Logstash, Elasticsearch and Kibana
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Logstash is an open source tool for collecting, parsing, and storing logs and other event data. It can input data from multiple sources, parse and transform the data, and output it to multiple destinations such as Elasticsearch. Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene. It allows storing, searching, and analyzing large volumes of data quickly and in near real-time. Together, Logstash can collect and parse log files, enriching the data, and outputting it to Elasticsearch for storage, search, and visualization, making log event data searchable and analyzable.
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Organizations are collecting an ever-increasing amount of data from numerous sources such as log systems, click streams, and connected devices. Launched in 2009, Elasticsearch —an open-source analytics and search engine— has emerged as a popular tool for real-time analytics and visualization of data. Some of the most common use cases include risk assessment, error detection, and sentiment analysis. However, as data volumes and applications grow, managing Elasticsearch clusters can consume significant IT resources while adding little or no differentiated value to the organization. Amazon Elasticsearch Service (Amazon ES) is a managed service that makes it easy to deploy, operate, and scale Elasticsearch clusters in the AWS Cloud. Amazon ES offers the benefits of a managed service, including cluster provisioning, easy configuration, replication for high availability, scaling options, data durability, security, and node monitoring. This session presents a technical deep dive on Amazon ES. Attendees learn: Common challenges with real-time data analytics and visualization and how to address them; the benefits, reference architecture, and best practices for using Amazon ES; and data ingestion options with Amazon DynamoDB, AWS Lambda, and Amazon Kinesis.
Data saturday malta - ADX Azure Data Explorer overview
This is a step-by-step approach the entire ecosystem of features driven by Azure Data eXplorer. You can find many examples using Kusto dialect, in order to acquire data, process and build up complete web interfaces using only one service: ADX.
Introduction to Solr
This document provides a summary of the Solr search platform. It begins with introductions from the presenter and about Lucid Imagination. It then discusses what Solr is, how it works, who uses it, and its main features. The rest of the document dives deeper into topics like how Solr is configured, how to index and search data, and how to debug and customize Solr implementations. It promotes downloading and experimenting with Solr to learn more.
Practical Machine Learning for Smarter Search with Spark+Solr
This document discusses using Apache Spark and Apache Solr together for practical machine learning and data engineering tasks. It provides an overview of Spark and Solr, why they are useful together, and then gives an example of using them together to explore a dataset of Apache mailing list archives through data visualization, clustering, classification, and recommender systems.
Practical Machine Learning for Smarter Search with Solr and Spark
This document discusses using Apache Spark and Apache Solr together for practical machine learning and data engineering tasks. It provides an overview of Spark and Solr, why they are useful together, and then gives an example of exploring and analyzing mailing list archives by indexing the data into Solr with Spark and performing both unsupervised and supervised machine learning techniques.
Elasticsearch - Scalability and Multitenancy
A talk that discusses two topics regarding Elasticsearch - multitenancy and scalability and what are the technical details to achieving them efficiently
Elasticsearch JVM-MX Meetup April 2016
This document provides a 3 sentence summary of an Elasticsearch meetup in Mexico City in April 2016. It introduces Elasticsearch as a open-source search and analytics engine, discusses its features including real-time data, being massively distributed and schema-free, and provides examples of creating an index, mapping types, indexing documents, and searching in Elasticsearch using HTTP requests.
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Presentation for meetup http://www.meetup.com/Liferay-Budapest-Tech-Meetup/events/229996198/
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Graph databases for SQL Server profesionnals
20 avril 2015
Groupe SQL
Sujet:Graph databases for SQL Server profesionnals
Graph databases are used to represent graph structures with nodes, edges and properties. Neo4j, an open-source graph database is reliable and fast for managing and querying highly connected data. Will explore how to install and configure, create nodes and relationships, query with the Cypher Query Language, importing data and using Neo4j in concert with SQL Server... Providing answers and insight with visual diagrams about connected data that you have in your SQL Server Databases!
SQL Server MVP, Stéphane Fréchette is a Data & BI Architect who is very passionate about architecting, designing and building solutions that matter. Specialized in SQL Server, Microsoft Business Intelligence, Data Analytics, Data Visualization, Big Data and the Cloud. He also works and his fluent with several other database platforms (MySQL, PostgreSQL, Oracle, Neo4j, MongoDB and other NoSQL type databases). A self proclaimed Open Data Hacker/Advocate Stéphane founded Gatineau Ouverte a citizen led initiative which aims to promote open access to civic data of the city of Gatineau and his the licensee and organizer of TEDxGatineau.
Episerver and search engines
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Introduction to elasticsearch
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.
Accelerating Delivery of Data Products - The EBSCO Way
EBSCO Information Services (EBSCO) is the leading provider of electronic journals, magazines, eBooks, audioBooks, and online research content for libraries, including hundreds of research databases, historical archives, point-of-care medical reference, and corporate learning tools serving millions of end users at tens of thousands of institutions worldwide. The EBSCO platform is a widely used platform serving the needs of researchers at all levels in academic institutions, schools, public libraries, hospitals, medical institutions, corporations and government institutions. Data is our business, and delivering new products quickly is our competitive advantage. We build hundreds of data products and accelerating the analysis, transformation of new datasets translates to revenue and competitiveness. And since our data is so varied, using MognoDB to store data flexibly and JSON Studio to analyze this data allows us to deliver products to market faster. In this session we will describe this process that helped us expedite delivery of new datasets, and give real examples of how data is used, analyzed and processed.
AWS October Webinar Series - Introducing Amazon Elasticsearch Service
Running Elasticsearch often requires specialized expertise and significant resources to operate and manage infrastructure and Elasticsearch software.
Amazon Elasticsearch Service makes it easy to deploy, operate, and scale Elasticsearch in AWS.
In this webinar, we will walk through how to launch a fully functional Amazon Elasticsearch domain, load your data, and analyze it using the built-in Kibana integration. We will also cover the CloudWatch Logs integration, which enables you to have your log data, such as VPC logs, automatically loaded into your Amazon Elasticsearch domain for analysis and exploration.
Getting Started with Elasticsearch
See webinar recording of this presentation at: https://resource.alibabacloud.com/webinar/live.htm?&webinarId=67
In this presentation, you will learn all you need to know about Elasticsearch, one of the most widely used open source search platforms in the world. We will walk you through what Elasticsearch is, why you need it, and show common use cases. First, we will introduce Elastic Search and the best practices for deploying it, as well as show what some of the salient features of the platform are.
In the second part of the webinar, we delve into the various use cases for Elasticsearch and show why it is an excellent platform to query a large dataset. This includes a demo on querying a cluster. Finally, we will show how you can launch an elastic cluster on Alibaba Cloud and how to use Elasticsearch to query a large dataset for an autocomplete use case.
Learn more about Alibaba Cloud’s Elasticsearch offering:
https://www.alibabacloud.com/product/elasticsearch
Solr Recipes Workshop
This document summarizes a Solr Recipes Workshop presented by Erik Hatcher of Lucid Imagination. It introduces Lucene and Solr, describes how to index different content sources into Solr including CSV, XML, rich documents, and databases, and provides an overview of using the DataImportHandler to index from a relational database.
Similar to Logstash, Elasticsearch and Kibana (20)
(BDT209) Launch: Amazon Elasticsearch For Real-Time Data Analytics
(BDT209) Launch: Amazon Elasticsearch For Real-Time Data Analytics
Data saturday malta - ADX Azure Data Explorer overview
Data saturday malta - ADX Azure Data Explorer overview
Practical Machine Learning for Smarter Search with Spark+Solr
Practical Machine Learning for Smarter Search with Spark+Solr
Practical Machine Learning for Smarter Search with Solr and Spark
Practical Machine Learning for Smarter Search with Solr and Spark
In search of: A meetup about Liferay and Search 2016-04-20
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Accelerating Delivery of Data Products - The EBSCO Way
Accelerating Delivery of Data Products - The EBSCO Way
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AWS October Webinar Series - Introducing Amazon Elasticsearch Service
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Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
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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.
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Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
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Logstash, Elasticsearch and Kibana
- 2. Agenda • Introduction • What is logstash • logstash in action • What is Elasticsearch • Elasticsearch in action
- 3. Introduction • Issue : find out TPS based on ELB ’s log files • What is the goals • make life is easier • No repeat to write program again and agains • Make log event searchable • Able to analyse log event
- 5. • Free & open source • Event processing log file • Support multiple sources and destinations • logstash can manipulate data • pipeline = input + (filter) + output • codec is change data representation • Flexible configuration • Not limit only process log event
- 6. • Middle guy that sit between sources and destinations that • manage event and logs • collect data • parse data • enrich data • store data (search & visualise)
- 7. logstash = inputs + filters + outputs
- 8. Elastic search • NoSQL: Document Oriented - Insert, delete, update, retrieve, analytic and search • Built on top of apache lucene - lucene is most popular java based full text search index implement • Distributed text search engine - Inverted Index - Cluster
- 10. Why Free & open source Easy to scale (distributed) Everything is one JSON call (Restful API) Unleash power of Lucene under the hood • • • • • Excellent query DSL • Support for advance search features (full text search) Document oriented Schema free • • • Active community
- 11. What does it added to lucene? • RESTFUL Service - JSON API over HTTP • High Available & Performance - node form cluster - distributed data using shard - replicas request load, fault tolerance • Long terms persistency - write through persistent storage system
- 12. Document Oriented name address hired_date department Ball Paris 22/06/2015 Business JenJa Tokyo 18/01/2016 Accounting Kook London 1/04/2017 Marketing { ….. “hit”: [ { “_index”: “general” “_type”: “employee”, “name”: “Ball”, “address”: “Paris”, “hired_date”: “22/06/2015”, “department”: “business” }, { “_index”: “general” “_type”: “employee”, “name”: “JenJa”, “address”: “Tokyo”, “hired_date”: “18/01/2016”, “department”: “Accounting” }, ….. ] } Table: employee Database: general
- 13. Elastic search Elasticsearch Relational MySQL Index Database Type Table Document Row Field Column
- 14. Who use elasticsearch ?
- 15. Conclusion • Logstash used to load,parse and structured data to elasticsearch • Elasticsearch used to find number of TPS for each API
- 16. Q & A Thanks you