Big Data Driven At Eway
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This document discusses how Eway uses big data and cloud computing. It describes how Eway collects raw transaction data using Google Cloud Compute Engine instances, converts the data to Apache Parquet files for efficient storage and querying, uploads the files to Google Cloud Storage, and then explores the data using tools like Google Cloud Datalab and Grafana/PowerBI for analytics and visualization. The goals are to generate business and operational insights, power product features, and monitor systems. Principles for building such a data system include keeping it simple, avoiding duplication, having single responsibilities, and focusing on scalability and cost efficiency.
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Big Data Driven At Eway
- 1. (Big) Data Driven at Eway Tu Pham - CTO @ Eway Journey to Google Cloud -- Ha Noi - 03/2019 --
- 2. About Me - CTO at Eway JSC - Google Developer Expert on Cloud Platform - Open source contributor, blogger, father - 8 years experience on Big data and Cloud Computing
- 17. How Do We Use This Data
- 18. Use Case - Reporting - Business Analytics - Operational Analytics - Product Features - System Monitoring
- 19. - Reporting to: - Partners - Advertisers - Publishers Reporting
- 20. Business Analytics - Analyzing - Growth - Users behavior - Sign up funnels - Sign up referrals - ...
- 21. Operational Analytics - Analyzing - Root cause analysis - Latency analysis - Error analysis - Better - Threshold alerts - Security alerts - Capacity planning (server, bandwidth)
- 22. Product Features - Product Features - Top Products - Adflex publisher challenge - Signup referrals - A/B Testing
- 24. Samole: End-To-End Flow For Mining User Behavior
- 25. How do we collect this data?
- 26. Step 1: GC Compute Engine Instances Collect Raw Data - Technology: Cloud Load Balancing, Compute Engine - Why Cloud Load Balancing: - TCP/UDP Load Balancing - Seamless Autoscaling - Scalable - Why Compute Engine: - High-Performance - Scalable - Low Cost - Fast Networking - Custom Machine Types
- 27. Step 1: GC Compute Engine Instances Collect Raw Data
- 28. How do we process this data?
- 29. Step 2: GC Compute Engine Instances Convert Raw Data To Apache Parquet Files - Technology: Compute Engine, Parquet file format - Why Parquet: - Self-describing, columnar storage format - Language-independent - High query-performance - Spark SQL is much faster with Parquet - High compression (up to 70%)- less disk IO
- 30. Step 2: GC Compute Engine Instances Convert Raw Data To Apache Parquet Files
- 31. Step 2: GC Compute Engine Instances Convert Raw Data To Apache Parquet Files
- 32. - Technology: Compute Engine, Parquet file format, Cloud Storage - Why Cloud Storage: - Four storage classes - Easy to integrate - Object Lifecycle Management - Fast Networking Step 3: GC Compute Engine Upload Parquet File To GC Cloud Storage
- 33. Step 3: GC Compute Engine Upload Parquet File To GC Cloud Storage
- 34. Step 3: GC Compute Engine Upload Parquet File To GC Cloud Storage
- 35. Step 3: GC Compute Engine Upload Parquet File To GC Cloud Storage
- 36. Step 3: GC Compute Engine Upload Parquet File To GC Cloud Storage
- 37. How do we visualize this data
- 38. Step 4a: Explore Dataset Using GC Datalab - Technology: Cloud Datalab - Why Datalab: - Integrated with: Cloud BigQuery, Cloud Machine Learning Engine, Cloud Storage, and Stackdriver Monitoring - IPython Support & Notebook Format - Interactive Data Visualization - Multi-Language Support: Python, SQL, and JavaScript (for BigQuery user-defined functions
- 39. Step 4b: Explore Dataset Using BI Tools - Technology: Grafana, PowerBI - Why: - Support >40 data sources (File, Database, Log Stream, Zabbix, Google Analytic, Google Calendar, AWS Cloudwatch, Jira, ...) - Query, visualize, alert on and understand your metrics - Create, explore, and share dashboards with your team
- 40. Step 4a: Explore Dataset Using GC Datalab
- 45. Become Geek
- 46. Where Are The AI / ML
- 47. Create Your Principles Principles: - KISS (Keep it simple, stupid) - DRY (Don’t Repeat Yourself) - Single Responsibility - Low Cost - Scalable
- 48. Be 1% better everyday tips Create your system principles Design system architecture, data flow, data model, data structure first Separate realtime and batch flows Separate data storage strategies between data types Save the cost by network cost, instances cost, storage cost by metric monitoring & alert system
- 49. We Are Hiring ● Product Owner ● Backend Java Developer ● Full Stack PHP Developer ● Full Stack Python Developer ● DevOps Engineer
- 50. Thank You - Q&A ● Eway: https://eway.vn ● My Contact: tupp@eway.vn