We went over what Big Data is and it's value. This talk will cover the details of Elasticsearch, a Big Data solution. Elasticsearch is an NoSQL-backed search engine using a HDFS-based filesystem.
We'll cover:
• Elasticsearch basics
• Setting up a development environment
• Loading data
• Searching data using REST
• Searching data using NEST, the .NET interface
• Understanding Scores
Finally, I show a use-case for data mining using Elasticsearch.
You'll walk away from this armed with the knowledge to add Elasticsearch to your data analysis toolkit and your applications.
Big Data has become the new buzzword like “Agile” and “Cloud”. Like those two others, it’s a transformative technology. We’ll be discussing:
•What is it?
•Technology key words
•HDFS
•Hadoop
•MapReduce
This will be part 1 of 2 (at least). This first talk will not be overly technical. We’ll go over the concepts and terms you’ll encounter when considering a big data solution.
Elasticsearch Arcihtecture & What's New in Version 5Burak TUNGUT
General architectural concepts of Elasticsearch and what's new in version 5? Examples are prepared with our company business therefore these are excluded from presentation.
Introduction to Elastic Search
Elastic Search Terminology
Index, Type, Document, Field
Comparison with Relational Database
Understanding of Elastic architecture
Clusters, Nodes, Shards & Replicas
Search
How it works?
Inverted Index
Installation & Configuration
Setup & Run Elastic Server
Elastic in Action
Indexing, Querying & Deleting
An introduction to elasticsearch with a short demonstration on Kibana to present the search API. The slide covers:
- Quick overview of the Elastic stack
- indexation
- Analysers
- Relevance score
- One use case of elasticsearch
The query used for the Kibana demonstration can be found here:
https://github.com/melvynator/elasticsearch_presentation
Big Data has become the new buzzword like “Agile” and “Cloud”. Like those two others, it’s a transformative technology. We’ll be discussing:
•What is it?
•Technology key words
•HDFS
•Hadoop
•MapReduce
This will be part 1 of 2 (at least). This first talk will not be overly technical. We’ll go over the concepts and terms you’ll encounter when considering a big data solution.
Elasticsearch Arcihtecture & What's New in Version 5Burak TUNGUT
General architectural concepts of Elasticsearch and what's new in version 5? Examples are prepared with our company business therefore these are excluded from presentation.
Introduction to Elastic Search
Elastic Search Terminology
Index, Type, Document, Field
Comparison with Relational Database
Understanding of Elastic architecture
Clusters, Nodes, Shards & Replicas
Search
How it works?
Inverted Index
Installation & Configuration
Setup & Run Elastic Server
Elastic in Action
Indexing, Querying & Deleting
An introduction to elasticsearch with a short demonstration on Kibana to present the search API. The slide covers:
- Quick overview of the Elastic stack
- indexation
- Analysers
- Relevance score
- One use case of elasticsearch
The query used for the Kibana demonstration can be found here:
https://github.com/melvynator/elasticsearch_presentation
ElasticSearch - index server used as a document databaseRobert Lujo
Presentation held on 5.10.2014 on http://2014.webcampzg.org/talks/.
Although ElasticSearch (ES) primary purpose is to be used as index/search server, in its featureset ES overlaps with common NoSql database; better to say, document database.
Why this could be interesting and how this could be used effectively?
Talk overview:
- ES - history, background, philosophy, featureset overview, focus on indexing/search features
- short presentation on how to get started - installation, indexing and search/retrieving
- Database should provide following functions: store, search, retrieve -> differences between relational, document and search databases
- it is not unusual to use ES additionally as an document database (store and retrieve)
- an use-case will be presented where ES can be used as a single database in the system (benefits and drawbacks)
- what if a relational database is introduced in previosly demonstrated system (benefits and drawbacks)
ES is a nice and in reality ready-to-use example that can change perspective of development of some type of software systems.
ELK Stack (Elasticsearch, Logstash, Kibana) as a Log-Management solution for the Microsoft developer presented at the .net Usergroup in Munich in June 2015.
Talk given for the #phpbenelux user group, March 27th in Gent (BE), with the goal of convincing developers that are used to build php/mysql apps to broaden their horizon when adding search to their site. Be sure to also have a look at the notes for the slides; they explain some of the screenshots, etc.
An accompanying blog post about this subject can be found at http://www.jurriaanpersyn.com/archives/2013/11/18/introduction-to-elasticsearch/
Philly PHP: April '17 Elastic Search Introduction by Aditya BhamidpatiRobert Calcavecchia
Philly PHP April 2017 Meetup: Introduction to Elastic Search as presented by Aditya Bhamidpati on April 19, 2017.
These slides cover an introduction to using Elastic Search
Check out my Elasticsearch course and get it for only $10:
https://www.udemy.com/elasticsearch-complete-guide/?couponCode=SLIDESHARE10&utm_source=slideshare&utm_campaign=slideshare&utm_medium=referral
Interactive learning analytics dashboards with ELK (Elasticsearch Logstash Ki...Andrii Vozniuk
My workshop at the Learning Analytics Summer Institute (LASI) 2016: http://lasi16.snola.es/#!/schedule/113
Educational data continues to grow in volume, velocity and variety. Making sense of the educational data in such conditions requires deployment and usage of appropriate scalable, real-time processing tools supporting a flexible data schema. Elasticsearch is one of the popular open-source tools meeting the enlisted requirements. Initially envisioned as a search engine capable of operating at scale and in real time, Elasticsearch is used by organisations such as Wikimedia and Github, which deal with big data on daily basis. In addition, Elasticsearch is used increasingly often as analytics platform thanks to its scalable architecture and expressive query language. Until recently, the exploitation of Elasticsearch for (learning) analytical purposes by practitioners was hindered by a high entrance barrier due to the complexity of the query language and the query specificities. This is currently changing with the ongoing development of Kibana, an open-source tool that allows to conduct analysis and build visualisations of Elasticsearch data through a graphical user interface. Kibana does not require the user to dive into technical details of the queries (although it is still possible) and hence makes big educational data visualisations accessible to regular users. The additional value of Kibana comes in play whenever several visualisations are combined on a single dashboard, enabling to use multiple coordinated views for an interactive explorative analysis. Both Elasticsearch and Kibana, together with Logstash are part of an analytics stack often referred to as ELK. Logstash supports data acquisition from multiple sources (including twitter, RSS, event logs) thanks to its rich set of available connectors. Custom connectors can be developed for case-specific sources. In addition to the mentioned values, ELK enables building analytics infrastructures decoupled from the learning platform, i.e., it allows to host separately the learning environment (with the analytics functionalities) and the data storage without affecting the end-user experience.
A Powerpoint presentation from the Connecticut Freedom of Information Commission, posted on its website: http://www.ct.gov/foi/cwp/view.asp?a=3171&Q=504946
Elasticsearch, Logstash, Kibana. Cool search, analytics, data mining and more...Oleksiy Panchenko
In the age of information and big data, ability to quickly and easily find a needle in a haystack is extremely important. Elasticsearch is a distributed and scalable search engine which provides rich and flexible search capabilities. Social networks (Facebook, LinkedIn), media services (Netflix, SoundCloud), Q&A sites (StackOverflow, Quora, StackExchange) and even GitHub - they all find data for you using Elasticsearch. In conjunction with Logstash and Kibana, Elasticsearch becomes a powerful log engine which allows to process, store, analyze, search through and visualize your logs.
Video: https://www.youtube.com/watch?v=GL7xC5kpb-c
Scripts for the Demo: https://github.com/opanchenko/morning-at-lohika-ELK
Nested and Parent/Child Docs in ElasticSearchBeyondTrees
A key part of the architecture of RefWorks Flow, a new document workflow tool for researchers, is an ElasticSearch cluster used for citation canonicalization. We will present our findings of how to use the "nested" type and parent-child relations in ElasticSearch to do complex where-clause queries in an efficient way
ElasticSearch - index server used as a document databaseRobert Lujo
Presentation held on 5.10.2014 on http://2014.webcampzg.org/talks/.
Although ElasticSearch (ES) primary purpose is to be used as index/search server, in its featureset ES overlaps with common NoSql database; better to say, document database.
Why this could be interesting and how this could be used effectively?
Talk overview:
- ES - history, background, philosophy, featureset overview, focus on indexing/search features
- short presentation on how to get started - installation, indexing and search/retrieving
- Database should provide following functions: store, search, retrieve -> differences between relational, document and search databases
- it is not unusual to use ES additionally as an document database (store and retrieve)
- an use-case will be presented where ES can be used as a single database in the system (benefits and drawbacks)
- what if a relational database is introduced in previosly demonstrated system (benefits and drawbacks)
ES is a nice and in reality ready-to-use example that can change perspective of development of some type of software systems.
ELK Stack (Elasticsearch, Logstash, Kibana) as a Log-Management solution for the Microsoft developer presented at the .net Usergroup in Munich in June 2015.
Talk given for the #phpbenelux user group, March 27th in Gent (BE), with the goal of convincing developers that are used to build php/mysql apps to broaden their horizon when adding search to their site. Be sure to also have a look at the notes for the slides; they explain some of the screenshots, etc.
An accompanying blog post about this subject can be found at http://www.jurriaanpersyn.com/archives/2013/11/18/introduction-to-elasticsearch/
Philly PHP: April '17 Elastic Search Introduction by Aditya BhamidpatiRobert Calcavecchia
Philly PHP April 2017 Meetup: Introduction to Elastic Search as presented by Aditya Bhamidpati on April 19, 2017.
These slides cover an introduction to using Elastic Search
Check out my Elasticsearch course and get it for only $10:
https://www.udemy.com/elasticsearch-complete-guide/?couponCode=SLIDESHARE10&utm_source=slideshare&utm_campaign=slideshare&utm_medium=referral
Interactive learning analytics dashboards with ELK (Elasticsearch Logstash Ki...Andrii Vozniuk
My workshop at the Learning Analytics Summer Institute (LASI) 2016: http://lasi16.snola.es/#!/schedule/113
Educational data continues to grow in volume, velocity and variety. Making sense of the educational data in such conditions requires deployment and usage of appropriate scalable, real-time processing tools supporting a flexible data schema. Elasticsearch is one of the popular open-source tools meeting the enlisted requirements. Initially envisioned as a search engine capable of operating at scale and in real time, Elasticsearch is used by organisations such as Wikimedia and Github, which deal with big data on daily basis. In addition, Elasticsearch is used increasingly often as analytics platform thanks to its scalable architecture and expressive query language. Until recently, the exploitation of Elasticsearch for (learning) analytical purposes by practitioners was hindered by a high entrance barrier due to the complexity of the query language and the query specificities. This is currently changing with the ongoing development of Kibana, an open-source tool that allows to conduct analysis and build visualisations of Elasticsearch data through a graphical user interface. Kibana does not require the user to dive into technical details of the queries (although it is still possible) and hence makes big educational data visualisations accessible to regular users. The additional value of Kibana comes in play whenever several visualisations are combined on a single dashboard, enabling to use multiple coordinated views for an interactive explorative analysis. Both Elasticsearch and Kibana, together with Logstash are part of an analytics stack often referred to as ELK. Logstash supports data acquisition from multiple sources (including twitter, RSS, event logs) thanks to its rich set of available connectors. Custom connectors can be developed for case-specific sources. In addition to the mentioned values, ELK enables building analytics infrastructures decoupled from the learning platform, i.e., it allows to host separately the learning environment (with the analytics functionalities) and the data storage without affecting the end-user experience.
A Powerpoint presentation from the Connecticut Freedom of Information Commission, posted on its website: http://www.ct.gov/foi/cwp/view.asp?a=3171&Q=504946
Elasticsearch, Logstash, Kibana. Cool search, analytics, data mining and more...Oleksiy Panchenko
In the age of information and big data, ability to quickly and easily find a needle in a haystack is extremely important. Elasticsearch is a distributed and scalable search engine which provides rich and flexible search capabilities. Social networks (Facebook, LinkedIn), media services (Netflix, SoundCloud), Q&A sites (StackOverflow, Quora, StackExchange) and even GitHub - they all find data for you using Elasticsearch. In conjunction with Logstash and Kibana, Elasticsearch becomes a powerful log engine which allows to process, store, analyze, search through and visualize your logs.
Video: https://www.youtube.com/watch?v=GL7xC5kpb-c
Scripts for the Demo: https://github.com/opanchenko/morning-at-lohika-ELK
Nested and Parent/Child Docs in ElasticSearchBeyondTrees
A key part of the architecture of RefWorks Flow, a new document workflow tool for researchers, is an ElasticSearch cluster used for citation canonicalization. We will present our findings of how to use the "nested" type and parent-child relations in ElasticSearch to do complex where-clause queries in an efficient way
Searching Relational Data with Elasticsearchsirensolutions
Second Galway Data Meetup, 29th April 2015
Elasticsearch was originally developed for searching flat documents. However, as real world data is inherently more complex, e.g., nested json data, relational data, interconnected documents and entities, Elasticsearch quickly evolves to support more advanced search scenarios. In this presentation, we will review existing features and plugins to support such scenarios, discuss their advantages and disadvantages, and understand which one is more appropriate for a particular scenario.
Every second thousands of Netflix members hit the play button to stream content on more than 1000 different Netflix device types. The playback experience is comprised of multiple dimensions - device, customer, network, content, country, languages. At Netflix’s scale, these dimensions are beyond what a human can comprehend. To enable understanding of what’s working and not working across the globe for Netflix customers, we need to blend humans and machines. Elasticsearch and Kibana augment our human analysis and enable automated root causing of streaming problems.
In this talk, we’ll discuss:
- The dimensions of data used in generating an optimal playback experience
- How we use Elasticsearch and Kibana to slice and dice these dimensions for our insights
- The automation workflow we have in place for finding the issues auto-magically and identifying the root cause of the issue.
You can reach Suudhan at @suudhan and https://www.linkedin.com/in/suudhan
Elasticsearch Introduction to Data model, Search & AggregationsAlaa Elhadba
An overview of Elasticsearch features and explains performing smart search, data aggregations, and relevancy through scoring functions. How Elasticsearch works as a distributed scalable data storage. Finally, showcasing some use cases that are currently becoming core functionalities in Zalando.
ElasticSearch in Production: lessons learnedBeyondTrees
With Proquest Udini, we have created the worlds largest online article store, and aim to be the center for researchers all over the world. We connect to a 700M solr cluster for search, but have recently also implemented a search component with ElasticSearch. We will discuss how we did this, and how we want to use the 30M index for scientific citation recognition. We will highlight lessons learned in integrating ElasticSearch in our virtualized EC2 environments, and challenges aligning with our continuous deployment processes.
Moving From MySQL to Elasticsearch for AnalyticsPercolate
This presentation gives a technical overview of Percolate next generation Analytics system. It describes the first generation system, it challenges, and how Percolate uses the latest technology to build its new Analytics system.
Attack monitoring using ElasticSearch Logstash and KibanaPrajal Kulkarni
With growing trend of Big data, companies are tend to rely on high cost SIEM solutions. However, with introduction of open source and lightweight cluster management solution like ElasticSearch this has been the highlight of the year. Similarly, the log aggregation has been simplified by logstash and kibana providing a visual look to the complex data structure. This presentation will exactly cater to this need of having a appropriate log analysis+Detecting Intrusion+Visualizing data in a powerful interface.
Xen e CoreOS: solução para data mining com NodeJS e ElasticSearchBernardo Donadio
Palestra apresentada no FISL17 pelos diretores da Alligo Tecnologia, Bernardo Donadio e Emerson Luiz, acerca da escalabilidade e otimização de ROI de aplicações intensivas.
Deep Dive on ElasticSearch Meetup event on 23rd May '15 at www.meetup.com/abctalks
Agenda:
1) Introduction to NOSQL
2) What is ElasticSearch and why is it required
3) ElasticSearch architecture
4) Installation of ElasticSearch
5) Hands on session on ElasticSearch
Elasticsearch is a search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. ElasticSearchis a free and open source distributed inverted index. So it’s a bunch of indexed documents in a repository. As well as it’s fast, incisive search against large volumes of data. And directly accessed to the data in the denormaliz document storage. Additionally in general distributable and highly scalable DB.
Filebeat Elastic Search Presentation.pptxKnoldus Inc.
In this session, we will figure out how you can use Filebeat to monitor the Elasticsearch log files, collect log events, and ship them to the monitoring cluster. And how your recent logs are visible on the Monitoring page in Kibana.
This slide deck talks about Elasticsearch and its features.
When you talk about ELK stack it just means you are talking
about Elasticsearch, Logstash, and Kibana. But when you talk
about Elastic stack, other components such as Beats, X-Pack
are also included with it.
what is the ELK Stack?
ELK vs Elastic stack
What is Elasticsearch used for?
How does Elasticsearch work?
What is an Elasticsearch index?
Shards
Replicas
Nodes
Clusters
What programming languages does Elasticsearch support?
Amazon Elasticsearch, its use cases and benefits
Deep dive to ElasticSearch - معرفی ابزار جستجوی الاستیکیEhsan Asgarian
در این اسلاید به مباحث زیر می پردازیم:
مقدمات پایگاه داده های غیر اس.کیو.ال، مبانی جستجوگرها
سپس معرفی ابزار جستجوی الاستیکی، کاربردها، معماری کلی، مقایسه با ابزارهای مشابه
افزودن تحلیلگر متن و در نهایت لینک آن با دات نت
ا
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Level: Intermediate
Speakers:
Tony Nguyen - Senior Consultant, ProServe, AWS
Hannah Marlowe - Consultant - Federal, AWS
Data Analytics Week at the San Francisco Loft
Using Data Lakes
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
John Mallory - Principal Business Development Manager Storage (Object), AWS
Hemant Borole - Sr. Big Data Consultant, AWS
An Open Talk at DeveloperWeek Austin 2017 by Kimberly Wilkins (@dba_denizen), Principal Engineer - Databases at ObjectRocket. Featuring new use cases like Bitcoin, AI, IoT, and all the cool things.
Elastic Search Capability Presentation.pptxKnoldus Inc.
Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON document. Distributed search and analytics engine, part of the Elastic Stack. It indexes and analyzes data in real-time, providing powerful and scalable search capabilities for diverse applications.
Big Data Architecture Workshop - Vahid Amiridatastack
Big Data Architecture Workshop
This slide is about big data tools, thecnologies and layers that can be used in enterprise solutions.
TopHPC Conference
2019
Need help learning Twitter Bootstrap? This is your chance! We're covering:
• What is it?
• Why is it so popular?
• Installation
• Theming
• Usage (grid, controls, etc...)
You need some knowledge of CSS, HTML, and JavaScript to get the most out of this. Contact me if you need a primer reference.
Continuous integration (CI) allows you to check the quality of your project on every developer commit. It's a key part of any Agile environment.
We will start with a 5 year old ASP.NET WebPages application and put it under CI with unit testing and other key metrics. The web project, has no modern best practices at all. This talk will demonstrate, step-by-step and line-by-line, adding a project to a continuous integration (CI) server.
The end result will be a project with the following:
- Automatic build on check-in
- Automatic reporting of unit tests
- Code duplication reporting
- Warning analysis
- REM: Automatic database upgrade
This will be great if you work with legacy code and feel the CI hurdle is big.
This talk was give at the South Shore .NET Users Group.
Unit Testing is now considered a required skill for developers. There are a ton of tools out there. However, there's nothing that shows you how to tie them all together to make your software fast, testable, and flexible. This talk will go over my toolset:
• MSTest
• Moq - Mocking framework
• NCover - for coverage
• MSBuild - for automation
• Dotcover - coverage from VS
• Unity - for dependency injection
This talk has a very long demo
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.
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.
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.
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.
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/
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.
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.
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
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
4. Big Data
“Big data is an all-encompassing term for any collection of data sets so
large and complex that it becomes difficult to process using traditional
data processing applications.”
- Wikipedia
5. The 3 Vs
• Volume
• A few Gigabytes -> Petabyte
• Velocity
• Arrives quickly
• Variety
• Multiple types of data
6. What is ElasticSearch?
• You know, for search…
• Elasticsearch is a search server based on Lucene. It provides a
distributed, multitenant-capable full-text search engine with a RESTful
web interface and schema-free JSON documents. Elasticsearch is
developed in Java and is released as open source under the terms of
the Apache License.
7. Let’s break that down…
• Distributed
• Run on multiple servers simultaneously
• Multitenant
• The same system serving different groups of data
• REST
• Web-based programming interface
• NoSQL for storage
• Uses JSON
• Open Source
8. So what is ElasticSearch?
• It’s a search engine
• Stores data on multiple machines
• Stores multiple types of data
• Stores in JSON format
• REST interface
• There are managed and unmanaged programming interfaces
• .NET
• Java
• NodeJs
• JavaScript
• Scala
• Clojure
• PHP
• Perl
• Python
• Ruby
• Haskell
• Erlang
• ColdFusion
• SmallTalk
• Ocaml
• CommandLine
• EventMachine
• Go
10. Definitions
• Cluster
• One or more nodes
• Document
• A stored record
• Field
• A document has a list of fields, or key-value pairs
• Index
• Think of this as a database
• Term
• This is an exact value to be matched (“FOO”, “Foo”, “foo”) are not the same term
• Type
• Similar to a database
• Text
• Field value
• Analyzed into terms
• Stored in the index