Getting started with Graph Databases & Neo4jSuroor Wijdan
The presentation gives a brief information about Graph Databases and its usage in today's scenario. Moving on the presentation talks about the popular Graph DB Neo4j and its Cypher Query Language i.e., used to query the graph.
Challenges in the Design of a Graph Database Benchmark graphdevroom
Graph databases are one of the leading drivers in the emerging, highly heterogeneous landscape of database management systems for non-relational data management and processing. The recent interest and success of graph databases arises mainly from the growing interest in social media analysis and the exploration and mining of relationships in social media data. However, with a graph-based model as a very flexible underlying data model, a graph database can serve a large variety of scenarios from different domains such as travel planning, supply chain management and package routing.
During the past months, many vendors have designed and implemented solutions to satisfy the need to efficiently store, manage and query graph data. However, the solutions are very diverse in terms of the supported graph data model, supported query languages, and APIs. With a growing number of vendors offering graph processing and graph management functionality, there is also an increased need to compare the solutions on a functional level as well as on a performance level with the help of benchmarks. Graph database benchmarking is a challenging task. Already existing graph database benchmarks are limited in their functionality and portability to different graph-based data models and different application domains. Existing benchmarks and the supported workloads are typically based on a proprietary query language and on a specific graph-based data model derived from the mathematical notion of a graph. The variety and lack of standardization with respect to the logical representation of graph data and the retrieval of graph data make it hard to define a portable graph database benchmark. In this talk, we present a proposal and design guideline for a graph database benchmark. Typically, a database benchmark consists of a synthetically generated data set of varying size and varying characteristics and a workload driver. In order to generate graph data sets, we present parameters from graph theory, which influence the characteristics of the generated graph data set. Following, the workload driver issues a set of queries against a well-defined interface of the graph database and gathers relevant performance numbers. We propose a set of performance measures to determine the response time behavior on different workloads and also initial suggestions for typical workloads in graph data scenarios. Our main objective of this session is to open the discussion on graph database benchmarking. We believe that there is a need for a common understanding of different workloads for graph processing from different domains and the definition of a common subset of core graph functionality in order to provide a general-purpose graph database benchmark. We encourage vendors to participate and to contribute with their domain-dependent knowledge and to define a graph database benchmark proposal.
How Graph Databases efficiently store, manage and query connected data at s...jexp
Graph Databases try to make it easy for developers to leverage huge amounts of connected information for everything from routing to recommendations. Doing that poses a number of challenges on the implementation side. In this talk we want to look at the different storage, query and consistency approaches that are used behind the scenes. We’ll check out current and future solutions used in Neo4j and other graph databases for addressing global consistency, query and storage optimization, indexing and more and see which papers and research database developers take inspirations from.
Getting started with Graph Databases & Neo4jSuroor Wijdan
The presentation gives a brief information about Graph Databases and its usage in today's scenario. Moving on the presentation talks about the popular Graph DB Neo4j and its Cypher Query Language i.e., used to query the graph.
Challenges in the Design of a Graph Database Benchmark graphdevroom
Graph databases are one of the leading drivers in the emerging, highly heterogeneous landscape of database management systems for non-relational data management and processing. The recent interest and success of graph databases arises mainly from the growing interest in social media analysis and the exploration and mining of relationships in social media data. However, with a graph-based model as a very flexible underlying data model, a graph database can serve a large variety of scenarios from different domains such as travel planning, supply chain management and package routing.
During the past months, many vendors have designed and implemented solutions to satisfy the need to efficiently store, manage and query graph data. However, the solutions are very diverse in terms of the supported graph data model, supported query languages, and APIs. With a growing number of vendors offering graph processing and graph management functionality, there is also an increased need to compare the solutions on a functional level as well as on a performance level with the help of benchmarks. Graph database benchmarking is a challenging task. Already existing graph database benchmarks are limited in their functionality and portability to different graph-based data models and different application domains. Existing benchmarks and the supported workloads are typically based on a proprietary query language and on a specific graph-based data model derived from the mathematical notion of a graph. The variety and lack of standardization with respect to the logical representation of graph data and the retrieval of graph data make it hard to define a portable graph database benchmark. In this talk, we present a proposal and design guideline for a graph database benchmark. Typically, a database benchmark consists of a synthetically generated data set of varying size and varying characteristics and a workload driver. In order to generate graph data sets, we present parameters from graph theory, which influence the characteristics of the generated graph data set. Following, the workload driver issues a set of queries against a well-defined interface of the graph database and gathers relevant performance numbers. We propose a set of performance measures to determine the response time behavior on different workloads and also initial suggestions for typical workloads in graph data scenarios. Our main objective of this session is to open the discussion on graph database benchmarking. We believe that there is a need for a common understanding of different workloads for graph processing from different domains and the definition of a common subset of core graph functionality in order to provide a general-purpose graph database benchmark. We encourage vendors to participate and to contribute with their domain-dependent knowledge and to define a graph database benchmark proposal.
How Graph Databases efficiently store, manage and query connected data at s...jexp
Graph Databases try to make it easy for developers to leverage huge amounts of connected information for everything from routing to recommendations. Doing that poses a number of challenges on the implementation side. In this talk we want to look at the different storage, query and consistency approaches that are used behind the scenes. We’ll check out current and future solutions used in Neo4j and other graph databases for addressing global consistency, query and storage optimization, indexing and more and see which papers and research database developers take inspirations from.
The openCypher Project - An Open Graph Query LanguageNeo4j
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
Neo4j is a highly scalable native graph database that leverages data relationships as first-class entities, helping enterprises build intelligent applications to meet today’s evolving data challenges.
این دیتابیس توسط Neo Technology در سال ۲۰۰۷ ایجاد شد و به صورت Opensource در اختیار کاربران قرار گرفت. آخرین نسخه Stable، ورژن ۳.۱ هست.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Ryan Boyd, Developer Relations at Neo4j
Ryan is a SF-based software engineer focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.
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.
An Introduction to Graph: Database, Analytics, and Cloud ServicesJean Ihm
Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.
Presented at Analytics and Data Summit, March 20, 2018
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
This presentation covers several aspects of modeling data and domains with a graph database like Neo4j. The graph data model allows high fidelity modeling. Using the first class relationships of the graph model allow to use much higher forms of normalization than you would use in a relational database.
Video here: https://vimeo.com/67371996
Hadoop and Neo4j: A Winning Combination for Bioinformaticsosintegrators
This presentation includes an intro to bioinformatics with an emphasis on human genome re-sequencing and how Hadoop and Neo4j can be used together to open striking possibilities.
This tutorial will provide you with a basic understanding of graph database technology and the ability to quickly begin development of a graph database application. You will have the capability to recognize graph-based problems and present the benefits of using graph technology for problem resolution.
The tutorial will give you an understanding of:
• Graph theory - origins and concepts
• Benefits of graph databases
• Different types of graph databases
• Typical graph database API
• Programming basics
• Use cases
Bring your laptops for a hands-on opportunity to practice some sample codes. A basic understanding of Java programming is a recommended prerequisite to understand this course. This session is led by the InfiniteGraph technical team and the demonstration code will be drawn from InfiniteGraph examples, however the broader educational presentation is product-neutral and not a commercial presentation of their products.
To participate in the hands-on portion of the graph tutorial users must have:
• Java programming experience
• Java Developer Kit (JDK)
• Current InfiniteGraph installed on laptop. (To download visit www.objectivity.com/infinitegraph)
• HelloGraph test – Upon installing IG, run HelloGraph to test the install. (HelloGraph can be found online at http://wiki.infinitegraph.com/2.1/w/index.php?title=Download_Sample_Code)
Leon Guzenda was one of the founding members of Objectivity in 1988 and one of the original architects of Objectivity/DB. He currently works with Objectivity's major customers to help them effectively develop and deploy complex applications and systems that use the industry's highest-performing, most reliable DBMS technology, Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products, he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom, delivering major projects for NATO and leading multinationals. He was also design and development manager for ICL's 2900 IDMS product. He spent the first 7 years of his career working in defense and government systems. Leon has a B.S. degree in Electronic Engineering from the University of Wales.
Not only, is our data is getting not just more complex but also more connected. In order not to lose sight of the web of information, but to use it as a source of new insights and opportunities, technologies such as graph databases can help.
For both analytical and transactional use cases, they allow efficient storage, retrieval, and processing of networked data without loss of detail. In this talk, we want to get to know existing tools and techniques for graph data processing.
During this presentation, Will covers the updates made in the Neo4j 3.0 release. He introduces Bolt (Neo4j's new binary protocol), and shows how developers can start using the Neo4j official drivers, build a stored procedure and take advantage of advanced support for cloud, container and on-premise.
The openCypher Project - An Open Graph Query LanguageNeo4j
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
Neo4j is a highly scalable native graph database that leverages data relationships as first-class entities, helping enterprises build intelligent applications to meet today’s evolving data challenges.
این دیتابیس توسط Neo Technology در سال ۲۰۰۷ ایجاد شد و به صورت Opensource در اختیار کاربران قرار گرفت. آخرین نسخه Stable، ورژن ۳.۱ هست.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Ryan Boyd, Developer Relations at Neo4j
Ryan is a SF-based software engineer focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.
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.
An Introduction to Graph: Database, Analytics, and Cloud ServicesJean Ihm
Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.
Presented at Analytics and Data Summit, March 20, 2018
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
This presentation covers several aspects of modeling data and domains with a graph database like Neo4j. The graph data model allows high fidelity modeling. Using the first class relationships of the graph model allow to use much higher forms of normalization than you would use in a relational database.
Video here: https://vimeo.com/67371996
Hadoop and Neo4j: A Winning Combination for Bioinformaticsosintegrators
This presentation includes an intro to bioinformatics with an emphasis on human genome re-sequencing and how Hadoop and Neo4j can be used together to open striking possibilities.
This tutorial will provide you with a basic understanding of graph database technology and the ability to quickly begin development of a graph database application. You will have the capability to recognize graph-based problems and present the benefits of using graph technology for problem resolution.
The tutorial will give you an understanding of:
• Graph theory - origins and concepts
• Benefits of graph databases
• Different types of graph databases
• Typical graph database API
• Programming basics
• Use cases
Bring your laptops for a hands-on opportunity to practice some sample codes. A basic understanding of Java programming is a recommended prerequisite to understand this course. This session is led by the InfiniteGraph technical team and the demonstration code will be drawn from InfiniteGraph examples, however the broader educational presentation is product-neutral and not a commercial presentation of their products.
To participate in the hands-on portion of the graph tutorial users must have:
• Java programming experience
• Java Developer Kit (JDK)
• Current InfiniteGraph installed on laptop. (To download visit www.objectivity.com/infinitegraph)
• HelloGraph test – Upon installing IG, run HelloGraph to test the install. (HelloGraph can be found online at http://wiki.infinitegraph.com/2.1/w/index.php?title=Download_Sample_Code)
Leon Guzenda was one of the founding members of Objectivity in 1988 and one of the original architects of Objectivity/DB. He currently works with Objectivity's major customers to help them effectively develop and deploy complex applications and systems that use the industry's highest-performing, most reliable DBMS technology, Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products, he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom, delivering major projects for NATO and leading multinationals. He was also design and development manager for ICL's 2900 IDMS product. He spent the first 7 years of his career working in defense and government systems. Leon has a B.S. degree in Electronic Engineering from the University of Wales.
Not only, is our data is getting not just more complex but also more connected. In order not to lose sight of the web of information, but to use it as a source of new insights and opportunities, technologies such as graph databases can help.
For both analytical and transactional use cases, they allow efficient storage, retrieval, and processing of networked data without loss of detail. In this talk, we want to get to know existing tools and techniques for graph data processing.
During this presentation, Will covers the updates made in the Neo4j 3.0 release. He introduces Bolt (Neo4j's new binary protocol), and shows how developers can start using the Neo4j official drivers, build a stored procedure and take advantage of advanced support for cloud, container and on-premise.
Bryan Thompson, Chief Scientist and Founder, SYSTAP, LLC at MLconf ATLMLconf
I will discuss current research on the MapGraph platform. MapGraph is a new and disruptive technology for ultra-fast processing of large graphs on commodity many-core hardware. On a single GPU you can analyze the bitcoin transaction graph in .35 seconds. With MapGraph on 64 NVIDIA K20 GPUs, you can traverse a scale-free graph of 4.3 billion directed edges in .13 seconds for a throughput of 32 Billion Traversed Edges Per Second (32 GTEPS). I will explain why GPUs are an interesting option for data intensive applications, how we map graphs onto many-core processors, and what the future looks like for the MapGraph platform.
MapGraph provides a familiar vertex-centric abstraction, but its GPU acceleration is 100s of times faster than main memory CPU-only technologies and up to 100,000 times faster than graph technologies based on MapReduce or key-value stores such as HBase, Titan, and Accumulo. Learn more at http://MapGraph.io.
Using Big Data techniques to query and store OpenStreetMap data. Stephen Knox...huguk
This talk will describe his research into using Hadoop to query and manage big geographic datasets, specifically OpenStreetMap(OSM). OSM is an “open-source” map of the world, growing at a large rate, currently around 5TB of data. The talk will introduce OSM, detail some aspects of the research, but also discuss his experiences with using the SpatialHadoop stack on Azure and Google Cloud.
BDM8 - Near-realtime Big Data Analytics using ImpalaDavid Lauzon
Quick overview of all informations I've gathered on Cloudera Impala. It describes use cases for Impala and what not to use Impala for. Presented at Big Data Montreal #8 at RPM Startup Center.
Ciel, mes données ne sont plus relationnellesXavier Gorse
Quand la gestion des données de nos applications web dépasse la simple persistance dans une base de données relationnelle (type SGBD), l’utilisation de technologies alternatives dites « NoSql » est nécessaire. Nous aborderons les 4 grandes familles de NoSql (Key/Value, Document, Column-oriented et Graph) ainsi que leur intégration dans des applications PHP.
Thorny path to the Large-Scale Graph Processing (Highload++, 2014)Alexey Zinoviev
Alexey Zinoviev presented this paper on the Highload++ conference http://www.highload.ru/2014/abstracts/1516.html
This paper covers next topics: Pregel, Graph Theory, Giraph, Okapi, GraphX, GraphChi, Spark, Shrotest Path Problem, Road Network, Road Graph
Analytics methods for big data have two requirements above and beyond analytics methods for normal-sized data. First, the analytics can not assume that all the data will fit in memory, or even fit on one server. Second, the choice of analysis methods must avoid high-order algorithms. We illustrate the point with one algorithm: Locality Sensitive Hashing
These slides are from a talk Ted Dunning gave at Lawrence Livermore Labs in 2011.
The talk gives an architectural outline of the MapR system and then discusses how this architecture facilitates large scale machine learning algorithms.
Improving computer vision models at scale presentationDr. Mirko Kämpf
Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. When testing data is present at the petabyte scale, the ability to seamlessly access all the images that have been assigned specific labels poses a technical challenge by itself.
Marton Balassi, Mirko Kämpf, and Jan Kunigk share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable. Images and labels are stored in HBase. The model is encapsulated in a PySpark program, while the images are indexed with Solr and can be accessed from a Hue dashboard.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
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.
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Data Model
ColumnFamily: ColumnFamily is a single structure that can group Columns and SuperColumns with ease.
Key: the permanent name of the record. Keys have different numbers of columns, so the database can scale in an irregular way.
Keyspace: This defines the outermost level of an organization, typically the name of the application. For example, ‘3PillarDataBase’ (database name).
Column: It has an ordered list of elements aka tuple with a name and a value defined.
In comparison, most relational DBMS store data in rows, the benefit of storing data in columns, is fast search/ access and data aggregation
polyglot persistence will come at a cost - but it will come because the benefits are worth it.
A Graph Database + Lucene Index
Property Graph
Full ACID (atomicity, consistency, isolation, durability)
High Availability (with Enterprise Edition)
32 Billion Nodes, 32 Billion Relationships, 64 Billion Properties
Embedded Server
REST API