AWS Analytics Immersion Day - Build BI System from Scratch

- Hands-on Lab
https://serverless-bi-system-from-scratch.workshop.aws/
- 웨비나 동영상
https://youtu.be/FX5iWHFn1v0
- 데모 영상
https://youtu.be/d76zNxO2Qtk

Transcript

1. Sungmin Kim Solutions Architect, AWS Analytics Immersion Day Build BI System from Scratch
2. Itinerary to Analytics on
3. What is Architecting? Art of Trade-off
4. Data The world’s most valuable resource is no longer oil, but data.* *Copyright: The Economist, 2017, David Parkins “ ”
5. Answers & Insights Collect Consume Store Process/ Analyze Data 1 4 0 9 5 *Copyright: Fractionating column, 2009 science-resources.co.uk
6. 3+1 Vs of Big Data
7. Structured, Unstructured, and Semi-Structured
8. Data Temperature Spectrum Structure Hot data Warm data Cold data Low High High Request rate Low High Cost / GB Low High Latency Low High Data Volume Low In-Memory SQL NoSQL Search Object Storage Archive Storage Graph
9. Simplify Big Data Processing Collect Consume Store Process/ Analyze Data 1 4 0 9 5 Answers & Insights Time to answer(Latency) Throughput Cost
10. Let’s build Business Intelligence System
11. Business Intelligence System CRM IoT WEB Messages CDC* Event Streams * CDC: Change Data Capture
12. Business Intelligence System QuickSight Amazon RDS
13. Relational Databases Flat Files And Many Others! Retail Data Ops Data Marketing Data Amazon QuickSight Fast BI Service with Pay-per-Session Pricing and ML Insights for everyone
14. Is it Well-Architected? QuickSight Amazon RDS
15. Is it Well-Architected? QuickSight Amazon RDS
16. AWS Well-Architecture Framework Performance efficiency Cost optimization Security Reliability Operational excellence Set of questions you can use to evaluate how well an architecture is aligned to AWS best practices
17. Is it Well-Architected? QuickSight Amazon RDS Operation excellence ü Scale-up: CPU, Memory, Disk ü Connection Pooling ü Schema changes: alter table ü Schema validation Reliability ü High availability: Master-Slave fail-over ü Data loss Performance efficiency ü Slow SQL queries caused by poor database indexing ü Number of writes >>> Number of reads Cost optimization ü Management of RDMS ü Compute(Instance), Storage cost Security ü Encryption data at rest and in transition
18. Key considerations for storing data • Storage Volume • Data Model • Data Scheme • Access Patterns ü SQL Supported ü Put/Get(key, value) ü Range Query ü Join Query • Serverless or Managed Service • Current skill set
19. Business Intelligence System QuickSight Redshift S3 Amazon RDS DocumentDB (with MongoDB compatability) DynamoDB Object Storage Data Warehouse NoSQL Elasticsearch Service
20. Business Intelligence System QuickSight Redshift S3 Elasticsearch Service DocumentDB (with MongoDB compatability) DynamoDB Object Storage Data Warehouse NoSQL DynamoDB
21. Business Intelligence System QuickSight DocumentDB (with MongoDB compatibility) Redshift S3 Elasticsearch Service DocumentDB (with MongoDB compatability) DynamoDB Object Storage Data Warehouse NoSQL
22. Business Intelligence System QuickSight Elasticsearch Service Redshift S3 Elasticsearch Service DocumentDB (with MongoDB compatability) DynamoDB Object Storage Data Warehouse NoSQL
23. Business Intelligence System QuickSight Redshift Redshift S3 Elasticsearch Service DocumentDB (with MongoDB compatability) DynamoDB Object Storage Data Warehouse NoSQL
24. Business Intelligence System QuickSight S3 Redshift S3 Elasticsearch Service DocumentDB (with MongoDB compatability) DynamoDB Object Storage Data Warehouse NoSQL
25. Storage Engine Comparison S3 Redshift Elasticsearch DocumentDB DynamoDB Storage volume Unlimited Limited (~ tens of TB) Limited (~ tens of TB) Limited (~ tens of TB) Unlimited Data model Object Relational Document Document Key-value Data scheme Schema-free Fixed schema Schema-free (JSON) Schema-free (JSON) (Key, value) SQL supported Query engine dependent Yes Yes No No Put/Get (key, value) Yes Yes Yes Yes Yes Range query Query engine dependent Yes Yes Yes Difficult Join query Query engine dependent Yes No Difficult Very Difficult Serverless Yes No No Yes Yes v https://db-engines.com/en/system/Amazon+DocumentDB%3BAmazon+DynamoDB%3BAmazon+Redshift%3BElasticsearch
26. Use Amazon S3 as Your Persistent Data Store • Natively supported by big data frameworks (Spark, Hive, Presto, etc.) • Decouple storage and compute • No need to run compute clusters for storage (unlike HDFS) • Can run transient Amazon EMR clusters with Amazon EC2 Spot Instances • Multiple & heterogeneous analysis clusters and services can use the same data • Designed for 99.999999999% durability • No need to pay for data replication within a region • Secure: SSL, client/server-side encryption at rest • Low cost
27. Business Intelligence System S3 QuickSight Ingestion Query engine
28. Business Intelligence System Kinesis Data Firehose S3 QuickSight
29. Amazon Kinesis Data Firehose Prepare and load real-time data streams into data stores and analytics tools
30. Business Intelligence System Kinesis Data Firehose S3 QuickSight Glue Athena EMR Batch Interactive
31. Comparison of SQL Processing engines Data Structure Semi Semi Semi Full Languages API/SQL SQL SQL SQL Data Store S3 (Glue), S3/HDFS (Spark) S3/HDFS S3 Local Use case Transformation SQL Queries for S3/HDFS Serverless SQL Queries for S3 Fully Featured SQL Database Performance AWS Glue Amazon Athena Amazon Redshift
32. Business Intelligence System Kinesis Data Firehose S3 Athena QuickSight
33. Data Processing: ETL vs ELT Source1 Source2 Target Source1 Source2 Staging tables Final tables Target (MPP database) Extract Transform Load E → T → L Extract & Load Transform E → L → T Source3 Source3
34. How about Real-time Analytics? Kinesis Data Firehose S3 Athena QuickSight Real-time Analysis Layer ü Streaming data processing ü Latency: milliseconds ~ seconds Messages CDC Event Streams
35. Real-time Analytics Kinesis Data Firehose S3 Athena QuickSight Real-time Analysis Layer SQS Kinesis Data Streams MSK ü Streaming data processing ü Latency: milliseconds ~ seconds
36. • Decouple producers & consumers • Persistent buffer • Collect multiple streams • Preserve client ordering • Parallel consumption • Streaming MapReduce 4 4 3 3 2 2 1 1 4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 4 4 3 3 2 2 1 1 Producer 1 shard 1 / partition 1 shard 2 / partition 2 Consumer 1 Count of red = 4 Count of violet = 4 Consumer 2 Count of blue = 4 Count of green = 4 Producer 2 Producer 3 Producer n Key = Red Key = Green Key = Blue Key = Violet Amazon Kinesis Data Streams Amazon Managed Streaming for Kafka … Why Stream Storage?
37. • Decouple producers & consumers • Persistent buffer • Collect multiple streams • No client ordering (standard) • FIFO queue preserves client ordering • No streaming MapReduce • No parallel consumption • Amazon SNS can publish to multiple SNS subscribers (queues or Lambda functions) Consumers 4 3 2 1 1 2 3 4 4 3 2 1 1 2 3 4 2 1 3 4 1 3 3 4 2 Standard FIFO Producers Amazon SQS Queue Publisher Amazon SNS Topic AWS Lambda function Amazon SQS queue Queue Subscriber What about Amazon SQS?
38. Real-time Analytics Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Real-time Analysis Layer ü Streaming data processing ü Latency: milliseconds ~ seconds
39. Real-time Analytics Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Kinesis Data Analytics on EMR Elasticsearch Service Real-time Analysis Layer on EMR
40. RDD Operator RDD RDD … Operator Operator Data Sink Operator Operator Data Source Amazon EMR Applications Framework Process Layer Data Layer Infrastructure S3 EMRFS Amazon S3 Instances Spot Instances
41. Real-time Analytics with Spark or Flink on EMR, RDS Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams QuickSight EMR Amazon RDS
42. Real-time Analytics with Spark or Flink on EMR, DynamoDB Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams EMR DynamoDB real-time dashboard
43. Real-time Analytics with Spark or Flink on EMR, ElastiCache Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams EMR real-time dashboard ElastiCache
44. Amazon Kinesis Data Analytics Easily process and analyze streaming data with standard SQL
45. Real-time Analytics with Kinesis Data Analytics, RDS Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Kinesis Data Analytics Lambda function QuickSight Amazon RDS Kinesis Data Streams
46. Real-time Analytics with Kinesis Data Analytics, DynamoDB Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Kinesis Data Analytics Lambda function Kinesis Data Streams real-time dashboard DynamoDB
47. Real-time Analytics with Kinesis Data Analytics, ElastiCache Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Kinesis Data Analytics Lambda function Kinesis Data Streams real-time dashboard ElastiCache
48. Amazon Elasticsearch Service Fully managed, scalable, and secure Elasticsearch service
49. Real-time Analytics with Elasticsearch Service Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Lambda function Elasticsearch Service Kibana
50. 3 ways to build Real-time Analytics Kinesis Data Streams Lambda function Elasticsearch Service Kibana EMR real-time dashboard ElastiCache Kinesis Data Analytics Lambda function QuickSight Amazon RDS Kinesis Data Streams DynamoDB 1 2 3
51. Business Intelligence System Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Lambda function Elasticsearch Service Kibana
52. Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Lambda function Elasticsearch Service Kibana Batch Layer Serving Layer Speed Layer Business Intelligence System Lambda Architecture
53. Lambda vs Kappa Architecture Lambda Kappa
54. Beyond Data Analytics: Expand Business Intelligence • Network(Graph) Analysis • Social networking • Recommendation engines • Fraud detection • Knowledge Graphs • Recommendation • Personalized recommendations • Personalized search • Personalized notifications • Machine Learning • Forecast(Predictive Analytics) • Classification • Sentiment Analysis • and more …
55. Amazon Neptune Fully managed graph database Social Networking Recommendation Engines Fraud Detection Knowledge Graphs
56. Beyond Data Analytics – Network Analysis(with Neptune) Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Lambda function Elasticsearch Service Kibana Neptune Lambda function API Gateway Lambda function
57. Deliver high-quality recommendations Deliver personalization in days, not months Real-time Works with any product or content Amazon Personalize Real-time personalization and recommendation, based on the same technology used at Amazon.com
58. Beyond Data Analytics: Recommendation(with Personalize) Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Lambda function Elasticsearch Service Kibana S3 ElastiCache API Gateway Event (time-based) Lambda function Personalize Lambda function EMR
59. Fully managed hosting with auto scaling One-click deployment Notebook instances Built-in, high- performance algorithms Automatic model tuning One-click training Build Train Deploy Amazon SageMaker Build, Train, and Deploy Machine Learning Models Quickly & Easily, at Scale
60. Beyond Data Analytics: Machine Learning(with SageMaker) Kinesis Data Firehose S3 Athena QuickSight Kinesis Data Streams Lambda function Elasticsearch Service Kibana Train Notebook Model Models bucket SageMaker Endpoint SageMaker
61. Business Intelligence System
62. Journey to Analytics on
63. Collect Consume Store Process/ Analyze Data 1 4 0 9 5 Answers & Insights Amazon Kinesis Data Firehose Amazon Kinesis Data Streams Amazon Managed Streams for Kafka Amazon S3 Amazon Kinesis Data Analytics AWS Glue Amazon EMR Amazon Athena Amazon QuickSight Amazon Redshift Amazon Elasticsearch Service Amazon Machine Learning AWS Lambda
64. Lessons Learned: Architectural Principles • Build decoupled systems - Data → Store → Process → Store → Analyze → Answers • Use the right tool for the job - Data structure, latency, throughput, access patterns • Leverage managed and serverless services - Scalable/elastic, available, reliable, secure, no/low admin • Use log-centric design patterns - Immutable logs (data lake), materialized views • Be cost-conscious - Big data ≠ Big cost • Working backwards - Design from consume to collect
65. Learn more - Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AWS re:Invent 2018 https://www.slideshare.net/AmazonWebServices/big-data-analytics-architectural-patterns-and-best- practices-ant201r1-aws-reinvent-2018 - Everything You Need to Know About Big Data: From Architectural Principles to Best Practices https://www.slideshare.net/AmazonWebServices/everything-you-need-to-know-about-big-data- from-architectural-principles-to-best-practices - Big Data Analytics Options on AWS https://d1.awsstatic.com/whitepapers/Big_Data_Analytics_Options_on_AWS.pdf - AWS Big Data Blog https://aws.amazon.com/ko/blogs/big-data - AWS Well-Architected Labs https://wellarchitectedlabs.com
66. Q & A