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
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.
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
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
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/
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.
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.
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
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
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/
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.
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.
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 introduction talk. Overview of the API, functionality, use cases. What can be achieved, how to scale? What is Kibana, how it can benefit your business.
This presentation contains differences between Elasticsearch and relational Databases. Along with that it also has some Glossary Of Elasticsearch and its basic operation.
Visualize some of Austin's open source data using Elasticsearch with Kibana. ObjectRocket's Steve Croce presented this talk on 10/13/17 at the DBaaS event in Austin, TX.
An introduction to and a couple of examples and tips on how to use Elasticsearch for general data analytics. Examples are based on Elasticsearch version 2.x.
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.
Whether you're a developer or just curious about the tech behind search engines, Elasticsearch is worth checking out. From quick search results to analyzing large datasets, Elasticsearch has got you covered. Dive in and explore the endless possibilities.
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.
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 introduction talk. Overview of the API, functionality, use cases. What can be achieved, how to scale? What is Kibana, how it can benefit your business.
This presentation contains differences between Elasticsearch and relational Databases. Along with that it also has some Glossary Of Elasticsearch and its basic operation.
Visualize some of Austin's open source data using Elasticsearch with Kibana. ObjectRocket's Steve Croce presented this talk on 10/13/17 at the DBaaS event in Austin, TX.
An introduction to and a couple of examples and tips on how to use Elasticsearch for general data analytics. Examples are based on Elasticsearch version 2.x.
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.
Whether you're a developer or just curious about the tech behind search engines, Elasticsearch is worth checking out. From quick search results to analyzing large datasets, Elasticsearch has got you covered. Dive in and explore the endless possibilities.
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.
A horizontally-scalable, distributed database built on Apache’s Lucene that delivers a full-featured search experience across terabytes of data with a simple yet powerful API.
Learn more at http://infochimps.com
Elasticsearch, a distributed search engine with real-time analyticsTiziano Fagni
An overview of Elasticsearch: main features, architecture, limitations. It includes also a description on how to query data both using REST API and using elastic4s library, with also a specific interest into integration of the search engine with Apache Spark.
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
AWS October Webinar Series - Introducing Amazon Elasticsearch ServiceAmazon Web Services
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.
(BDT209) Launch: Amazon Elasticsearch For Real-Time Data AnalyticsAmazon Web Services
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.
These slides contain amazon web service guidelines and services, the global infrastructure, and Different basic services and overviews.
EC2, EBS, ELB, Autoscaling, IAM, RDS, Elasticache,Aurora DB
This slide has will cover the basics of cloud computing like what is cloud computing, what are cloud computing service models and why it is important.
"The cloud" refers to servers that are accessed over the Internet.
Examples, service models, cloud deployment models. It is #1 of the AWS Cloud series.
This Slide Pack contains the basics of Linux, what is linux, when it is created, what is opensource, some basic commands, the things you need to know about Linux.
This slide pack will help you with the concepts of how to write an effective email. Nowadays every communication is held with the help of electronic media and deliver your message in a written way.
So tried creating a slide and consolidate all the basic facts at one place for effective email writing
This slide pack is full of knowledge around cybersecurity and the major terms in that domain. It will help you to learn and increase your understanding of cybersecurity.
This slide presentation will brief you about what is AWS Config and what are the key points to remember for this service. Aws config is also known as compliance as a code.
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/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
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.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
3. So, what is the ELK Stack?
● "ELK" is the acronym for three open source projects:
Elasticsearch, Logstash, and Kibana.
● Elasticsearch is a search and analytics engine.
● Logstash is a server‑side data processing pipeline that
ingests data from multiple sources simultaneously,
transforms it, and then sends it to a "stash" like
Elasticsearch.
● Kibana lets users visualize data with charts and graphs
in Elasticsearch.
4. ELK vs Elastic stack
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.
5. Elasticsearch
● Elasticsearch is a distributed, open-source, RESTful,
Highly Scalable search and analytics engine based on the
Apache Lucene Library and works for all types of data,
including textual, numerical, geospatial, structured, and
unstructured.
● It is the Heart of elastic stack.
● Elasticsearch is an open source developed in Java and
used by many big organizations around the world.
● It is licensed under the Apache license version 2.0.
6. What is Elasticsearch used for?
● Application search
● Website search
● Enterprise search
● Logging and log analytics
● Infrastructure metrics and container monitoring
● Application performance monitoring
● Geospatial data analysis and visualization
● Security analytics
● Business analytics
7. How does Elasticsearch work?
Raw data flows into Elasticsearch from a variety of sources,
including logs, system metrics, and web applications.
Data ingestion is the process by which this raw data is
parsed, normalized, and enriched before it is indexed in
Elasticsearch.
Once indexed in Elasticsearch, users can run complex queries
against their data and use aggregations to retrieve complex
summaries of their data.
From Kibana, users can create powerful visualizations of
their data, share dashboards, and manage the Elastic Stack.
8.
9. Document
It is a collection of fields in a specific manner defined in
JSON format.
Every document belongs to a type and resides inside an
index.
Every document is associated with a unique identifier called
the UID.
11. What is an Elasticsearch index?
An Elasticsearch index is a collection of documents that are related to each
other. Elasticsearch stores data as JSON documents. Each document correlates a
set of keys (names of fields or properties) with their corresponding values
(strings, numbers, Booleans, dates, arrays of values, geolocations, or other
types of data).
Elasticsearch uses a data structure called an inverted index, which is
designed to allow very fast full-text searches. An inverted index lists every
unique word that appears in any document and identifies all of the documents
each word occurs in.
During the indexing process, Elasticsearch stores documents and builds an
inverted index to make the document data searchable in near real-time.
Indexing is initiated with the index API, through which you can add or update
a JSON document in a specific index.
12. Shards
Indexes are horizontally subdivided into shards.
This means each shard contains all the properties of
document but contains less number of JSON objects than
index.
The horizontal separation makes shard an independent node,
which can be store in any node. Primary shard is the
original horizontal part of an index and then these primary
shards are replicated into replica shards.
13. Shards
● Shard is like a partition(piece) of an Index.
● Shard splits the index horizontally.
● You can define the number of shards in an index at the time of Index
creation.
● The main shard which is used for write is called as Primary shard.
● In Elasticsearch, replication is done with the help of Replica shards.
14. Replicas
● Elasticsearch allows a user to create replicas of their indexes and
shards. Replication not only helps in increasing the availability of data
in case of failure, but also improves the performance of searching by
carrying out a parallel search operation in these replicas.
● Replica contains the same data as its primary shards.
● The replicas are never allocated to the same node as the primary shard.
● Allows for fault tolerance.
● Scales search throughput.
15.
16. Node
● A single server in a cluster called Node.
● A node has a unique name in the cluster.
17. Cluster
● It is a collection of one or more servers.
● It allows searching and indexing across all nodes in
the cluster.
● One node is one Lucene instance.
● Every cluster is identified by its UNIQUE name. (This
is Important for multi-cluster setup)
18.
19. Cluster Status
Your cluster will be either of 3 stats of cluster depends on primary and
replica shards.
● Green, when all the primary, as well as replica shards, are allocated.
● Yellow, when all the primary shards are allocated where one or more
replica shards are unallocated
● Red, when one or more primary shards are unallocated.
21. Node Types
Master Eligible Node (Default: True)
It is responsible for all the master cluster management, operations like create, update, delete,
read as well as tracking of all the clusters and shard allocation.
Data Node (Default: True)
Data nodes contain the shards. Index, Delete, Search and other operations are performed on data
nodes.
Ingest Node (Default: True)
Preprocessing of the data is done by the index node. (Logstash)
22. Node Types
Coordinating Only Node (Default: false)
Coordinating only nodes acts as a smart load balancer that routes the requests to the
nodes.
It also handles search reduction.
Distributes bulk indexing.
Machine Learning Node
It is a feature of X-pack which is not free.
In this node, you can run machine learning jobs and API requests.
23.
24.
25.
26. What programming languages does Elasticsearch support?
Elasticsearch supports a variety of languages and official
clients are available for:
● Java
● JavaScript (Node.js)
● Go
● .NET (C#)
● PHP
● Perl
● Python
● Ruby
27. Amazon Elasticsearch
Amazon Elasticsearch Service is a fully managed service that makes it
easy for you to deploy, secure, and run Elasticsearch cost effectively
at scale.
You can build, monitor, and troubleshoot your applications using the
tools you love, at the scale you need.
The service provides support for open source Elasticsearch APIs,
managed Kibana, integration with Logstash and other AWS services, and
built-in alerting and SQL querying.
Amazon Elasticsearch Service lets you pay only for what you use – there
are no upfront costs or usage requirements. With Amazon Elasticsearch
Service, you get the ELK stack you need, without the operational
overhead.
28. Benefits
Easy to deploy and manage
With Amazon Elasticsearch Service you can deploy your
Elasticsearch cluster in minutes. The service simplifies
management tasks such as hardware provisioning, software
installation and patching, failure recovery, backups, and
monitoring.
To monitor your clusters, Amazon Elasticsearch service includes
built-in event monitoring and alerting so you can get notified on
changes to your data to proactively address any issues.
29. Benefits
Highly scalable and available
Amazon Elasticsearch Service lets you store up to 3 PB of data in
a single cluster, enabling you to run large log analytics
workloads via a single Kibana interface.
You can easily scale your cluster up or down via a single API
call or a few clicks in the AWS console.
Amazon Elasticsearch Service is designed to be highly available
using multi-AZ deployments, which allows you to replicate data
between three Availability Zones in the same region.
30. Benefits
Highly secure
For your data in Elasticsearch Service, you can achieve
network isolation with Amazon VPC, encrypt data at-rest and
in-transit using keys you create and control through AWS
KMS, and manage authentication and access control with
Amazon Cognito and AWS IAM policies.
Amazon Elasticsearch Service is also HIPAA eligible, and
compliant with PCI DSS, SOC, ISO, and FedRamp standards to
help you meet industry-specific or regulatory requirements.
31. Benefits
Cost-effective
With Amazon Elasticsearch Service, you pay only for the resources you
consume.
You can select on-demand pricing with no upfront costs or long-term
commitments, or achieve significant cost savings via our Reserved
Instance pricing.
As a fully managed service, Amazon Elasticsearch Service further lowers
your total cost of operations by eliminating the need for a dedicated
team of Elasticsearch experts to monitor and manage your clusters.
32.
33. Use Cases
Application monitoring
Store, analyze, and correlate application and infrastructure log data to find
and fix issues faster and improve application performance.
Enable trace data analysis for your distributed applications to quickly
identify performance issues. You can receive automated alerts if your
application is underperforming, enabling you to proactively address any
issues.
An online travel company, for example, can use Amazon Elasticsearch Service to
analyze logs from its applications to identify and resolve performance
bottlenecks or availability issues, ensuring streamlined booking experience.
34. Use Cases
Security information and event management (SIEM)
Centralize and analyze logs from disparate applications and
systems across your network for real-time threat detection
and incident management.
A telecom company, for example, can use Amazon Elasticsearch
Service with Kibana to quickly index, search, and visualize
logs from its routers, applications, and other devices to
find and prevent security threats such as data breaches,
unauthorized login attempts, DoS attacks, and fraud.
35. Use Cases
Search
Provide a fast, personalized search experience for your applications,
websites, and data lake catalogs, allowing your users to quickly find
relevant data.
For example, a real estate business can use Amazon Elasticsearch
Service to help its consumers find homes in their desired location, in
a certain price range from among millions of real-estate properties.
You get access to all of Elasticsearch’s search APIs, supporting
natural language search, auto-completion, faceted search, and
location-aware search.
36. Use Cases
Infrastructure monitoring
Collect logs and metrics from your servers, routers, switches,
and virtualized machines to get a comprehensive visibility into
your infrastructure, reducing mean time to detect (MTTD) and
resolve (MTTR) issues and lowering system downtime.
A gaming company, for example, can use Amazon Elasticsearch
Service to monitor and analyze server logs to identify any server
performance issues that could lead to application downtime.
37. Advantages
● Elasticsearch is developed on Java, which makes it compatible on almost every
platform.
● Elasticsearch is real time, in other words after one second the added document is
searchable in this engine
● Elasticsearch is distributed, which makes it easy to scale and integrate in any
big organization.
● Creating full backups are easy by using the concept of gateway, which is present
in Elasticsearch.
● Handling multi-tenancy is very easy in Elasticsearch when compared to Apache Solr.
● Elasticsearch uses JSON objects as responses, which makes it possible to invoke
the Elasticsearch server with a large number of different programming languages.
● Elasticsearch supports almost every document type except those that do not support
text rendering.