Telecommunications networks are vast, complex graphs upon a map. Why is it then, that Telcos typically do not use graph technology as means to understand and traverse their networks of devices, systems and customers?
This webinar explores ways for Telecommunications and media vendors to experience their networks as graphs from Neo4j.
Presented at the MLConf in Seattle, this presentation offers a quick introduction to Apache Spark, followed by an overview of two novel features for data science
An introduction to Neo4j and Graph Databases. Learn about the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
Building Robust Production Data Pipelines with Databricks DeltaDatabricks
"Most data practitioners grapple with data quality issues and data pipeline complexities—it's the bane of their existence. Data engineers, in particular, strive to design and deploy robust data pipelines that serve reliable data in a performant manner so that their organizations can make the most of their valuable corporate data assets.
Databricks Delta, part of Databricks Runtime, is a next-generation unified analytics engine built on top of Apache Spark. Built on open standards, Delta employs co-designed compute and storage and is compatible with Spark API’s. It powers high data reliability and query performance to support big data use cases, from batch and streaming ingests, fast interactive queries to machine learning. In this tutorial we will discuss the requirements of modern data pipelines, the challenges data engineers face when it comes to data reliability and performance and how Delta can help. Through presentation, code examples and notebooks, we will explain pipeline challenges and the use of Delta to address them. You will walk away with an understanding of how you can apply this innovation to your data architecture and the benefits you can gain.
This tutorial will be both instructor-led and hands-on interactive session. Instructions in how to get tutorial materials will be covered in class. WHAT
YOU’LL LEARN:
– Understand the key data reliability and performance data pipelines challenges
– How Databricks Delta helps build robust pipelines at scale
– Understand how Delta fits within an Apache Spark™ environment – How to use Delta to realize data reliability improvements
– How to deliver performance gains using Delta
PREREQUISITES:
– A fully-charged laptop (8-16GB memory) with Chrome or Firefox
– Pre-register for Databricks Community Edition"
Speakers: Steven Yu, Burak Yavuz
Deep-dive into Microservices Patterns with Replication and Stream Analytics
Target Audience: Microservices and Data Architects
This is an informational presentation about microservices event patterns, GoldenGate event replication, and event stream processing with Oracle Stream Analytics. This session will discuss some of the challenges of working with data in a microservices architecture (MA), and how the emerging concept of a “Data Mesh” can go hand-in-hand to improve microservices-based data management patterns. You may have already heard about common microservices patterns like CQRS, Saga, Event Sourcing and Transaction Outbox; we’ll share how GoldenGate can simplify these patterns while also bringing stronger data consistency to your microservice integrations. We will also discuss how complex event processing (CEP) and stream processing can be used with event-driven MA for operational and analytical use cases.
Business pressures for modernization and digital transformation drive demand for rapid, flexible DevOps, which microservices address, but also for data-driven Analytics, Machine Learning and Data Lakes which is where data management tech really shines. Join us for this presentation where we take a deep look at the intersection of microservice design patterns and modern data integration tech.
Graph Machine Learning in Production with Neo4jNeo4j
In our presentation at Data Innovation Summit 2023, we explained how you could accelerate AI and machine learning innovation by using graph data science.
This comes down to three things: 1. Getting your data into a graph
2. Use graph algorithms to find what’s important
3. Use machine learning to make predictions on your graph
We covered these three key steps with code examples and discussed some key considerations when moving your ML workloads to production.
Transforming AI with Graphs: Real World Examples using Spark and Neo4jDatabricks
Graphs – or information about the relationships, connection, and topology of data points – are transforming machine learning. We’ll walk through real world examples of how to get transform your tabular data into a graph and how to get started with graph AI. This talk will provide an overview of how we to incorporate graph based features into traditional machine learning pipelines, create graph embeddings to better describe your graph topology, and give you a preview of approaches for graph native learning using graph neural networks. We’ll talk about relevant, real world case studies in financial crime detection, recommendations, and drug discovery. This talk is intended to introduce the concept of graph based AI to beginners, as well as help practitioners understand new techniques and applications. Key take aways: how graph data can improve machine learning, when graphs are relevant to data science applications, what graph native learning is and how to get started.
Presented at the MLConf in Seattle, this presentation offers a quick introduction to Apache Spark, followed by an overview of two novel features for data science
An introduction to Neo4j and Graph Databases. Learn about the primary use cases for Graph Databases and explore the properties of Neo4j that make those use cases possible.
Building Robust Production Data Pipelines with Databricks DeltaDatabricks
"Most data practitioners grapple with data quality issues and data pipeline complexities—it's the bane of their existence. Data engineers, in particular, strive to design and deploy robust data pipelines that serve reliable data in a performant manner so that their organizations can make the most of their valuable corporate data assets.
Databricks Delta, part of Databricks Runtime, is a next-generation unified analytics engine built on top of Apache Spark. Built on open standards, Delta employs co-designed compute and storage and is compatible with Spark API’s. It powers high data reliability and query performance to support big data use cases, from batch and streaming ingests, fast interactive queries to machine learning. In this tutorial we will discuss the requirements of modern data pipelines, the challenges data engineers face when it comes to data reliability and performance and how Delta can help. Through presentation, code examples and notebooks, we will explain pipeline challenges and the use of Delta to address them. You will walk away with an understanding of how you can apply this innovation to your data architecture and the benefits you can gain.
This tutorial will be both instructor-led and hands-on interactive session. Instructions in how to get tutorial materials will be covered in class. WHAT
YOU’LL LEARN:
– Understand the key data reliability and performance data pipelines challenges
– How Databricks Delta helps build robust pipelines at scale
– Understand how Delta fits within an Apache Spark™ environment – How to use Delta to realize data reliability improvements
– How to deliver performance gains using Delta
PREREQUISITES:
– A fully-charged laptop (8-16GB memory) with Chrome or Firefox
– Pre-register for Databricks Community Edition"
Speakers: Steven Yu, Burak Yavuz
Deep-dive into Microservices Patterns with Replication and Stream Analytics
Target Audience: Microservices and Data Architects
This is an informational presentation about microservices event patterns, GoldenGate event replication, and event stream processing with Oracle Stream Analytics. This session will discuss some of the challenges of working with data in a microservices architecture (MA), and how the emerging concept of a “Data Mesh” can go hand-in-hand to improve microservices-based data management patterns. You may have already heard about common microservices patterns like CQRS, Saga, Event Sourcing and Transaction Outbox; we’ll share how GoldenGate can simplify these patterns while also bringing stronger data consistency to your microservice integrations. We will also discuss how complex event processing (CEP) and stream processing can be used with event-driven MA for operational and analytical use cases.
Business pressures for modernization and digital transformation drive demand for rapid, flexible DevOps, which microservices address, but also for data-driven Analytics, Machine Learning and Data Lakes which is where data management tech really shines. Join us for this presentation where we take a deep look at the intersection of microservice design patterns and modern data integration tech.
Graph Machine Learning in Production with Neo4jNeo4j
In our presentation at Data Innovation Summit 2023, we explained how you could accelerate AI and machine learning innovation by using graph data science.
This comes down to three things: 1. Getting your data into a graph
2. Use graph algorithms to find what’s important
3. Use machine learning to make predictions on your graph
We covered these three key steps with code examples and discussed some key considerations when moving your ML workloads to production.
Transforming AI with Graphs: Real World Examples using Spark and Neo4jDatabricks
Graphs – or information about the relationships, connection, and topology of data points – are transforming machine learning. We’ll walk through real world examples of how to get transform your tabular data into a graph and how to get started with graph AI. This talk will provide an overview of how we to incorporate graph based features into traditional machine learning pipelines, create graph embeddings to better describe your graph topology, and give you a preview of approaches for graph native learning using graph neural networks. We’ll talk about relevant, real world case studies in financial crime detection, recommendations, and drug discovery. This talk is intended to introduce the concept of graph based AI to beginners, as well as help practitioners understand new techniques and applications. Key take aways: how graph data can improve machine learning, when graphs are relevant to data science applications, what graph native learning is and how to get started.
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.
GraphFrames: DataFrame-based graphs for Apache® Spark™Databricks
These slides support the GraphFrames: DataFrame-based graphs for Apache Spark webinar. In this webinar, the developers of the GraphFrames package will give an overview, a live demo, and a discussion of design decisions and future plans. This talk will be generally accessible, covering major improvements from GraphX and providing resources for getting started. A running example of analyzing flight delays will be used to explain the range of GraphFrame functionality: simple SQL and graph queries, motif finding, and powerful graph algorithms.
How to Utilize MLflow and Kubernetes to Build an Enterprise ML PlatformDatabricks
In large enterprises, large solutions are sometimes required to tackle even the smallest tasks and ML is no different. At Comcast we are building a comprehensive, configuration based, continuously integrated and deployed platform for data pipeline transformations, model development and deployment. This is accomplished using a range of tools and frameworks such as Databricks, MLflow, Apache Spark and others. With a Databricks environment used by hundreds of researchers and petabytes of data, scale is critical to Comcast, so making it all work together in a frictionless experience is a high priority. The platform consists of a number of components: an abstraction for data pipelines and transformation to allow our data scientists the freedom to combine the most appropriate algorithms from different frameworks , experiment tracking, project and model packaging using MLflow and model serving via the Kubeflow environment on Kubernetes. The architecture, progress and current state of the platform will be discussed as well as the challenges we had to overcome to make this platform work at Comcast scale. As a machine learning practitioner, you will gain knowledge in: an example of data pipeline abstraction; ways to package and track your ML project and experiments at scale; and how Comcast uses Kubeflow on Kubernetes to bring everything together.
A NOSQL Overview And The Benefits Of Graph Databases (nosql east 2009)Emil Eifrem
Presentation given at nosql east 2009 in Atlanta. Introduces the NOSQL space by offering a framework for categorization and discusses the benefits of graph databases. Oh, and also includes some tongue-in-cheek party poopers about sucky things in the NOSQL space.
Taking Splunk to the Next Level - ArchitectureSplunk
This session led by Michael Donnelly will teach you how to take your Splunk deployment to the next level. Learn about Splunk high availability architectures with Splunk Search Head Clustering and Index Replication. Additionally, learn how to manage your deployment with Splunk’s operational and management controls to manage Splunk capacity and end user experience
SOC presentation- Building a Security Operations CenterMichael Nickle
Presentation I used to give on the topic of using a SIM/SIEM to unify the information stream flowing into the SOC. This piece of collateral was used to help close the largest SIEM deal (Product and services) that my employer achieved with this product line.
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...James Serra
Discover, manage, deploy, monitor – rinse and repeat. In this session we show how Azure Machine Learning can be used to create the right AI model for your challenge and then easily customize it using your development tools while relying on Azure ML to optimize them to run in hardware accelerated environments for the cloud and the edge using FPGAs and Neural Network accelerators. We then show you how to deploy the model to highly scalable web services and nimble edge applications that Azure can manage and monitor for you. Finally, we illustrate how you can leverage the model telemetry to retrain and improve your content.
Data Privacy with Apache Spark: Defensive and Offensive ApproachesDatabricks
In this talk, we’ll compare different data privacy techniques & protection of personally identifiable information and their effects on statistical usefulness, re-identification risks, data schema, format preservation, read & write performance.
We’ll cover different offense and defense techniques. You’ll learn what k-anonymity and quasi-identifier are. Think of discovering the world of suppression, perturbation, obfuscation, encryption, tokenization, watermarking with elementary code examples, in case no third-party products cannot be used. We’ll see what approaches might be adopted to minimize the risks of data exfiltration.
Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies?
In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into the architecture of our current data quality solution. We’ll cover what worked, what didn’t work so well, and what we're working on next. We’ll conclude with some tips & lessons learned for ensuring high quality on big data.
This talk was presented at DataWorks/Hadoop Summit 2017 on June 13, 2017.
SOC Lessons from DevOps and SRE by Anton ChuvakinAnton Chuvakin
SOC Lessons from DevOps and SRE by Dr Anton Chuvakin - RSA 2023 Google Cloud sideshow presentation focused on using select DevOps and SRE lessons to make your SOC better
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.
GraphFrames: DataFrame-based graphs for Apache® Spark™Databricks
These slides support the GraphFrames: DataFrame-based graphs for Apache Spark webinar. In this webinar, the developers of the GraphFrames package will give an overview, a live demo, and a discussion of design decisions and future plans. This talk will be generally accessible, covering major improvements from GraphX and providing resources for getting started. A running example of analyzing flight delays will be used to explain the range of GraphFrame functionality: simple SQL and graph queries, motif finding, and powerful graph algorithms.
How to Utilize MLflow and Kubernetes to Build an Enterprise ML PlatformDatabricks
In large enterprises, large solutions are sometimes required to tackle even the smallest tasks and ML is no different. At Comcast we are building a comprehensive, configuration based, continuously integrated and deployed platform for data pipeline transformations, model development and deployment. This is accomplished using a range of tools and frameworks such as Databricks, MLflow, Apache Spark and others. With a Databricks environment used by hundreds of researchers and petabytes of data, scale is critical to Comcast, so making it all work together in a frictionless experience is a high priority. The platform consists of a number of components: an abstraction for data pipelines and transformation to allow our data scientists the freedom to combine the most appropriate algorithms from different frameworks , experiment tracking, project and model packaging using MLflow and model serving via the Kubeflow environment on Kubernetes. The architecture, progress and current state of the platform will be discussed as well as the challenges we had to overcome to make this platform work at Comcast scale. As a machine learning practitioner, you will gain knowledge in: an example of data pipeline abstraction; ways to package and track your ML project and experiments at scale; and how Comcast uses Kubeflow on Kubernetes to bring everything together.
A NOSQL Overview And The Benefits Of Graph Databases (nosql east 2009)Emil Eifrem
Presentation given at nosql east 2009 in Atlanta. Introduces the NOSQL space by offering a framework for categorization and discusses the benefits of graph databases. Oh, and also includes some tongue-in-cheek party poopers about sucky things in the NOSQL space.
Taking Splunk to the Next Level - ArchitectureSplunk
This session led by Michael Donnelly will teach you how to take your Splunk deployment to the next level. Learn about Splunk high availability architectures with Splunk Search Head Clustering and Index Replication. Additionally, learn how to manage your deployment with Splunk’s operational and management controls to manage Splunk capacity and end user experience
SOC presentation- Building a Security Operations CenterMichael Nickle
Presentation I used to give on the topic of using a SIM/SIEM to unify the information stream flowing into the SOC. This piece of collateral was used to help close the largest SIEM deal (Product and services) that my employer achieved with this product line.
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...James Serra
Discover, manage, deploy, monitor – rinse and repeat. In this session we show how Azure Machine Learning can be used to create the right AI model for your challenge and then easily customize it using your development tools while relying on Azure ML to optimize them to run in hardware accelerated environments for the cloud and the edge using FPGAs and Neural Network accelerators. We then show you how to deploy the model to highly scalable web services and nimble edge applications that Azure can manage and monitor for you. Finally, we illustrate how you can leverage the model telemetry to retrain and improve your content.
Data Privacy with Apache Spark: Defensive and Offensive ApproachesDatabricks
In this talk, we’ll compare different data privacy techniques & protection of personally identifiable information and their effects on statistical usefulness, re-identification risks, data schema, format preservation, read & write performance.
We’ll cover different offense and defense techniques. You’ll learn what k-anonymity and quasi-identifier are. Think of discovering the world of suppression, perturbation, obfuscation, encryption, tokenization, watermarking with elementary code examples, in case no third-party products cannot be used. We’ll see what approaches might be adopted to minimize the risks of data exfiltration.
Netflix is a famously data-driven company. Data is used to make informed decisions on everything from content acquisition to content delivery, and everything in-between. As with any data-driven company, it’s critical that data used by the business is accurate. Or, at worst, that the business has visibility into potential quality issues as soon as they arise. But even in the most mature data warehouses, data quality can be hard. How can we ensure high quality in a cloud-based, internet-scale, modern big data warehouse employing a variety of data engineering technologies?
In this talk, Michelle Ufford will share how the Data Engineering & Analytics team at Netflix is doing exactly that. We’ll kick things off with a quick overview of Netflix’s analytics environment, then dig into the architecture of our current data quality solution. We’ll cover what worked, what didn’t work so well, and what we're working on next. We’ll conclude with some tips & lessons learned for ensuring high quality on big data.
This talk was presented at DataWorks/Hadoop Summit 2017 on June 13, 2017.
SOC Lessons from DevOps and SRE by Anton ChuvakinAnton Chuvakin
SOC Lessons from DevOps and SRE by Dr Anton Chuvakin - RSA 2023 Google Cloud sideshow presentation focused on using select DevOps and SRE lessons to make your SOC better
Webinar: An Enterprise Architect’s View of MongoDBMongoDB
In the world of big data, legacy modernization, siloed organizations, empowered customers, and mobile devices, making informed choices about your enterprise infrastructure has become more important than ever. The alternatives are abundant, and the successful Enterprise Architect must constantly discern which new technology is just a shiny object and which will add true business value.
MongoDB is more than just a great application database for developers; it gives Enterprise Architects new capabilities to solve previously difficult architectural requirements much more easily. Take for example the challenge of many siloed systems at MetLife – with MongoDB, the Metlife team was able to successfully provide a single view into those 70 systems, in only 3 months.
In this webinar, we will:
Explore real life challenges enterprises face with case studies of their solutions
Consider how best to introduce MongoDB in the enterprise
Give an overview of how to optimize the use of MongoDB
During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability.
The speakers brought together best practice techniques with Apache Spark to achieve complete CDI.
Speakers:
Joe Caserta, President, Caserta Concepts
Kevin Rasmussen, Big Data Engineer, Caserta Concepts
Vida Ha, Lead Solutions Engineer, Databricks
The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included:
· Building an end-to-end CDI pipeline in Apache Spark
· What works, what doesn’t, and how do we use Spark we evolve
· Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior
· Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching
· Using GraphX for matching and scalable clustering
· Analyzing large data files with Spark
· Using Spark for ETL on large datasets
· Applying Machine Learning & Data Science to large datasets
· Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally
The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for.
For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/
Introduzione generale di che cos'è MongoDB e quali sono i benefici che può introdurre in ambito aziendale per migliorare processi aziendali e performances.
MongoDB è uno degli elementi tecnologici necessari per costruire le basi dell'internet delle cose e Big Data in ambito aziendale.
The Internet of Simulations and the agile development of Cyber-physical systemsSimware
Presentation made in the 2017 IEEE System of Systems conference. Co-authored by Stephen Clement, David McKee, Richard Romano and Jie Xu (University of Leeds), Jose-Maria Lopez (Simware Solutions) and David Battersby (Jaguar Land Rover)
INTTRA Technology Summit 2017: Sustainable Path for Building Networks in the ...INTTRA OceanMetrics
See how building networks in the cloud shape sustainable paths in ocean shipping. This deck was presented at the INTTRA Technology Summit on May 23, 2017 in Hamburg, Germany by Pater Spellman, INTTRA's Chief Technology Officer.
Shirley Bacso, Data Architect, Ingka Digital
“Linked Metadata by Design” represents the integration of the outcomes from human collaboration, starting from the design phase of data product development. This knowledge is captured in the Data Knowledge Graph. It not only enables data products to be robust and compliant but also well-understood and effectively utilized.
Atelier - Architecture d’applications de Graphes - GraphSummit ParisNeo4j
Atelier - Architecture d’applications de Graphes
Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...Neo4j
Romain CAMPOURCY – Architecte Solution, Sopra Steria
Patrick MEYER – Architecte IA Groupe, Sopra Steria
La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...Neo4j
Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin)
Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysisNeo4j
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesNeo4j
David Pond, Lead Product Manager, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Your enemies use GenAI too - staying ahead of fraud with Neo4jNeo4j
Delivered by Michael Down at Gartner Data & Analytics Summit London 2024 - Your enemies use GenAI too: Staying ahead of fraud with Neo4j.
Fraudsters exploit the latest technologies like generative AI to stay undetected. Static applications can’t adapt quickly enough. Learn why you should build flexible fraud detection apps on Neo4j’s native graph database combined with advanced data science algorithms. Uncover complex fraud patterns in real-time and shut down schemes before they cause damage.
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxNeo4j
Delivered by Sreenath Gopalakrishna, Director of Software Engineering at BT, and Dr Jim Webber, Chief Scientist at Neo4j, at Gartner Data & Analytics Summit London 2024 this presentation examines how knowledge graphs and GenAI combine in real-world solutions.
BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Future innovation plans include the exploration of uses of EKG + Generative AI.
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanNeo4j
Look beyond the hype and unlock practical techniques to responsibly activate intelligence across your organization’s data with GenAI. Explore how to use knowledge graphs to increase accuracy, transparency, and explainability within generative AI systems. You’ll depart with hands-on experience combining relationships and LLMs for increased domain-specific context and enhanced reasoning.
Workshop 1. Architecting Innovative Graph Applications
Join this hands-on workshop for beginners led by Neo4j experts guiding you to systematically uncover contextual intelligence. Using a real-life dataset we will build step-by-step a graph solution; from building the graph data model to running queries and data visualization. The approach will be applicable across multiple use cases and industries.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
13. Money
Transferring
Purchases Bank
Services Relational
database
Data Lake
+ Good for Map Reduce
+ Good for Analytical Workloads
– No holistic view
– Non-operational workloads
– Weeks-to-months processes Develop Patterns
Data Science-team
Merchant
Data
Credit
Score
Data
Other 3rd
Party Data
14. Money
Transferring
Purchases Bank
Services
Graph powers
360° view of
transactions in
real-time
Graph
Cluster
SENSE
Transaction
stream
RESPOND
Alerts &
notification
LOAD RELEVANT DATA
Relational
database
Data Lake
Visualization UI
Fine Tune Patterns
Develop Patterns
Data Science-team
Merchant
Data
Credit
Score
Data
Other 3rd
Party Data
15. Money
Transferring
Purchases Bank
Services
Graph powers
360° view of
transactions in
real-time
Graph
Cluster
SENSE
Transaction
stream
RESPOND
Alerts &
notification
LOAD RELEVANT DATA
Relational
database
Data Lake
Visualization UI
Fine Tune Patterns
Develop Patterns
Data Science-team
Merchant
Data
Credit
Score
Data
Other 3rd
Party Data
Data-set used
to explore
new insights
19. At Write Time:
data is connected
as it is stored
At Read Time:
Lightning-fast retrieval of data and relationships via
pointer chasing
Index free adjacency
Graph Optimized Memory & Storage
20. 2
0
Example HR Query in SQL The Same Query using openCypher
MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total
Project Impact
Less time writing queries
• More time understanding the answers
• Leaving time to ask the next question
Less time debugging queries:
• More time writing the next piece of code
• Improved quality of overall code base
Code that’s easier to read:
• Faster ramp-up for new project members
• Improved maintainability & troubleshooting
Productivity Gains with Graph Query Language
The query asks: “Find all direct reports and how many people they manage, up to three levels down”
21. Connectedness and Size of Data Set
ResponseTime
Relational and Other
NoSQL Databases
0 to 2 hops
0 to 3 degrees
Thousands of connections
1000x
Advantage
Tens to hundreds of hops
Thousands of degrees
Billions of connections
Graph
“Minutes to
milliseconds”
“Minutes to Milliseconds” Real-Time Query Performance
22. NoSQL Databases Don’t Handle Relationships
• No data structures to model or store
relationships
• No query constructs to support data
relationships
• Relating data requires “JOIN logic”
in the application
• No ACID support for transactions
… making NoSQL databases inappropriate when
data relationships are valuable in real-time
26. Why Graph: Key Technology Benefits
ACID Transactions
• ACID transactions with causal consistency
• Security Foundation delivers enterprise-
class security and control
Hardware Efficiency
• Native graph query processing and storage
requires 10x less hardware
• Index-free adjacency requires 10x less CPU
Agility
• Native property graph model
• Modify schema as business changes
without disrupting existing data
Developer Productivity
• Easy to learn, declarative graph query language
• Procedural language extensions
• Open library of procedures and functions
• Worldwide developer network
… all backed by Neo’s track record of leadership
and product roadmap
Performance
• Index-free adjacency delivers millions of
hops per second
• In-memory pointer chasing for fast query
results
28. Background
• Oslo-based telcom provider is #1 in Nordic
countries and #10 in world
• Online, mission-critical, self-serve system lets
users manage subscriptions and plans
• availability and responsiveness is critical to
customer satisfaction
Business Problem
• Logins took minutes to retrieve relational
access rights
• Massive joins across millions of plans,
customers, admins, groups
• Nightly batch production required 9 hours
and produced stale data
Solution and Benefits
• Shifted authentication from Sybase to Neo4j
• Moved resource graph to Neo4j
• Replaced batch process with real-time login
response measured in milliseconds that delivers
real-time data, not yesterday’s snapshot
• Mitigated customer retention risks
Identity and Access Management
Telenor COMMUNICATIONS
SUBSCRIBED_BY
CONTROLLED_BY
PART_OFUSER_ACCESS
Account
Customer
CustomerUser
Subscription
28
29. Background
• Second largest communications company
in France
• Based in Paris, part of Vivendi Group,
partnering with Vodafone
Solution and Benefits
• Flexible inventory management supports
modeling, aggregation, troubleshooting
• Single source of truth for entire network
• New apps model network via near-1:1 mapping
between graph and real world
• Schema adapts to changing needs
Network and IT Operations
SFR COMMUNICATIONS
Business Problem
• Infrastructure maintenance took week to plan
due to need to model network impacts
• Needed what-if to model unplanned outages
• Identify network weaknesses to uncover need
for additional redundancy
• Info lived on 30+ systems, with daily changes
LINKED
LINKED
DEPENDS_ON
Router Service
Switch Switch
Router
Fiber Link Fiber Link
Fiber Link
Oceanfloor
Cable
29
30. Background
• World’s largest provider of IT infrastructure,
software and services
• Unified Correlation Analyzer (UCA) helps
comms operators manage large networks
with carrier-class resource and service
management, root cause and impact analysis
Business Problem
• Use network topology to identify root problems
causes on the network
• Simplify and speed alarm handling by operators
• Automate handling of certain types of alarms
• Filter/group/eliminate redundant alarms via
event correlation
Solution and Benefits
• Accelerated product development time
• Extremely fast network-topology queries
• Graph representation a perfect domain fit
• 24x7 carrier-grade reliability with Neo4j
High Availability clustering
• Met objective in under six months
Hewlett Packard WEB/ISV COMMUNICATIONS
Network and IT Operations30
31. Background
• Hong Kong-based telephony provider
branching into VOIP services via Maaii app,
white-label services, and VOIP APIs
• Exclusive China Mobile partner for toll-free
services, SMS hub and other offerings
• 2012 Red Herring Top 100 Global Winner
Business Problem
• Maaii app allows consumers to communicate
by voice and text – similar to Line, Viber,
Rebtel and VoxOx
• Must relate devices, users and contacts via
user address books and central database
• 3 million users with 200 million graph nodes
Solution and Benefits
• Provide fast transactions for key operations such
as suggesting friends, updating contacts, and
blocking calls
• Deliver high availability via Neo4j clusters
• Embedded Neo4j is great architecture fit
Social and Mobile Communications
Maaii COMMUNICATIONS
31
32. Master Data Management
Background
• Part of Hutchison Whampoa, one of the
world’s largest telecom conglomerates
• Operates in the Nordics and UK
• Moving toward real-time customer profiling
and analytics
Solution and Benefits
• Customer-facing apps access Neo4j cluster
containing a billing-information graph
• Graph model gives services reps timely and
insightful customers profiles
• Much faster query performance
• Faster app and feature development
Business Problem
• New business requirement to give customers
more insight into their own usage patterns
• Changing data model was slow and painful
• New queries were difficult to write
• Very large RDBMS data sets creating serious
connected query (>L2) performance issues
Tre TELECOMMUNICATIONS
32
33. Background
• Started in 2011 in Lyon, France
• Offers video communication and collaboration
accessed in one click from social networks
• Patented interface brings an unlimited number
of online participants together in a virtual
meeting space
Solution and Benefits
• Designed a competitive platform in one-third
the anticipated development time
• Introduced both real-time and social graphs
• Enjoyed huge performance improvements,
regardless of query complexity
Business Problem
• Store all contacts from all social networks in a
graph, and manage all real-time interactions
• Original app represented users in graphs, but
used SQL to display and read them
• Displaying complex queries proved impossible
Glowbl COMMUNICATIONS
Social Networks33
34. Graph-Based Search
Background
• Communications equipment giant ranks #91 in
the Global 2000 with $44B in annual sales
• Had success with Neo4j in Master Data
Management and Real-Time Recommendations
apps, so wanted to use it for this Content
Management and Graph-Based Search problem
Solution and Benefits
• Created Intelligent Query Service, an internal
document discovery system with automated
keyword assignment
• Time required to find precisely the right sales
asset slashed from 2 weeks to 20 minutes
Business Problem
• Sales reps wasted days looking for appropriate
materials to send to prospects
• Keyword indexing system was too slow
• Deal sales cycles were suffering
Cisco COMMUNICATIONS
INTELLIGENT QUERY SERVICE
34
37. Neo4j for the Enterprise
ENTERPRISE-CLASS PRODUCT
Ready for Production
• Performance & Scalability
• Clustered Replication across Data Centers
• Unlimited graph sizes
• Intelligent online space reuse
• Enterprise lock manager
• Compiled Runtime for common queries
• Monitoring & Administration:
• Advanced Monitoring by role
• Cypher Query Tracing
• Hot backups
• Enterprise Security
• Enterprise Schema Features
• Property Existence Constraints
• Composite and Node key constraints
ENTERPRISE-CLASS SERVICES
Dedication to customer success
• Certified & hardened for Production
• World-Class Support with SLAs
• Access to Professional Services
• Training and deployment services
• Access to Support Portal & Knowledge Base
Growing Innovation Network
• Growing service provider network
• Growing OEM & VAR network
• Growing Technology partner network
• Growing Contribution network