Gartner predicts that "the application of graph processing and graph databases will grow 100% annually through 2022 to continuously accelerate data preparation and enable more-complex and adaptive data science." The jury is in; performing these sorts of graph algorithms or employing Graph technology is a must-have now for many enterprises. However Graph technology remains a relatively young field with many offerings from which to choose. How do you approach selecting from the 15+ Graph vendors, and which features are most important? There is an overwhelming amount of information and complexity. Join us for this webinar as we de-mystify the complexity and bring clarity to the selection process.
During this webinar, we'll dive into the factors that business and technical leaders should consider when making technology choices for Graph processing and how to apply them for your particular industry and use cases.
KEY LEARNING TOPICS:
1. Organizational Readiness:
- Overview: Aren't all the products the same? Why do I need a selection?
- Graph vs. Others: I have selected other DBs like NoSQL and SQL, why can't I just use those selection criteria?
- Use Cases: Why different use cases can materially effect your performance and thus product selection.
- Categories, Terminology, & Differences of Graphs: Property, Time Series & NoSQL, RDF, OLAP, OLTP (Why does this matter? How do they play together?)
2. Graph Product Shoot Out
- How use cases definitions drive outcomes: fraud, customer 360, supply chain, network dependence and deep link analysis, ontologies, hierarchies, data
- What kind of shoot out and what specs matter: deep hops, wide queries, OLAP, OLTP, and effect on Graph calculations and processing times and HW
- Data matters: how to specify speeds-n-feeds in order to measure and compare; how to parse quoted benchmarks & statistics.
- High level test & timing: things to review for test overview & metrics and measurement of success.
- Time: how the concept of time in Graph effects your use cases and impacts performance depends upon data model approaches.
- Score Cards: what is your weighting, value of features, and which matters more to you and your use case & why it matters to your score.
- Which Algo's Matter: use case derivation & focus
- ML & Integration: how to integrate, co-opt and with Python, Pytorch tools and external notebooks and other data science tools.
3. How to Build Enterprise Applications
- Now you have a Graph DB: how to build an enterprise application.
- Graph Integration: Integrating Graph into a large complex architecture that maximizes existing investments and technologies.
- Data Connections: streaming, ETL, data file transformation and other connectivity.
- Visualization: API's, and Use Case Driven - customer 360, fraud supply chain, and custom UI templates. Why is Graph so different from traditional visualization?
4. Bringing it Together: Scoring, Hidden Costs & Intangibles
- How to build a total cost comparison: hardware, cloud, transaction costs, real time batch, support, FTEs.
- Operations and Maintenance: intangible elements around training, operational costs, and maintenance.
- Tooling & Algorithms Ecosystem: partners, plug ins, git hub, and vibrant external communities contributions.
Gartner predicts that "the application of graph processing and graph databases will grow 100% annually through 2022 to continuously accelerate data preparation and enable more-complex and adaptive data science." The jury is in; performing these sorts of graph algorithms or employing Graph technology is a must-have now for many enterprises. However Graph technology remains a relatively young field with many offerings from which to choose. How do you approach selecting from the 15+ Graph vendors, and which features are most important? There is an overwhelming amount of information and complexity. Join us for this webinar as we de-mystify the complexity and bring clarity to the selection process.
During this webinar, we'll dive into the factors that business and technical leaders should consider when making technology choices for Graph processing and how to apply them for your particular industry and use cases.
KEY LEARNING TOPICS:
1. Organizational Readiness:
- Overview: Aren't all the products the same? Why do I need a selection?
- Graph vs. Others: I have selected other DBs like NoSQL and SQL, why can't I just use those selection criteria?
- Use Cases: Why different use cases can materially effect your performance and thus product selection.
- Categories, Terminology, & Differences of Graphs: Property, Time Series & NoSQL, RDF, OLAP, OLTP (Why does this matter? How do they play together?)
2. Graph Product Shoot Out
- How use cases definitions drive outcomes: fraud, customer 360, supply chain, network dependence and deep link analysis, ontologies, hierarchies, data
- What kind of shoot out and what specs matter: deep hops, wide queries, OLAP, OLTP, and effect on Graph calculations and processing times and HW
- Data matters: how to specify speeds-n-feeds in order to measure and compare; how to parse quoted benchmarks & statistics.
- High level test & timing: things to review for test overview & metrics and measurement of success.
- Time: how the concept of time in Graph effects your use cases and impacts performance depends upon data model approaches.
- Score Cards: what is your weighting, value of features, and which matters more to you and your use case & why it matters to your score.
- Which Algo's Matter: use case derivation & focus
- ML & Integration: how to integrate, co-opt and with Python, Pytorch tools and external notebooks and other data science tools.
3. How to Build Enterprise Applications
- Now you have a Graph DB: how to build an enterprise application.
- Graph Integration: Integrating Graph into a large complex architecture that maximizes existing investments and technologies.
- Data Connections: streaming, ETL, data file transformation and other connectivity.
- Visualization: API's, and Use Case Driven - customer 360, fraud supply chain, and custom UI templates. Why is Graph so different from traditional visualization?
4. Bringing it Together: Scoring, Hidden Costs & Intangibles
- How to build a total cost comparison: hardware, cloud, transaction costs, real time batch, support, FTEs.
- Operations and Maintenance: intangible elements around training, operational costs, and maintenance.
- Tooling & Algorithms Ecosystem: partners, plug ins, git hub, and vibrant external communities contributions.
