Digital Advertising on AWS - Pop-up Loft Tel Aviv
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Digital Advertising on AWS summarizes trends in digital advertising and examples of how AWS solutions can help. The document discusses how the industry is fragmented with many intermediaries, and areas of high growth like mobile, real-time bidding (RTB), big data, and video. It provides examples of AWS solutions that help customers with RTB by optimizing compute scaling, data access, and networking. Another solution ingests 150TB of data daily for RTB data collection. The document advocates working with customers to identify "undifferentiated heavy lifting" that AWS can solve through industry solutions.
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Digital Advertising on AWS - Pop-up Loft Tel Aviv
- 1. Digital Advertising on AWS Dmitri Tchikatilov AdTech BD, AWS dmitrit@amazon.com
- 2. Agenda 1. Goals and how AWS works with AdTech 2. Observations and latest trends 3. Industry solutions approach 4. Examples
- 3. What's dangerous is not to evolve... Jeff Bezos
- 4. Goals for Industry Business Development Better understand our customers business and technical challenges. Collect the data and draw conclusions how AWS can help with solutions. Evolve. Accelerate change and new product development at AWS through solutions.
- 5. Advertiser Solutions Ad Networks and Exchanges Publisher Solutions Content Publishers Brands and Marketers Data Providers Digital Advertising Industry Scope for AWS Industry Scope
- 6. Observations 1. Industry is very fragmented – barrier to enter is low 2. Value chain from Advertiser to Publisher is complex – too many intermediaries (take about 60% of the Advertising $$$) 3. Overall market is growing - $170B in 2015 to over $200B in 2017 4. Some areas ground much faster – Mobile, Video, RTB
- 8. Mobile US – Market is very fragmented Smaller companies are AWS customers High growth (40% YoY) Very competitive
- 9. RTB High growth YoY Ad Exchanges Low margin - efficient operators have advantage
- 10. Video High growth, high eCPMs Fraud issues Ad blocking Server side ad stitching New VAST 3.0 is just published
- 11. Industry Solutions Dec 2006 Web 2.0 Summit – Tim O’Reilly interviewed Jeff Bezos and Jeff mentioned “undifferentiated heavy lifting” Industry solutions – try to identify new “undifferentiated heavy lifting” and solve the problem with AWS.
- 12. Some Examples: Real Time Bidding Differentiators Algorithms for RTB ML Models Heavy Lifting Scaling EC2 instances Low latency NoSQL Heavy Lifting Data access libraries Optimizing Networking
- 13. Example RTB – Cost Curve Do I need co- located bidders? Do I need a dedicated networking team? Do I need to own networking equipment? $0 $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 1 2 5 7 10 USD Time (ms) Monthly RTB Fleet Spend vs. Roundrip Exchange Latency (ms)
- 14. Customer Perspective on RTB Solutions Customers have very different perspectives on what their business considers as competitive advantage What is your competitive advantage? What do you consider shareable knowledge? Customer 1 Customer 2 Customer 3 More advanced customers are sharing more and raising the HL bar HL HL HL
- 15. “We run the RTB platform on more than 2,500 machines, approximately eight hours a day globally, at a cost of less than $0.05 per day per machine...” “Because we’re running on AWS, we’re able to focus 95 percent of our staff on new product development. Using AWS allows us to focus on innovating our platform and solving customer problems.” Valentino Volonghi, CTO AdRoll Example: Enabling Real Time Bidding Advertiser Solutions
- 16. Example: RTB Data Collection Improved speed (mins to secs), simplicity & cost reduction Reducing data latency to seconds Ingesting approximately 150TB daily
- 17. Big Data This business is all about data – competitive advantage Everyone does it, but still easy to overspend Apache Spark Druid Analytics on streaming data
- 18. Solution Example: Ad Exchanges Outside AWS 18 Equinix AdIX AWS Customer Ad Exchange Provider Ad Exchange Provider Ad Exchange Provider Equinix AdIX Ad Exchange Provider Ad Exchange Provider Ad Exchange Provider Ashburn New York Partner provides: Channel on NNI (<1GB) or Dedicated port (>1GB) Private IP Public IP Public IP DX Partner DX Partner Reduce AWS traffic spend ~25% Predictable latency (vs. Internet) Reduced latency
- 19. Solution Example: Druid + Spark Everyone knows and loves Apache Spark Druid is less popular but was “quietly” implemented in a number of leading Advertising companies Druid is an real time OLAP engine and can be used together with Apache Spark
- 20. Druid Architecture Real Time Nodes Broker Nodes Historical Nodes Hand Off Data Segments Query API Real Time Data
- 21. Druid + Spark Solution Technology Improves query performance Uses Druid index under RDD, no change to Spark user experience Uses open source library by Sparkline Data to rewrite the queries and redirect to Druid.
- 22. Druid + EMR (Spark) Demo
- 23. Solutions Ideas and Feedback 1. RTB 2. Ad Exchange Connectivity 3. Low latency user stores 4. Data Science • Advertiser problems on sample data – attribution examples, etc. • Streaming data analytics (Kinesis + EMR/Spark) 5. Mobile (SDK IAB compliance, mobile services etc.) 6. Ad content delivery 7. Cost savings with EC2 Spot
- 24. Dmitri Tchikatilov Digital Advertising BD dmitrit@amazon.com
Hinweis der Redaktion
- If we look at the advertising value chain – all the companies between the Marketers and Content Publishers, we see that AWS is enabling clients with wide variety of business models. On the advertiser side of the business (Advertiser Solution Providers) we see Ad Agencies like Razorfish, Demand Side Platforms like DataXu and on the publisher side we see Publisher Solution Providers or Supply Side Platforms like Fiksu and Zedo. We also have a very active group of advertising networks ad exchanges rapidly growing on AWS especially in Mobile and Video segments. Our clients see the value of significantly improving the economics of their business by using AWS. In addition, having multiple companies co-exist in the same AWS Regions creates a number of new opportunities for B2B collaboration between them. Digital Advertising business frequently requires low latency communications as well as data analytics on Petabyte size data sets. These scenarios are becoming very popular and easily deployable on AWS.
- Customers find their operating ranges they are comfortable with and make economic sense
- More advanced customers are sharing more and elevating overall knowledge You can see that in other industries too
- NO Undifferentiated heavy lifting – Compare and contrast with traditional companies A great example of stunning growth and success is a company from California called AdRoll. AdRoll is an Advertiser Solution Provider and global leader in retargeting with more than 10,000 active advertisers across over 100 countries. The company is razor focused on delivering new business functionality to their clients and leaving the heavy undifferentiated lifting to AWS. What sets this company apart is significant simplification of the infrastructure which is achieved through latest advancements in several AWS technologies. Good question to think about - how many people in your organization are focused on new product development vs. maintenance of the existing solutions? Video (3 mins) https://aws.amazon.com/solutions/case-studies/adroll/
- Here is an example of large scale data ingest solution implemented by AdRoll The key benefit is reducing data latency and insight from minutes to seconds. It implements a streaming service called Kinesis which received the data from multiple sources and makes it available for real time batch and real time applications. Batching records to save $ in Kinesis Removing large numbers of small files Ingesting approximately 150TB daily AdRoll Kinesis ingest http://tech.adroll.com/blog/data/2015/06/26/kinesis.html
- Stream is coming in Real time nodes – buffer them – bring in data and once in a while they hand off to historical data nodes Brokers – you query them – they understand how to locate the data Real time node – periodic buffer into the disk Real time node queries into Segment – is sent to the Deep Storage/historical node Availability – how is it handled (failure scenarios) Losing the process and losing the disk Losing the process – bring it back up what it has seen drom the disk Lose process and the disk – that data did not make it to the deep storage Features of the message bus – how do you deliver the message to the bus – be able to replay the messages Replicate the feed onto multiple machines. Need to take data from Kafka (read back from) Configure multiple machines to handle the same data – to do HA on the real time node