Webinar - Bringing connected graph data to Cassandra with DSE Graph

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For today’s always-connected customer, modern digital cloud applications need to manage highly connected data with seemingly endless data relationships. DataStax Enterprise with DSE Graph is the only distributed data platform able to support the transactional and analytical complex data relationships contained in such systems. Learn how DSE Graph can support your highly connected systems and answer questions such as, how do my customers interact with my business, where is the bottleneck in my supply chain, what recommendation makes the most sense for my customer in a particular moment?

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1 .Overview of DataStax Enterprise Graph Scalable graph capabilities with the same powerful scale-out architecture as Apache Cassandra™ May 24, 2017 Jeremy Hanna, Solutions Architect Andrew Lampitt, Product Marketing

2 . Agenda 1. About DataStax 2. Introduction to DataStax Enterprise (DSE) Graph 3. Graph Use Case 4. DSE Graph – an always-on Graph solution 5. Solving the Use Case, DSE Graph’s Implementation Details 6. Getting Started with DSE Graph © DataStax, All Rights Reserved. DataStax, Titan, and TitanDB are registered trademark of DataStax, Inc. and its subsidiaries in the United States and/or other countries. Apache Cassandra, Apache, Tomcat, Lucene, Solr, Hadoop, Spark, TinkerPop, and Cassandra are trademarks of the Apache Software 2 Foundation or its subsidiaries in Canada, the United States and/or other countries.

3 . From validation to momentum 400+ $190M 500+ Employees Funding Customers Founded in April 2010 Santa Clara • San Francisco • Austin • London • Paris • Berlin • Tokyo • Sydney (Series E – Sept. 2014) 30% + 2016 World’s Best Ranked #1 in multiple operational 100 Cloud Companies database categories © DataStax, All Rights Reserved.

12 .DataStax Enterprise Graph The world’s most highly available, scalable graph database Built on the best distribution of Apache Cassandra™ • Designed for real-time use cases – E.g. Customer 360°, Real-time Recommendations, Fraud Detection, etc. • Manage complex and highly connected data • Discover commonalities and anomalies in data © DataStax, All Rights Reserved.

15 . • C360 Examples of Graph- Understand and analyze customer Shaped relationships consolidated across business units for a holistic view Business Problems (1 of 2) • Personalization Tailor the experience • Recommendations Offer relevant products and services • Fraud detection Determine which entity, transaction or interaction is fraudulent, poses a security risk, or is a compliance concern 15 © DataStax, All Rights Reserved.

16 . • IoT, Asset Management, Networks Examples of Graph- Easily perform analysis on relationships among data elements. Understand how they relate to Shaped each other over time. Business Problems (2 of 2) 16 © DataStax, All Rights Reserved.

18 . Typical Needs/Requirements for Graph Category Item Data Access Fast, flexible, relationship-centric data access Relationship-centric, exploratory data access Batch export of records Real-time record manipulation (Create, Update, Delete) Security Security (encryption, authorization, authentication, etc) Data Integration Batch ingestion capabilities across many different data sources Real-time data ingestion capabilities (streaming, events, services) Identity Matching Business logic layer (e.g. identification, match, merge, general data enrichment) Persistence Scalable and always available data repository Analysis Data exploration, visualization, ad hoc analysis tools 18 © DataStax, All Rights Reserved.

19 . DSE Graph Approach Typical Use Case Conceptual Architecture … DSE Advanced Security Data Access Analysis File rdbms Drivers and Dev Tools DSE Graph Drivers Visualization Studio other OLAP Traversal and Customer or 3rd OLTP Traversal OLTP Traversal DSEGraphFrames Batch Export Party Provided Logic DSE Graph Indicates Partner Real Time Data Ingestion via DSE Driver Graph Loader and DSEGraphFrames Streaming via DSE Analytics Spark Streaming Identification, Masking, Match/Merge and Enrichment Processing Events Stream CDC File rdbms other 19 © DataStax, All Rights Reserved.

22 . DSE Graph – Persistence | Adjacency List • Adjacency list storage optimized for graph database performance • Cassandra storage system optimized for availability and scale Consecutive, Sorted List Storage Vertex 1 Edge 1 Edge 2 Edge 3 Edge 4 Edge … 22 © DataStax, All Rights Reserved.

23 . DSE Graph – Data Access | Traversals • Query Access => Apache TinkerPop Gremlin • Traversal style data navigation Find all orders purchased by Lisa g.V().has('customer', 'name', 'Lisa').out('ordered').values('number') Find all products purchased by Lisa’s Friends g.V().has('customer', 'name', 'Lisa') .outE('related').has('Type','friend').inV().out('ordered') .out('purchased').values('name’) Recommendation - find all products purchased by Lisa’s Friends, not purchased by Lisa g.V().has('customer', 'name', 'Lisa') .as('customer').out('ordered').out('purchased') .aggregate('products').in('purchased').in('ordered') .where(neq('customer')).out('ordered').out('purchased') .where(without('products')).values('name') 23 © DataStax, All Rights Reserved.

24 . DSE Graph – Data Access | Global Indexes • DSE Graph has seamless integration with DSE Search • DSE Graph’s query optimizer automatically uses the right Index behind the scenes • Search workloads can be separated from OLTP workloads. • DSE Graph maintains the DSE Search core • Advanced users can configure the core directly • Three Options Available • DSE Search Integration • Cassandra 2is • Materialized Views • DSE Search Indexes Supports • Fuzzy and Phrase indexing for Text searching • Geospatial indexing/searching for Points, LineStrings, and Polygons 24 © DataStax, All Rights Reserved.

25 . DSE Graph – Data Access | Analytics • DSE Graph has seamless integration with DSE Analytics • OLAP workloads can be separated from OLTP workloads. • Key OLAP Features: • Deep Traversals • Scan Traversals • Subgraph Snapshots – persisted RDDs • Counts • Two options for analytics • DSE Graph Frames • Gremlin OLAP • Both options leverage Spark but do so differently 25 © DataStax, All Rights Reserved.

26 . DSE Graph – Data Access | DSE Drivers ● Recommended path is DSE Drivers ● All DSE Drivers support graph – Java, Python, C#, node.js, C/C++, Ruby, PHP ● Gremlin Fluent API – Starting with Java and Python ● Cluster aware ● Node health aware 26 © DataStax, All Rights Reserved.

27 . DSE Graph – Security | Authentication & Authorization • Internal authentication and LDAP support • Kerberos authentication support • Object level permissions are at CQL table level • Future roadmap item for DSE Graph 27 © DataStax, All Rights Reserved.

28 . DSE Graph – Security | Encryption • Client to node encryption with DSE Drivers • Node to node encryption • Transparent data encryption • Data auditing is available 28 © DataStax, All Rights Reserved.

29 . DSE Graph – Data Ingestion | Graph Loader • Simplifies loading large amounts of data from various sources into DSE Graph efficiently and robustly • Inspects incoming data for schema compliance • Uses declarative data mappings and custom transformations to handle diverse types of data • Streamlined write path for custom vertex IDs (5.1) 29 © DataStax, All Rights Reserved.

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