Http4s, Doobie and Circe: The Functional Web StackGaryCoady
Http4s, Doobie and Circe together form a nice platform for building web services. This presentations provides an introduction to using them to build your own service.
Alternatives of JPA
Requery provide simple Object Mapping & Generate SQL to execute without reflection and session, so fast than JPA, simple and easy to learn.
Http4s, Doobie and Circe: The Functional Web StackGaryCoady
Http4s, Doobie and Circe together form a nice platform for building web services. This presentations provides an introduction to using them to build your own service.
Alternatives of JPA
Requery provide simple Object Mapping & Generate SQL to execute without reflection and session, so fast than JPA, simple and easy to learn.
Slides for presentation on Cloudera Impala I gave at the DC/NOVA Java Users Group on 7/9/2013. It is a slightly updated set of slides from the ones I uploaded a few months ago on 4/19/2013. It covers version 1.0.1 and also includes some new slides on HortonWorks' Stinger Initiative.
Effective testing for spark programs scala bay preview (pre-strata ny 2015)Holden Karau
We all know testing is important, but often end up cutting corners because its too much effort. Come learn how to make testing Spark programs less effort and save your self from future production disasters when your recommendation system starts to return no results. We will explore how to quickly make tests for regular Spark programs, working with DataFrames, and special considerations for making effective unit tests for Spark Streaming. If you are super excited about the subject of testing Spark programs, make sure to also checkout the corresponding Strata NY talk for even more Spark testing fun. http://strataconf.com/big-data-conference-ny-2015/public/schedule/detail/42993
Non-Relational Postgres / Bruce Momjian (EnterpriseDB)Ontico
Postgres has always had strong support for relational storage. However, there are many cases where relational storage is either inefficient or overly restrictive. This talk shows the many ways that Postgres has expanded to support non-relational storage, specifically the ability to store and index multiple values, even unrelated ones, in a single database field. Such storage allows for greater efficiency and access simplicity, and can also avoid the negatives of entity-attribute-value (eav) storage. The talk will cover many examples of multiple-value-per-field storage, including arrays, range types, geometry, full text search, xml, json, and records.
This presentation will demonstrate how you can use the aggregation pipeline with MongoDB similar to how you would use GROUP BY in SQL and the new stage operators coming 3.4. MongoDB’s Aggregation Framework has many operators that give you the ability to get more value out of your data, discover usage patterns within your data, or use the Aggregation Framework to power your application. Considerations regarding version, indexing, operators, and saving the output will be reviewed.
Cassandra 3.0 - JSON at scale - StampedeCon 2015StampedeCon
This session will explore the new features in Cassandra 3.0, starting with JSON support. Cassandra now allows storing JSON directly to Cassandra rows and vice versa, making it trivial to deploy Cassandra as a component in modern service-oriented architectures.
Cassandra 3.0 also delivers other enhancements to developer productivity: user defined functions let developers deploy custom application logic server side with any language conforming to the Java scripting API, including Javascript. Global indexes allow scaling indexed queries linearly with the size of the cluster, a first for open-source NoSQL databases.
Finally, we will cover the performance improvements in Cassandra 3.0 as well.
Slick: Bringing Scala’s Powerful Features to Your Database Access Rebecca Grenier
This talk will teach you how to use Slick in practice, based on our experience at EatingWell Media Group. Slick is a totally different (and better!) relational database mapping tool that brings Scala’s powerful features to your database interactions, namely: static-checking, compile-time safety, and compositionality.
Here at EatingWell, we have learned quite a bit about Slick over the past two years as we transitioned from a PHP website to Scala. I will share with you tips and tricks we have learned, as well as everything you need to get started using Slick in your Scala application.
I will begin with Slick fundamentals: how to get started making your connection, the types of databases it can access, how to actually create table objects and make queries to and from them. We will using these fundamentals to demonstrate the powerful features inherited from the Scala language itself: static-checking, compile-time safety, and compositionality. And throughout I will share plenty of tips that will help you in everything from getting started to connection pooling options and configuration for use at scale.
Slides for presentation on Cloudera Impala I gave at the DC/NOVA Java Users Group on 7/9/2013. It is a slightly updated set of slides from the ones I uploaded a few months ago on 4/19/2013. It covers version 1.0.1 and also includes some new slides on HortonWorks' Stinger Initiative.
Effective testing for spark programs scala bay preview (pre-strata ny 2015)Holden Karau
We all know testing is important, but often end up cutting corners because its too much effort. Come learn how to make testing Spark programs less effort and save your self from future production disasters when your recommendation system starts to return no results. We will explore how to quickly make tests for regular Spark programs, working with DataFrames, and special considerations for making effective unit tests for Spark Streaming. If you are super excited about the subject of testing Spark programs, make sure to also checkout the corresponding Strata NY talk for even more Spark testing fun. http://strataconf.com/big-data-conference-ny-2015/public/schedule/detail/42993
Non-Relational Postgres / Bruce Momjian (EnterpriseDB)Ontico
Postgres has always had strong support for relational storage. However, there are many cases where relational storage is either inefficient or overly restrictive. This talk shows the many ways that Postgres has expanded to support non-relational storage, specifically the ability to store and index multiple values, even unrelated ones, in a single database field. Such storage allows for greater efficiency and access simplicity, and can also avoid the negatives of entity-attribute-value (eav) storage. The talk will cover many examples of multiple-value-per-field storage, including arrays, range types, geometry, full text search, xml, json, and records.
This presentation will demonstrate how you can use the aggregation pipeline with MongoDB similar to how you would use GROUP BY in SQL and the new stage operators coming 3.4. MongoDB’s Aggregation Framework has many operators that give you the ability to get more value out of your data, discover usage patterns within your data, or use the Aggregation Framework to power your application. Considerations regarding version, indexing, operators, and saving the output will be reviewed.
Cassandra 3.0 - JSON at scale - StampedeCon 2015StampedeCon
This session will explore the new features in Cassandra 3.0, starting with JSON support. Cassandra now allows storing JSON directly to Cassandra rows and vice versa, making it trivial to deploy Cassandra as a component in modern service-oriented architectures.
Cassandra 3.0 also delivers other enhancements to developer productivity: user defined functions let developers deploy custom application logic server side with any language conforming to the Java scripting API, including Javascript. Global indexes allow scaling indexed queries linearly with the size of the cluster, a first for open-source NoSQL databases.
Finally, we will cover the performance improvements in Cassandra 3.0 as well.
Slick: Bringing Scala’s Powerful Features to Your Database Access Rebecca Grenier
This talk will teach you how to use Slick in practice, based on our experience at EatingWell Media Group. Slick is a totally different (and better!) relational database mapping tool that brings Scala’s powerful features to your database interactions, namely: static-checking, compile-time safety, and compositionality.
Here at EatingWell, we have learned quite a bit about Slick over the past two years as we transitioned from a PHP website to Scala. I will share with you tips and tricks we have learned, as well as everything you need to get started using Slick in your Scala application.
I will begin with Slick fundamentals: how to get started making your connection, the types of databases it can access, how to actually create table objects and make queries to and from them. We will using these fundamentals to demonstrate the powerful features inherited from the Scala language itself: static-checking, compile-time safety, and compositionality. And throughout I will share plenty of tips that will help you in everything from getting started to connection pooling options and configuration for use at scale.
MongoDB + Java - Everything you need to know Norberto Leite
Learn everything you need to know to get started building a MongoDB-based app in Java. We'll explore the relationship between MongoDB and various languages on the Java Virtual Machine such as Java, Scala, and Clojure. From there, we'll examine the popular frameworks and integration points between MongoDB and the JVM including Spring Data and object-document mappers like Morphia.
Learn everything you need to know to get started building a MongoDB-based app in Java. We'll explore the relationship between MongoDB and various languages on the Java Virtual Machine such as Java, Scala, and Clojure. From there, we'll examine the popular frameworks and integration points between MongoDB and the JVM including Spring Data and object-document mappers like Morphia.
Backbone.js — Introduction to client-side JavaScript MVCpootsbook
Using Backbone.js to move state to the client-side and the benefits of using a JavaScript MVC framework.
Delivered at SuperMondays, Newcastle upon Tyne, on 26th September 2011.
You may all know that JSON is a subset of JavaScript, but… Did you know that HTML5 implements NoSQL databases? Did you know that JavaScript was recommended for REST by HTTP co-creator Roy T. Fielding himself? Did you know that map & reduce are part of the native JavaScript API? Did you know that most NoSQL solutions integrate a JavaScript engine? CouchDB, MongoDB, WakandaDB, ArangoDB, OrientDB, Riak…. And when they don’t, they have a shell client which does. The story of NoSQL and JavaScript goes beyond your expectations and opens more opportunities than you might imagine… What better match could you find than a flexible and dynamic language for schemaless databases? Isn’t an event-driven language what you’ve been waiting for to manage consistency? When NoSQL doesn’t come to JavaScript, JavaScript comes to NoSQL. And does it very well.
Flask and Angular: An approach to build robust platformsAyush Sharma
AngularJS is a really powerful and extensible Javascript library that can be used for all number of applications. The team that up with Flask and you've got a great power and maintainability.
Developing application for Windows Phone 7 in TDDMichele Capra
A real example of how to develop an application for Windows Phone 7 with Test Driven Development approach. In this presentation you'll see also hoew to implements the Model-View-ViewModel (MVVM) pattern.
This week in Neo4j - 3rd February 2018Mark Needham
Discover what's new in the Neo4j community for the week of 3 February 2018, including projects around Thomson Reuters’ OpenPermID, Data Lineage at UBS, and Kubernetes on Google Cloud.
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
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
3. Neo4j Developer Surface
Native LanguageDrivers
BOLT User Defined
Procedure
2000-2010 0.x Embedded Java API
2010-2014 1.x REST
2014-2015 2.x Cypher over HTTP
2016 3.0.x Bolt, Official Language Drivers, User Defined Procedures
2016 3.1.x User Defined Functions
2017 3.2.x User Defined Aggregation Functions
4. Neo4j Developer Surface
Native LanguageDrivers
BOLT User Defined
Procedure
2000-2010 0.x Embedded Java API
2010-2014 1.x REST
2014-2015 2.x Cypher over HTTP
2016 3.0.x Bolt, Official Language Drivers, User Defined Procedures
2016 3.1.x User Defined Functions
2017 3.2.x User Defined Aggregation Functions
8. Neo4j Developer Surface
Native LanguageDrivers
BOLT User Defined
Procedure
2000-2010 0.x Embedded Java API
2010-2014 1.x REST
2014-2015 2.x Cypher over HTTP
2016 3.0.x Bolt, Official Language Drivers, User Defined Procedures
2016 3.1.x User Defined Functions
2017 3.2.x User Defined Aggregation Functions
50. UNWIND the array of elements
CALL apoc.load.xml('http://overpass.osm.rambler.ru/cgi/xapi_meta?*[bbox=11.54,48.14,11.543,48.145]')
YIELD value
UNWIND value["_children"] AS child
WITH child WHERE child["_type"] = "node"
WITH child.id AS id,
child.lat AS latitude,
child.lon AS longitude,
child["user"] AS userName
MERGE (point:Point {id: id})
SET point.latitude = latitude,
point.longitude = longitude
MERGE (user:User {name: userName})
MERGE (user)-[:EDITED]->(point)
54. apoc.load.json
WITH "https://api.stackexchange.com/2.2/questions?pagesize=100&order=desc&sort=creation&tagged=neo4j&site=stackoverflow&filter=!5-i6Zw8Y)4W7vpy91PMYsKM-k9yzEsSC1_Uxlf" AS url
CALL apoc.load.json(url) YIELD value
UNWIND value.items AS q
MERGE (question:Question {id:q.question_id})
ON CREATE SET question.title = q.title,
question.share_link = q.share_link,
question.favorite_count = q.favorite_count
MERGE (owner:User {id:q.owner.user_id})
ON CREATE SET owner.display_name = q.owner.display_name
MERGE (owner)-[:ASKED]->(question)
FOREACH (tagName IN q.tags |
MERGE (tag:Tag {name:tagName}) MERGE (question)-[:TAGGED]->(tag))
FOREACH (a IN q.answers |
MERGE (question)<-[:ANSWERS]-(answer:Answer {id:a.answer_id})
MERGE (answerer:User {id:a.owner.user_id})
ON CREATE SET answerer.display_name = a.owner.display_name
MERGE (answer)<-[:PROVIDED]-(answerer)
)
55. WITH "https://api.stackexchange.com/2.2/questions?pagesize=100&order=desc&sort=creation&tagged=neo4j&site=stackoverflow&filter=!5-i6Zw8Y)4W7vpy91PMYsKM-k9yzEsSC1_Uxlf" AS url
CALL apoc.load.json(url) YIELD value
UNWIND value.items AS q
MERGE (question:Question {id:q.question_id})
ON CREATE SET question.title = q.title,
question.share_link = q.share_link,
question.favorite_count = q.favorite_count
MERGE (owner:User {id:q.owner.user_id})
ON CREATE SET owner.display_name = q.owner.display_name
MERGE (owner)-[:ASKED]->(question)
FOREACH (tagName IN q.tags |
MERGE (tag:Tag {name:tagName}) MERGE (question)-[:TAGGED]->(tag))
FOREACH (a IN q.answers |
MERGE (question)<-[:ANSWERS]-(answer:Answer {id:a.answer_id})
MERGE (answerer:User {id:a.owner.user_id})
ON CREATE SET answerer.display_name = a.owner.display_name
MERGE (answer)<-[:PROVIDED]-(answerer)
)
Use FOREACH for arrays within a row
FOREACH (tagName IN q.tags |
MERGE (tag:Tag {name:tagName}) MERGE (question)-[:TAGGED]->(tag))
FOREACH (a IN q.answers |
MERGE (question)<-[:ANSWERS]-(answer:Answer {id:a.answer_id})
MERGE (answerer:User {id:a.owner.user_id})
ON CREATE SET answerer.display_name = a.owner.display_name
MERGE (answer)<-[:PROVIDED]-(answerer)
)
96. MATCH (d:District)
WITH collect(d) AS districts
CALL spatial.addNodes('geom', districts) YIELD node
RETURN count(*)
Add nodes to spatial index
97. CALL spatial.withinDistance('geom',
{latitude: 37.563440, longitude: -122.322265}, 1)
YIELD node AS d
WITH d, d.wkt AS wkt, d.state AS state, d.district AS district
LIMIT 1
MATCH (d)<-[:REPRESENTS]-(l:Legislator)
MATCH (l)-[:SERVES_ON]->(c:Committee)
MATCH (c)<-[:REFERRED_TO]-(b:Bill)
MATCH (b)-[:DEALS_WITH]->(s:Subject)
RETURN *
Query nodes by location
98. CALL spatial.withinDistance('geom',
{latitude: 37.563440, longitude: -122.322265}, 1)
YIELD node AS d
WITH d, d.wkt AS wkt, d.state AS state, d.district AS district
LIMIT 1
MATCH (d)<-[:REPRESENTS]-(l:Legislator)
MATCH (l)-[:SERVES_ON]->(c:Committee)
MATCH (c)<-[:REFERRED_TO]-(b:Bill)
MATCH (b)-[:DEALS_WITH]->(s:Subject)
RETURN *
Finds nodes within 1km
99. CALL spatial.withinDistance('geom',
{latitude: 37.563440, longitude: -122.322265}, 1)
YIELD node AS d
WITH d, d.wkt AS wkt, d.state AS state, d.district AS district
LIMIT 1
MATCH (d)<-[:REPRESENTS]-(l:Legislator)
MATCH (l)-[:SERVES_ON]->(c:Committee)
MATCH (c)<-[:REFERRED_TO]-(b:Bill)
MATCH (b)-[:DEALS_WITH]->(s:Subject)
RETURN *
Continue with the rest of the query
104. CREATE (n:News)
SET n.text = "Scores of people were already lying dead or
injured inside a crowded Orlando nightclub,
and the police had spent hours trying to connect with the
gunman and end the situation without further violence.
But when Omar Mateen threatened to set off explosives, the
police decided to act, and pushed their way through a
wall to end the bloody standoff."
Annotating text
119. GraphQL is a query language for your API, and a
server-side runtime for executing queries by using a
type system you define for your data.
What is it?
120. GraphQL is a query language for your API, and a
server-side runtime for executing queries by using a
type system you define for your data.
What is it?
121. GraphQL is a query language for your API, and a
server-side runtime for executing queries by using a
type system you define for your data.
What is it?
122. GraphQL is a query language for your API, and a
server-side runtime for executing queries by using a
type system you define for your data.
What is it?
type Planet {
name: String
climate: String
}
type Character {
name: String
friends: [Character]
homeWorld: Planet
species: Species
}
type Species {
name: String
lifespan: Int
origin: Planet
}
129. CALL graphql.idl('
type Movie {
title: String!
released: Int
actors: [Person] @relation(name:"ACTED_IN",direction:IN)
}
type Person {
name: String!
born: Int
movies: [Movie] @relation(name:"ACTED_IN")
}'
)
130. WITH '{ Person(born: 1961) { name, born } }' as query, {} as params
CALL graphql.execute(query,params)
YIELD result
UNWIND result.Person as p
RETURN p.name, p.born