Building a Big Data and Analytics Practice Partner Opportunities with AWS Craig Stires, Head Big Data and Analytics, APAC Title: Building a Big Data and Analytics Practice - Partner Opportunities with AWS Synopsis: Across all major vertical industries, we see customers searching for ways to become more data-driven organizations. The market opportunity is substantial, and investment from customers is growing in solutions that leverage Big Data and Analytics. As our customers have ambitions to grow beyond their existing capabilities, they often depend on partners to build and deliver these solutions. In this presentation, we will talk about some of the opportunities we see in the market, and how our partners can work with us to fulfill these market demands. We will also share two models to consider: first -- enterprise integration, second -- software-as-a-service.

Content Market trends of Big Data investments Trax retail analytics Becoming an AWS Big Data partner

Three big indicators of individual behavior Movement Purchases Influence

A platform to build business outcomes from data Purchases Movement Influence Revenue Lift Market acquisition Customer delight Brand advocacy Inventory optimization Supply chain efficiency ... 1 4 9 5 Ingest/ Collect Process/ analyze Consume/ visualize Store

Business case determines platform design START HERE WITH A BUSINESS CASE 1 4 9 5 Data Answers & Insights Ingest/ Collect Process/ analyze Consume/ visualize Store

Excellence in analytics Six common categories of investments in Big Data Modernization Go mobile Data platform Automation Engagement IOT Excellence in data Excellence in analytics

Automate insights at large scale

41,000+ INDEXES Operates financial exchanges around the world Leading stock index provider with 41,000+ INDEXES across asset classes and geographies Provides technology, trading, intelligence, surveillance, and listing services 100+ data product offerings supporting 2.5+ million investment professionals and users in 98 countries Speaker notes: Founded in 1971 as the world’s first electronic stock market, NASDAQ have transformed their business from predominately a U.S. equities exchange to a global provider of corporate, trading, technology, and information solutions. 1 They are currently the leading stock index provider, with more than 41,000 indexes across asset classes and geographies.2 It’s these stock indexes that provide investors and financial managers with a measurement of the value of specific sections of the stock market so they can compare the return on specific investments. In addition to processing large volumes of data every day in the form of orders, trades, and quotes, they also must be able to run a wide variety of analytic and surveillance systems, all on the same data sets. 3 The data also powers over 100 data product offerings for millions of investment professionals around the world. __________________________________________________________________ 1AWS re:Invent 2014 | (FIN401) Seismic Shift: Nasdaq's Migration to Amazon Redshift 2AWS re:Invent 2014 | (FIN401) Seismic Shift: Nasdaq's Migration to Amazon Redshift 3Big Data on AWS course: Mod15_BigDataEcosystem

They wanted to accelerate innovation and time to market, and also lower their data warehouse costs without compromising on performance of their analytics tools and services, or jeopardize the security of the sensitive data they collect. Costly legacy warehouse ($1.16M /yr) High volume of data (4-8B rows daily) Limited capacity (1 year of data) Disparate historical data sources Modernization Go mobile Data platform Automation Engagement IOT Speaker notes: Now let’s look at some of the things NASDAQ achieved as a result of implementing an AWS solution: Migration to new data warehouse in 7 man-months NASDAQ was able to migrate their entire legacy data warehouse off the on-premises solution and onto Amazon Redshift, including all production traffic, in only seven man-months. 57% reduction in data warehouse costs They reduced their data warehouse costs by 57% compared to the original legacy budget for the same data set. Even more dramatically, the budget of $1.16 million for the original legacy system didn’t begin to compare to the size of the up-front CapEx investment they would have made for a new on-premises upgrade. Instead, they converted that budget from CapEx to OpEx, and pay monthly as they go, making use of 3-year reserved instances to further reduce costs. Ingest up to 14B rows of data per day They were able to move approximately 1,100 data tables into Redshift, and throughout 2014 they averaged 5.5 billion rows of data flowing into the cluster every day, with the highest daily levels reaching 14 billion rows of data. Faster performance than legacy system Not only is their Redshift solution performing faster than their legacy system, they are well within performance windows and can continue to scale elastically up and down and set performance to whatever they need it to be in the future. To give some context around the improvement to performance, the legacy system had been struggling to complete the loading of data by the start of each trading day. The stock market closes at 4:00 pm Eastern time, at which point the ingest of the data from that day begins, and must be completed by the next morning. With AWS, the ingest process is completed, with data available to clients in Redshift, before 11:00 pm Eastern time.

Answers & Insights Data Speaker notes: 1 4 9 5 Answers & Insights Data Ingest/ Collect Process/ analyze Consume/ visualize Store End of day analysis Listed companies reports Trade anamolies … Amazon Direct Connect Amazon S3 Data lake Amazon RedShift Daily trades Amazon SNS Amazon SQS Client Alerts virtual private cloud Speaker notes: Instead of making an upfront CapEx investment for new hardware, NASDAQ decided to replace their legacy on-premises data warehouse and move to an OpEx model in the cloud with Amazon Redshift. They had been looking into Pivotal’s Greenplum and IBM’s Netezza, but neither could compare to Amazon Redshift compare in terms of cost-savings. NASDAQ were one of Amazon’s first Redshift customers, and Rahul Pathak, who was the Principle Product Manager for Redshift at the time, was able to establish a relationship with the NASDAQ team and two of their senior architects. The AWS team involved the NASDAQ architects on a deeply technical level, and presented the solution through an executive briefing. During the development pre-release phase of Redshift, NASDAQ worked with AWS to test the service, and NASDAQs architects were able to make a lot of suggestions around security and how the servers should operate. NASDAQ continued with testing once Redshift was officially announced at re:Invent, and has been able to provide AWS with feedback on any bugs or issues as the service has evolved. AWS has continued to maintain a good relationship with the NASDAQ team, and the architects have since become members of the Customer Advisory Board (CAB) and continue to contribute their guidance. So let’s look at the solution that NASDAQ put in place at the time they migrated to Amazon Redshift: Ingest/Collect Security is a significant concern due to the sensitivity of the data they collect, as well as reliability of bandwidth for their data stream, so NASDAQ employ Amazon Direct Connect as a private line from their data center to Amazon, so that none of the data ever travels over the public internet. Store Data flows from multiple systems in their on-premises data center, through Direct Connect and into Amazon S3, where the load files for Amazon Redshift are staged and massaged into CSV files that match the Redshift schemas. All of the data is encrypted and compressed before being loaded into Redshift, and broken down into even smaller files to enable faster, parallel loading of the files into Redshift. Process/Analyze Redshift is also run inside an Amazon Virtual Private Cloud or VPC, which means security groups can be used to further lock down access, with regular snapshots of Redshift sent to S3 as a backup. Certification is used within Redshift to ensure data is encrypted both in transit and at rest. Consume/Visualize  NASDAQ is now working with a partner solution, Pentaho, for self-service tools that empower BI users to integrate new data sources, create their own analytics, dashboards, and reports without requiring development involvement. Amazon SNS and Amazon SQS are used to automatically notify clients each night as soon as a data table is available, which saves clients the arduous task of constantly checking for updates.

Modernize and decouple to innovate

Large Vietnamese Manufacturing Conglomerate One of Vietnam's largest private sector companies Leads the market in many segments of CPG, and serves retailers nationwide with a diverse range of food and beverage products Due to the transportation infrastructure, it must manage a complex supply and delivery chain, with limited downstream visibility With visionaries that are driving the group to new innovations, they need to modernize and decouple legacy technologies

Large Vietnamese Manufacturing Conglomerate Created mobile applications that track the delivery performance of the various contractors and employees, and the consumption of goods at the point of sale. The mobile apps use behavioral analysis to understand what is impacting performance and a gamification engine has been introduced. Need to create incentives for motorbike delivery team to fill underserved markets Modernization Go mobile Data platform Automation Engagement IOT

Large Vietnamese Manufacturing Conglomerate 1 4 9 5 Data Answers & Insights Ingest/ Collect Process/ analyze Consume/ visualize Store Mobile usage Amazon EMR Performance by category, channel, and region Demand forecast Incentives to fill underserved markets Gamification of channel sales Supply chain visibility … Amazon S3 Data lake Transactions On-premises Transactional Database Amazon RedShift Legacy BI / Report Users CRM On-premises CRM system

Capture windows of opportunity

Was fully AWS-native since day one A full-service residential real estate brokerage Redfin manages data on hundreds of millions of properties and millions of customers The Hot Homes algorithm automatically calculates the likelihood by analyzing more than 500 attributes of each home Was fully AWS-native since day one https://aws.amazon.com/solutions/case-studies/redfin/

Hot Homes There's an 80% chance this home will sell in the next 11 days – go tour it soon. Modernization Go mobile Data platform Automation Engagement IOT Sifting through data is challenging. Need a solution to store and process them and translate them into knowledge and insights Matchmaking millions of users with 100million of properties with thousands of agents. Users: Clickstream (View, Search, ) Contacts, Tours, Open Houses, Offers... Properties: Property facts & history Neighborhood & POI Agents: Availability Performance, Survey…

Users: Properties: Agents: Data Answers & Insights 1 4 9 5 Data Answers & Insights Ingest/ Collect Process/ analyze Consume/ visualize Store Amazon S3 Data lake Properties Amazon EMR User Profile Recommendation Hot Homes Similar Homes Agent Follow-up Agent Scorecard Marketing A/B Testing Real Time Data … BI / Reporting Agents Amazon Kinesis Amazon RedShift Users Amazon DynamoDB Hot Homes "Redfin Hot Homes gives my clients the ultimate insider information," said Keith Thomas, a Redfin real estate agent in Orange County. "Now we know which homes we need to see today, and which ones can wait until next week." Users: Clickstream (View, Search, ) Contacts, Tours, Open Houses, Offers... Properties: Property facts & history Neighborhood & POI Agents: Availability Performance, Survey…

Free steak campaign Mars exploration ops Securities Trading Data Archiving Ticket pricing optimization SAP & SharePoint Disaster recovery Marketing web site Financial markets analytics Interactive TV apps Streaming webcasts Media streaming Web and mobile apps Demand forecasting Social Media Monitoring Mobile analytics Facebook app Consumer social app Big data analytics Consumer social app Consumer social app Ground campaign Operations insights Digital media IT operations Core IT and media We have had many customers from startups to enterprises, government agencies and banks for big data workloads such as analytics on recommendations of where to eat

Content Market trends of Big Data investments Trax retail analytics Becoming an AWS Big Data partner

Trax is the among the world leaders in image recognition for retail Providing leading technology and market data services to tier one manufacturers in over 30 countries Top brands such as Coca-Cola, AB InBev, Heineken, Nestle and Henkel leverage Trax globally to increase revenues at all points of sale Provides services for shelf analytics, cooler analytics, outlet analytics, and more http://www.traxretail.com/technology/

Content Market trends of Big Data investments Trax retail analytics Becoming an AWS Big Data partner

Use a best-fit combination of highly interoperable services Data Warehouse Semi-structured Streaming NoSQL Predictive Models Other Apps Amazon Redshift Amazon Elastic MapReduce Amazon Kinesis Amazon DynamoDB Amazon Machine Learning Amazon Simple Storage Service Data Lake Archive Amazon Glacier

Professional Services Partner Ecosystem Training & Certification Solutions Architects Account Management Security & Pricing Reports Support Technical & Business Support Virtual Desktop Sharing & Collaboration Business Email Enterprise Applications Analytics App Services Developer Tools & Operations Mobile Services Hadoop Queuing & Notifications Transcoding Identity Deployment Resource Templates Real-time Streaming Data Sync Workflow Email DevOps Containers Data Warehouse Mobile Analytics Application Lifecycle Management App Streaming Search Event-driven Computing Data Pipelines Push Notifications Platform Services Identity Management Access Control Resource & Usage Auditing Key Management & Storage Monitoring & Logs Administration & Security Compute (VMs, Auto-scaling & Load Balancing) Storage (Object, Block and Archival) Databases (Relational, NoSQL, Caching) CDN Networking (VPC, DX, DNS) Core Services Regions Availability Zones Points of Presence Infrastructure AWS has developed the broadest collection of services available from any cloud provider. Our approach to regions, availability zones, and POPs provides global coverage for high availability, low latency applications. Foundation services across compute, storage, security, and networking offer customers flexibility in their architecture. We have a full spectrum of options to meet most price-to-performance scenarios. We offer the capability for both managed and unmanaged database options. The offerings for Analytics and Application Services enable advanced data processing and workloads. AWS Redshift, our cloud-based data warehouse, is the fastest growing service in the history of AWS. Our management tools offer a lot of insight and flexibility to let you manage your AWS resources through either our tools or the management tools you’re already familiar with. Recent expansion into enterprise applications has been entirely driven by customer feedback on where they’d like us to deliver value.

AWS Big Data Competency Key Benefits: Enables Differentiation Featured on the AWS Solutions Pages Prioritized Marketing Activities Highlighted in Partner Directory “Being an APN competency holder sends a signal to the market that we have the right capabilities to serve our customers.” – Cognizant QUALIFICATIONS https://aws.amazon.com/partners/competencies/

Build knowledge and best practices Commercial AWS Partner Solution Training – AWS Big Data & Analytics Solutions Technical Big Data on AWS Architecting on AWS Big Data ProServe bootcamp Partners committed to Accreditations and AWS Certifications are achieving at least 10 times more revenue

re:Think For Big Data For Partners AWS AWS is providing POC services credits to qualified customers Existing, substantial AWS customer Usage of AWS strategic services Redshift, DynamoDB, Kinesis, Aurora, Lambda Usage depends on implementation capabilities For Partners AWS is providing POC implementation funds to qualified partners APN Advanced or higher Existing customer references on services AWS Big Data Competency (preferred)

Moving forward as an AWS Big Data partner Find suitable analytics-driven opportunities Get the competency, advanced tier, support, trainings Visit the AWS Big Data expo booth today Modernization Go mobile Data platform Automation Engagement IOT Sifting through data is challenging. Need a solution to store and process them and translate them into knowledge and insights Matchmaking millions of users with 100million of properties with thousands of agents. Users: Clickstream (View, Search, ) Contacts, Tours, Open Houses, Offers... Properties: Property facts & history Neighborhood & POI Agents: Availability Performance, Survey…