This presentation contains differences between Elasticsearch and relational Databases. Along with that it also has some Glossary Of Elasticsearch and its basic operation.
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 Tutorial | Getting Started with Elasticsearch | ELK Stack Train...Edureka!
( ELK Stack Training - https://www.edureka.co/elk-stack-trai... )
This Edureka Elasticsearch Tutorial will help you in understanding the fundamentals of Elasticsearch along with its practical usage and help you in building a strong foundation in ELK Stack. This video helps you to learn following topics:
1. What Is Elasticsearch?
2. Why Elasticsearch?
3. Elasticsearch Advantages
4. Elasticsearch Installation
5. API Conventions
6. Elasticsearch Query DSL
7. Mapping
8. Analysis
9 Modules
Elasticsearch is a free and open source distributed search and analytics engine. It allows documents to be indexed and searched quickly and at scale. Elasticsearch is built on Apache Lucene and uses RESTful APIs. Documents are stored in JSON format across distributed shards and replicas for fault tolerance and scalability. Elasticsearch is used by many large companies due to its ability to easily scale with data growth and handle advanced search functions.
In this presentation, we are going to discuss how elasticsearch handles the various operations like insert, update, delete. We would also cover what is an inverted index and how segment merging works.
Redis is an open source, in-memory data structure store that can be used as a database, cache, or message broker. It supports data structures like strings, hashes, lists, sets, sorted sets with ranges and pagination. Redis provides high performance due to its in-memory storage and support for different persistence options like snapshots and append-only files. It uses client/server architecture and supports master-slave replication, partitioning, and failover. Redis is useful for caching, queues, and other transient or non-critical data.
A brief presentation outlining the basics of elasticsearch for beginners. Can be used to deliver a seminar on elasticsearch.(P.S. I used it) Would Recommend the presenter to fiddle with elasticsearch beforehand.
This document provides an overview and introduction to Elasticsearch. It discusses the speaker's experience and community involvement. It then covers how to set up Elasticsearch and Kibana locally. The rest of the document describes various Elasticsearch concepts and features like clusters, nodes, indexes, documents, shards, replicas, and building search-based applications. It also discusses using Elasticsearch for big data, different search capabilities, and text analysis.
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 Tutorial | Getting Started with Elasticsearch | ELK Stack Train...Edureka!
( ELK Stack Training - https://www.edureka.co/elk-stack-trai... )
This Edureka Elasticsearch Tutorial will help you in understanding the fundamentals of Elasticsearch along with its practical usage and help you in building a strong foundation in ELK Stack. This video helps you to learn following topics:
1. What Is Elasticsearch?
2. Why Elasticsearch?
3. Elasticsearch Advantages
4. Elasticsearch Installation
5. API Conventions
6. Elasticsearch Query DSL
7. Mapping
8. Analysis
9 Modules
Elasticsearch is a free and open source distributed search and analytics engine. It allows documents to be indexed and searched quickly and at scale. Elasticsearch is built on Apache Lucene and uses RESTful APIs. Documents are stored in JSON format across distributed shards and replicas for fault tolerance and scalability. Elasticsearch is used by many large companies due to its ability to easily scale with data growth and handle advanced search functions.
In this presentation, we are going to discuss how elasticsearch handles the various operations like insert, update, delete. We would also cover what is an inverted index and how segment merging works.
Redis is an open source, in-memory data structure store that can be used as a database, cache, or message broker. It supports data structures like strings, hashes, lists, sets, sorted sets with ranges and pagination. Redis provides high performance due to its in-memory storage and support for different persistence options like snapshots and append-only files. It uses client/server architecture and supports master-slave replication, partitioning, and failover. Redis is useful for caching, queues, and other transient or non-critical data.
A brief presentation outlining the basics of elasticsearch for beginners. Can be used to deliver a seminar on elasticsearch.(P.S. I used it) Would Recommend the presenter to fiddle with elasticsearch beforehand.
This document provides an overview and introduction to Elasticsearch. It discusses the speaker's experience and community involvement. It then covers how to set up Elasticsearch and Kibana locally. The rest of the document describes various Elasticsearch concepts and features like clusters, nodes, indexes, documents, shards, replicas, and building search-based applications. It also discusses using Elasticsearch for big data, different search capabilities, and text analysis.
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/
What I learnt: Elastic search & Kibana : introduction, installtion & configur...Rahul K Chauhan
This document provides an overview of the ELK stack components Elasticsearch, Logstash, and Kibana. It describes what each component is used for at a high level: Elasticsearch is a search and analytics engine, Logstash is used for data collection and normalization, and Kibana is a data visualization platform. It also provides basic instructions for installing and running Elasticsearch and Kibana.
An introduction to KrakenD, the ultra-high performance API Gateway with middlewares. An opensource tool built using go that is currently serving traffic in major european sites.
운영하는 서비스의 전체 또는 일부분을 클라우드의 이점을 100% 얻으며 옮겨가기 위해 서버리스는 가장 좋은 선택입니다. 서버리스 환경은 개발자가 애플리케이션을 개발하고 배포하는 방식을 바꾸고 있습니다. 본 세션에서는 서버리스 개발자가 애플리케이션 수명주기 관리, CI/CD, 모니터링 및 진단에 사용할 수 있는 모범 사례를 살펴 봅니다. AWS CodePipeline, AWS CodeBuild 및 AWS CloudFormation을 사용하여 서버리스 애플리케이션을 자동으로 구축, 테스트 및 배포하는 CI/CD 파이프 라인을 구축하는 방법에 대해 설명합니다. 또한 기능 및 API의 여러 버전, 단계 및 환경을 만들기 위해 Lambda 및 API Gateway의 기본 제공 기능에 대해 설명합니다. 마지막으로, Amazon CloudWatch 및 AWS X-Ray로 람다 기능의 모니터링 및 진단에 대해 소개합니다.
An overview of the Amazon ElastiCache managed service, with examples of how it can be used to increase performance, lower costs and augment other database services and databases to make things faster, easier and less expensive.
Centralized log-management-with-elastic-stackRich Lee
Centralized log management is implemented using the Elastic Stack including Filebeat, Logstash, Elasticsearch, and Kibana. Filebeat ships logs to Logstash which transforms and indexes the data into Elasticsearch. Logs can then be queried and visualized in Kibana. For large volumes of logs, Kafka may be used as a buffer between the shipper and indexer. Backups are performed using Elasticsearch snapshots to a shared file system or cloud storage. Logs are indexed into time-based indices and a cron job deletes old indices to control storage usage.
This document classifies and describes different types of APIs. It discusses web service APIs like REST and SOAP, library-based APIs that interface with programming languages like JavaScript, class-based APIs for platforms like Java and Android, OS APIs that allow access to system functions and hardware, and object remoting APIs like CORBA. Examples are provided for many API types. The document is intended to provide an overview of the various ways that software applications can communicate through defined programming interfaces.
The talk covers how Elasticsearch, Lucene and to some extent search engines in general actually work under the hood. We'll start at the "bottom" (or close enough!) of the many abstraction levels, and gradually move upwards towards the user-visible layers, studying the various internal data structures and behaviors as we ascend. Elasticsearch provides APIs that are very easy to use, and it will get you started and take you far without much effort. However, to get the most of it, it helps to have some knowledge about the underlying algorithms and data structures. This understanding enables you to make full use of its substantial set of features such that you can improve your users search experiences, while at the same time keep your systems performant, reliable and updated in (near) real time.
This document discusses Elasticsearch, an open source search engine that can handle large volumes of data in real time. It is based on Apache Lucene, a full-text search engine, and was developed by Shay Banon in 2010. Elasticsearch stores data in JSON documents and works by indexing these documents so they can be quickly searched. Some key advantages include being RESTful, scalable, simple and transparent, and fast. Disadvantages include only supporting JSON for requests and responses as well as some challenges around processing. The document recommends starting with the official Elasticsearch documentation.
My presentation from Nordic APIs 2014 in Stockholm, Sweden.
How can the architecture of one API platform look like? How can you break down things to make this challenge easier?
Slidedeck presented at http://devternity.com/ around MongoDB internals. We review the usage patterns of MongoDB, the different storage engines and persistency models as well has the definition of documents and general data structures.
Elasticsearch is a distributed, open source search and analytics engine that allows full-text searches of structured and unstructured data. It is built on top of Apache Lucene and uses JSON documents. Elasticsearch can index, search, and analyze big volumes of data in near real-time. It is horizontally scalable, fault tolerant, and easy to deploy and administer.
This document provides an overview and introduction to NoSQL databases. It begins with an agenda that explores key-value, document, column family, and graph databases. For each type, 1-2 specific databases are discussed in more detail, including their origins, features, and use cases. Key databases mentioned include Voldemort, CouchDB, MongoDB, HBase, Cassandra, and Neo4j. The document concludes with references for further reading on NoSQL databases and related topics.
- DynamoDB is a fully managed NoSQL database service by Amazon that provides fast and predictable performance with seamless scalability.
- It uses an eventually consistent, distributed architecture to store data across multiple servers and provides automatic scaling of read and write throughput capacity.
- DynamoDB uses vector clocks to track multiple versions of data that may exist due to asynchronous replication and eventual consistency, applying both syntactic and semantic reconciliation of data conflicts.
The document provides an introduction to the ELK stack, which is a collection of three open source products: Elasticsearch, Logstash, and Kibana. It describes each component, including that Elasticsearch is a search and analytics engine, Logstash is used to collect, parse, and store logs, and Kibana is used to visualize data with charts and graphs. It also provides examples of how each component works together in processing and analyzing log data.
This document provides an introduction and overview of Elasticsearch. It discusses installing Elasticsearch and configuring it through the elasticsearch.yml file. It describes tools like Marvel and Sense that can be used for monitoring Elasticsearch. Key terms used in Elasticsearch like nodes, clusters, indices, and documents are explained. The document outlines how to index and retrieve data from Elasticsearch through its RESTful API using either search lite queries or the query DSL.
1) The document discusses information retrieval and search engines. It describes how search engines work by indexing documents, building inverted indexes, and allowing users to search indexed terms.
2) It then focuses on Elasticsearch, describing it as a distributed, open source search and analytics engine that allows for real-time search, analytics, and storage of schema-free JSON documents.
3) The key concepts of Elasticsearch include clusters, nodes, indexes, types, shards, and documents. Clusters hold the data and provide search capabilities across nodes.
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
Elasticsearch is a powerful open source search and analytics engine. It allows for full text search capabilities as well as powerful analytics functions. Elasticsearch can be used as both a search engine and as a NoSQL data store. It is easy to set up, use, scale, and maintain. The document provides examples of using Elasticsearch with Rails applications and discusses advanced features such as fuzzy search, autocomplete, and geospatial search.
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/
What I learnt: Elastic search & Kibana : introduction, installtion & configur...Rahul K Chauhan
This document provides an overview of the ELK stack components Elasticsearch, Logstash, and Kibana. It describes what each component is used for at a high level: Elasticsearch is a search and analytics engine, Logstash is used for data collection and normalization, and Kibana is a data visualization platform. It also provides basic instructions for installing and running Elasticsearch and Kibana.
An introduction to KrakenD, the ultra-high performance API Gateway with middlewares. An opensource tool built using go that is currently serving traffic in major european sites.
운영하는 서비스의 전체 또는 일부분을 클라우드의 이점을 100% 얻으며 옮겨가기 위해 서버리스는 가장 좋은 선택입니다. 서버리스 환경은 개발자가 애플리케이션을 개발하고 배포하는 방식을 바꾸고 있습니다. 본 세션에서는 서버리스 개발자가 애플리케이션 수명주기 관리, CI/CD, 모니터링 및 진단에 사용할 수 있는 모범 사례를 살펴 봅니다. AWS CodePipeline, AWS CodeBuild 및 AWS CloudFormation을 사용하여 서버리스 애플리케이션을 자동으로 구축, 테스트 및 배포하는 CI/CD 파이프 라인을 구축하는 방법에 대해 설명합니다. 또한 기능 및 API의 여러 버전, 단계 및 환경을 만들기 위해 Lambda 및 API Gateway의 기본 제공 기능에 대해 설명합니다. 마지막으로, Amazon CloudWatch 및 AWS X-Ray로 람다 기능의 모니터링 및 진단에 대해 소개합니다.
An overview of the Amazon ElastiCache managed service, with examples of how it can be used to increase performance, lower costs and augment other database services and databases to make things faster, easier and less expensive.
Centralized log-management-with-elastic-stackRich Lee
Centralized log management is implemented using the Elastic Stack including Filebeat, Logstash, Elasticsearch, and Kibana. Filebeat ships logs to Logstash which transforms and indexes the data into Elasticsearch. Logs can then be queried and visualized in Kibana. For large volumes of logs, Kafka may be used as a buffer between the shipper and indexer. Backups are performed using Elasticsearch snapshots to a shared file system or cloud storage. Logs are indexed into time-based indices and a cron job deletes old indices to control storage usage.
This document classifies and describes different types of APIs. It discusses web service APIs like REST and SOAP, library-based APIs that interface with programming languages like JavaScript, class-based APIs for platforms like Java and Android, OS APIs that allow access to system functions and hardware, and object remoting APIs like CORBA. Examples are provided for many API types. The document is intended to provide an overview of the various ways that software applications can communicate through defined programming interfaces.
The talk covers how Elasticsearch, Lucene and to some extent search engines in general actually work under the hood. We'll start at the "bottom" (or close enough!) of the many abstraction levels, and gradually move upwards towards the user-visible layers, studying the various internal data structures and behaviors as we ascend. Elasticsearch provides APIs that are very easy to use, and it will get you started and take you far without much effort. However, to get the most of it, it helps to have some knowledge about the underlying algorithms and data structures. This understanding enables you to make full use of its substantial set of features such that you can improve your users search experiences, while at the same time keep your systems performant, reliable and updated in (near) real time.
This document discusses Elasticsearch, an open source search engine that can handle large volumes of data in real time. It is based on Apache Lucene, a full-text search engine, and was developed by Shay Banon in 2010. Elasticsearch stores data in JSON documents and works by indexing these documents so they can be quickly searched. Some key advantages include being RESTful, scalable, simple and transparent, and fast. Disadvantages include only supporting JSON for requests and responses as well as some challenges around processing. The document recommends starting with the official Elasticsearch documentation.
My presentation from Nordic APIs 2014 in Stockholm, Sweden.
How can the architecture of one API platform look like? How can you break down things to make this challenge easier?
Slidedeck presented at http://devternity.com/ around MongoDB internals. We review the usage patterns of MongoDB, the different storage engines and persistency models as well has the definition of documents and general data structures.
Elasticsearch is a distributed, open source search and analytics engine that allows full-text searches of structured and unstructured data. It is built on top of Apache Lucene and uses JSON documents. Elasticsearch can index, search, and analyze big volumes of data in near real-time. It is horizontally scalable, fault tolerant, and easy to deploy and administer.
This document provides an overview and introduction to NoSQL databases. It begins with an agenda that explores key-value, document, column family, and graph databases. For each type, 1-2 specific databases are discussed in more detail, including their origins, features, and use cases. Key databases mentioned include Voldemort, CouchDB, MongoDB, HBase, Cassandra, and Neo4j. The document concludes with references for further reading on NoSQL databases and related topics.
- DynamoDB is a fully managed NoSQL database service by Amazon that provides fast and predictable performance with seamless scalability.
- It uses an eventually consistent, distributed architecture to store data across multiple servers and provides automatic scaling of read and write throughput capacity.
- DynamoDB uses vector clocks to track multiple versions of data that may exist due to asynchronous replication and eventual consistency, applying both syntactic and semantic reconciliation of data conflicts.
The document provides an introduction to the ELK stack, which is a collection of three open source products: Elasticsearch, Logstash, and Kibana. It describes each component, including that Elasticsearch is a search and analytics engine, Logstash is used to collect, parse, and store logs, and Kibana is used to visualize data with charts and graphs. It also provides examples of how each component works together in processing and analyzing log data.
This document provides an introduction and overview of Elasticsearch. It discusses installing Elasticsearch and configuring it through the elasticsearch.yml file. It describes tools like Marvel and Sense that can be used for monitoring Elasticsearch. Key terms used in Elasticsearch like nodes, clusters, indices, and documents are explained. The document outlines how to index and retrieve data from Elasticsearch through its RESTful API using either search lite queries or the query DSL.
1) The document discusses information retrieval and search engines. It describes how search engines work by indexing documents, building inverted indexes, and allowing users to search indexed terms.
2) It then focuses on Elasticsearch, describing it as a distributed, open source search and analytics engine that allows for real-time search, analytics, and storage of schema-free JSON documents.
3) The key concepts of Elasticsearch include clusters, nodes, indexes, types, shards, and documents. Clusters hold the data and provide search capabilities across nodes.
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
Elasticsearch is a powerful open source search and analytics engine. It allows for full text search capabilities as well as powerful analytics functions. Elasticsearch can be used as both a search engine and as a NoSQL data store. It is easy to set up, use, scale, and maintain. The document provides examples of using Elasticsearch with Rails applications and discusses advanced features such as fuzzy search, autocomplete, and geospatial search.
Elasticsearch is a distributed, open source search and analytics engine based on Apache Lucene. It allows storing, searching, and analyzing big volumes of data quickly. Elasticsearch uses an inverted index to search text, and indexes documents into shards and replicas for scalability and fault tolerance. Write operations in Elasticsearch are logged in a transaction log and memory buffer before being flushed to segments on disk. Updates create a new version rather than modifying documents in place. Reads are routed to shards, sorted, and returned to the client from the coordinating node.
The talk at TYPO3 DevDays 2015 in Nuremberg which explains the deep insights of how search works. TF-IDF algorithm, vector space model and how that is used in Lucene and therefore Solr and 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
History of Types in Elasticsearch
Why They are being removed
How to migrate from old ES version using multiple types per index to the new version with one type per index or custom type fields
Elasticsearch concepts include nodes, clusters, shards, and replicas. Nodes can be master-eligible, data, or client nodes. Shards hold index data and can have replicas for redundancy. The mapping process defines how documents are stored. Analyzers tokenize text for indexing and searching. Thread pools manage threads for different operations. Capacity planning involves calculating needed shards based on data size. Configurations specify settings like shards, replicas, heap size, and disabling swapping.
by Mikhail Prudnikov, Sr. Solutions Architect, AWS
Elasticsearch is a popular open-source distributed search and analytics engine, widely used for log analytics and text search – and increasingly used as a primary data store. Amazon Elasticsearch Service makes it easy to deploy, secure, operate, and scale Elasticsearch. We’ll take a look at how to use Elasticsearch Service to manage these different use cases.
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
The document provides an overview of how search engines and the Lucene library work. It explains that search engines use web crawlers to index documents, which are then stored and searched. Lucene is an open source library for indexing and searching documents. It works by analyzing documents to extract terms, indexing the terms, and allowing searches to match indexed terms. The document details Lucene's indexing and searching process including analyzing text, creating an inverted index, different query types, and using the Luke tool.
This presentation slide is a condensed theoretical overview of Elasticsearch prepared by going through the official ES Definitive Guide and Practical Guide.
Elasticsearch is a distributed, RESTful, free and open source search engine based on Apache Lucene. It allows for fast full text searches across large volumes of data. Documents are indexed in Elasticsearch to build an inverted index that allows for fast keyword searches. The index maps words or numbers to their locations in documents for fast retrieval. Elasticsearch uses Apache Lucene to create and manage the inverted index.
This document provides an overview of Elasticsearch, including:
- It is a NoSQL database that indexes and searches JSON documents in real-time. Documents are distributed across a cluster of servers for high performance and availability.
- Elasticsearch uses Lucene under the hood for indexing and search. It is part of the ELK (Elasticsearch, Logstash, Kibana) stack and is open source.
- Documents are organized into indexes and types, similar to databases and tables. Documents can be created, updated, and deleted via a RESTful API.
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
The document discusses different types of index structures used in databases, including dense and sparse indexes, primary and secondary indexes, B-trees, and inverted indexes. It explains that indexes associate key values with pointers to data records to allow efficient retrieval of records matching a search key. B-trees automatically maintain multiple levels of balanced indexes and keep blocks at least half full. Inverted indexes are used for text search where each word is a key associated with documents containing that word.
This document discusses XML query language XPath and navigation. It describes how XPath allows querying XML documents by addressing elements and text using a path-like notation. XPath expressions are evaluated based on a context node and node-set. The document also covers XPointer for pointing to specific data within XML documents, and how XPath can be used with the XML DOM and XPathNavigator class in .NET.
Full text search allows searching large documents and databases by examining all words in stored documents to match search terms, rather than just searching for exact matches. It works by indexing documents, including word positions, and applying rules like removing common words. Queries can then search for keywords, use wildcards, and order results by relevance. Common full text search solutions include APIs like Lucene and Xapian, and servers like Sphinx and Solr, which are used by many large companies and websites to enable powerful searching of large amounts of text data.
XML (eXtensible Markup Language) is a meta markup language that allows defining custom markup languages. It became a W3C recommendation in 1998 and uses a tag-based syntax similar to HTML. XML allows defining tags to represent different types of text documents and data in a well-structured, machine-readable format. It is not a replacement for other technologies but can be converted to and used with many formats and languages.
Similar to Elasticsearch V/s Relational Database (20)
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
2. Agenda
● Basic Difference Between Elasticsearch And Relational Database
● Use Cases where Relational Db are not suitable
● Basic Terminology Of Elasticsearch
● Elasticsearch – CRUD operations
3. Basic Difference
● Elasticsearch is a No sql Database.
● It has no relations, no constraints, no joins, no transactional
behaviour.
● Easier to scale as compared to a relational Database.
Relational DB Elasticsearch
DataBase Index
Table Type
Row/Record Document
Column Name Field
4. Usecases where Relational Databases
are not suitable
● Relevance based searching
● Searching when entered spelling of search term is wrong
● Full text search
● Synonym search
● Phonetic search
● Log analysis
5. Relevance Based searcching
● By default, results are returned sorted by relevance—with the most
relevant docs first.
● The relevance score of each document is represented by a positive
floating-point number called the _score. The higher the _score, the
more relevant the document.
● A query clause generates a _score for each document. How that
score is calculated depends on the type of query clause.
6. Relevance Representation in ES
{
"_index": "test",
"_type": "product",
"_id": "AV0iKK_ZJJfvpLB9dSHl",
"_score": 0.51623213, ====> Relevance Score calculated by ES
"_source": {
"id": 2,
"name": "Red Shirt"
}
}
8. Full Text Search
● Whenever a full-text is given to Elasticsearch, special analyzers are
applied in order to simplify it and make it searchable.
● It does not store the text as it is visible. This means that the original
text would be modified following special rules before being stored in
the Inverted index.
● This process is called the “analysis phase,” and it is applied to all full-
text fields.
11. Synonym search
● Synonyms are used to broaden the scope of what is considered a
matching document.
● Perhaps no documents match a query for “Top Doctor's College,” but
documents that contain “Top Medical Institutions” would probably be
considered a good match.
12. Phonetic Searching
● Elasticsearch can search for words that sound similar, even if their
spelling differs.
● The Phonetic Analysis plugin provides token filters which convert
tokens to their phonetic representation using Soundex, Metaphone,
and a variety of other algorithms.
● Generally used while searching for names that sound similar.
Consider 'Smith', 'Smythe'. Elasticsearch analyser will produce same
tokens for both.
13. Log Analysis Using Elasticsearch
● Elasticsearch is vastly used as a centralized location for storing logs.
● For the purpose of indexing and searching logs, there is a bundled
solution offered at the Elasticsearch page - ELK stack, which stands
for elasticsearch, logstash and kibana.
●
14. Elasticsearch Terminology
● Elasticsearch: It is a horizontally distributed,data storage, search server,
aggregation engine, based on lucene library. It is written in java. Elasticsearch
5.5 is the latest one.
● Cluster: A cluster consists of one or more nodes which share the same cluster
name. Each cluster has a single master node which can be replaced if the
current master node fails.
● Node: A node is a running instance of elasticsearch which belongs to a cluster.
Multiple nodes can be started on a single server. At startup, a node will use
unicast to discover an existing cluster with the same cluster name and will try
to join that cluster.
● Primary Shard: Each document is stored in a single primary shard. When you
index a document, it is indexed first on the primary shard, then on all replicas
of the primary shard. By default, an index has 5 primary shards.
15. Elasticsearch Terminology Ctd.
● Replica Shard: Each primary shard can have zero or more replicas. A replica
is a copy of the primary shard. By Default there are 1 replica for each primary
shards.
● Document: A document is a JSON document which is stored in elasticsearch.
It is like a row in a table in a relational database. Each document is stored in
an index and has a type and an id. A document is a JSON object which
contains zero or more fields, or key-value pairs.
● ID: The ID of a document identifies a document. The index/type/id of a
document must be unique. If no ID is provided, then it will be auto-generated.
● Mapping: A mapping is like a schema definition in a relational database. Each
index has a mapping, which defines each type within the index, plus a number
of index-wide settings.
16. Create Index/Document
● Index Creation:
PUT employee
● Document Creation
POST employee/employee/1
{
"name" : "John"
}
17. Delete Document
● Delete By Id
DELETE employee/employee/1
● Delete By query
POST employee/employee/_delete_by_query
{
"query": {
"match": {
"name": "John"
}
}
}
18. Update Document
● Update By Id:
POST employee/employee/1/_update
{
"doc": {
"name": "Johny"
}
}
● Update By Query:
POST employee/_update_by_query
{
"script": {
"inline": "ctx._source.age++",
"lang": "painless"
},
"query": {
"match": {
"name": "john"
}
}
}
19. Read/Query Document
● Read By Id
GET employee/employee/1
● Read By query
GET employee/_search
{
"query": {
"match": {
"name": "John"
}
}
}