Learn how to enhance your application by using Neo4j and MongoDB together. Polyglot persistence is the concept of taking advantage of the strengths of different database technologies to improve functionality and enhance your application. In this webinar we will examine some use cases where it makes sense to use a document database (MongoDB) with a graph database (Neo4j) in a single application. Specifically, we will show how MongoDB can be used to provide search and browsing functionality for a product catalog while using Neo4j to provide personalized product recommendations. Finally we will look at the Neo4j Doc Manager project which facilitates syncing data from MongoDB to Neo4j to make polyglot persistence with MongoDB and Neo4j much easier.
Polyglot Persistence vs Multi-Model DatabasesLuca Garulli
Many complex applications scale up by using several different databases, i.e. selecting the best DBMS for each use case. This tends to complicate modern architecture with many products by different vendors, no standards, and a lot of ETL which ultimately causes unpredictable results and a lot of headaches. Multi-Model DBMSs were created to make your life easier, giving you the option of using one NoSQL product with powerful multi-purpose engines capable of handling complex domains. Could one DBMS handle all your needs including speed and scalability in the times of Big Data? Luca will walk you through the benefits and trade-offs of multi-model DBMSs and will show you how easy it is to setup one open source database to handle many different use cases, saving you time and money.
Presented at Data Day Texas - Austin (TX) - USA
Presented by Rags Srinivas, Developer Advocate/Architect at Datastax at Kubernetes Community Days, Washington DC, September 14, 2022.
Cassandra is designed for multi-region
● Partition tolerant
● Each node in the cluster maintains the full topology
● Nodes automatically route traffic to nearby neighbors
● Data is automatically and asynchronously replicated
● The cluster is homogenous
● Any node can service any client request
● Clients can be configured to automatically route traffic to the local datacenter
Kubernetes was not designed for multi-region
● Increased latencies
● The cost is higher consensus request latency from crossing data center boundaries
● Loss of connectivity to ectd could cause outages
● Services should route traffic to nearby endpoints
Neo4j Spatial - Backing a GIS with a true graph databaseCraig Taverner
Geographic data is naturally structured like a graph, and topological analyses view GIS data as graphs, but until now no-one has tried to make use of a real graph database as the backing store for a GIS. The developers of Neo4j have added features to the popular open source graph database to provide for support for spatial indexing, storage and topology. In addition to these core components, there are a number of useful utilities for importing and exporting data from other popular data sources, and enabling the use of this database in well known libraries and applications in the open source GIS environment.
We will discuss the advantages of using a graph database for geographic data, the performance and scalability implications, and the opportunities enabled by this approach. In today's highly connected social web, there is an increasing need for graph-based data management. At the same time applications are becoming more and more location aware. The time is right for the first geographic graph database.
MongoDB is the leading NoSQL database due to a plenitude of reasons, open source, general purpose, document oriented database supported by a large community and educational platform. It's horizontal scalability features allows this to fit in the operational big data scenarios where the business needs point to realtime analytics and ever-increasing data sets. This talk will focus on the usage of MongoDB for big data operational purposes and why it's ideal to be used in such scenarios. Also integration with other notable big data technology out there like Hadoop and BI tools.
Norberto Leite - Senior Solutions Architect, @MongoDB.
Mongo DB presentation during the Pentaho & Big Data Ecosystem - Live Seminar 2013
A Brief History of Database Management (SQL, NoSQL, NewSQL)Abdelkader OUARED
What's the Difference Between SQL, NoSQL, and NewSQL
SQL is a relational database management system (RDBMS) based on ... NewSQL tries to bring some of the features and scalability of NoSQL to SQL.
Polyglot Persistence vs Multi-Model DatabasesLuca Garulli
Many complex applications scale up by using several different databases, i.e. selecting the best DBMS for each use case. This tends to complicate modern architecture with many products by different vendors, no standards, and a lot of ETL which ultimately causes unpredictable results and a lot of headaches. Multi-Model DBMSs were created to make your life easier, giving you the option of using one NoSQL product with powerful multi-purpose engines capable of handling complex domains. Could one DBMS handle all your needs including speed and scalability in the times of Big Data? Luca will walk you through the benefits and trade-offs of multi-model DBMSs and will show you how easy it is to setup one open source database to handle many different use cases, saving you time and money.
Presented at Data Day Texas - Austin (TX) - USA
Presented by Rags Srinivas, Developer Advocate/Architect at Datastax at Kubernetes Community Days, Washington DC, September 14, 2022.
Cassandra is designed for multi-region
● Partition tolerant
● Each node in the cluster maintains the full topology
● Nodes automatically route traffic to nearby neighbors
● Data is automatically and asynchronously replicated
● The cluster is homogenous
● Any node can service any client request
● Clients can be configured to automatically route traffic to the local datacenter
Kubernetes was not designed for multi-region
● Increased latencies
● The cost is higher consensus request latency from crossing data center boundaries
● Loss of connectivity to ectd could cause outages
● Services should route traffic to nearby endpoints
Neo4j Spatial - Backing a GIS with a true graph databaseCraig Taverner
Geographic data is naturally structured like a graph, and topological analyses view GIS data as graphs, but until now no-one has tried to make use of a real graph database as the backing store for a GIS. The developers of Neo4j have added features to the popular open source graph database to provide for support for spatial indexing, storage and topology. In addition to these core components, there are a number of useful utilities for importing and exporting data from other popular data sources, and enabling the use of this database in well known libraries and applications in the open source GIS environment.
We will discuss the advantages of using a graph database for geographic data, the performance and scalability implications, and the opportunities enabled by this approach. In today's highly connected social web, there is an increasing need for graph-based data management. At the same time applications are becoming more and more location aware. The time is right for the first geographic graph database.
MongoDB is the leading NoSQL database due to a plenitude of reasons, open source, general purpose, document oriented database supported by a large community and educational platform. It's horizontal scalability features allows this to fit in the operational big data scenarios where the business needs point to realtime analytics and ever-increasing data sets. This talk will focus on the usage of MongoDB for big data operational purposes and why it's ideal to be used in such scenarios. Also integration with other notable big data technology out there like Hadoop and BI tools.
Norberto Leite - Senior Solutions Architect, @MongoDB.
Mongo DB presentation during the Pentaho & Big Data Ecosystem - Live Seminar 2013
A Brief History of Database Management (SQL, NoSQL, NewSQL)Abdelkader OUARED
What's the Difference Between SQL, NoSQL, and NewSQL
SQL is a relational database management system (RDBMS) based on ... NewSQL tries to bring some of the features and scalability of NoSQL to SQL.
Data Discovery at Databricks with AmundsenDatabricks
Databricks used to use a static manually maintained wiki page for internal data exploration. We will discuss how we leverage Amundsen, an open source data discovery tool from Linux Foundation AI & Data, to improve productivity with trust by surfacing the most relevant dataset and SQL analytics dashboard with its important information programmatically at Databricks internally.
We will also talk about how we integrate Amundsen with Databricks world class infrastructure to surface metadata including:
Surface the most popular tables used within Databricks
Support fuzzy search and facet search for dataset- Surface rich metadata on datasets:
Lineage information (downstream table, upstream table, downstream jobs, downstream users)
Dataset owner
Dataset frequent users
Delta extend metadata (e.g change history)
ETL job that generates the dataset
Column stats on numeric type columns
Dashboards that use the given dataset
Use Databricks data tab to show the sample data
Surface metadata on dashboards including: create time, last update time, tables used, etc
Last but not least, we will discuss how we incorporate internal user feedback and provide the same discovery productivity improvements for Databricks customers in the future.
Postgres vs Mongo / Олег Бартунов (Postgres Professional)Ontico
РИТ++ 2017, Backend Conf
Зал Конгресс-холл, 6 июня, 17:00
Тезисы:
http://backendconf.ru/2017/abstracts/2781.html
Я хочу немного порушить стереотипы, что Postgres - это чисто реляционная СУБД из прошлого века, плохо приспособленная под реалии современных проектов. Недавно мы прогнали YCSB для последних версий Postgres и Mongodb и увидели их плюсы и минусы на разных типах нагрузки, о которых я буду рассказывать. ...
Definitive Guide to Select Right Data Warehouse (2020)Sprinkle Data Inc
Choosing the right data warehouse is a big challenge for organisations. In this doc, we have made an end to end comparison of leading data warehouses. Snowflake vs Redshift vs BigQuery vs Hive vs Athena
Sprinkledata.com
Active Governance Across the Delta Lake with AlationDatabricks
Alation provides a single interface to provide users and stewards to provide active and agile data governance across Databricks Delta Lake and Databricks SQL Analytics Service. Understand how Alation can expand adoption in the data lake while providing safe and responsible data consumption.
Building Data Lakes with Apache AirflowGary Stafford
Build a simple Data Lake on AWS using a combination of services, including Amazon Managed Workflows for Apache Airflow (Amazon MWAA), AWS Glue, AWS Glue Studio, Amazon Athena, and Amazon S3.
Blog post and link to the video: https://garystafford.medium.com/building-a-data-lake-with-apache-airflow-b48bd953c2b
The analysis of movement is an important research topic in, for example, geography, ecology, visual analytics, GIScience as well as in application domains such as urban, maritime, and aviation research. Movement data analysis requires tools for the manipulation and visualization of movement or trajectory data. This talk presents the new Python library MovingPandas.org
Avoiding Deadlocks: Lessons Learned with Zephyr Health Using Neo4j and MongoD...Neo4j
Z-Platform is the new innovative powerful and complex platform to ingest data of any kind and store the data in the form of JSON documents in MongoDB and represent a sparse representation of the same in Neo4j graph database. Mahesh discusses how he tackled deadlocks and improved the performance of the system significantly. The test environment included small graphs (ranging up to 10000 relationships to very large graphs (ranging up to 39 million relationships). The average performance of the system is 3741 relationships per minute.
Data Discovery at Databricks with AmundsenDatabricks
Databricks used to use a static manually maintained wiki page for internal data exploration. We will discuss how we leverage Amundsen, an open source data discovery tool from Linux Foundation AI & Data, to improve productivity with trust by surfacing the most relevant dataset and SQL analytics dashboard with its important information programmatically at Databricks internally.
We will also talk about how we integrate Amundsen with Databricks world class infrastructure to surface metadata including:
Surface the most popular tables used within Databricks
Support fuzzy search and facet search for dataset- Surface rich metadata on datasets:
Lineage information (downstream table, upstream table, downstream jobs, downstream users)
Dataset owner
Dataset frequent users
Delta extend metadata (e.g change history)
ETL job that generates the dataset
Column stats on numeric type columns
Dashboards that use the given dataset
Use Databricks data tab to show the sample data
Surface metadata on dashboards including: create time, last update time, tables used, etc
Last but not least, we will discuss how we incorporate internal user feedback and provide the same discovery productivity improvements for Databricks customers in the future.
Postgres vs Mongo / Олег Бартунов (Postgres Professional)Ontico
РИТ++ 2017, Backend Conf
Зал Конгресс-холл, 6 июня, 17:00
Тезисы:
http://backendconf.ru/2017/abstracts/2781.html
Я хочу немного порушить стереотипы, что Postgres - это чисто реляционная СУБД из прошлого века, плохо приспособленная под реалии современных проектов. Недавно мы прогнали YCSB для последних версий Postgres и Mongodb и увидели их плюсы и минусы на разных типах нагрузки, о которых я буду рассказывать. ...
Definitive Guide to Select Right Data Warehouse (2020)Sprinkle Data Inc
Choosing the right data warehouse is a big challenge for organisations. In this doc, we have made an end to end comparison of leading data warehouses. Snowflake vs Redshift vs BigQuery vs Hive vs Athena
Sprinkledata.com
Active Governance Across the Delta Lake with AlationDatabricks
Alation provides a single interface to provide users and stewards to provide active and agile data governance across Databricks Delta Lake and Databricks SQL Analytics Service. Understand how Alation can expand adoption in the data lake while providing safe and responsible data consumption.
Building Data Lakes with Apache AirflowGary Stafford
Build a simple Data Lake on AWS using a combination of services, including Amazon Managed Workflows for Apache Airflow (Amazon MWAA), AWS Glue, AWS Glue Studio, Amazon Athena, and Amazon S3.
Blog post and link to the video: https://garystafford.medium.com/building-a-data-lake-with-apache-airflow-b48bd953c2b
The analysis of movement is an important research topic in, for example, geography, ecology, visual analytics, GIScience as well as in application domains such as urban, maritime, and aviation research. Movement data analysis requires tools for the manipulation and visualization of movement or trajectory data. This talk presents the new Python library MovingPandas.org
Avoiding Deadlocks: Lessons Learned with Zephyr Health Using Neo4j and MongoD...Neo4j
Z-Platform is the new innovative powerful and complex platform to ingest data of any kind and store the data in the form of JSON documents in MongoDB and represent a sparse representation of the same in Neo4j graph database. Mahesh discusses how he tackled deadlocks and improved the performance of the system significantly. The test environment included small graphs (ranging up to 10000 relationships to very large graphs (ranging up to 39 million relationships). The average performance of the system is 3741 relationships per minute.
We provide an overview of the expressive object model, secondary indexes, high availability, write scalability, query language support, performance benchmarks - database model, performance benchmarks - load characteristics, performance benchmarks - consistency requirements, ease of use, and navigation aggregation.
Want to improve your Cypher skills? This is just the session for you! You don't need any experience of coding/programming. We will be writing queries using Neo4j's query language but we'll start from scratch and work from there through the session.
This 3 hour session will be a mixture of theory and hands-on practice sessions, and you will quickly learn how easy it is to work with a powerful graph database using Cypher as the query language.
We will use the well known Game Of Thrones book and TV series as dataset because it is fun and rich in relationships of many kinds. We will create, import and query the data to gain some new and surprising insights and confirm things we already know.
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.
Introducing Neo4j 3.1: New Security and Clustering Architecture Neo4j
Neo4j 3.1, now in public beta, introduces many new exciting features. It improves upon existing security features to provide enterprise class user management, including role based authentication and AD/LDAP integration. The release introduces a new clustering architecture called Causal Clustering that enables very large clusters of Neo4j to be deployed across data centers while maintaining the data integrity that is is critical for the property graph model. Other highlights include database kernel and operations advances, user defined functions, a new Cypher command line interface, and Neo4j Browser improvements.
In this webinar we will cover these new features in detail, including a live demo where we will show how to deploy a Neo4j 3.1 cluster and manage users using the new security features.
Polyglot Persistence & Multi-Model Databases by Michael Hackstein @mchacki
In many modern applications the database side is realized using polyglot persistence – store each data format (graphs, documents, etc.) in an appropriate separate database. This approach yields several benefits, databases are optimized for their specific duty, however there are also drawbacks:
- keep all databases in sync
- queries might require data from several databases
- experts needed for all used systems
A multi-model database is not restricted to one data format, but can cope with several of them. In this talk I will present how a multi-model database can be used in a polyglot persistence setup and how it will reduce the effort drastically.
http://www.jugh.de/termine/polyglot-persistence-multi-model-nosql-databases-69.html
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
Shoot Me a Token: OpenAM as an OAuth2 ProviderForgeRock
Presented by Victor Ake, OpenAM Product Manager and ForgeRock Co-Founder at ForgeRock Open Stack Identity Summit. June 2013
Learn more about ForgeRock Access Management:
https://www.forgerock.com/platform/access-management/
Learn more about ForgeRock Identity Management:
https://www.forgerock.com/platform/identity-management/
In this talk I discussed the various ways in which we utilise Neo4j and data modelling with graphs to helps us model and automate the complex in-game economy in our MMORPG title Here Be Monsters.
Managing Connected Big Data in Art with Neo4j Graph Database - Lorenzo Speran...Codemotion
The fundamental aspect of Vincent Van Gogh's artwork was his continuous research for colors. By modeling his journey as an artist in a graph database, we are able to make new inferences on different artists, cities, climates and other nodes that influenced the development of Vincent Van Gogh as an artist. Aside from the case of Van Gogh and his artwork, there remains unexpected connections in the world around us. This talk discusses the value of a graph databases for your own projects in revealing new insights from the connections inherent in your data.
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.
No se pierda esta oportunidad de conocer las ventajas de NoSQL. Participe en nuestro seminario web y descubra:
Qué significa el término NoSQL
Qué diferencias hay entre los almacenes clave-valor, columna ancha, grafo y de documentos
Qué significa el término «multimodelo»
A gentle introduction to Neo4j:
- What is a Graph Database?
- Why use a Graph Database?
- Cypher Query Language
- Neo4j UI Walkthrough
- Relational vs. Graph Databases
- My Personal Journey with Graph Databases
- Best Practices in Data Modeling
Combine Spring Data Neo4j and Spring Boot to quicklNeo4j
Speakers: Michael Hunger (Neo Technology) and Josh Long (Pivotal)
Spring Data Neo4j 3.0 is here and it supports Neo4j 2.0. Neo4j is a tiny graph database with a big punch. Graph databases are imminently suited to asking interesting questions, and doing analysis. Want to load the Facebook friend graph? Build a recommendation engine? Neo4j's just the ticket. Join Spring Data Neo4j lead Michael Hunger (@mesirii) and Spring Developer Advocate Josh Long (@starbuxman) for a look at how to build smart, graph-driven applications with Spring Data Neo4j and Spring Boot.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
12. MongoDB
Features
• NoSQL database
• Document datamodel
• JSON-like documents (BSON)
• Flexible data model
• Horizontal scalability (sharding)
• Complex queries
{
"session": {
"title": "12 Years of Spring: An Open Source Journey",
"abstract": "Spring emerged as a core open source project in early 2003 and
evolved to a broad portfolio of open source projects up until 2015."
},
"topics": [
"keynote",
"spring"
],
"room": "Auditorium",
"timeslot": "Wed 29th, 09:30-10:30",
"speaker": {
"name": "Juergen Hoeller",
"bio": "Juergen Hoeller is co-founder of the Spring Framework open source
project.",
"twitter": "https://twitter.com/springjuergen",
"picture": "http://www.springio.net/wp-content/uploads/2014/11/
juergen_hoeller-220x220.jpeg"
}
}
13. MongoDB
Use Cases
• Product catalog
• User profiles
• Metadata
• Content
• Events
• Analytics
{
"session": {
"title": "12 Years of Spring: An Open Source Journey",
"abstract": "Spring emerged as a core open source project in early 2003 and
evolved to a broad portfolio of open source projects up until 2015."
},
"topics": [
"keynote",
"spring"
],
"room": "Auditorium",
"timeslot": "Wed 29th, 09:30-10:30",
"speaker": {
"name": "Juergen Hoeller",
"bio": "Juergen Hoeller is co-founder of the Spring Framework open source
project.",
"twitter": "https://twitter.com/springjuergen",
"picture": "http://www.springio.net/wp-content/uploads/2014/11/
juergen_hoeller-220x220.jpeg"
}
}
https://www.mongodb.com/use-cases/
14. MongoDB
Use Cases
• Product catalog
• User profiles
• Metadata
• Content
• Events
• Analytics
{
"session": {
"title": "12 Years of Spring: An Open Source Journey",
"abstract": "Spring emerged as a core open source project in early 2003 and
evolved to a broad portfolio of open source projects up until 2015."
},
"topics": [
"keynote",
"spring"
],
"room": "Auditorium",
"timeslot": "Wed 29th, 09:30-10:30",
"speaker": {
"name": "Juergen Hoeller",
"bio": "Juergen Hoeller is co-founder of the Spring Framework open source
project.",
"twitter": "https://twitter.com/springjuergen",
"picture": "http://www.springio.net/wp-content/uploads/2014/11/
juergen_hoeller-220x220.jpeg"
}
}
https://www.mongodb.com/use-cases/
15. Neo4j
Graph Database
• Property graph data model
• Nodes and relationships
• Native graph processing
• Cypher query language
16. Property Graph Model Components
Nodes
• The objects in the graph
• Can have name-value properties
• Can be labeled
Relationships
• Relate nodes by type and
direction
• Can have name-value properties
CAR
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
LOVES
LOVES
LIVES WITH
OW
NS
PERSON PERSON
17. Cypher: SQL for graphs
CREATE (:Person { name:“Dan”} ) -[:LOVES]-> (:Person { name:“Ann”} )
LOVES
Dan Ann
LABEL PROPERTY
NODE NODE
LABEL PROPERTY
18. NEO4J USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
19. NEO4J USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
GRAPH THINKING:
Real Time Recommendations
VIEWED
VIEWED
BOUGHT
VIEWED
BOUGHT
BOUGHT
BOUGHT
BOUGHT
20. NEO4J USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
GRAPH THINKING:
Master Data Management
MANAGES
MANAGES
LEADS
REGION
M
ANAG
ES
MANAGES
REGION
LEADS
LEADS
COLLABORATES
21. NEO4J USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
GRAPH THINKING:
Fraud Detection
O
PENED_ACCO
UNT
HAS
IS_ISSUED
HAS
LIVES
LIVES
IS_ISSUED
OPENED_ACCOUNT
22. GRAPH THINKING:
Graph Based Search
NEO4J USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
PUBLISH
INCLUDE
INCLUDE
CREATE
CAPTURE
IN
IN
SOURCE
USES
USES
IN
IN
USES
SOURCE
SOURCE
23. NEO4J USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
BROWSES
CONNECTS
BRIDGES
ROUTES
POWERS
ROUTES
POWERS
POWERS
HOSTS
QUERIES
GRAPH THINKING:
Network & IT-Operations
24. GRAPH THINKING:
Identity And Access Management
NEO4J USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
TRUSTS
TRUSTS
ID
ID
AUTHENTICATES
AUTHENTICATES
O
W
NS
OWNS
CAN_READ
34. SYSTEMS OF RECORD
Relational Database Model
Structured
Pre-computed
Based on rigid rules
SYSTEMS OF ENGAGEMENT
NoSQL Database Model
Highly Flexible
Real-Time Queries
Highly Contextual
38. Polyglot Persistence
• Different types of data in different ways
• Take advantage of strengths of different databases
http://martinfowler.com/bliki/PolyglotPersistence.html
39. Polyglot Persistence
Functionality Database type
Shopping Cart Rapid session
reads / writes
Key-value store
Orders / Product
Catalog
Frequent reads Document
Customer social
graph
Recommendation Graph
http://www.jamesserra.com/archive/2015/07/what-is-polyglot-persistence/
59. Java Stored Procedures
User-defined procedures are written in Java,
deployed into the database, and called from Cypher.
http://neo4j.com/docs/developer-manual/current/#procedures
78. Document to property graph
{
"session": {
"title": "12 Years of Spring: An Open Source Journey",
"abstract": "Spring emerged as a core open source
project in early 2003 and evolved to a broad portfolio of
open source projects up until 2015."
},
"topics": [
"keynote",
"spring"
],
"room": "Auditorium",
"timeslot": "Wed 29th, 09:30-10:30",
"speaker": {
"name": "Juergen Hoeller",
"bio": "Juergen Hoeller is co-founder of the Spring
Framework open source project.",
"twitter": "https://twitter.com/springjuergen",
"picture": "http://www.springio.net/wp-content/
uploads/2014/11/juergen_hoeller-220x220.jpeg"
}
}
79. {
"session": {
"title": "12 Years of Spring: An Open Source Journey",
"abstract": "Spring emerged as a core open source
project in early 2003 and evolved to a broad portfolio of
open source projects up until 2015."
},
"topics": [
"keynote",
"spring"
],
"room": "Auditorium",
"timeslot": "Wed 29th, 09:30-10:30",
"speaker": {
"name": "Juergen Hoeller",
"bio": "Juergen Hoeller is co-founder of the Spring
Framework open source project.",
"twitter": "https://twitter.com/springjuergen",
"picture": "http://www.springio.net/wp-content/
uploads/2014/11/juergen_hoeller-220x220.jpeg"
}
}
Document to property graph
82. Using Data Relationships for
Recommendations
Content-based filtering
Recommend items based on what
users have liked in the past
Collaborative filtering
Predict what users like based on the
similarity of their behaviors,
activities and preferences to others
Movie
Person
Person
RATED
SIMILARITY
rating: 7
value: .92
