Better Together: How Graph database enables easy data integration with Spark and Kafka in the Cloud
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The document provides an overview of how TigerGraph integrates with Apache Spark and Kafka for data ingestion and real-time processing in the cloud. It discusses system architecture, data pipeline features, and the benefits of using TigerGraph for analytics, machine learning, and data integration. It also highlights various data loading approaches, including bulk loading and real-time streaming, to enhance data handling capabilities.
Better Together: How Graph database enables easy data integration with Spark and Kafka in the Cloud
- 1. Better Together: How Graph database enables easy data integration with Spark and Kafka in the Cloud September 30th 2020 1
- 2. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Today's Speakers Emma Liu Product Manager ● BS in Engineering from Harvey Mudd College, MS in Engineering Systems from MIT ● Prior work experience at Oracle and MarkLogic ● Focus - Cloud, Containers, Enterprise Infra, Monitoring, Management, Connectors Rayees Pasha Product Manager ● MS in Computer Science from University of Memphis ● Prior Lead PM and ENG positions at Workday, Hitachi and HP ● Expertise in Database Management and Big Data Technologies 2
- 3. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | 1 TigerGraph Architecture and Data Ingestion Overview TigerGraph and Spark Data Pipeline TigerGraph and Kafka Data Pipeline Today’s Outline 3 2 3
- 4. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | SYSTEM ARCHITECTURE OVERVIEW 4
- 5. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | The TigerGraph Difference Feature Design Difference Benefit Real-Time Deep-Link Querying ● Native Graph design ● C++ engine, for high performance ● Storage Architecture ● Uncovers hard-to-find patterns ● Operational, real-time ● HTAP: Transactions+Analytics Handling Massive Scale ● Distributed DB architecture ● Massively parallel processing ● Compressed storage reduces footprint and messaging ● Integrates all your data ● Automatic partitioning ● Elastic scaling of resource usage In-Database Analytics ● GSQL: High-level yet Turing-complete language ● User-extensible graph algorithm library, runs in-DB ● ACID (OLTP) and Accumulators (OLAP) ● Avoids transferring data ● Richer graph context ● In-DB machine learning 5 to 10+ hops deep 5
- 6. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | TigerGraph Architecture
- 7. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Data Ingestion 7 Step 3 Each GPE consumes the partial data updates, processes it and puts it on disk. Loading Jobs and POST use UPSERT semantics: ● If vertex/edge doesn't yet exist, create it. ● If vertex/edge already exists, update it. ● Idempotent Step 1 Data integration through the following ways to ingest in user source data. ● Bulk load of data files or a Kafka stream in CSV or JSON format ● HTTP POSTs via REST services (JSON) ● GSQL Insert commands Step 2 Dispatcher takes in the data ingestion requests in the form of updates to the database. 1. Query IDS to get internal IDs 2. Convert data to internal format 3. Send data to one or more corresponding GPEs
- 8. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Data Ingestion 8 Incremental Data Nginx Restpp GPE GPE GPE Disk Disk Disk CSV/JSON Insert/Update/Delete Vertices and Edges Listen to corresponding topic for new messages Acknowledge Response Incoming Outgoing Synchronize data to disk GSE(IDS) ID Translation Kafka Kafka Kafka Server 1 Server 2 Server 3 Kafka Cluster In-memory copy of data
- 9. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Spark and TigerGraph 9
- 10. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Spark + TigerGraph Data Pipeline
- 11. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Typical Spark + TigerGraph Integration ● Data Preparation and Integration (TigerGraph/Spark) ● Unsupervised Learning (TigerGraph) ● Feature Extraction for Supervised Learning (TigerGraph/Spark) ● Model Training (Spark) ● Validate and Apply Model (TigerGraph) ● Visualize and Explore Interconnected Data (TigerGraph) 11
- 12. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Spark and TigerGraph Data Pipeline Static Data Sources TigerGraph JDBC Driver Streaming Data Sources 12
- 13. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | JDBC Driver ● Type 4 driver ● Support Read and Write bi-directional data flow to TigerGraph ● Read: Converts ResultSet to DataFrame ● Write: Load DataFrame and files to vertex/edge in TigerGraph ● Supports REST endpoints of built-in, compiled and interpreted GSQL queries from TigerGraph ● Open Source: ● https://github.com/tigergraph/ecosys/tree/master/tools/etl/tg-jdbc-driver 13
- 14. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Supervised ML with TigerGraph - Detecting Phone-Based Fraud by Analyzing Network or Graph Relationship Features at China Mobile Download the solution brief at - https://info.tigergraph.com/MachineLearning 14
- 15. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | DEMO 15
- 16. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Kafka and TigerGraph 16
- 17. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Kafka and TigerGraph Data Pipeline Static Data Sources Streaming Data Sources Kafka Loader 17
- 18. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Kafka Loader - Speed to Value from Real-time Streaming Data • Reduce Data Availability Gap and Accelerate Time to Value • Native Integration with Real-time Streaming Data and Batch Data • Enables Real-time Graph Feature Updates with Streaming Data in Machine Learning Use Cases • Decrease Learning Curve With Familiar Syntax • GSQL Support with Consistent Data Loading Syntax • Maintain Separation of Control for Data Loading • Designed with Built-in MultiGraph Support 18
- 19. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Kafka Loader : Three Steps Consistent with GSQL Data Loading Steps Step 1: Define the Data Source Step 2: Create a Loading Job Step 3: Run the Loading Job 19
- 20. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | Kafka Loader High Level Architecture ● Connect to External Kafka Cluster ● User Commands Through GSQL Server ● Configuration Settings: ○ Config 1: Kakfa Cluster Configuration ○ Config 2: Topic/Partition/Offset Info 20
- 21. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | DEMO 21
- 22. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | TigerGraph Architecture + Spark + Kakfa 22
- 23. Get Started for Free ● Try TigerGraph Cloud ( tgcloud.io ) ● Download TigerGraph’s Developer Edition ● Take a Test Drive - Online Demo ● Get TigerGraph Certified ● Join the Community @TigerGraphDB /tigergraph /TigerGraphDB /company/TigerGraph 23