Downloaded 24 times
Uploaded bySteven Hsieh
AWS 2019 Taipei Summit - Building Serverless Analytics Platform on AWS
This document discusses building serverless analytics solutions on AWS. It describes how serverless analytics can provide on-demand analytics on data lakes with no infrastructure to manage. Key services mentioned include Amazon S3 for storage, AWS Glue for ETL and data cataloging, Amazon Athena for interactive queries, and Amazon QuickSight for visualization. The document provides examples of using these services together for automated reporting, query monitoring with workgroups, and embedding dashboards in applications.
AWS 2019 Taipei Summit - Building Serverless Analytics Platform on AWS
- 1. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T BuildingServerlessAnalytics onAWS Ivan Cheng Solutions Architect AWS Steven Hsieh Engineer TrendMicro
- 2. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T COLLECT STORE PROCESS/ ANALYZE CONSUME Data Answers Time to answer (Latency) Throughput Cost Data Processing START HERE WITH A BUSINESS CASE
- 3. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T To answer new questions quickly, we look to a modern data architecture design Massive upfront costs Overprovisioned capacity Long implementation times Pay as you go, for what you use Decoupled pipelines and engines Experimentation platform Ingest/ Collect Consume/ visualize Store Process/ analyze 1 4 0 9 5
- 4. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Data Is Changing Analytics Are Adopting Capture and store new data at PB-EB scale Do new type of analytics in a cost effective way • Machine learning • Big data processing • Real-time analytics • Full-text search New types of analytics
- 5. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T More data lakes and analytics than anywhere else More than 10,000 data lakes onAWS
- 6. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Data Movement Analytics AWS Analytics Portfolio Broadest and deepest portfolio, purpose-built for builders + 10 more Redshift EMR (Spark & Hadoop) Athena Elasticsearch Service Kinesis Data Analytics Glue (Spark & Python) S3/Glacier GlueLake Formation Visualization, Engagement, & Machine Learning QuickSight SageMaker Comprehen d Le x Polly Rekognition Translate Transcribe Deep Learning AMIs Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams | Managed Streaming for Kafka Data Lake Infrastructure & Management
- 7. Agility and InnovationAre Key Amazon SageMaker AWS Deep LearningAMIs Amazon Rekognition Amazon Lex AWS DeepLens Amazon Comprehend AmazonTranslate AmazonTranscribe Amazon Polly Amazon Athena Amazon EMR Amazon Redshift Amazon Elasticsearch Service Amazon Kinesis Amazon QuickSight AWS Direct Connect AWS Snowball AWS Snowmobile AWS Database Migration Service AWS IoT Core Amazon Kinesis Data Firehose Amazon Kinesis Data Streams Amazon KinesisVideo Streams Data Lake on AWS Storage | Archival Storage | Data Catalog AnalyticsMachine learning Real-time dataOn-premises movementdata movement
- 8. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Snowball Snowmobile Kinesis Data Firehose Kinesis Data Streams S3 Redshift EMR Athena Kinesis Elasticsearch Service Kinesis Video Streams AI Services QuickSight Durable and available; Exabyte scale Secure, compliant, auditable Rapid ingest and transformation Schema on read Decoupling of compute and storage On-demand resources, tiering, cost choices Robust Infrastructure
- 9. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Your choice of Amazon S3 storage classes Access FrequencyFrequent Infrequent • Active, frequently accessed data • Milliseconds access • > 3 AZ • $0.0210/GB • Data with changing access patterns • Milliseconds access • > 3 AZ • $0.0210 to $0.0125/GB • Monitoring fee per Obj. • Min storage duration • Infrequently accessed data • Milliseconds access • > 3 AZ • $0.0125/GB • Retrieval fee per GB • Min storage duration • Min object size S3 Standard S3 S-IA S3 Z-IA Amazon Glacier • Re-creatable, less accessed data • Milliseconds access • 1 AZ • $0.0100/GB • Retrieval fee per GB • Min storage duration • Min object size • Archive data • Select minutes or hours • > 3 AZ • $0.0040/GB • Retrieval fee per GB • Min storage duration • Min object size S3 INT
- 10. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Ingest Consume Amazon Kinesis BI Tools Data Analytics Pipeline Database Migration Service AWS Snowball Amazon MSK Amazon Athena Amazon EMR Amazon Redshift Amazon Elasticsearch Process & Analyze Jupyter Notebooks Amazon API Gateway Amazon QuickSight Catalog AWS Glue Store Amazon S3 Store Amazon S3 Data sources Web logs / cookies ERP Connected devices
- 11. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Virtual machines Managed services Serverless Cloud Services Evolution
- 12. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Serverless analytics Deliver on-demand analytics on the data lake S3 Data lake Glue (ETL & Data Catalog) Athena QuickSight Serverless. Zero infrastructure. Zero administration Never pay for idle resources $ Availability and fault tolerance built in Automatically scales resources with usage AI/ML Devices Web Sensors Social Kinesis Data Firehose
- 13. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Athena-Interactive Analysis Interactive query service to analyze data in Amazon S3 using standard SQL No infrastructure to set up or manage and no data to load Supports Multiple Data Formats – Define Schema on Demand Fast. Really Fast. Interactive performance even for large datasets. Athena automatically executes queries in parallel, so most results come back within seconds. Open. Powerful. Standard Start Querying Instantly Pay Per Query Athena is serverless. Just point to your data in Amazon S3, define the schema, and start querying using the built- in query editor. Amazon Athena uses Presto with ANSI SQL support and works with a variety of standard data formats, including CSV, JSON, ORC, Avro, and Parquet With Amazon Athena, you pay only for the queries that you run. You are charged $5 per terabyte scanned by your queries.
- 14. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon S3 Amazon Athena Data catalog Data Engineer Data Consumer AWS Tools and SDKs AWS Management Console Amazon QuickSight Amazon SageMaker User Analyst Data Scientist Use PyAthena to query Athena tables directly from Amazon SageMaker notebooks
- 15. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Data consumption – Automated Reporting athena.startQueryExecution("SELECT * FROM business_view”) 1 2 3 4 5 1. Schedule query 2. Track QueryID for status 3. Query results to Amazon S3 4. New file trigger 5. Job complete notification Email notification Query_ID
- 16. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Athena Workgroups Athena Workgroups are used to isolate queries between different teams, workloads or applications, and to set limits on amount of data each query or the entire workgroup can process Workload Isolation Query Metrics Cost Controls
- 17. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Workgroups – Cost Controls • Per query data scanned threshold; exceeding, will cancel query • Trigger alarms to notify of increasing usage and cost • Disable Workgroup when all queries exceed a maximum threshold Any Athena metric: successful/failed & total queries, query run time, etc.
- 18. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Visualize your data with your favorite tools Featured Athena Partners Amazon QuickSight
- 19. © 2018, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Amazon QuickSight is a fully managed, serverless, cloud business intelligence system
- 20. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Why QuickSight Scalable From 10 users to 10,000, QuickSight seamlessly grows with you with no need for additional servers or infrastructure. No Servers to Manage QuickSight is a fully managed cloud service. There is no infrastructure to maintain or upgrade and no upfront costs. Fully integrated QuickSight integrates with your other AWS services and data sources giving you everything you need to build an end-to-end cloud analytics solution. Pay For What You Use Instead of buying costly licenses for all of your users, QuickSight allows you to share dashboards and reports and only pay when users access them.
- 21. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Connect to your data, wherever it is QuickSight allows you to connect to AWS data sources, Private VPC subnets, on-premise and hosted databases and third party business applications. On-premises Securely connect to on-premise databases and flat files like Excel and CSV In the cloud Connect to hosted database, big data formats, and secure VPCs Applications Connect directly to third party business applications • Salesforce • Square • Adobe Analytics • Jira • ServiceNow • Twitter • Github • Redshift • RDS • S3 • Athena • Aurora • Teradata • MySQL • Presto • Spark • SQL Server • Postgre SQL • MariaDB • Snowflake • IoT Analytics • Excel • CSV • Teradata • MySQL • SQL Server • PostgreSQL
- 22. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Embedding Dashboards In Your Application QuickSight allows you to seamlessly integrate interactive dashboards and analytics into your own applications • Enhance your applications with rich analytics and dashboards • Easy maintenance, no servers to manage • Fast! No Custom development or domain expertise needed • Leverage new features as we add them • Utilizes Pay-per-Session Pricing.
- 23. S U MM I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 24. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon S3 (Processed Data) Amazon Athena Amazon QuickSight Demo Scenario Glue Data catalog
- 25. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Building AWS Multi Account Cost Analytics Solution at Scale Steven Hsieh Engineer TrendMicro
- 26. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T
- 27. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T About Me Steven Hsieh
- 28. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Background
- 29. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Pillars of
- 30. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Design Principles for Cost Optimization • Adopt a consumption model • Measure overall efficiency • Stop spending money on data center operations • Analyze and attribute expenditure • Use managed services to reduce cost of ownership Pay as you go / need
- 31. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Challenges Large Scale Accounts • Almost 400 accounts • Hard management via AWS console Multiple Data Sources • Billing data • Utilization data of AWS services ( e.g., EC2, S3)
- 32. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Challenges Permission Management • Multiple teams • Authorization of different team Insight for Better Design • Finding insight for design improvement • Providing utilization visibility for design change
- 33. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Other solution we have tried… AWS Billing Console • Hard to use in large scale • Single data source Amazon Redshift • Cost Model • ETL 3rd Party BI Tool • Expensive license fee • Additional operation cost
- 34. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Ideas + + • Data persistence in Amazon S3 • Data querying via Amazon Athena • Dashboard / Reporting via Amazon QuickSight
- 35. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Challenges
- 36. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Global Accelerator
- 37. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T • Using SQS to trigger parallel tasks • Lambda limitation: • Timeout: 15 minutes • /tmp: 512 MB • Spot instance interruptions • Fargate limitation: • Container storage: 10 GB • Run-task: 10 • Using assume role to collect data across accounts
- 38. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T • Using SNS to trace data uploading result • Preprocessing data before uploading to S3 • Only creator can modify datasets in QuickSight • Create view in Athena
- 39. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Global Accelerator • Web application host in Fargate • Lambda Integration with QuickSight for embedded URL. • Using ALB to handle all HTTPS interaction. • Permission & Metadata in DynamoDB • ADFS Federation using Cognito • Performance Improvement via AWS Global Accelerator • Web Security Enhancement via AWS WAF
- 40. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Quick Development & Evaluation
- 41. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Low Utilization & Right Sizing • Trusted Advisor Checks • Low utilization EC2 instances: CPU was 10% or less and network I/O was 5 MB or less on 4 or more days during last 14 days • Right Sizing • Analysis metric data to recommend proper instance type and size • Awareness of NIC driver and Linux virtualization type issue
- 42. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Saving Polar Bear • Analyzing the CPU utilization pattern • Tuning off non-production instances can saving almost 70% cost
- 43. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Recap • Using cost effective way to build the end-to-end BI solution • 2 power users $36 + ALB $18 = $54 • Using flexible reporting architecture to integrate with multiple data sources • Quick win & timely data driven decision • Validating innovation idea （e.g., the potential saving of polar bear project）
- 44. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Summary • More organizations building datalake on cloud to stay competitive • AWS provides the broadest and deepest portfolio of databases and analytics services includes machine learning. • Serverless Analytics helps you build modern data pipeline with increased agility and lower cost. • Learn more at: https://aws.amazon.com/big-data/datalakes-and-analytics/
- 45. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.S U M M I T Thank you! S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Ivan Cheng Solutions Architect AWS Steven Hsieh Engineer TrendMicro
Editor's Notes
- #6 As a result of the advantages shown on the previous slides, there are more people running their data lakes, and analytics on AWS than anywhere else. This includes customers like FINRA, Netflix, Nasdaq, Amazon.com, Atlassian, Sysco, Airbnb, iRobot, CrowdStrike, Viber, 21st Century Fox, Vanguard, Takeda, Movable Inc, Expedia.com, Zillow, Yelp, Amgen, JustGiving, NTT Docomo, to name a few. In later slides, we will share details on a number of customers and how they use specific services to achieve their goals.
- #7 To enable this for customers, AWS provides the broadest and deepest portfolio of databases and analytics services than any other cloud provider, many of them named above to match with the function they perform (as shown on the previous slide.) AWS offers at least: 10 data movement services 13 analytics services 18 machine learning and AI services 17 security and governance services Maybe more since this slide was created!
- #9 <timing 2 minute> Backup notes the way to deal with massive amounts of data is to use S3 to store your data. It can store exabytes of data without breaking a sweat so u can store all your relational and non relational data and not have to throw anything away because your data base does not scale like was the case on-premise. We call if your Data Lake. It has all your data. To get data into the Data Lake, we have data movement services and devices. These bring all the data into the Data Lake from on-premise systems using snowball devices, migrate databases into s3 using database migration service and bring data from IOT and real time ingestion using kinesis. And we have a set of purpose built tools that work directly on data in S3. Redshift is our exabyte scale Data Warehouse that can work directly on data in S3. Quicksight is our BI tool that can query the data directly in S3. Athena is our adhoc query service for data in S3 that you can use in place of Redshift if you have need to do adhoc query to data sitting in S3. EMR supports Spark, and Hadoop to directly make sense out of data in S3. And these tools are priced so inexpensively that you can make sense out of all your data and not have to delete data to save money. No need to do a ROI analysis on data. Redshift costs $1,000/TB per year instead of 10k to 50k like Teradata and Oracle on-premise systems. Athena can query data at a rate of ½ cent per GB and S3 can store GB of data for a whole month for 2.3 cents. Quicksight can query data for 30 cents for a 30 mt session instead of legacy tools that can cost you lot more money per user per month. You can give all your users access to all your data without breaking the bank and truly be a data driven organization.
- #13 AWS continues to innovate and find more efficient ways for you to analyze. We have an emerging serverless analytics stack – you can put all these systems together with zero infrastructure to manage. This lets you pay per use, with close to zero costs when things are ideal. Scales automatically; systems are highly available and fault tolerant by default. IoT data is a great example where an “always on” system provides continuous sensor data, but the analytics is on-demand and you pay for those services only when you use them. We also deliver our AI/ML services this way. While we offer GPUs and frameworks for experts, we also make higher level services for image and video analysis, transcription, translation etc, available via APIs with nothing for you to manage Simply put, we do a bunch of work behind the scenes, so you don’t have to.
- #29 As clouding services increases, cloud spending increases. We have to find where we can improve and optimize Provide visibility to prove the insight and lead to
- #31 Consumption model: turn off when work off -> pay as your demand Measure: business output Stop ? Analyze: Managed services: operation cost
- #32 More Detail
- #33 More Detail
- #34 3rd Analytic Tools
- #36 頁面美觀
- #38 描述詳細一點
- #39 描述詳細一點
- #40 描述詳細一點 補designer角色slide 看相聲 抑揚頓挫 笑話三個
- #42 Stop spending money on data center operations
- #43 Stop spending money on data center operations