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Modern Data Architectures for Business Insights at Scale

Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.

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Modern Data Architectures for Business Insights at Scale

  1. 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Olivier Klein 奧樂凱 Emerging Technologies Solutions Architect, Asia-Pacific Modern Data Architectures for Business Insights at Scale
  2. 2. Data analysis for a better customer experience • Your business creates and stores data and logs all the time • Data points and logs allow you to understand individual customer experience and improve it • Analysis of logs and trails help gain insights
  3. 3. Ever Increasing Amount of Data Volume Velocity Variety
  4. 4. Generation Collection & Storage Analytics & Computation Collaboration & Sharing
  5. 5. More devices Lower cost Higher throughput Generation Collection & Storage Analytics & Computation Collaboration & Sharing
  6. 6. Highly constrained More devices Lower cost Higher throughput Generation Collection & Storage Analytics & Computation Collaboration & Sharing
  7. 7. 95% of the 1.2 zettabytes of data in the digital universe is unstructured 70% of of this is user- generated content Unstructured data growth explosive, with estimates of compound annual growth (CAGR) at 62% from 2008 – 2012. Source: IDC GB TB PB ZB EB Big Data: Unconstrained data growth
  8. 8. Gartner: User Survey Analysis: Key Trends Shaping the Future of Data Center Infrastructure Through 2011 IDC: Worldwide Business Analytics Software 2012–2016 Forecast and 2011 Vendor Shares Available for analysis Generated data Data volume - Gap 1990 2000 2010 2020
  9. 9. Cloud Computing helps remove constraints
  10. 10. Big Data: • Potentially massive datasets • Iterative, experimental style of data manipulation and analysis • Frequently not a steady-state workload; peaks and valleys • Data is a combination of structured and unstructured data in many formats AWS Cloud: • Virtually unlimited capacity • Iterative, experimental usage cost through on-demand infrastructure • Fully scalable infrastructure for highly variable workloads • Tools & Services for managing structured, unstructured and stream data
  11. 11. Let’s talk business outcomes of data analytics!
  12. 12. Outcome 1 : Modernize and consolidate • Insights to enhance business applications and create new digital services Outcome 2 : Innovate for new revenues • Personalization, demand forecasting, risk analysis Outcome 3 : Real-time engagement • Interactive customer experience, event-driven automation, fraud detection Outcome 4 : Automate for expansive reach • Automation of business processes and physical infrastructure Driving Business Outcomes via Data Analytics
  13. 13. Amazon Redshift Amazon Elastic MapReduce Data Warehouse Semi-structured Amazon GlacierAmazon Simple Storage Service Data Storage Archive Amazon DynamoDB Amazon Machine Learning Amazon Kinesis NoSQL Predictive Models Other AppsStreaming Use optimal combination of interoperable services
  14. 14. 2 . S o u r c e D a t a S 3 U p l o a d K i n e s i s F i r e h o s e n a m o D B S t r e a m s S n o w b a l l S n o w b a l l E d g e S n o w m o b i l e 3 . L i f e c yc l e m a n a g e m e n t a n d c o l d s t o r a g e 5 . D a t a g o v e r n a n c e , s e c u r i t y, p r i v a c y Analytics D a t a b a s e M i g r a t i o n S e r v i c e 1 . I n g e s t i o n D a t a s t o r e t a r g e t 4 . M e t a d a t a c a p t u r e 6 . S e l f - s e r v i c e d i s c o v e r y, s e a r c h , a c c e s s 7 . M a n a g i n g d a t a q u a l i t y A W S G l u e S 3 E F S D yn a m o D B R D S E B S 8 . P r e p a r i n g f o r An a l yt i c s 9 . O r c h e s t r a t i o n a n d j o b s c h e d u l i n g 1 0 . C a p t u r i n g d a t a c h a n g e s G l a c i e r E M R At h e n a E M R E l a s t i c S e a r c h R e d s h i f t AI M a c h i n e L e a r n i n g Q u i c k s i g h t Modern Data Architecture on AWS
  15. 15. Insights to enhance business applications, new digital services Technology: Backend system integration, on-prem data center extension, business application integration, BI provisioning, data lakes, external APIs, access control and logging Common initiatives Insights: 360 view of the business • Legacy data systems migration to enable self-service for business analysts • Integration of all customer data, from orders, payments, interactions • Supplier performance for inventory and vendor management Digitization: Web-service that gives on-demand insights • Delivery of digital content, with behavior tracking, and upsell (or ads) • Ordering system for enterprise customers or consumers Data monetization: Enrich, aggregate, and sell business data • External data enrichment API, including digital marketing platforms • Purchasable data sets of anonymized, domain-enriched insights Outcome 1 : Modernize and Consolidate
  16. 16. Ingest ServingData sources Speed (Real-time) Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Enhancing business applications and creating new digital services takes a few steps. Business goals often consist of being an agile, well-run organization, and to stop missing opportunities because people are making decisions without accurate insights. These initiatives are focused on giving important personas fast and secure access to business-relevant insights.
  17. 17. Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers 1. Define personas and use case requirements (including UI) Data analysts
  18. 18. Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers Transactions Web logs / cookies ERP 2. Locate the data sources that have the information to extract Data analysts
  19. 19. Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers Transactions Web logs / cookies ERP Ingest AWS Database Migration Service AWS Direct Connect AWS Storage Gateway Internet Interfaces Changed Data 3. Ingest data through incremental or full loads, across secure connections Data analysts
  20. 20. Fluentd: Open Source Log Collection https://github.com/fluent/fluentd/ • Fluentd is an open source data collector to unify data collection and consumption • Integration into many data sources (App Logs, Syslogs, Twitter etc.) • Direct integration into AWS <source> type tail format apache2 path /var/log/apache2/access_log tag s3.apache.access </source> <match s3.*.*> type s3 s3_bucket myweblogs path logs/ </match>
  21. 21. Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers Transactions Web logs / cookies ERP Ingest AWS Database Migration Service AWS Direct Connect AWS Storage Gateway Internet Interfaces Changed Data 4. Use Hadoop for large scale ETL, data quality, and preparation [*EMRFS] AWS Glue Amazon S3 Raw Data Amazon EMR ETL Data analysts Amazon S3 Clean Data
  22. 22. Amazon S3 • Highly available object storage • Designed for 99.999999999% annual data durability • Replicated across 3 facilities • Virtually unlimited scale • Pay only for what you use, you don’t need to pre-provision • Allows event notifications to trigger further action Amazon S3
  23. 23. Amazon EMR • Amazon EMR is a fully managed Hadoop cluster • Transient and long running clusters • Direct integration into Amazon S3 • Easy to scale and enable burstable capacity • Integration with AWS Spot Market
  24. 24. 1 instance x 100 hours = 100 instances x 1 hour (and with Spot Pricing not only faster but also cheaper)
  25. 25. Amazon EMR • Amazon EMR supports all common Hadoop Frameworks such as: • Spark, Pig, Hive, Hue, Oozie … • Hbase, Presto, Impala … • Decouples storage from compute • Allows independent scaling • Direct Integration with DynamoDB and S3 Amazon S3Amazon DynamoDB Amazon EMR
  26. 26. Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers Transactions Web logs / cookies ERP Ingest AWS Database Migration Service AWS Direct Connect AWS Storage Gateway Internet Interfaces Changed Data 5. Stage all data into centralized, highly available, durable storage for further access AWS Glue Amazon S3 Raw Data Data analysts Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data
  27. 27. Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers Transactions Web logs / cookies ERP Ingest AWS Database Migration Service AWS Direct Connect AWS Storage Gateway Internet Interfaces Changed Data 6. Load semi-structured into Hadoop, structured into the DWH, and application data into managed legacy application databases AWS Glue Amazon S3 Raw Data Amazon EMR Semi-structured Amazon RedShift Data Warehouse Amazon RDS Legacy Apps Data analysts Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data
  28. 28. Amazon Redshift • Fully managed petabyte-scale data warehouse • Scalable amount of cluster nodes • ODBC/JDBC connector for BI tools using SQL • Supports Amazon DynamoDB and Amazon S3 to load data • Less than a 10th of a cost of traditional solutions Amazon Redshift
  29. 29. Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers Transactions Web logs / cookies ERP Ingest AWS Database Migration Service AWS Direct Connect AWS Storage Gateway Internet Interfaces Changed Data 7. Data is protected through identity and access management and logging AWS Glue Amazon S3 Raw Data Amazon EMR Semi-structured Amazon RedShift Data Warehouse Amazon RDS Legacy Apps Data analysts AWS Cloud TrailAWS IAM Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data
  30. 30. AWS Cloud TrailAWS IAM Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers Transactions Web logs / cookies ERP Ingest AWS Database Migration Service AWS Direct Connect AWS Storage Gateway Internet Interfaces Changed Data 8. Data analysts use BI tools of choice to access all serving services AWS Glue Amazon S3 Raw Data Amazon EMR Semi-structured Amazon RedShift Data Warehouse Amazon RDS Legacy Apps Data analysts Amazon QuickSight Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data
  31. 31. Amazon Quicksight • Fast, cloud-powered, BI service that makes it easy to build visualizations, perform ad-hoc analysis, and get insights from data. • Connectors for files, third party platforms, AWS services and other partner BI tools • In-memory calculation engine (SPICE) to accelerate analysis and visualization • $9 per user per month
  32. 32. AWS Marketplace • Pre-Configured machine images ready to be launched into virtual server instances • Launch applications with 1-Click • Pay software licenses by the hour or bring your own license (BYOL)
  33. 33. AWS Cloud TrailAWS IAM Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers Transactions Web logs / cookies ERP Ingest AWS Database Migration Service AWS Direct Connect AWS Storage Gateway Internet Interfaces Changed Data 9. Business users have enterprise applications enhanced by analytics AWS Glue Amazon S3 Raw Data Amazon EMR Semi-structured Amazon RedShift Data Warehouse Amazon RDS Legacy Apps Data analysts Amazon QuickSight Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data
  34. 34. AWS Cloud TrailAWS IAM Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers Transactions Web logs / cookies ERP Ingest AWS Database Migration Service AWS Direct Connect AWS Storage Gateway Internet Interfaces Changed Data 10. External parties can buy services or data in a governed, secure way AWS Glue Amazon S3 Raw Data Amazon EMR Semi-structured Amazon RedShift Data Warehouse Amazon RDS Legacy Apps Data analysts Amazon QuickSight Amazon API Gateway Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data
  35. 35. Speed (Real-time) Ingest ServingData sources Scale (Batch) Modernize and consolidate Insights to enhance business applications, new digital services Business users External buyers Transactions Web logs / cookies ERP Ingest AWS Database Migration Service AWS Direct Connect AWS Storage Gateway Internet Interfaces Changed Data AWS Glue Amazon S3 Raw Data Amazon EMR Semi-structured Amazon RedShift Data Warehouse Amazon RDS Legacy Apps Data analysts Amazon QuickSight Amazon API Gateway AWS Cloud TrailAWS IAM Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Athena
  36. 36. Decouple Storage and Compute Traditionally analytical workloads required large databases or data warehouses, with storage and compute close to each other Big Data often benefits from decoupling storage and compute Amazon S3 offers virtually unlimited storage at a per GB/month rate
  37. 37. No need to move data Query S3 directly & right away No infrastructure to setup & manage Fast results within seconds Pay for just the queries you run Amazon Athena Interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL
  38. 38. Athena & Quicksight Demo Amazon S3 Amazon Athena Amazon Quicksight Analyze past flight performance data stored in S3 Bureau of Transportation Flight Data Statistics www.transtats.bts.gov Create visualizations from S3 with Athena & Quicksight
  39. 39. Personalization, demand forecasting, risk analysis Technology: Advanced analytics, customer segmentations, high volume transactional data, un/semi- structured data, design of experiment, A/B & hypothesis testing, machine learning Common initiatives Personalization: Refine market approaches based on optimal segments • Offer products to new customers based on clusters of similar individuals • Launch share of wallet initiatives, understanding likely total spend • Targeted marketing to capture interests and increase conversion rates Predict demand: Guide business owners to select the best scenarios • Launch items or promotions at the optimal time to maximize response • Modeling for store assortment, product selection, and merchandizing • New product design, based on known market propensities Risk measurement: Create freedom to act by quantifying exposures • Scenario simulation to encourage investments and new offerings • Supply chain analytics allows for faster confirmation of goods to customers Outcome 2 : Innovate for new revenues
  40. 40. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Driving net new revenues is realized by business teams that have access to skilled analysts, using platforms that can scale up and out, without IT bottlenecks. Organizations start operating based on what they know about their customers, and can approach new ventures in terms of confidence levels. Product launches, campaigns, supply chain management, packaged services, and customized offerings are designed and executed based on predictive models.
  41. 41. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis AWS Cloud TrailAWS IAM Amazon CloudWatch Data analysts Data scientists Business users Engagement platforms 1. Personas involved in generating new revenues are data scientists, data analysts (often embedded), business users, and customers/suppliers
  42. 42. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Direct Connect AWS Cloud TrailAWS IAM Amazon CloudWatch Data analysts Data scientists Business users Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data AWS Glue 2. Advanced analytics are built from a base of traditional data processing Amazon EMR Amazon RedShift Amazon RDS
  43. 43. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Direct Connect AWS Cloud TrailAWS IAM Amazon CloudWatch Data analysts Data scientists Business users Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data AWS Glue 3. On-premise storage and databases are connected and converted Amazon EMR Amazon RedShift Amazon RDS AWS Database Migration Service AWS Storage Gateway
  44. 44. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon CloudWatch Data analysts Data scientists Business users Web logs / cookies Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data AWS Glue 4. Internet-native data sources, like web and mobile, are captured Amazon EMR Amazon RedShift Amazon RDS AWS Database Migration Service AWS Storage Gateway
  45. 45. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data AWS Glue 5. Streaming un/semi-structured data feeds, like social and devices are captured Amazon EMR Amazon RedShift Amazon RDS
  46. 46. Stream in Real Time: Amazon Kinesis • Real-Time Data Processing over large distributed streams • Elastic capacity that scales to millions of events per second • React In real-time upon incoming stream events • Reliable stream storage replicated across 3 facilities Amazon Kinesis
  47. 47. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon S3 Schemaless AWS Glue 6. Log files and other schemaless data converted to Parquet and staged Amazon EMR Amazon RedShift Amazon RDS
  48. 48. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon S3 Schemaless AWS Glue 7. Data scientists test hypothesis against un/semi-structured data Amazon RedShift Amazon RDS
  49. 49. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine Learning Amazon S3 Schemaless AWS Glue 8. Simple analytical models are built against Amazon Machine Learning Amazon EMR Amazon RedShift Amazon RDS Amazon Athena Amazon ElasticSearch
  50. 50. Amazon Machine Learning • Easy to use, managed machine learning service built for developers • Machine learning technology based on Amazon’s internal systems • Create models using data stored in Amazon S3, Amazon RDS or Amazon Redshift • Request predictions on batch or real- time Amazon Machine Learning
  51. 51. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless AWS Glue 9. Complex analytical models are built against EMR (Spark) clusters Amazon EMR Amazon RedShift Amazon RDS Amazon Athena Amazon ElasticSearch
  52. 52. Apache Spark • In-memory analytics cluster using RDD (Resilient Distributed Dataset) for fast processing • Spark MLlib offers machine learning out of the box • Apache Spark can read directly from Amazon S3 data = sc.textFile("s3://...") parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')])) model = KMeans.train(parsedData, 2, maxIterations=10, initializationMode="random") model.save(sc, "MyModel") sameModel = KMeansModel.load(sc, "MyModel")
  53. 53. Machine Learning Algorithms • Classification • Sentiment analysis – Do people like my new product? • Linear Regression • Trend prediction – How much revenue next month? • Clustering • Recommendation - Other people bought this! • Association • Market basket analysis – Bundled products • Neural Networks • Pattern recognition - Speech recognition Amazon Machine Learning Amazon EMR + Spark Mlib GPU Optimized EC2 Instance
  54. 54. Intel® Processor Technologies Intel® AVX – Dramatically increases performance for highly parallel HPC workloads such as life science engineering, data mining, financial analysis, media processing Intel® AES-NI – Enhances security with new encryption instructions that reduce the performance penalty associated with encrypting/decrypting data Intel® Turbo Boost Technology – Increases computing power with performance that adapts to spikes in workloads Intel Transactional Synchronization (TSX) Extensions – Enables execution of transactions that are independent to accelerate throughput P state & C state control – provides granular performance tuning for cores and sleep states to improve overall application performance
  55. 55. New X1 Instance - Tons of Memory • Designed for large-scale, in-memory applications in the cloud • Ideal for in-memory databases like SAP HANA and big data processing apps like Spark and Presto • Powered by Intel® Xeon® E7 8880 v3 Haswell processors • Features up to 2TB of memory and up to 128 vCPUs per instance • 8X the memory offered by any other Amazon EC2 instance
  56. 56. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless AWS Glue 10. Predictive models are published to data staging Amazon EMR Amazon RedShift Amazon RDS Amazon Athena Amazon ElasticSearch
  57. 57. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless AWS Glue 11. Analysts use DWH, EMR, ES to find patterns & measure performance Amazon RedShift Amazon RDS
  58. 58. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless AWS Glue 12. Risk models evaluated to create new products and assess customers Amazon RDS
  59. 59. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless AWS Glue 13. Demand forecasts loaded into supply chain management systems
  60. 60. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless AWS Glue 14. Personalized offers are broadcast out over notification channels Amazon SNS Amazon Pinpoint
  61. 61. Amazon SNS & Amazon Pinpoint • Amazon SNS is a fully managed, cross-platform mobile push intermediary service • Fully scalable to millions of devices • Amazon Pinpoint allows to created targeted campaigns and measure engagement and results Amazon SNS Apple APNS Google GCM Amazon ADM Windows WNS and MPNS Baidu CP Android Phones and Tablets Apple iPhones and iPads Kindle Fire Devices Android Phones and Tablets in China iOS Windows Phone Devices Amazon SNS
  62. 62. Ingest ServingData sources Speed (Real-time) Scale (Batch) Innovate for new revenues Personalization, demand forecasting, risk analysis Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless AWS Glue Amazon SNS Amazon Pinpoint
  63. 63. Elastic GPUs For EC2 U s e G r a p h i c s G P U s A s I f T h e y W e r e E B S Vo l u m e s
  64. 64. Elastic GPUs: GPU Acceleration on-demand Current Generation EC2 Instance
  65. 65. 1GiB GPU Memory 2 GiB 4 GiB 8 GiB Current Generation EC2 Instance Elastic GPUs: GPU Acceleration on-demand
  66. 66. BREAK Next up: Real-Time Analytics and Engagement
  67. 67. Interactive customer experience, event-driven automation, fraud detection Technology: Clickstream/mobile apps/sensor/video (computer vision)/audio (intent comprehension), event detection and pipelining, in-line scoring, serverless compute, computer vision, deep learning Common initiatives Interactive CX: Natural customer journeys with adaptive interfaces • Behavior-based recommendations, improving personalization along the journey • Seamless session transfer across UI, from browser to mobile to physical location • Voice-driven commands, and use of gestures and other natural interfaces Event-driven automation: Full execution of business process driven by an action • Order fulfillment, with real-time update notifications to customer • Fast response to customer complaints/comments over direct or social channels Fraud detection: Protect customer and business w/ real-time anomaly detection • Purchase and payment verification, using behavioral models and location assessment • Application and account opening validation Outcome 3 : Real-time Engagement
  68. 68. Artificial Intelligence
  69. 69. Alexa, Hello!
  70. 70. The Power of Speech: Alexa Alexa, the voice service that powers Echo, provides capabilities, or skills, that enable customers to interact with devices using voice Alexa Skills Kit (ASK) allows everyone to build and publish their own skills Skills can be powered by AWS Lambda
  71. 71. Build your own Alexa Skill! Amazon Echo Alexa Skills Kit AWS Lambda Facebook Page
  72. 72. Personalized content - Account access - Track spending - Check balances - Pay bills - Prevent fraud
  73. 73. Unlimited Replays Returns an MP3 or audio stream Lightning Fast Response Fully Managed and Low Cost Amazon Polly Turn text into lifelike speech using deep learning technologies to synthesize speech that sounds like a human voice
  74. 74. Amazon Polly “The temperature in WA is 75°F” “The temperature in Washington is 75 degrees Fahrenheit” Amazon Polly: Text In, Life-like Speech Out
  75. 75. Amazon Lex Conversational interfaces for your applications, powered by the same Natural Language Understanding (NLU) & Automatic Speech Recognition (ASR) models as Alexa Integrated development in AWS console Trigger AWS Lambda functions Multi-step conversations Continually improving ASR & NLU models Enterprise connectors Fully Managed
  76. 76. Intents A particular goal that the user wants to achieve Utterances Spoken or typed phrases that invoke your intent Slots Data the user must provide to fulfill the intent Prompts Questions that ask the user to input data Fulfillment The business logic required to fulfill the user’s intent BookHotel
  77. 77. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Provide superior customer service by responding to opportunities in real time. Fulfill requests for products or services in an automated fashion to create a strong competitive advantage over those that are unable to. Assurance becomes a different challenge, when speeds increase, and fraud prevention must be adaptive and fast. Adding another layer of opportunity and complexity is the use of vast streams of data from devices that are measuring location, video, behaviors, environmental conditions, and more.
  78. 78. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection AWS Cloud TrailAWS IAM Amazon CloudWatch Data analysts Data scientists Business users Engagement platforms Automation / events 1. Real-time engagement requires personas that develop the analytics, and platforms for engaging and automating processes
  79. 79. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless AWS Glue 2. Real-time systems are built from a base of advanced data processing
  80. 80. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless AWS Glue Amazon Kinesis 3. Events are pipelined through Kinesis, into multiple streams, at scale
  81. 81. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis 4. Event data is given context and structure in EMR and pushed for batch
  82. 82. Also possible with Spark Streaming! Amazon Kinesis EMR with Spark Streaming KinesisUtils.createStream(‘twitter-stream’) .filter(_.getText.contains(‘Big Data’)) .countByWindow(Seconds(5)) Counting tweets on a sliding window
  83. 83. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Amazon Kinesis Firehose Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis 5. Kinesis Firehose pumps events into a DWH for near real-time analysis
  84. 84. Amazon Kinesis Firehose • Fully managed data streaming service to ingest and capture data into your storage or data warehouse • Ability to batch load, compress or encrypt streaming data • Elastic to scale to any throughput (no more sharding) • Charged only per GB processed ($0.035 per GB)
  85. 85. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Amazon Kinesis Firehose Event Scoring AWS Lambda Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis 6. The event is streamed to a scoring server for processing
  86. 86. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Amazon Kinesis Firehose Event Scoring Amazon AI AWS Lambda Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis 7. Language, intent, and image processing are run and sent for scoring
  87. 87. Amazon Rekognition Image Recognitions and Analysis powered by Deep Learning which allows to search, verify and organize millions of images Easy to use Batch Analysis Real-time Analysis Continually Improving Low Cost
  88. 88. Maple Villa Plant Garden Water Swimming Pool Tree Potted Plant Backyard
  89. 89. Demographic Data Facial Landmarks Sentiment Expressed Image Quality Brightness: 25.84 Sharpness: 160 General Attributes
  90. 90. Serverless Rekognition Demo Serverless website that uses Rekognition to identify faces and classify pictures Amazon S3 AWS Lambda Amazon API Gateway Amazon DynamoDB Amazon Rekognition Mobile CodeFor.Cloud/image
  91. 91. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Amazon Kinesis Firehose Event Scoring Amazon AI AWS Lambda Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis 8. Simple analytical models are checked on-demand against Amazon ML
  92. 92. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Amazon Kinesis Firehose Event Scoring Amazon AI AWS Lambda Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis 9. Complex analytical models are scored against coded models (PMML)
  93. 93. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS AWS Storage Gateway Amazon CloudWatch Amazon Kinesis Firehose Event Scoring Amazon AI AWS Lambda AWS Lambda Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis 10. Scored response to the event is processed to be pushed for action
  94. 94. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS Amazon DynamoDB AWS Storage Gateway Amazon CloudWatch Amazon Kinesis Firehose Event Scoring Amazon AI AWS Lambda AWS Lambda Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis 11. Recommendations are pushed to DynamoDB for low latency serving
  95. 95. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS Amazon SQS AWS Storage Gateway Amazon CloudWatch Amazon Kinesis Firehose Event Scoring Amazon AI AWS Lambda AWS Lambda Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis 12. Actions are pushed to RDS and SQS for business process automation Amazon DynamoDB
  96. 96. Ingest ServingData sources Speed (Real-time) Scale (Batch) Real-time engagement Interactive customer experience, event-driven automation, fraud detection Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS Amazon DynamoDB Amazon SQS AWS Storage Gateway Amazon CloudWatch Amazon Kinesis Firehose Event Scoring Amazon AI AWS Lambda AWS Lambda Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis
  97. 97. Amazon Kinesis Twitter Stream Amazon Lambda Demo: Live Twitter Feed Analysis * https://blog.twitter.com/2013/new-tweets-per-second-record-and-how Twitter Blog* - On a typical day (in 2013): • More than 500 million Tweets sent • Average 5,700 TPS Amazon Elasticsearch Service
  98. 98. Kinesis for Real- Time 10TB/day Amazon S3
  99. 99. • Robinhood’s lean staff used AWS to create a massively scalable securities trading app with strong built-in security and compliance features that supported hundreds of thousands of users at launch • Saved customers $22 million in commissions since launch, and transacted over $1 billion. All of this scaled up with 2 DevOps resources • Amazon Redshift has allowed the data science team to identify fraud and fight money laundering, without needing to hire a data science infrastructure team Robinhood Launches Popular No-fee Brokerage Trading Platform on AWS Robinhood is an investment platform that offers free trades for everyone. It is based in Palo Alto, CA. We can look at real-time analytics and behaviors on our platform, that wouldn't be available at our scale if we weren't using AWS. ” “ Miles Wellesley Head of Business Development
  100. 100. Automation of self-service, deployment, policy, and quality assurance Technology: Self-service, on-demand provisioning, DevOps, spot pricing, Cloud Formations, security automation, performance monitoring (CW&XR), global rollouts Common initiatives Self-service: • Application catalog or portal for all employees, availability determined by role • Service provisioning backed by automation of policy and governance Agile development: Use of DevOps to allow very few resources to deploy globally • CI/CD for software release, build/test, and deployment automation • Templated infrastructure provisioning, and configuration management • Business rules and policies are "gold coded" to be used for all deployments • Use of Security by Design (SbD) to codify network, O/S, and encryption Comprehensive monitoring: Assurance of SLA and issue remediation • Logging and monitoring of all API calls and executions to ensure SLAs are met • Analysis of performance variance for faster root cause analysis Outcome 4 : Automate for expansive reach
  101. 101. Ingest ServingData sources Speed (Real-time) Scale (Batch) Automate for expansive reach Automation of self-service, deployment, policy, and quality assurance Transactions AWS Database Migration Service AWS Direct Connect Internet Interfaces Amazon S3 Stream Data AWS Cloud TrailAWS IAM Amazon Kinesis Amazon Athena Amazon EMR Amazon ElasticSearch Amazon RedShift Amazon RDS Amazon DynamoDB Amazon SQS AWS Storage Gateway Amazon CloudWatch Amazon Kinesis Firehose Event Scoring Amazon AI AWS Lambda AWS Lambda Data analysts Data scientists Business users Connected devices Web logs / cookies Social media Engagement platforms Automation / events ERP Amazon S3 Raw Data Amazon S3 Staged Data (Data Lake) Amazon EMR ETL Amazon S3 Clean Data Amazon Machine LearningAmazon EMR MLlib Amazon S3 Schemaless Amazon EMR AWS Glue Amazon Kinesis AWS DevOps
  102. 102. AWS Glue Easily understand your data sources, prepare the data, and load it reliably to data stores and your analytics pipeline Integrated with: S3, RDS, Redshift & any JDBC- compliant data store
  103. 103. Build Your Data Catalog
  104. 104. Generate And Edit Transformations
  105. 105. Schedule And Run Your Jobs
  106. 106. AWS Lambda • Use AWS Lambda to clean and massage incoming data • Write code to load data sources (S3, DynamoDB) automatically in your data warehouse (e.g. Amazon Redshift) • React in real-time to incoming events in Amazon Kinesis Amazon Lambda Amazon Redshift Amazon Kinesis
  107. 107. AdRoll: AWS Lambda for log files Valentino Volonghi CTO, AdRoll “Polling is not a scalable strategy to figure out when new files are added to S3, especially when you add 17M of them per month. So we moved Lambda in front of S3.” • Cross-platform, cross-device advertising platform • Offers retargeting based on clickstream data 300TB new data/mont h
  108. 108. Remember everything is an API: SDKs Java Python (boto) PHP .NET Ruby Node.js iOS Android Go JavaScript C++
  109. 109. Affordable Petabyte-scale Analytics AWS helps customers maximize the value of Big Data investments while reducing overall IT costs Secure, Highly Durable storage $28.16 / TB / month Data Archiving $7.16 / TB / month Real-time streaming data load $0.035 / GB 10-node Spark Cluster $0.15 / hr Petabyte-scale Data Warehouse $0.25 / hr Amazon Glacier Amazon S3 Amazon RedshiftAmazon EMRAmazon Kinesis
  110. 110. Call To Action • Attend the official AWS Training course organized by AWS Authorized local training partner – Iverson Associates Sdn Bhd (www.iverson.com.my). • Join the AWS Jumpstart (2 hr) session and hear from our customers and partners on how they enabled their teams and successfully deployed on AWS. Also stand a chance to win free seat to the above courses. • Point of contact – Cheryl Wong - cheryl.wong@iverson.com.my Courses Date Architecting on AWS 28 Feb - 2 March System Operations on AWS 8-10 March Developing on AWS 15-17 March Big Data on AWS 19-21 April Date Venue 17 Mar 2017 Iverson Associates Sdn Bhd (303330-M), Suites T113-T114, 3rd Floor, Centrepoint, Lebuh Bandar Utama, Bandar Utama, 47800 Petaling Jaya, Selangor
  111. 111. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. http://bit.ly/awssummitkl April 18 | One World Hotel | Kuala Lumpur Register Now!
  112. 112. Join AWS User Group MY https://www.facebook.com/groups/awsugmy/
  113. 113. Thank You! Next up: Q&A

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