Data Modeling with Neo4j

The document discusses how graph databases can help governments address challenges like fraud detection, cybersecurity, and intelligence analysis. It provides examples of how Neo4j has helped organizations like Lockheed Martin, the US Army, and NASA optimize processes and save time and money by integrating diverse data sources and analyzing relationships within the data. The document promotes Neo4j's graph data platform for its flexibility, performance, and ability to handle large, interconnected datasets in real-time.

Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.

Neo4j Founder and CEO Emil Eifrem shares his story on the origins of Neo4j and how graph technology has the potential to answer the world's most important data questions.

This document outlines an upcoming workshop on graph technology and data science using Neo4j. The workshop will cover knowledge graphs, graph algorithms, graph machine learning techniques, and use cases. It will include demonstrations of algorithms like node similarity and centrality measures on graphs. Attendees will learn how graph databases like Neo4j can power graph analytics and machine learning to gain insights from connected data.

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 native 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.

The document is a presentation deck on building a supply chain twin using Neo4j and Google technologies. It discusses how supply chain data can be modeled as a graph and stored in Neo4j to power use cases like identifying product and part shortfalls, evaluating supply chain risk, and enabling scenario planning. The deck outlines an architecture that ingests supply chain data from Google BigQuery into Neo4j, then leverages Neo4j technologies like Graph Data Science, Bloom, and Keymaker to operationalize queries and deliver insights to applications.

Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt. Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com. Chris Riccomini works as a Software Engineer at WePay.

This document provides an overview of the Neo4j Graph Data Platform. Some key points: - Neo4j is a native graph database that is well-suited for connected data use cases that are growing exponentially. Graph databases can handle relationships better than relational databases and support relationship queries better than NoSQL databases. - The Neo4j Graph Data Platform includes the native graph database, development tools, data science and analytics capabilities, and an ecosystem of integrations. It can be deployed anywhere including as a service on AuraDB. - Neo4j has pioneered the graph database category since 2010 and continues to drive innovation with features like graph-RBAC security, graph data

The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.

The document discusses graph data science techniques in Neo4j. It provides an overview of graph algorithms categories including pathfinding and search, centrality and importance, community detection, similarity, heuristic link prediction, and node embeddings and machine learning. It also summarizes 60+ graph data science techniques available in Neo4j across these categories and how they can be accessed and deployed. Finally, it discusses graph embeddings and graph native machine learning in Neo4j, covering techniques like Node2Vec, GraphSAGE, and FastRP.

With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.

This document provides an overview of graph databases and their use cases. It begins with definitions of graphs and graph databases. It then gives examples of how graph databases can be used for social networking, network management, and other domains where data is interconnected. It provides Cypher examples for creating and querying graph patterns in a social networking and IT network management scenario. Finally, it discusses the graph database ecosystem and how graphs can be deployed for both online transaction processing and batch processing use cases.

Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization. Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support. In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.

The document discusses best practices for graph data modeling in Neo4j. It describes different types of modeling including whiteboarding, instance modeling, logical modeling, physical modeling, and tuned modeling. Each type of modeling has a different focus such as conceptual understanding, answering questions, enabling data loading, and optimizing performance. The document provides tips for each modeling type and examples to illustrate graph structures. It also covers topics like relationship types, constraints, indexing, and validating the model.

The document discusses Neo4j's graph data science capabilities. It highlights that Neo4j provides tools for graph algorithms, machine learning pipelines for tasks like node classification and link prediction, and a graph catalog for managing graph projections from the underlying database. The document also notes that Neo4j's capabilities allow users to leverage relationships in connected data to answer business questions.

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.

Making Data Mesh simple, Open Source and available to all; without vendor lock-in, without complex tooling and to use an approach centered around ‘specifications’, existing tools and baking in a ‘domain’ model.

Short presentation on the graph database and processing support in SQL Server 2017 (plus new stuff in 2019) as presented at Live 360! SQL Live

The document provides an outline for a presentation on graph-based data models. It introduces some key concepts about graphs and how they are used to model real-world interconnected data. It discusses how early adopters of graph technologies grew by focusing on data relationships. The document also covers graph data structures, graph databases, and graph query languages like Cypher and Gremlin.

An Introduction to Neo4j Graph database, Cypher query language. Comparison between Neo4j and Graph Database

Graph databases address one of the great macroscopic business trends of today: leveraging complex and dynamic relationships in highly connected data to generate insight and competitive advantage. Whether we want to understand relationships between customers, elements in a telephone or data center network, entertainment producers and consumers, or genes and proteins, the ability to understand and analyze vast graphs of highly connected data will be key in determining which companies outperform their competitors over the coming decade. In this session, I am going to cover following graph database concepts mainly w.r.t Neo4j. High level view of Graph Space Power of Graph Databases Data Modeling with Graphs Cypher : Graph Query language Building a Graph Database Application Graphs in Real World / Common Use cases Predictive Analysis with Graph Theory

The document provides an overview of NoSQL databases and MongoDB. It discusses: - What NoSQL is and why it was created - The different categories of NoSQL databases, including key-value stores, document databases, column family stores, and graph databases - MongoDB specifically, including its flexible schema, horizontal scalability, replication support, and data modeling approach - Comparisons between relational and NoSQL databases

This document provides an overview of SQL and NoSQL databases. It discusses how relational databases using SQL emerged as the dominant data storage approach but faced challenges in scaling to big data workloads. NoSQL databases were developed to address these scaling needs by using non-relational data models like key-value, document, and column-oriented structures that are better suited to distributed architectures. The document outlines the history and characteristics of SQL and relational databases and how NoSQL databases address needs like scalability that drove their emergence in the big data era.

The document provides an overview of NoSQL and MongoDB. It discusses that NoSQL databases were built for large datasets and cloud applications. It covers some of the main types of NoSQL databases like document stores, key-value stores, and column family stores. The document also compares NoSQL to SQL/relational databases, discussing how NoSQL is more flexible and scales horizontally. MongoDB is presented as a popular document-oriented NoSQL database, covering its flexible schema, horizontal scaling, and replication features.

The document discusses using graph databases for insights into connected data. It provides an overview of graph databases, comparing them to relational databases and NoSQL stores. It discusses how graph databases are better suited than other models for richly connected data due to their native support of relationships. The document also covers graph data modeling, the Cypher query language, examples of graph databases in real world domains, and aspects of graph database internals like scalability.

Incontro del 23/05/2018 Nowadays graph visualization and analysis is a fundamental tool for developers, analysts, business executives, and really anyone who needs to understand his data in order to extract information from it and see all the present interactions. Unfortunately, most graph visualization tools do not have the ability to integrate with a relational database. Arcade Analytics is a graph visualization tool that enables users to have more control over their data: it sits on top of the user's database and allows the users to query data and show it in a graph. One of the most attractive features of Arcade Analytics is that it allows users to query data from a relational database and visualize the relational database content as a graph. Arcade's RDBMS connector allows users to perform a graph analysis over your RDBMS without any migration, in this way you can visually inspect relationships and connections within your RDBMS and treat your data as a graph. Speaker: Gabriele Ponzi

Graph processing and graph databases have been with us for a while. However, since their physical implementations are the same for every database in production (Node connected to node, or triplets), there's a perception that data modeling (and data modelers) have no role on projects where graph databases are used. This month we'll talk about where graph databases are a best fit in a modern data architecture and where data models add value.

This document provides an overview of different database models, including the relational, document, and graph models. The graph model stores data as connected vertices and edges, allowing for index-free adjacency and flexible, schema-agnostic modeling of relationships. This makes the graph model well-suited for applications involving social networks, recommendations, and other domains where relationships are important. The document compares querying and traversing graph data to other models using examples involving modeling a social network.

This document summarizes a research paper that evaluates Cassandra and MongoDB NoSQL databases for processing unstructured data using Hadoop streaming. It proposes a system with three stages: data preparation where data is downloaded from Cassandra servers to file systems; data transformation where JSON data is converted to other formats using MapReduce; and data processing where non-Java executables run on the transformed data. The document reviews related work on Cassandra and Hadoop performance and discusses the data models of key-value, document, column-oriented, and graph databases. It concludes that comparing Cassandra and MongoDB can help process unstructured data and outline new approaches.

NoSql is an approach to database design that enables the storage and querying of data outside the traditional structures found in relational databases

Lingua talk: Italiano. Descrizione: In questo talk parleremo di come integrare e utilizzare ArangoDB, un database multi-modello con supporto nativo ai grafi, con R. Presenteremo quindi aRangodb, il package che abbiamo sviluppato per interfacciarsi in modo più semplice e intuitivo al database. Nel corso del talk mostreremo come il package possa essere utilizzato in ambito data science usando alcuni case studies concreti. Speaker: Gabriele Galatolo - Data Scientist - Kode srl

Abstract: How graphs became just another big data primitive Graph-shaped data is used in product recommendation systems, social network analysis, network threat detection, image de-noising, and many other important applications. And, a growing number of these applications will benefit from parallel distributed processing for graph featuring engineering, model training, and model serving. But today’s graph tools are riddled with limitations and shortcomings, such as a lack of language bindings, streaming support, and seamless integration with other popular data services. In this talk, we’ll argue that the key to doing more with graphs is doing less with specialized systems and more with systems already good at handling data of other shapes. We’ll examine some practical data science workflows to further motivate this argument and we’ll talk about some of the things that Intel is doing with the open source community and industry to make graphs just another big data primitive.

Graph databases are a type of NoSQL database designed to handle large networks of structured, semi-structured, or unstructured data. They are well-suited for domains involving entities and relationships between entities. Some examples of graph databases include Neo4j, Oracle NoSQL DB, and Graphbase. Graph databases prioritize relationships between data, unlike traditional SQL databases. They are useful for applications involving large, dynamic networks like social media sites.

Domain Driven Design is a software development process that focuses on finding a common language for the involved parties. This language and the resulting models are taken from the domain rather than the technical details of the implementation. The goal is to improve the communication between customers, developers and all other involved groups. Even if Eric Evan's book about this topic was written almost ten years ago, this topic remains important because a lot of projects fail for communication reasons. Relational databases have their own language and influence the design of software into a direction further away from the Domain: Entities have to be created for the sole purpose of adhering to best practices of relational database. Two kinds of NoSQL databases are changing that: Document stores and graph databases. In a document store you can model a "contains" relation in a more natural way and thereby express if this entity can exist outside of its surrounding entity. A graph database allows you to model relationships between entities in a straight forward way that can be expressed in the language of the domain. In this talk I want to look at the way a multi model database that combines a document store and a graph database can help you to model your problems in a way that is understandable for all parties involved, and explain the benefits of this approach for the software development process.

Andreas presents 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.

The document discusses how graph databases and graph data science can be used to enhance machine learning models by incorporating relationship data. It provides examples of how organizations are using Neo4j's graph data science platform to improve predictive models in areas like fraud detection, health outcomes, and supply chain reliability. The platform includes over 50 graph algorithms, graph-native machine learning workflows, and the ability to train, apply, and manage predictive models on graph data.

With SQL Server and Cosmos Db we now have graph databases broadly available, after being studied for decades in Db theory, or being a niche approach in Open Source with Neo4J. And then there are services like Microsoft Graph and Azure Digital Twins that give us vertical implementations of graph. So let's make a walkaround of graphs in the MIcrosoft ecosystem.

Presented at Gartner Data & Analytics, London Maty 2024. 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. Join this session to hear their story, the lessons they learned along the way and how their future innovation plans include the exploration of uses of EKG + Generative AI.

Gursev Pirge, PhD Senior Data Scientist - JohnSnowLabs

Tomaz Bratanic Graph ML and GenAI Expert - Neo4j

Katja Glaß OpenStudyBuilder Community Manager - Katja Glaß Consulting Marius Conjeaud Principal Consultant - Neo4j

Dmitrii Kamaev, PhD Senior Product Owner - QIAGEN

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 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.

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.

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.

Thibault Gossé, Chief Data & AI Officer, FLOA Martin Schubenel, Fraud Project Manager, FLOA

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.

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.

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.

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.

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.

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.

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.

As a popular open-source library for analytics engineering, dbt is often used in combination with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models. This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through: - Standard ways of running dbt (and when to utilize other methods) - How Cosmos can be used to run and visualize your dbt projects in Airflow - Common challenges and how to address them, including performance, dependency conflicts, and more - How running dbt projects in Airflow helps with cost optimization Webinar given on 9 July 2024

Everything that I found interesting about engineering leadership last month

YOUR RELIABLE WEB DESIGN & DEVELOPMENT TEAM — FOR LASTING SUCCESS WPRiders is a web development company specialized in WordPress and WooCommerce websites and plugins for customers around the world. The company is headquartered in Bucharest, Romania, but our team members are located all over the world. Our customers are primarily from the US and Western Europe, but we have clients from Australia, Canada and other areas as well. Some facts about WPRiders and why we are one of the best firms around: More than 700 five-star reviews! You can check them here. 1500 WordPress projects delivered. We respond 80% faster than other firms! Data provided by Freshdesk. We’ve been in business since 2015. We are located in 7 countries and have 22 team members. With so many projects delivered, our team knows what works and what doesn’t when it comes to WordPress and WooCommerce. Our team members are: - highly experienced developers (employees & contractors with 5 -10+ years of experience), - great designers with an eye for UX/UI with 10+ years of experience - project managers with development background who speak both tech and non-tech - QA specialists - Conversion Rate Optimisation - CRO experts They are all working together to provide you with the best possible service. We are passionate about WordPress, and we love creating custom solutions that help our clients achieve their goals. At WPRiders, we are committed to building long-term relationships with our clients. We believe in accountability, in doing the right thing, as well as in transparency and open communication. You can read more about WPRiders on the About us page.

This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator. Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/ Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.

Stream processing is a crucial component of modern data infrastructure, but constructing an efficient and scalable stream processing system can be challenging. Decoupling compute and storage architecture has emerged as an effective solution to these challenges, but it can introduce high latency issues, especially when dealing with complex continuous queries that necessitate managing extra-large internal states. In this talk, we focus on addressing the high latency issues associated with S3 storage in stream processing systems that employ a decoupled compute and storage architecture. We delve into the root causes of latency in this context and explore various techniques to minimize the impact of S3 latency on stream processing performance. Our proposed approach is to implement a tiered storage mechanism that leverages a blend of high-performance and low-cost storage tiers to reduce data movement between the compute and storage layers while maintaining efficient processing. Throughout the talk, we will present experimental results that demonstrate the effectiveness of our approach in mitigating the impact of S3 latency on stream processing. By the end of the talk, attendees will have gained insights into how to optimize their stream processing systems for reduced latency and improved cost-efficiency.

Cybersecurity is a major concern in today's connected digital world. Threats to organizations are constantly evolving and have the potential to compromise sensitive information, disrupt operations, and lead to significant financial losses. Traditional cybersecurity techniques often fall short against modern attackers. Therefore, advanced techniques for cyber security analysis and anomaly detection are essential for protecting digital assets. This blog explores these cutting-edge methods, providing a comprehensive overview of their application and importance.

This is a slide deck that showcases the updates in Microsoft Copilot for May 2024

How do we build an IoT product, and make it profitable? Talk from the IoT meetup in March 2024. https://www.meetup.com/iot-sweden/events/299487375/

In the modern digital era, social media platforms have become integral to our daily lives. These platforms, including Facebook, Instagram, WhatsApp, and Snapchat, offer countless ways to connect, share, and communicate.

Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation

Everything that I found interesting last month about the irresponsible use of machine intelligence

Java Servlet programs

Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.

Invited Remote Lecture to SC21 The International Conference for High Performance Computing, Networking, Storage, and Analysis St. Louis, Missouri November 18, 2021

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MuleSoft Meetup on APM and IDP

Kief Morris rethinks the infrastructure code delivery lifecycle, advocating for a shift towards composable infrastructure systems. We should shift to designing around deployable components rather than code modules, use more useful levels of abstraction, and drive design and deployment from applications rather than bottom-up, monolithic architecture and delivery.

Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr "A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models" arXiv2023 https://arxiv.org/abs/2307.12980

This is a powerpoint that features Microsoft Teams Devices and everything that is new including updates to its software and devices for May 2024

This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization. Key Takeaways: * Understand why connection pooling is essential for high-traffic applications * Explore various connection poolers available for PostgreSQL, including pgbouncer * Learn the configuration options and functionalities of pgbouncer * Discover best practices for monitoring and troubleshooting connection pooling setups * Gain insights into real-world use cases and considerations for production environments This presentation is ideal for: * Database administrators (DBAs) * Developers working with PostgreSQL * DevOps engineers * Anyone interested in optimizing PostgreSQL performance Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services