DataStax Enterprise (DSE) is a multi-model data platform that supports various data models through a unified backend, primarily aimed at enhancing OLTP and OLAP workloads. It features a highly scalable graph database that allows for the efficient management of complex relationships within data, making it ideal for applications in IoT, customer data analytics, and network management. The platform integrates seamlessly with Apache Cassandra and supports real-time analytics and query capabilities through the Gremlin graph query language.

1. DataStax Enterprise: The Multi-Model Data Platform

2. 1 Multi-Model Defined
2 The evolution of Models
3 Graph Databases Overview
4 The DataStax Approach to Graph Databases
5 DataStax in the Open Source Graph Community
6 Conclusion
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3. Multi Model Defined
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Most database management systems are
organized around a single data model that
determines how data can be organized,
stored, and manipulated. In contrast, a multi-
model database is designed to support
multiple data models against a single,
integrated backend.
Source - https://en.wikipedia.org/wiki/Multi-model_database

4. The evolution of Models
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Application
Application
Application
(OLTP and OLAP)
Data Access Abstraction
RDBMS / SQL Polyglot
Persistence
Multi
Model

5. DSE Multi Model
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Multiple (polygot) Models exposed via
cohesive interaction mechanisms, (APIs)
for OLTP and OLAP workloads, (mixed workload)
with a unified persistence layer, (Apache Cassandra)
providing GR, always on characteristics
within a TCO efficient data base platform
for a variety of use cases
Cassandra

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6
DataStax Enterprise – Wide Row / MVs
C1 C2
MV1c1
MV1c2
Agg1c1
Agg2c1

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7
DataStax Enterprise - JSON
Inserting JSON data is easy
Reading JSON data is easy
Finding JSON errors is easy

8. Introduction to Graph Databases
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9. What is a Graph Database?
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• Store, manage and query highly connected data
• Data stored as nodes (vertices), edges and properties to represent
a domain model
• By explicitly embedding relationships in the data model, you store
a more logical business model using the natural data access
language Gremlin
• Think of a graph as
a pre-joined
database

10. Choose the Right Model to Fit your Business Needs
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DSE Wide Row
- Build and maintain models using
CQL’s DDL features
- Super-fast CRUD at scale with CQL
DML features
- Good option for denormalized
schemas with high-throughput
requirements
- Perfect fit for IoT applications that
require consuming enormous
amounts of data with specific
data retrieval requirements:
product catalogs, high-volume
messaging systems, collecting
and storing sensor data
DSE Graph
- Flexible schema that is easy to modify
and maintain with Gremlin
- Clearly maps business semantics to a
logical model for easy maintenance
and understanding
- Ideal model for highly-connected data
models
- Perfect fit for social-engagement models,
recommendation engines and IT
network / device management
- Update and query the graph in real-time
with easy-to-learn open-source
Gremlin language
- Good option for transitioning from slow
RDBMS 3NF models with lots of
JOINs

11. What is DataStax Enterprise (DSE) Graph?
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• Highly scalable graph database for modern web, mobile, and IoT applications that
need to manage highly connected and heterogeneous data
• Built-in support for real-time search, and analytic graph queries via tight integration
with the DSE platform
• A property graph model native inside the DataStax product, engineered specifically
for Cassandra
• Store & find relationships in data fast and easy on huge graphs

12. DSE Graph: Built-in Scalability
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• Scale out Graph vs. Scale up only
• Graph partitioning built on Cassandra’s scale-out architecture
• Graph index structures integrated into Cassandra
• Domain model maps more naturally to data model, allowing for greater
understanding between business and IT
• Traverse millions of relationships in a short period of
time, faster than modeling the data in RDBMS
• Flexible data model that can be easily adapted to
business changes

13. DSE Graph: Integrated Search, Analytics and Ops
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• Real-time traversals over complex-structured graph data
• Integrates with DSE Search to mix search with traversal queries
• Integrates with DSE Analytics and Spark to support OLAP and breadth-first
graph traversals
• Iterative graph analytics like PageRank or other centrality measures
• Reporting and aggregates over graph data
• Integrated with DataStax OpsCenter

14. Graph Database Use Cases
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15. Additional Graph Use Cases
360 Degree View of Your Customer
• Collect massive amounts of data point about your customer
• Data collected from social networks, web analytics, digital ads, mobile devices, CRM
• Bring heterogeneous customer data together into DSE Graph
• Uncover buying patterns and customer behaviors
• Graph becomes a master data hub for customer data
• Use the graph customer hub to build better products
for your target customers
• Keep the customers you already have with
customer intelligence
Customer
360 View
Social
Store
Sensors
Email
CRM
Mobile
Weblogs

16. Additional Graph Use Cases
IT Network and Device Management
• Allows IT to monitor, manage and protect corporate networks and devices
(laptops, iPads, mobile phones, etc.)
• Requires understanding of network topologies and relationships between
devices, interfaces, equipment, people, services …
• A traditional RDBMS would require
expensive query-time joins
• A graph model intrinsically knows how
to traverse the topology because the
relationships are already stored
• This makes for quick & easy recognition
of problems, root cause analysis and
event correlation

17. DataStax in the Graph Open Source
Community
TinkerPop / Gremlin
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18. DataStax Role in TinkerPop
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• DataStax utilizes the TinkerPop framework for the DSE Graph
product
• DataStax will contribute to the TinkerPop community and is
heavily invested in the success of the Gremlin language
• DataStax will provide resource guides, documentation,
samples and training on building and querying graphs
with Gremlin, using DSE Graph as the graph engine

19. Gremlin
The open-source standard graph query language
• DataStax contributes and supports the Apache TinkerPop community, along with the Gremlin Graph query
language
• g.V.hasLabel('person').as('a').out('knows').as('b').select('a','b').by('age')
.by('age')
"for all people in the graph, give me the ages of the people on each end of a friendship relationship“
• g.V.has('name','marko').out('knows').out('mother').outE('worksFor').
has('time',between(2001,2002)).inV.name
“what are the names of the places that marko's friends' mothers worked for from 2001 to 2002”
Deep traversal == multiple levels of query-time joins in RDBMS

20. Recommendation Query – RDBMS vs. Graph
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SELECT TOP (5) [t14].[ProductName]
FROM (SELECT COUNT(*) AS [value],
[t13].[ProductName]
FROM [customers] AS [t0]
CROSS APPLY (SELECT [t9].[ProductName]
FROM [orders] AS [t1]
CROSS JOIN [order details] AS [t2]
INNER JOIN [products] AS [t3]
ON [t3].[ProductID] = [t2].[ProductID]
CROSS JOIN [order details] AS [t4]
INNER JOIN [orders] AS [t5]
ON [t5].[OrderID] = [t4].[OrderID]
LEFT JOIN [customers] AS [t6]
ON [t6].[CustomerID] = [t5].[CustomerID]
CROSS JOIN ([orders] AS [t7]
CROSS JOIN [order details] AS [t8]
INNER JOIN [products] AS [t9]
ON [t9].[ProductID] = [t8].[ProductID])
WHERE NOT EXISTS(SELECT NULL AS [EMPTY]
FROM [orders] AS [t10]
CROSS JOIN [order details] AS [t11]
INNER JOIN [products] AS [t12]
ON [t12].[ProductID] = [t11].[ProductID]
WHERE [t9].[ProductID] = [t12].[ProductID]
AND [t10].[CustomerID] = [t0].[CustomerID]
AND [t11].[OrderID] = [t10].[OrderID])
AND [t6].[CustomerID] <> [t0].[CustomerID]
AND [t1].[CustomerID] = [t0].[CustomerID]
AND [t2].[OrderID] = [t1].[OrderID]
AND [t4].[ProductID] = [t3].[ProductID]
AND [t7].[CustomerID] = [t6].[CustomerID]
AND [t8].[OrderID] = [t7].[OrderID]) AS [t13]
WHERE [t0].[CustomerID] = N'ALFKI'
GROUP BY [t13].[ProductName]) AS [t14]
ORDER BY [t14].[value] DESC
g.V('customerId','ALFKI').as('customer')
.out('ordered').out('contains').out('is').as('products')
.in('is').in('contains').in('ordered').except('customer')
.out('ordered').out('contains').out('is').except('products')
.groupCount().cap().orderMap(T.decr)[0..<5].productName
VS.

21. Thank you