Big Data & The Cloud


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Joe Ziegler's presentation at the 5th Elephant conference in Bangalore.

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  • The more misspelled words you collect, the better is your spellcheck application
  • Data volume. As the data volume increases, it becomes increasingly difficult to process the data. Easy for 1 box: Harder for many boxes. When the data exceeds the capacity of one place.Data structure. Data comes in variety of formats from logs files to database schema to images. The diversity in data structures and format grows as well. To analyze this data holistically it is required to consolidate data across multiple data sources and multiple formats. Since valuable data comes from various companies like facebook, and linked-in it is also required to consolidate data across businesses.
  • According to IDC, 95% of the 1.2 zettabytes of data in the digital universe is unstructured; and 70% of of this is user-generated content. Unstructured data is also projected for explosive growth, with estimates of compound annual growth (CAGR) at 62% from 2008 - 2012.ChallengesUnconstrained growth
  • Finally complexity increases because demands on data are changing. Business requires faster response time on fresher data. Sampling is not good enough, history is important. Did the customer purchase something in February because his friend has a birthday or because it was a valentine's day – this information can help figure out how to help this customer next February. SQL is simply not enough to drive some of the answers. Data scientist require access to other statistical tools or other programing languages. Finally and most importantly users demand inexpensive experimentation. Often times we don’t know what products or facts will come out of our analytics so we cannot justify large upfront investment.
  • Computers typically generate data as byproduct of interacting with people or other with other device. The more interactions, typically there is more data. This data comes in a variety of formats from semi-structured logs to in unstructured binaries. This data can be extremely valuable. It can be used to understand and track application or service behavior so that we can find errors or suboptimal user experience. We can mind it for patterns and correlations to generate recommendations.For example ecommerce sites can analyze user access logs to provide product recommendations, social networking sites provide new friends recommendations, dating sites find qualified soul mates, and so fourth.
  • Big data is important.
  • Now the Philosophy around data has changed. The philosophy is collect as much data as possible before you know what questions you are going to ask and most importantly you don't know which algorithms you are going to ask because you don't know what type of questions I might need in future. The ultimate mantra of collect and measure everything. How you are going to refine those algorithms, how much data, how much processing power, you really don't know how much resources you really need. Big data is what clouds are for. Its Big data analysis and cloud computing is the perfect marriage.Free of constraintsCollect and Store without limitsCompute and Analyze without limitsVisualize without limites
  • Data is the next industrial revolutionToday, the core of any successful company is the data it manages and its ability to effectively model, analyze and process that data quickly – almost in real time - so that it can make the right decision faster and rise to the top.
  • These resources are even more precious because of the rarity of skills.
  • Our goal, and what our customers tell us they see, is that this ratio is inverted after moving to AWS. When you move your infrastructure to the cloud, this changes things drastically. Only 30% of your time should be spent architecting for the cloud and configuring your assets. This gives you 70% of your time to focus on your business. Project teams are free to add value to the business and it's customers, to innovate more quickly, and to deliver products to market quickly as well.
  • Our goal, and what our customers tell us they see, is that this ratio is inverted after moving to AWS. When you move your infrastructure to the cloud, this changes things drastically. Only 30% of your time should be spent architecting for the cloud and configuring your assets. This gives you 70% of your time to focus on your business. Project teams are free to add value to the business and it's customers, to innovate more quickly, and to deliver products to market quickly as well.
  • There are many patterns of usage that make capacity planning a complex science. From on and off usage patterns, where capacity is only needed at fixed times and not at others, fast growth where an online service becomes so successful that step changes in traditional capacity need to be added, variable peaks - where you just don't know what demand will be when and best guess applies, to predictable peaks such as during commute times as customers use mobile devices to access your service.
  • Each of these examples is typified by wasted IT resources. Where you planned correctly, the IT resources will be over provisioned so that services are not impacted and customers lost during high demand. In the worst cases, that capacity will not be enough, and customer dissatisfaction will result. Most businesses have a mix differing patterns at play, and much time and resource is dedicated to planning and management to ensure services are always available. And when a new online service is really successful, you often can't ship in new capacity fast enough. Some say that's a nice problem to have, but those that have lived through it will tell you otherwise!
  • Elasticity with AWS enables your provisioned capacity to follow demand. To scale up when needed and down when not. And as you only pay for what is used, the savings can be significant.
  • You control how and when your service scales, so you can closely match increasing load in small increments, scale up fast when needed, and cool off and reduce the resources being used at any time of day. Even the most variable and complex demand patterns can be matched with the right amount of capacity - all automatically handled by AWS.
  • Vertical scaling on commodity hardware. Perfect for Hadoop.
  • New model is collect as much data as possible – “Data-First Philosophy”Allows us to collect data and ask questions laterAsk many different kinds of questions
  • And scale is something AWS is used to dealing with. The Amazon Simple Storage Service, S3, recently passed 1 trillion objects in storage, with a peak transaction rate of 750 thousand per second. That's a lot of objects, all stored with 11 9's of durability.
  • And just like an electricity grid, where you would not wire every factory to the same power station, the AWS infrastructure is global, with multiple regions around the globe from which services are available. This means you have control over things like where you applications run, where you data is stored, and where best to serve your customers from.
  • Global reach (North Pole, Space)Native app every smartphoneSMSwebmobile-web10M+ users, 15M+ venues, ~1B check-insTerabytes of log data
  • Bank at least 400,000 simulations to get realistic results.23 hours to 20 minutes and dramatically reduced processing, with the ability to reduce even further when required.Bankinter uses Amazon Web Services (AWS) as an integral part of their credit-risk simulation application, developing complex algorithms to simulate diverse scenarios in order to evaluate the financial health of their clients. “This requires high computational power,” says Bankinter Director of New Technologies Pedro Castillo. “We need to execute at least 400,000 simulations to get realistic results.”
  • One result of such experimentation is Taste Test which is a recommendations product that helps Etsy figure out your tastes and to offer you relevant products. It works like this, you see 6 images at a time and you pick an image you like the most. You iterate through these sets of images a few times (you can also skip a set if you don’t like any images) and after a few iterations, Etsy displays the products that are most relevant to you. I encourage you to try – it’s a lot of fun.Today, Etsy uses Amazon Elastic MapReduce for web log analysis and recommendation algorithms. Because AWS easily and economically processes enormous amounts of data, it’s ideal for the type of processing that Etsy performs. Etsy copies its HTTP server logs every hour to Amazon S3, and syncs snapshots of the production database on a nightly basis. The combination of Amazon’s products and Etsy’s syncing/storage operation provides substantial benefits for Etsy. As Dr. Jason Davis, lead scientist at Etsy, explains, “the computing power available with [Amazon Elastic MapReduce] allows us to run these operations over dozens or even hundreds of machines without the need for owning the hardware.”Dr. Davis goes on to say, “Amazon Elastic MapReduce enables us to focus on developing our Hadoop-based analysis stack without worrying about the underlying infrastructure. As our cycles shift between development and research, our software and analysis requirements change and expand constantly, and [Amazon Elastic MapReduce] effectively eliminates half of our scaling issues, allowing us to focus on what is most important.”Etsy has realized improved results and performance by architecting their application for the cloud, with robustness and fault tolerance in mind, while providing a market for users to buy and sell handmade items online.
  • Another example of such innovation is gift ideas. A lot of us struggle to pic the right present for our friends and so Etsy has a product that makes it easier. Etsy looks at your facebook social graph and learns about your interests and those of your friends. It uses this information to give you ideas for presents. For example, if your friend is an REM fan, Etsy may suggest a t-shirt with REM print on it.These innovative data products are just a few examples of innovation that is possible if we lower the cost barriers for data experimentation.
  • Yelp is also doing product recommendations based on location, people reviews, or people searches. For example, “people who viewed this, viewed that” feature can help customers discover other relevant options in the area. People can discover interesting facts about places with “People viewed this after searching for that” feature. In this example, the westin hotel probably has glass elevators and is likely offers the best location to stay in san francisco at least by some definition of best.There is also “review highlights” feature. Yelp analyses written reviews and provides highlights about the places, so that their customers don’t have to read through all the reviews to get basic ideas about the place. All these differentiating features were possible because of Hadoop and flexible infrastructure for data processing.
  • 500% increase in returns for advertising.Pedabytes of storage.Thereis a lot of data the retail business has about the users, it’s just never used it in advertising.For example, the retail knows that the customer has purchased a sports movie and is currently searching for video games, so it may make sense to advertise a sports video game for the customer.Efficient: Elastic infrastructure from AWS allows capacity to be provisioned as needed based on load, reducing cost and the risk of processing delays. Amazon Elastic MapReduce and Cascading lets Razorfish focus on application development without having to worry about time-consuming set-up, management, or tuning of Hadoop clusters or the compute capacity upon which they sit.Ease of integration: Amazon Elastic MapReduce with Cascading allows data processing in the cloud without any changes to the underlying algorithms.Flexible: Hadoop with Cascading is flexible enough to allow “agile” implementation and unit testing of sophisticated algorithms.Adaptable: Cascading simplifies the integration of Hadoop with external ad systems.Scalable: AWS infrastructure helps Razorfish reliably store and process huge (Petabytes) data sets.The AWS elastic infrastructure platform allows Razorfish to manage wide variability in load by provisioning and removing capacity as needed. Mark Taylor, Program Director at Razorfish, said, “With our implementation of Amazon Elastic MapReduce and Cascading, there was no upfront investment in hardware, no hardware procurement delay, and no additional operations staff was hired. We completed development and testing of our first client project in six weeks. Our process is completely automated. Total cost of the infrastructure averages around $13,000 per month. Because of the richness of the algorithm and the flexibility of the platform to support it at scale, our first client campaign experienced a 500% increase in their return on ad spend from a similar campaign a year before.”
  • Big Data & The Cloud

    1. 1. Amazon Web ServicesBig Data and the Cloud : A Best Friend Story
    2. 2. Joe ZieglerTechnical @jiyosub
    3. 3. Characteristics of Big Data How the Cloud Is Big Data’s Best Friend Big Data on the Cloud In the Real World
    4. 4. Characteristics of Big Data
    5. 5. BIG DATA When your data sets become so large that you have to startinnovating how to collect, store, organize, analyze and share it
    6. 6. Bigger Data isBetter Data
    7. 7. Features driven by MapReduce
    8. 8. Bigger Data isHarder Data
    9. 9. Big Data is Getting Bigger 2.7 Zetabytes in 2012 Over 90% will be unstructured Data spread across a wide array of silos
    10. 10. Why is Big Data Hard (and Getting Harder)? Changing Data Requirements Faster response time of fresher dataSampling is not good enough & history is important Increasing complexity of analytics Users demand inexpensive experimentation
    11. 11. Where is it Coming From?Computer Generated Human Generated• Application server logs • Twitter “Fire Hose” 50m (web sites, games) tweets/day 1,400% growth• Sensor data (weather, per year water, smart grids) • Blogs/Reviews/Emails/Pict• Images/videos (traffic, ures security cameras) • Social Graphs: Facebook, Linked-in, Contacts
    12. 12. The Role of Data is Changing
    13. 13. Until now, Questions you ask drove Data model New model is collect as much data as possible – “Data-First Philosophy”
    14. 14. Data is the new raw material forData is the new raw material for onbusiness on par any business any par with with capital, people, labor capital, people, labor
    15. 15. We Need Tools Built Specifically for Big Data
    16. 16. Hadoop• Scale out Easily • Solves some Problems• Parallel Computing • Complex to Run• Commodity Hardware • Special Skills to Maintain
    17. 17. How the Cloud IsBig Data’s Best Friend
    18. 18. How do we define the cloud? By Benefits!
    19. 19. No Cap Ex Pay Per Elasticity Use CloudFast Time to Market Focus on core competency
    20. 20. Why is the CloudBig Data’s Best Friend
    21. 21. We know we want collect, store,organize, analyze and share it.But we have limited resources.
    22. 22. The Cloud OptimizesPrecious IT Resources i.e. Skilled People
    23. 23. “Over the next decade, the number of files or containersthat encapsulate the information in the digital universewill grow by 75x.While the pool of IT staff available to manage them willgrow only slightly. At 1.5x” - 2011 IDC Digital Universe Study
    24. 24. Deploying a Hadoop cluster is hard
    25. 25. Cloud computing 30% 70%The Old Using Big Managing All of theIT World Data “Undifferentiated Heavy Lifting”
    26. 26. Cloud computing 30% 70% The Old Using Big Managing All of the IT World Data “Undifferentiated Heavy Lifting” Configuring Cloud-Based Analyzing and Using Big Data CloudInfrastructure Assets 70% 30%
    27. 27. ManagedReusability Services Scale Innovation
    28. 28. ManagedReusability Services Scale Innovation
    29. 29. ManagedReusability Services Scale Innovation
    30. 30. ManagedReusability Services Scale Innovation
    31. 31. ManagedReusability Services Scale Innovation
    32. 32. The Cloud OptimizesCapacity Resources
    33. 33. Elastic Compute Capacity On and Off Fast Growth Variable peaks Predictable peaks
    34. 34. Elastic Compute Capacity WASTE On and Off Fast Growth Variable peaks Predictable peaks CUSTOMER DISSATISFACTION
    35. 35. Elastic Compute CapacityCapacity Traditional IT capacity Elastic cloud capacity Time Your IT needs
    36. 36. Elastic Compute Capacity On and Off Fast Growth Variable peaks Predictable peaks
    37. 37. The CloudEmpowers Users to Balance Cost and Time
    38. 38. 1 instance for 500 hours =500 instances for 1 hour
    39. 39. The Cloud Reduces CostFor Experimentation
    40. 40. The CloudEnables Collection and Storage of Big Data
    41. 41. Simple Storage Service 1 Trillion 1000.000 750.000 500.000 250.000 0.000 750k+ peak transactions per second
    42. 42. Global Accessibility RegionUS-WEST (N. California) EU-WEST (Ireland) GOV CLOUD ASIA PAC (Tokyo) US-EAST (Virginia)US-WEST (Oregon) ASIA PAC (Singapore) SOUTH AMERICA (Sao Paulo)
    43. 43. Storage Costs are Declining
    44. 44. Big Data on the Cloud In the Real World
    45. 45. Big Data Verticals SocialMedia/Adverti Financial Oil & Gas Retail Life Sciences Security Network/Gami sing Services ng User Anti-virus Targeted Monte Carlo Demographics Recommend Advertising Simulations Seismic Genome Fraud Usage analysis Analysis Analysis Detection Image and Transactions Video Risk Analysis Analysis Image In-game Processing Recognition metrics
    46. 46. Visualizations
    47. 47. Bank – Monte Carlo Simulations “The AWS platform was a good fit for its unlimited and flexible computational power to23 Hours to our risk-simulation process requirements. With AWS, we now have the power to decide20 Minutes how fast we want to obtain simulation results, and, more importantly, we have the ability to run simulations not possible before due to the large amount of infrastructure required.” – Castillo, Director, Bankinter
    48. 48. Recommendations The Taste Test
    49. 49. RecommendationsGift Ideas for Facebook Friends
    50. 50. Click Stream Analysis User recently purchased a Targeted Adsports movie and (1.7 Million per day) is searching for video games
    51. 51. Characteristics of Big Data How the Cloud Is Big Data’s Best Friend Big Data on the Cloud In the Real World
    52. 52. Questions?
    53. 53. Joe ZieglerTechnical @jiyosub