Wrangling Customer Usage Data with Hadoop


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At Clearwire we have a big data challenge: Processing millions of unique usage records comprising terabytes of data for millions of customers every week. Historically, massive purpose-built database solutions were used to process data, but weren?t particularly fast, nor did they lend themselves to analysis. As mobile data volumes increase exponentially, we needed a scalable solution that could process usage data for billing, provide a data analysis platform, and inexpensively store the data indefinitely. The solution? A Hadoop-based platform allowed us to architect and deploy an end-to-end solution based on a combination of physical data nodes and virtual edge nodes in less than six months. This solution allowed us to turn off our legacy usage processing solution and reduce processing times from hours to as little as 15-min. This improvement has enabled Clearwire to deliver actionable usage data to partners faster and more predictably than ever before. Usage processing was just the beginning; we?re now turning to the raw data stored in Hadoop, adding new data sources, and starting to analyze the data. Clearwire is now able to put multiple data sources in the hands of our analysts for further discovery and actionable intelligence.

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Wrangling Customer Usage Data with Hadoop

  1. 1. Wrangling Customer Usage Data with Hadoop Clearwire – Thursday, June 27th Carmen Hall – IT Director Mathew Johnson – Sr. IT Manager
  2. 2. Starting With… • …a little ingenuITy!
  3. 3. ingenuITy Day @ Clearwire • Opportunity for everyone in IT to innovate and present new and even crazy ideas • One of those crazy ideas was from Roger Hosto • Roger had the solution for Clearwire’s Big Data problem: Hadoop
  4. 4. But Wait! • Now we had a solution for Big Data • We needed a Big Data opportunity • We had just the thing…
  5. 5. The Perfect Problem • Customer Usage Data – our commodity to Wholesale partners
  6. 6. Totally (un)Wired • Americans used more than 1,304 petabytes of wireless data in 2012 - an increase of 69.3% over the previous 12 months' usage (827 TB) • Clearwire processes over 3B individual usage detail records each month
  7. 7. Shifting Landscape • The U.S. wireless industry is a $195.5 billion enterprise - larger than publishing, agriculture, hotels and lodging, air transportation and movies – just to name a few • Prepaid/Pay-As-You-Go services' share of overall market penetration is 23.4% driving higher exposure of lost revenue if usage delivery is delayed. • In some cases, a customer can consume data faster than we can bill for it
  8. 8. Anatomy Of Latency - Legacy IT Usage Processing ASN GW PTS SPB Wholesale Partners Internet AAA OSS SDU 1 Hour Up to 90 Minutes
  9. 9. Let’s Talk Numbers • Assume a 2GB plan • An HD movie from Netflix consumes 2+ GB per hour • Assume wholesale price = $6/GB • Assume the retail price for a GB of data (as top up or overage) ranges from $20 – $100
  10. 10. As if that wasn’t enough - • Clearwire was locked into a very expensive vendor contract which handled both network provisioning and usage delivery needs • Legacy solution was not adaptable or flexible • We needed something innovative, reliable, internally supportable, scalable – and we needed it fast
  11. 11. Putting ingenuITy to Work! • Roger’s idea was suddenly a project • We needed to build a platform to ingest, process, and provide cleaned usage data for downstream applications – and quickly • We needed: • A Hadoop Cluster • 24x7 Operations • Code to ingest data and handle a myriad of business rules • Integration with legacy and new systems
  12. 12. Atlas was Born • Development work began immediately on Clearwire’s private cloud infrastructure • Selected BigTop Packaging of Apache Hadoop v1.0.1 • Custom code leveraging Hive and other common tools to ingest and process data was written • Infrastructure was built
  13. 13. Hybrid Approach to Hadoop • Virtual Edge Nodes • Leveraged our existing private cloud • Physical Data Nodes • Per Unit Cost (Storage & CPU) was lower than existing infrastructure • Smaller and more efficient than you think • 24 data nodes, each with 3TB of usable storage • Gives us 72TB of usable space • 3x block replication for production data • Deployed identical DR/Analytics platform
  14. 14. Operational in No Time • 2.5 months from project approval to production • Leveraged our existing support organizations • Solution leveraged common tools, did not require specialized teams • Fault tolerance inherent within Hadoop helps us minimize late night calls • An endless supply of data was quickly flowing through the system • The results were looking good!
  15. 15. Real Results • 65% improvement in end to end delivery times • From 2.5 hours to 1.3 hours • Reduced catch up time from upstream outages by more than half • Reduced outage impacts by introducing flexibility to deliver partial files • Eliminated 4 hour weekly usage delivery outages tied to provisioning system maintenance
  16. 16. Anatomy of Latency - Now ASN GW PTS SPB Wholesale Partners Internet AAA OSS SDU 1 Hour Average of 15 Minutes Atlas Medusa ~6 Minutes ~9 Minutes
  17. 17. Real (Financial) Results • 6 month return on investment • Delivered at 1/3 the cost of competing solutions • Foundational – Enabling Wholesale support plan of legacy platform migration • Saving Clearwire 10’s of millions of dollars over life of contract and internalizing support and development
  18. 18. The Intangibles • Proved to internal and external partners that we deliver what we promise with limited negative impacts to ongoing business • This was KEY to the speed at which we were able to migrate our billing platform • Delivered more than just a single, targeted process – delivered an enterprise usage platform to grow from • Kept true to our innovative spirit and the commitment to IT professionals that they can make a difference
  19. 19. Evolution – Proving More The Atlas Hadoop platform is now a go-to IT solution • LTE Usage Data – Now in production • Other Data Sources - ESR Data • Data Replication and real-time ETL • Exploring opportunities with network team to move closer to usage generation • Changing mindset of what IT can mean to an organization
  20. 20. Q & A