Neo4j provides professional services to help customers build intelligent solutions using graphs. These services include packaged services, staff augmentation, training, and managed services. Neo4j helps customers at every stage from requirements gathering to production support. Solution accelerators like frameworks and reference architectures help speed delivery. Customers see benefits like reduced complexity, data storage needs, and infrastructure costs when moving from traditional databases to Neo4j.
With the introduction of the Neo4j Graph Platform and increased adoption of graph database technology across all industries, now is a better time than ever to get started with graphs.
Join us for this introduction to Neo4j and graph databases. We'll discuss the primary use cases for graph databases and explore the properties of Neo4j that make those use cases possible.
Neo4j GraphTalks Oslo - Graph Your Business - Rik Van Bruggen, Neo4jNeo4j
The Neo4j graph database is the fastest growing database engine in the market and has hundreds of customer references across Europe and globally, solving significant technology problems for large Enterprises in Finance, Telco, Retail, Utilities, Logistics and Internet sectors. Typical use cases are Recommendations, Fraud Detection, MDM, Network and Software Analysis and Optimization, Identity and Access Management.
Network and IT Ops Series: Build Production Solutions Neo4j
Jeff Morris, Director, Neo4j:Are you building a breakthrough product or extending an existing one? Do you need introduce new capabilities based on insights from data relationships? If so, you should consider embedding a graph database.
For software providers building products to assure quality network operations or security, using an embedded graph database may open new customer opportunities. Watch this webinar to learn how you can easily differentiate your applications and take your solutions to market faster with a native graph database like Neo4j.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
With the introduction of the Neo4j Graph Platform and increased adoption of graph database technology across all industries, now is a better time than ever to get started with graphs.
Join us for this introduction to Neo4j and graph databases. We'll discuss the primary use cases for graph databases and explore the properties of Neo4j that make those use cases possible.
Neo4j GraphTalks Oslo - Graph Your Business - Rik Van Bruggen, Neo4jNeo4j
The Neo4j graph database is the fastest growing database engine in the market and has hundreds of customer references across Europe and globally, solving significant technology problems for large Enterprises in Finance, Telco, Retail, Utilities, Logistics and Internet sectors. Typical use cases are Recommendations, Fraud Detection, MDM, Network and Software Analysis and Optimization, Identity and Access Management.
Network and IT Ops Series: Build Production Solutions Neo4j
Jeff Morris, Director, Neo4j:Are you building a breakthrough product or extending an existing one? Do you need introduce new capabilities based on insights from data relationships? If so, you should consider embedding a graph database.
For software providers building products to assure quality network operations or security, using an embedded graph database may open new customer opportunities. Watch this webinar to learn how you can easily differentiate your applications and take your solutions to market faster with a native graph database like Neo4j.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
The New Normal – Delivering Remote Professional ServicesNeo4j
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In this webinar, Stefan Kolmar will present some of the Neo4j services packages and demonstrate examples of successful implementation and deployment of Neo4j based projects. The webinar will focus on adapting Neo4j services to the needs of today's world, maintaining productivity by enabling virtual teams to implement and deliver projects remotely.
We are country’s premium Solutions Organization: Awarded by Microsoft – Masters Service Agreement for Driving Solution Centric Engagements on Azure/ SPS / O365 / SQL Server. We are a Specialist Training & Consulting services company with over 20+ year of success story. The Depth of Deliverables is such that our services are banked by Top 15 SI and ISV for developing and driving SMAC competency within their Organization. By Far we are the “BEST “Microsoft – Azure, Sales and Pre-sales Competency Development Organization having delivered more than 50+ Sessions Globally.
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Blackboard has deployed cloud solutions for well over a decade and is very excited to launch our new SaaS offering at the Teaching and Learning conference. The session will explore Blackboard’s continued commitment to managed hosting, partnership with IBM/AWS and next generation SaaS offerings that offer institutions unique control over their innovation journey.
TLC2018 Thomas Haver: Transform with Enterprise AutomationAnna Royzman
Thomas Haver explains how to build a robust automation solution across the Enterprise to improve application quality, testing efficiency, and lower operational costs. He shows how to leverage all current resources to achieve this goal without affecting project delivery time at Test Leadership Congress 2018.
http://testleadershipcongress-ny.com
How Celtra Optimizes its Advertising Platformwith DatabricksGrega Kespret
Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Spark deployment, achieve faster project turnaround time, and empower people to make data-driven decisions.
In this webinar, you will learn how Databricks helps Celtra to:
- Utilize Apache Spark to power their production analytics pipeline.
- Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics.
- Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics.
A Connections-first Approach to Supply Chain OptimizationNeo4j
Supply chain optimization is an unusual balancing act that requires finesse, skill and timely data. Every supply chain’s the key questions to be answered are:
What to Buy? -- what are the factors in determining your optimal product mix and set of suppliers.
How much to Buy? -- what are the most and least popular items at any given time interval
When to Buy? -- long lags in delivery timing may tax limit your flexibility and influence your inventory management practices.
We will illustrate an API-based solution that utilizes a Graph database platform to add demonstrable value to Supply Planning.
Marvin é um ambicioso projeto de código aberto que se concentra em ajudar equipes a entregar soluções de machine learning de maneira ágil. A plataforma oferece uma arquitetura padronizada e agnóstica de linguagem, de alta escala e baixa latência enquanto simplifica o processo de exploração e modelagem de projetos de IA.
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Venez le découvrir lors de cette session ignite
4. 4
Neo4j PS in the real world
Solution Delivery
and Management
• Packaged Services
• Typically 5-25 days
• Neo4j advises
• Customer builds
• 80% of projects
• Custom Scoped
• 50+ man days
• Neo4j delivers
• Customer supports
• 20% of projects
5. PROFESSIONAL
SERVICES
GRAPH ACADEMY
SOLUTIONS
CUSTOMER SUPPORT
● Packaged Services
● Staff Augmentation
● Project/Solution Delivery
● Class room training
● Online/Virtual training
● Certification
● Innovation Labs
● Solution Workshops
● Solutions Development
● 24x7x365 & KB
● Platinum support
● Cloud Managed Services
● DBaaS (NEW)
● Agile Solution Support
Training
Enablement
Solution Delivery
& Management
Organization and offerings
8. • Agility -- constantly changing requirements
• Intuitiveness – so that everybody in your organization can
understand and influence the solution
• High Performance to support connected data scenarios
• Value Connections
• 360 degree views (customer, product, etc.)
• Finding patterns, traversing through the data
• Sysadmin friendliness
• Hardware efficiency8
Technical Requirements
(need of Neo4j Graph Based Solution)
13. Solution (Foundation) Framework
Neo4j Graph Platform
Recom Telco
App
Solution Foundation
Framework
Development &
Administration
Analytics
Tooling
Graph
Analytics
Graph
Transactions
Data Integration
Discovery & VisualizationDrivers
App App
14. Solution (Foundation) Framework
Neo4j Graph Platform
Recom Telco
App
Solution Foundation
Framework
Development &
Administration
Analytics
Tooling
Graph
Analytics
Graph
Transactions
Data Integration
Discovery & VisualizationDrivers
App App
API dev
3rd party
graph viz
Custom dev with
graph viz libraries
3rd party
analytics
Python,
Java ML, ...
Kettle 3rd party DI/EAI
Docker
Kubernetes
Git
Lineage
GRANDstack
26. Where AI and ML fit in
26
Development &
Administration
Analytics
Tooling
Graph
Analytics
Graph
Transactions
Data Integration
Discovery & VisualizationDrivers & APIs
AI
27. Differences between ML and Analytics
28
Machine learning:
• Determine domain parameters
• Historical-based discoveries
• Learn and improve without explicit
programming
28. Graph analytics:
• Uses inherent graph structures
• Uncover real-world networks
through their connections
• Forecast complex network
behavior and identify action
Differences between ML and Analytics
29. (Some) today challenges with Machine Learning:
• Doesn’t take multiple relationship hops into account
• Takes time to iteratively train a model
• Computational inefficiency of connecting data
Machine Learning Pipeline
Benefits of Mixing Graph Analytics with ML
Graphs bring:
• Context to machine learning
• Feature filtration
• Connected feature extraction
30. Neo4j has an ‘out of the box’ Graph Algorithms plugin:
• Pathfinding and Search
• Centrality and Importance
• Community Detection
• Similarity and Machine Learning Workflow
• Link Prediction
Many different ways to work with your ML algorithms in Neo4j:
• Support for many languages (Python, .Net, Java, Go, Ruby, etc.)
• Different data integration options
• Triggers, event-driven architecture, user-defined functions
31
Working with Graph Analytics and ML
31. Pathfinding
& Search
Centrality /
Importance
Community
Detection
Similarity
GraphConnect 2017 GraphConnect 2018
• Minimum Weight Spanning Tree
• Shortest Path
• Single-Source Shortest Path
• All Pairs Shortest Path
• PageRank
• Article Rank
• Betweenness Centrality
• Closeness Centrality
• Louvain
• Label Propagation
• Connected Components
• Harmonic Centrality
• Eigenvector Centrality
• Degree Centrality
• A* Shortest Path
• Yen’s K Shortest Path
• Random Walk
• Jaccard Similarity
• Cosine Similarity
• Pearson Similarity
• Strongly Connected
Components
• Triangle Count /
• Clustering Coefficient
• Balanced Triads
Machine Learning and Graph Algorithms in
Neo4j
• Euclidean Distance
• Overlap Similarity
Link
Prediction
• Adamic Adar
• Common Neighbors
• Preferential Attachment
• Resource Allocation
• Same Community
• Total Neighbors
32
neo4j.com/
graph-algorithms-
book/
32. IoT/Connected Home:
• Master Data Management
• Entity resolution using
community detection and similarity
Customer Experience Management:
• Customer journey path analysis
(path finding)
33
Graph Analytics and Algorithm Examples
33
33. Knowledge graph example:
• Using topic finding ML processes
e.g. Latent Dirichlet Allocation (LDA)
• Feeding the output into a graph database
• Search for topics, find related concepts, etc.
34
Graph and Machine Learning Examples
Recommendation engine example:
• Use ML processes such as collaborative filtering
• Enrich graph with the output
• Use graph as feedback for future iterations
36. Customer Use Case:
• Leading online platform to showcase and discover creative work
• More than 10 million members
• Allows creatives to share their work with millions of daily visitors
• Highlights Adobe software used in the creation process
• Drives people to the Adobe Creative Cloud
• Social platform for discovery, learning, and more
38
Adobe – Project Behance
Activity feed:
• Mongo (2011) - 125 nodes, dataset size of about 20tb
(terabytes)
• Cassandra (2015) - 48 nodes, dataset size of about 50tb
(terabytes)
• Neo4j (2018) - 3 nodes, dataset size of 40gb (gigabytes)
5 day Bootcamp
37. 39
Large Commercial Bank
Customer Journey
Innovation
Lab
Staff Augmentation
Campaign Management
Innovation
Lab
Fraud Project
80 person days
TBD
Another
Innovation Lab
38. 40
Conclusion
• Neo4j Professional Services makes customer projects successful
through:
• Enablement
• Project / solution delivery
• Graph Based Solutions as accelerators
• Neo4j is the foundation for AI and ML
• Customers are using Neo4j to drive success and deliver value
40. Our Neo4j activity implementation has led to a great decrease in complexity, storage, and
infrastructure costs. Our full dataset size is now around 40 GB, down from 50 TB of data
that we had stored in Cassandra. We’re able to power our entire activity feed infrastructure
using a cluster of 3 Neo4j instances, down from 48 Cassandra instances of pretty much
equal specs. That has also led to reduced infrastructure costs. Most importantly, it’s been
a breeze for our operations staff to manage since the architecture is simple and lean.”
David Fox, Adobe, Oct 2018
42
Customer Quote
How can Neo4j Services help you to get there?