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.
This talk is focused on tuning analysing and optimizing MongoDB query and index with the use of Database Profiler and "explain()" function.
Also, performance of database can also be impacted by configuring the underline ( Linux ) OS with some recommended settings which do not come by default.
EWD 3 Training Course Part 21: Persistent JavaScript ObjectsRob Tweed
This presentation is Part 21 of the EWD 3 Training Course. It explains how Document Node objects and its $() function allow the abstraction of Persistent JavaScript Objects from Global Storage
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.
This talk is focused on tuning analysing and optimizing MongoDB query and index with the use of Database Profiler and "explain()" function.
Also, performance of database can also be impacted by configuring the underline ( Linux ) OS with some recommended settings which do not come by default.
EWD 3 Training Course Part 21: Persistent JavaScript ObjectsRob Tweed
This presentation is Part 21 of the EWD 3 Training Course. It explains how Document Node objects and its $() function allow the abstraction of Persistent JavaScript Objects from Global Storage
EWD 3 Training Course Part 24: Traversing a Document's Leaf NodesRob Tweed
This presentation is Part 24 of the EWD 3 Training Course. It examines another way to iterate through Global Storage, via its leaf nodes. In some situations this can be a faster and more efficient technique.
For developers new to MongoDB and Node.js, however, some the common design patterns are very different than those of a RDBMS and traditional synchronous languages. Developers learning these technologies together may find it a bit bewildering. In reality, however, these tools fit perfectly together and enable I high degree of developer productivity and application performance.
This webinar will walk developers through common MongoDB development patterns in Node.js, such as efficiently loading data into MongoDB using MongoDB's bulk API, iterating through query results, and managing simultaneous asynchronous MongoDB queries to provide the best possible application performance. Working Node.js and MongoDB examples will be used throughout the presentation.
After a short introduction to the Java driver for MongoDB, we'll have a look at the more abtract persistence frameworks like Morphia, Spring Data, Jongo and Hibernate OGM.
Mythbusting: Understanding How We Measure the Performance of MongoDBMongoDB
Benchmarking, benchmarking, benchmarking. We all do it, mostly it tells us what we want to hear but often hides a mountain of misinformation. In this talk we will walk through the pitfalls that you might find yourself in by looking at some examples where things go wrong. We will then walk through how MongoDB performance is measured, the processes and methodology and ways to present and look at the information.
MongoDB Europe 2016 - Debugging MongoDB PerformanceMongoDB
Asya is back, and so is Sherlock Holmes and his techniques to gather and analyze data from your poorly performing MongoDB clusters. In this advanced talk we take a deep look at all the diagnostic data that lives inside MongoDB - how to interrogate and interpret it to help you solve those frustrating performance bottlenecks that we all face occasionally.
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.
Just a few years ago all software systems were designed to be monoliths running on a single big and powerful machine. But nowadays most companies desire to scale out instead of scaling up, because it is much easier to buy or rent a large cluster of commodity hardware then to get a single machine that is powerful enough. In the database area scaling out is realized by utilizing a combination of polyglot persistence and sharding of data. On the application level scaling out is realized by microservices. In this talk I will briefly introduce the concepts and ideas of microservices and discuss their benefits and drawbacks. Afterwards I will focus on the point of intersection of a microservice based application talking to one or many NoSQL databases. We will try and find answers to these questions: Are the differences to a monolithic application? How to scale the whole system properly? What about polyglot persistence? Is there a data-centric way to split microservices?
Just a few years ago all software systems were designed to be monoliths running on a single big and powerful machine. But nowadays most companies desire to scale out instead of scaling up, because it is much easier to buy or rent a large cluster of commodity hardware then to get a single machine that is powerful enough. In the database area scaling out is realized by utilizing a combination of polyglot persistence and sharding of data. On the application level scaling out is realized by microservices. In this talk I will briefly introduce the concepts and ideas of microservices and discuss their benefits and drawbacks. Afterwards I will focus on the point of intersection of a microservice based application talking to one or many NoSQL databases. We will try and find answers to these questions: Are the differences to a monolithic application? How to scale the whole system properly? What about polyglot persistence? Is there a data-centric way to split microservices?
Map/Confused? A practical approach to Map/Reduce with MongoDBUwe Printz
Talk given at MongoDb Munich on 16.10.2012 about the different approaches in MongoDB for using the Map/Reduce algorithm. The talk compares the performance of built-in MongoDB Map/Reduce, group(), aggregate(), find() and the MongoDB-Hadoop Adapter using a practical use case.
CouchDB Mobile - From Couch to 5K in 1 HourPeter Friese
In this talk, I explain how to use CouchDB mobile to connect your iPhone or Android phone with a a remote ChouchDB to build a RunKeeper clone. The code for this talk is available at https://github.com/peterfriese/CouchTo5K
These are slides from our Big Data Warehouse Meetup in April. We talked about NoSQL databases: What they are, how they’re used and where they fit in existing enterprise data ecosystems.
Mike O’Brian from 10gen, introduced the syntax and usage patterns for a new aggregation system in MongoDB and give some demonstrations of aggregation using the new system. The new MongoDB aggregation framework makes it simple to do tasks such as counting, averaging, and finding minima or maxima while grouping by keys in a collection, complementing MongoDB’s built-in map/reduce capabilities.
For more information, visit our website at http://casertaconcepts.com/ or email us at info@casertaconcepts.com.
EWD 3 Training Course Part 24: Traversing a Document's Leaf NodesRob Tweed
This presentation is Part 24 of the EWD 3 Training Course. It examines another way to iterate through Global Storage, via its leaf nodes. In some situations this can be a faster and more efficient technique.
For developers new to MongoDB and Node.js, however, some the common design patterns are very different than those of a RDBMS and traditional synchronous languages. Developers learning these technologies together may find it a bit bewildering. In reality, however, these tools fit perfectly together and enable I high degree of developer productivity and application performance.
This webinar will walk developers through common MongoDB development patterns in Node.js, such as efficiently loading data into MongoDB using MongoDB's bulk API, iterating through query results, and managing simultaneous asynchronous MongoDB queries to provide the best possible application performance. Working Node.js and MongoDB examples will be used throughout the presentation.
After a short introduction to the Java driver for MongoDB, we'll have a look at the more abtract persistence frameworks like Morphia, Spring Data, Jongo and Hibernate OGM.
Mythbusting: Understanding How We Measure the Performance of MongoDBMongoDB
Benchmarking, benchmarking, benchmarking. We all do it, mostly it tells us what we want to hear but often hides a mountain of misinformation. In this talk we will walk through the pitfalls that you might find yourself in by looking at some examples where things go wrong. We will then walk through how MongoDB performance is measured, the processes and methodology and ways to present and look at the information.
MongoDB Europe 2016 - Debugging MongoDB PerformanceMongoDB
Asya is back, and so is Sherlock Holmes and his techniques to gather and analyze data from your poorly performing MongoDB clusters. In this advanced talk we take a deep look at all the diagnostic data that lives inside MongoDB - how to interrogate and interpret it to help you solve those frustrating performance bottlenecks that we all face occasionally.
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.
Just a few years ago all software systems were designed to be monoliths running on a single big and powerful machine. But nowadays most companies desire to scale out instead of scaling up, because it is much easier to buy or rent a large cluster of commodity hardware then to get a single machine that is powerful enough. In the database area scaling out is realized by utilizing a combination of polyglot persistence and sharding of data. On the application level scaling out is realized by microservices. In this talk I will briefly introduce the concepts and ideas of microservices and discuss their benefits and drawbacks. Afterwards I will focus on the point of intersection of a microservice based application talking to one or many NoSQL databases. We will try and find answers to these questions: Are the differences to a monolithic application? How to scale the whole system properly? What about polyglot persistence? Is there a data-centric way to split microservices?
Just a few years ago all software systems were designed to be monoliths running on a single big and powerful machine. But nowadays most companies desire to scale out instead of scaling up, because it is much easier to buy or rent a large cluster of commodity hardware then to get a single machine that is powerful enough. In the database area scaling out is realized by utilizing a combination of polyglot persistence and sharding of data. On the application level scaling out is realized by microservices. In this talk I will briefly introduce the concepts and ideas of microservices and discuss their benefits and drawbacks. Afterwards I will focus on the point of intersection of a microservice based application talking to one or many NoSQL databases. We will try and find answers to these questions: Are the differences to a monolithic application? How to scale the whole system properly? What about polyglot persistence? Is there a data-centric way to split microservices?
Map/Confused? A practical approach to Map/Reduce with MongoDBUwe Printz
Talk given at MongoDb Munich on 16.10.2012 about the different approaches in MongoDB for using the Map/Reduce algorithm. The talk compares the performance of built-in MongoDB Map/Reduce, group(), aggregate(), find() and the MongoDB-Hadoop Adapter using a practical use case.
CouchDB Mobile - From Couch to 5K in 1 HourPeter Friese
In this talk, I explain how to use CouchDB mobile to connect your iPhone or Android phone with a a remote ChouchDB to build a RunKeeper clone. The code for this talk is available at https://github.com/peterfriese/CouchTo5K
These are slides from our Big Data Warehouse Meetup in April. We talked about NoSQL databases: What they are, how they’re used and where they fit in existing enterprise data ecosystems.
Mike O’Brian from 10gen, introduced the syntax and usage patterns for a new aggregation system in MongoDB and give some demonstrations of aggregation using the new system. The new MongoDB aggregation framework makes it simple to do tasks such as counting, averaging, and finding minima or maxima while grouping by keys in a collection, complementing MongoDB’s built-in map/reduce capabilities.
For more information, visit our website at http://casertaconcepts.com/ or email us at info@casertaconcepts.com.
Apache Spark for Library Developers with William Benton and Erik ErlandsonDatabricks
As a developer, data engineer, or data scientist, you’ve seen how Apache Spark is expressive enough to let you solve problems elegantly and efficient enough to let you scale out to handle more data. However, if you’re solving the same problems again and again, you probably want to capture and distribute your solutions so that you can focus on new problems and so other people can reuse and remix them: you want to develop a library that extends Spark.
You faced a learning curve when you first started using Spark, and you’ll face a different learning curve as you start to develop reusable abstractions atop Spark. In this talk, two experienced Spark library developers will give you the background and context you’ll need to turn your code into a library that you can share with the world. We’ll cover: Issues to consider when developing parallel algorithms with Spark, Designing generic, robust functions that operate on data frames and datasets, Extending data frames with user-defined functions (UDFs) and user-defined aggregates (UDAFs), Best practices around caching and broadcasting, and why these are especially important for library developers, Integrating with ML pipelines, Exposing key functionality in both Python and Scala, and How to test, build, and publish your library for the community.
We’ll back up our advice with concrete examples from real packages built atop Spark. You’ll leave this talk informed and inspired to take your Spark proficiency to the next level and develop and publish an awesome library of your own.
The CAP theorem is widely known for distributed systems, but it's not the only tradeoff you should be aware of. For datastores, there is also the FAB theory and just like with the CAP theorem you can only pick two:
Fast: Results are real-time or near real-time instead of batch-oriented.
Accurate: Answers are exact and don't have a margin of error.
Big: You require horizontal scaling and need to distribute your data.
While Fast and Big are relatively easy to understand, Accurate is a bit harder to picture. This talk shows some concrete examples of accuracy tradeoffs Elasticsearch can take for terms aggregations, cardinality aggregations with HyperLogLog++, and the IDF part of the full-text search. Or how to trade some speed or the distribution for more accuracy.
Demo presentation given at the Semantic Web Applications and Tools for Life Science (SWAT4LS) 2014 meeting in Berlin, Dec 10, 2014. http://www.swat4ls.org/workshops/berlin2014/scientific-programme/
Philipp Krenn | Make Your Data FABulous | Codemotion Madrid 2018Codemotion
The CAP theorem is widely known for distributed systems, but it's not the only tradeoff you should be aware of. For datastores there is also the FAB theory and just like with the CAP theorem you can only pick two: fast, accurate, big. While Fast and Big are relatively easy to understand, Accurate is a bit harder to picture. This talk shows some concrete examples of accuracy tradeoffs Elasticsearch can take for terms aggregations, cardinality aggregations with HyperLogLog++, and the IDF part of full-text search. Or how to trade some speed or the distribution for more accuracy.
Webinar: Data Processing and Aggregation OptionsMongoDB
MongoDB scales easily to store mass volumes of data. However, when it comes to making sense of it all what options do you have? In this talk, we'll take a look at 3 different ways of aggregating your data with MongoDB, and determine the reasons why you might choose one way over another. No matter what your big data needs are, you will find out how MongoDB the big data store is evolving to help make sense of your data.
Solutions for bi-directional Integration between Oracle RDMBS & Apache KafkaGuido Schmutz
A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Today’s enterprises have their core systems often implemented on top of relational databases, such as the Oracle RDBMS. Implementing a new solution supporting the digital strategy using Kafka and the ecosystem can not always be done completely separate from the traditional legacy solutions. Often streaming data has to be enriched with state data which is held in an RDBMS of a legacy application. It’s important to cache this data in the stream processing solution, so that It can be efficiently joined to the data stream. But how do we make sure that the cache is kept up-to-date, if the source data changes? We can either poll for changes from Kafka using Kafka Connect or let the RDBMS push the data changes to Kafka. But what about writing data back to the legacy application, i.e. an anomaly is detected inside the stream processing solution which should trigger an action inside the legacy application. Using Kafka Connect we can write to a database table or view, which could trigger the action. But this not always the best option. If you have an Oracle RDBMS, there are many other ways to integrate the database with Kafka, such as Advanced Queueing (message broker in the database), CDC through Golden Gate or Debezium, Oracle REST Database Service (ORDS) and more. In this session, we present various blueprints for integrating an Oracle RDBMS with Apache Kafka in both directions and discuss how these blueprints can be implemented using the products mentioned before.
Solutions for bi-directional integration between Oracle RDBMS and Apache Kafk...confluent
A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Today's enterprises have their core systems often implemented on top of relational databases, such as the Oracle RDBMS. Implementing a new solution supporting the digital strategy using Kafka and the ecosystem can not always be done completely separate from the traditional legacy solutions. Often streaming data has to be enriched with state data which is held in an RDBMS of a legacy application. It's important to cache this data in the stream processing solution, so that It can be efficiently joined to the data stream. But how do we make sure that the cache is kept up-to-date, if the source data changes? We can either poll for changes from Kafka using Kafka Connect or let the RDBMS push the data changes to Kafka. But what about writing data back to the legacy application, i.e. an anomaly is detected inside the stream processing solution which should trigger an action inside the legacy application. Using Kafka Connect we can write to a database table or view, which could trigger the action. But this not always the best option. If you have an Oracle RDBMS, there are many other ways to integrate the database with Kafka, such as Advanced Queueing (message broker in the database), CDC through Golden Gate or Debezium, Oracle REST Database Service (ORDS) and more. In this session, we present various blueprints for integrating an Oracle RDBMS with Apache Kafka in both directions and discuss how these blueprints can be implemented using the products mentioned before.
Conceptos básicos. Seminario web 4: Indexación avanzada, índices de texto y g...MongoDB
Este es el cuarto seminario web de la serie Conceptos básicos, en la que se realiza una introducción a la base de datos MongoDB. Este seminario se ve en la compatibilidad con índices de texto libre y geoespaciales.
Solutions for bi-directional integration between Oracle RDBMS & Apache KafkaGuido Schmutz
Apache Kafka is a popular distributed streaming data platform. A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Data sources flowing into Kafka are often native data streams such as social media streams, telemetry data, financial transactions and many others. But these data stream only contain part of the information. A lot of data necessary in stream processing is stored in traditional systems backed by relational databases. To implement new and modern, real-time solutions, an up-to-date view of that information is needed. So how do we make sure that information can flow between the RDBMS and Kafka, so that changes are available in Kafka as soon as possible in near-real-time? This session will present different approaches for integrating relational databases with Kafka, such as Kafka Connect, Oracle GoldenGate and bridging Kafka with Oracle Advanced Queuing (AQ).
AWS OpsWorks & Chef at the Hamburg Chef User Group 2014Jonathan Weiss
An introduction to AWS OpsWorks and how it uses Chef. Differences between OpsWorks and Chef server.
Presented by Jonathan Weiss on January 14th 2014 at the Hamburg Chef User Group.
An introduction into Backbone.js – a lightweight MVC framework. Backbone supplies structure to JavaScript-heavy applications by providing models with key-value binding and custom events, collections with a rich API of enumerable functions, views with declarative event handling, and connects it all to your existing application over a RESTful JSON interface.
Build your own clouds with Chef and MCollectiveJonathan Weiss
One important part of the DevOps movement is infrastructure automation, especially if you are running your application on top of services like Amazon EC2.
Everybody's dream is to be able to bootstrap and deploy hundreds or even thousands of machines with a few simple commands. This talk will tell you how you can do this using Open Source tools like Chef and mcollective. Chef manages your servers configuration using a nice Ruby DSL while mcollective orchestrates and commands all your nodes.
Platforms like Amazon EC2 promise scalable and redundant systems for a couple of pennies. As soon as you start to build complex systems or migrate existing apps there are many knobs to set. This talk will explain how you can create and deploy reliable and redundant applications to EC2 and will point out all the little things you need to know, like how to automatically provision new servers with tools like Chef.
Presented by Jonathan Weiss at PHP UK Conference 2011 in London.
Overview of how to manage deployments and clusters in the Amazon cloud. Introduction into Chef. Presented by Jonathan Weiss at RailsCamp DE in Cologne.
Rails in the Cloud - Experiences from running on EC2Jonathan Weiss
Overview of architectures in EC2 and services like EBS, ELB, RDS, and ElasticIPs. How to get your app on EC2. Configuration and deployment with Chef. Presented by Jonathan Weiss at RailsWayCon 2010 in Berlin
Jonathan Weiss gives an overview of the NoSQL databases. Why would you consider one, what are the tradeoffs? Given at BarCampRuhr3 2010 in Essen, Germany (Slides are English).
Ruby on CouchDB - SimplyStored and RockingChairJonathan Weiss
Presentation by Jonathan Weiss about Ruby on CouchDB at Ruby User Group Berlin in Marc 2010. Present SimplyStored, a nice wrapper for Ruby object. RockingChair is an in-memory CouchDB for speeding up your tests.
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.
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.
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.
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.
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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
2. Who am I?
Working for Peritor in Berlin, Germany
Written, maintain, or involved in
Webistrano
Capistrano
SimplyStored
Happening
The great fire of London
http://github.com/jweiss
@jweiss
2
30. Example Map Result
Map functions are similar to SQL indices
ID KEY VALUE
51ABFA211 Cap 1
ABC123456 Cappy 1
BCCD12CBB Helmet 1
BCCD12CBB Sombrero 1
Sorted by the key
Key can also be an array
Value can be complex JSON
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31. Query a view
GET /dbname/_design/hats/_view/all
HTTP Client
{"total_rows":348,"offset":0,"rows”:[
{"id":"A","key":"A","value":1},
{"id":"B","key":"B","value":1},
]}
31
32. Query a view
GET /dbname/_design/hats/_view/all?
include_docs=true
HTTP Client
32
41. RockingChair
In-memory CouchDB
Just a big Hash
Understands all SimplyStored generated views
Speeds up tests
Tests can run in parallel
Nice for debugging
BSD-licensed on
http://github.com/jweiss/rocking_chair
41