pipeline
- #5: Full view of our data platform Complicated at first glance, but easy to understand when we take the time to break it down Let’s break it down
- #6: Data platform when we were mainly a DVD company
- #7: Infinite hard drive Low cost to store our data reliably Stands for simple storage service So what do we store?
- #8: Our event data pipeline We get streaming events (e.g. when you start a movie/show, stop, pause, resume) Branch off of similar technology called Chukwa conceived at Yahoo!, then modified to meet our needs
- #9: Where we store our dimension data (e.g. titles, user accounts) Open source dbms, distributed reads quickly, writes, quickly And we’ve replaced Oracle with it
- #10: Review Keep in mind, we eventually want this data to be reported out of MSTR
- #11: Needed something to process our increased data volumes Hadoop fit the bill It’s a framework for processing large data sets Full discussion on how hadoop works out of scope for today But one thing to highlight is it’s designed to scale – if we need to process more data, we just add more servers And EMR allows us to add more servers with relative ease
- #12: Pig is an interface which makes it easy to write code to be executed within the hadoop framework to extract data Python is a high level programming language that we use to aid in data transformation
- #13: Hive, like pig makes it easy to write code to execute code within the hadoop framework The language resembles SQL We use it for adhoc queries and creating aggregate/summary tables
- #14: Review once again, Cassandra, and Honu data land in S3 For Cassandra, have an intermediary data extraction step before S3 On the right, we have hive, pig and python being used to process and aggregate data for reporting We then move that down to Teradata and then into MicroStrategy for reporting Lots of steps, what if we want to just explore data without processing, skip the ETL process?
- #15: Spin up a hive server in EMR Configure MicroStrategy to talk to it Query data directly out of AWS