Elk - An introduction

Nov 1, 20181 like516 views

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.

@KeyvanIraji
Keyvan.iraji@gmail.com
@keyvaniraji
@HosseinShemshadi
Hossein.Shemshadi
@gmail.com
@H_Shemshadi

What is our problems?
• Full text search on Persian messages.
3
• BI Dashboard
• ETL
• Search engine
• Log Analyzer
• Monitoring
• Sensor Monitoring
• CQRS – read side

Which companies uses ELK
6
Supporting e-commerce
search for 60+ countries
in 21+languages
Generate Actionable
value from game play
data and server events
Searching Across 800
million listings in sub
seconds
Powering the search for
Interplanetary discovery
Delivering a better help
experience for over a
billion users
Providing search on
azure and powering
social dynamics

Which companies uses ELK
7
Supporting e-commerce
search for 60+ countries
in 21+languages
Unlocking yesterday’s
content for the future of
media search
Reducing system
downtime at the basis of
cisco’s cloud native
Enhancing user
experience by processing
over a billion of events
every day
Aggregating business
metrics to control critical
marketplace behaviors
And many
others…

ElasticSearch vs Relational databases
14
ElasticSearch Relational databases
Index Database
Type Table
Document Row
Field Column
Analyze Index
_id Primary key
Mapping Schema
Shard Physical partition
Route Logical partition
Parent/child, nested Relational
DSL Query SQL

Is it suitable for you?
• Data Structure.
36
• You Need ACIDity?
• You need fast write?
• You are a startup?
• Your problem is small?

( ELK Stack Training - https://www.edureka.co/elk-stack-trai... ) This Edureka tutorial on What Is ELK Stack will help you in understanding the fundamentals of Elasticsearch, Logstash, and Kibana together and help you in building a strong foundation in ELK Stack. Below are the topics covered in this ELK tutorial for beginners: 1. Need for Log Analysis 2. Problems with Log Analysis 3. What is ELK Stack? 4. Features of ELK Stack 5. Companies Using ELK Stack

Introduction to ElasticsearchRuslan Zavacky

Introduction to KibanaVineet .

ELK StackPhuc Nguyen

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.

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.

Elastic stack PresentationAmr Alaa Yassen

The document introduces the ELK stack, which consists of Elasticsearch, Logstash, Kibana, and Beats. Beats ship log and operational data to Elasticsearch. Logstash ingests, transforms, and sends data to Elasticsearch. Elasticsearch stores and indexes the data. Kibana allows users to visualize and interact with data stored in Elasticsearch. The document provides descriptions of each component and their roles. It also includes configuration examples and demonstrates how to access Elasticsearch via REST.

Elk Caleb Wang

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

Customer Intelligence: Using the ELK Stack to Analyze ForgeRock OpenAM Audit ...ForgeRock

ELK StackEberhard Wolff

ElasticsearchDivij Sehgal

ELK introductionWaldemar Neto

This document discusses the ELK stack, which consists of Elasticsearch, Logstash, and Kibana. It describes each component and how they work together to parse, index, and visualize log data. Logstash is used to parse logs from various sources and apply filters before indexing the data into Elasticsearch. Kibana then allows users to visualize the indexed data through interactive dashboards and charts. The document also covers production deployments, monitoring, and security options for the ELK stack.

ElasticSearch Basic IntroductionMayur Rathod

Elastic search overviewABC Talks

ELK in Security Analytics nullowaspmumbai

ELK is a log analysis stack that consists of Elasticsearch for storage, Logstash for transport and processing, and Kibana for visualization. Beats are lightweight shippers that move logs to Logstash or Elasticsearch. The document discusses using ELK for security analytics by ingesting large volumes of logs from various sources for threat hunting, user behavior analytics, and forensic analysis to address limitations of SIEM tools.

Centralized Logging System Using ELK StackRohit Sharma

Centralized Logging System using ELK Stack The document discusses setting up a centralized logging system (CLS) using the ELK stack. The ELK stack consists of Logstash to capture and filter logs, Elasticsearch to index and store logs, and Kibana to visualize logs. Logstash agents on each server ship logs to Logstash, which filters and sends logs to Elasticsearch for indexing. Kibana queries Elasticsearch and presents logs through interactive dashboards. A CLS provides benefits like log analysis, auditing, compliance, and a single point of control. The ELK stack is an open-source solution that is scalable, customizable, and integrates with other tools.

Deep Dive Into ElasticsearchKnoldus Inc.

ElasticsearchHermeto Romano

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.

An Intro to Elasticsearch and KibanaObjectRocket

Introduction to elasticsearchpmanvi

Log management with ELKGeert Pante

ELK (Elasticsearch, Logstash, Kibana) is an open source toolset for centralized logging, where Logstash collects, parses, and filters logs, Elasticsearch stores and indexes logs for search, and Kibana visualizes logs. Logstash processes logs through an input, filter, output pipeline using plugins. It can interpret various log formats and event types. Elasticsearch allows real-time search and scaling through replication/sharding. Kibana provides browser-based dashboards and visualization of Elasticsearch query results.

The Elastic Stack as a SIEMJohn Hubbard

Video: https://www.youtube.com/watch?v=v69kyU5XMFI A talk I gave at the Philly Security Shell meetup 2019-02-21 on how the Elastic Stack works and how you can use it for indexing and searching security logs. Tools I mentioned: Github repo with script and demo data - https://github.com/SecHubb/SecShell_Demo Cerebro - https://github.com/lmenezes/cerebro Elastalert - https://github.com/Yelp/elastalert For info on my SANS teaching schedule visit: https://www.sans.org/instructors/john... Twitter: https://twitter.com/SecHubb

Introduction to elasticsearchhypto

Introducing ELKAllBits BVBA (freelancer)

This document introduces the (B)ELK stack, which consists of Beats, Elasticsearch, Logstash, and Kibana. It describes each component and how they work together. Beats are lightweight data shippers that collect data from logs and systems. Logstash processes and transforms data from inputs like Beats. Elasticsearch stores and indexes the data. Kibana provides visualization and analytics capabilities. The document provides examples of using each tool and tips for working with the ELK stack.

Big Data LDN 2017: Delivering Instant Experience with Redid EnterpriseMatt Stubbs

Elastic v5.0.0 Update uptoalpha3 v0.2 - 김종민NAVER D2

The document summarizes the new features and improvements in Elastic Stack v5.0.0, including updates to Kibana, Elasticsearch, Logstash, and Beats. Key highlights include a redesigned Kibana interface, improved indexing performance in Elasticsearch, easier plugin development in Logstash, new data shippers and filtering capabilities in Beats, and expanded subscription support offerings. The Elastic Stack aims to help users build distributed applications and solve real problems through its integrated search, analytics, and data pipeline capabilities.

More Related Content

What's hot (20)

Centralized log-management-with-elastic-stackRich Lee

Elastic stack PresentationAmr Alaa Yassen

Elk Caleb Wang

Elasticsearch Tutorial | Getting Started with Elasticsearch | ELK Stack Train...Edureka!

Customer Intelligence: Using the ELK Stack to Analyze ForgeRock OpenAM Audit ...ForgeRock

ELK StackEberhard Wolff

ElasticsearchDivij Sehgal

ELK introductionWaldemar Neto

ElasticSearch Basic IntroductionMayur Rathod

Elastic search overviewABC Talks

ELK in Security Analytics nullowaspmumbai

Centralized Logging System Using ELK StackRohit Sharma

Deep Dive Into ElasticsearchKnoldus Inc.

ElasticsearchHermeto Romano

An Intro to Elasticsearch and KibanaObjectRocket

Introduction to elasticsearchpmanvi

Log management with ELKGeert Pante

The Elastic Stack as a SIEMJohn Hubbard

Introduction to elasticsearchhypto

Introducing ELKAllBits BVBA (freelancer)

Centralized log-management-with-elastic-stackRich Lee

Elastic stack PresentationAmr Alaa Yassen

Elk Caleb Wang

Elasticsearch Tutorial | Getting Started with Elasticsearch | ELK Stack Train...Edureka!

Customer Intelligence: Using the ELK Stack to Analyze ForgeRock OpenAM Audit ...ForgeRock

ELK StackEberhard Wolff

ElasticsearchDivij Sehgal

ELK introductionWaldemar Neto

ElasticSearch Basic IntroductionMayur Rathod

Elastic search overviewABC Talks

ELK in Security Analytics nullowaspmumbai

Centralized Logging System Using ELK StackRohit Sharma

Deep Dive Into ElasticsearchKnoldus Inc.

ElasticsearchHermeto Romano

An Intro to Elasticsearch and KibanaObjectRocket

Introduction to elasticsearchpmanvi

Log management with ELKGeert Pante

The Elastic Stack as a SIEMJohn Hubbard

Introduction to elasticsearchhypto

Introducing ELKAllBits BVBA (freelancer)

Similar to Elk - An introduction (20)

Big Data LDN 2017: Delivering Instant Experience with Redid EnterpriseMatt Stubbs

Elastic v5.0.0 Update uptoalpha3 v0.2 - 김종민NAVER D2

Les objets connectés : de nombreux cas d'usage Jedha Bootcamp

Aujourd'hui, les objets connectés sont partout et nous entourent sans même s'en apercevoir : téléphones, transports, musique, montres, "The Internet of Things" (IoT) a pris une part importante dans notre vie. En nous montrant des cas d'usages des entreprises telles que la NASA, Airbus, Red bull et d'autres, Sean nous expliquera comment ils fonctionnent et comment sont gérées toutes ces données récoltées.

In-Memory Computing Webcast. Market Predictions 2017SingleStore

1) In-memory computing is growing rapidly, with the total data market expected to grow from $69 billion in 2015 to $132 billion in 2020. 2) In-memory databases are gaining popularity for applications that require fast response times, like telecommunications and mobile advertising, as memory access is faster than disk access. 3) Modern applications are driving adoption of in-memory solutions as they generate more data from more users and transactions and require faster performance to handle growing traffic. 4) Two examples presented were DellEMC using MemSQL for a real-time customer 360 application and an IoT logistics application called MemEx that processes sensor data from warehouses for predictive analytics.

Séminaire Big Data Alter Way - Elasticsearch - octobre 2014ALTER WAY

This document discusses Elasticsearch and how it can be used to search, analyze, and make sense of large amounts of data. It provides examples of how Elasticsearch is being used by large companies to handle petabytes of data and gain insights. Implementations in France are highlighted. The document concludes by demonstrating how easily Elasticsearch can be deployed and used to ingest and search sample data.

What's New in 6.3 + Data On-BoardingSplunk

The document summarizes Splunk Enterprise 6.3, highlighting key new features and capabilities. It discusses breakthrough performance and scale improvements including doubled search and indexing speed and 20-50% increased capacity. It also covers advanced analysis and visualization features like anomaly detection, geospatial mapping, and single-value display. New capabilities for high-volume event collection and an enterprise-scale platform with expanded management, custom alert actions, and data integrity control are also summarized.

ParStream - Big Data for Business UsersParStream Inc.

Top 17 web scraping tools for data extraction in 2022Aparna Sharma

Big Datayasinzamani

This document discusses big data and provides examples of how big data is generated and used. It notes that big data comes from both structured and unstructured sources, including social media posts, website activity, sensor data from IoT devices, and more. It also discusses how big data is stored, analyzed using techniques like machine learning, and visualized to help humans identify patterns and insights.

Create Your Future with z Systems CloudCA Technologies

The document discusses how the mainframe remains relevant despite predictions that it would become obsolete. It provides examples of how the mainframe continues to process a large volume of transactions and data efficiently. It then summarizes IBM's DevOps solutions for modernizing mainframe development and discusses how technologies like blockchain, analytics and APIs can help expose mainframe capabilities and drive innovation.

High-performance database technology for rock-solid IoT solutionsClusterpoint

Clusterpoint is a privately held database software company founded in 2006 with 32 employees. Their product is a hybrid operational database, analytics, and search platform that provides secure, high-performance distributed data management at scale. It reduces total cost of ownership by 80% over traditional relational databases by providing blazing fast performance, unlimited scalability, and bulletproof transactions with instant text search and security. Clusterpoint also offers their database software as a cloud database as a service to instantly scale databases on demand.

HUG Ireland Event - HPCC Presentation SlidesJohn Mulhall

This document provides a summary of HPCC Systems, including: 1. A brief history and overview of the architecture with a use case example of calculating insurance policy data within a specified radius. 2. Descriptions of the main components of HPCC Systems - Thor for batch processing, Roxie for real-time queries, and ECL as the data-oriented programming language. 3. Information on how HPCC Systems can be integrated with other systems and technologies through connectors, drivers, and the ability to embed other languages.

Big Data LDN 2018: DATABASE FOR THE INSTANT EXPERIENCEMatt Stubbs

Date: 14th November 2018 Location: Customer Experience Theatre Time: 12:30 - 13:00 Speaker: David Maitland Organisation: Redis Labs About: This session will cover the technology underpinning at the software infrastructure level required to deliver the instant experience to the end user and enterprises alike. Use cases and value derived by major brands will be shared in this insightful session based the world's most loved database REDIS.

ELK Solutions Enablement Session - 17th March'2020Ashnikbiz

Big Data Processing Beyond MapReduce by Dr. Flavio VillanustreHPCC Systems

Real-time big data analytics based on product recommendations case studydeep.bi

We started as an ad network. The challenge was to recommend the best product (out of millions) to the right person in a given moment (thousands of users within a second). We have delivered 5 billion ad views since 24 months. To put it in the scale context: If we would serve 1 ad per second it will take 160 years to serve 5 billion ads. So we needed a solution. SQL databases did not work. Popular NoSQL databases did not work. Standard data warehouse approaches (pre-aggregations, creating schemas) - did not work too. Re-thinking all the problems with huge data streams flowing to us every second we have built a complete solution based on open-source technologies and fresh, smart ideas from our engineering team. It is called deep.bi and now we make it available to other companies. deep.bi lets high-growth companies solve fast data problems by providing scalable, flexible and real-time data collection, enrichment and analytics. It was built using: - Node.js - API - Kafka - collecting and distributing data - Spark Streaming - ETL, data enrichments - Druid - real-time analytics - Cassandra - user events store - Hadoop + Parquet + Spark - raw data store + ad-hoc queries

Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...Maya Lumbroso

Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can Speed up the World" Bio: Ronan Corkery is a kdb+ engineer who has been working with Kx and First Derivatives for the past 4 years. Currently based in Total Gas and Power he spent his first 2 year working with Morgan Stanley. Abstract: Ronan's presentation will focus on the vertical industries the formally only finance based technologies Kx offers has been moving into. He will present proven solutions as well as introducing the overall architecture that Kx uses as well as laying out potential opportunities to work with Kx.

Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...Dataconomy Media

This document discusses how Wall Street technology from Kx Systems can speed up data processing for various industries. Kx's time-series database, kdb+, can process and analyze large volumes of real-time and historical data extremely fast with low latency. It is scalable, integrates with other technologies, and provides powerful tools and dashboards. Kx is currently used widely in financial services and is now being adopted in industries like manufacturing, utilities, healthcare, and more.

MongoDB .local Houston 2019: Building an IoT Streaming Analytics Platform to ...MongoDB

Kafka Summit SF 2017 - Providing Reliability Guarantees in Kafka at One Trill...confluent

In this presentation, I will talk about my firsthand experience dealing with the unique challenges of running Kafka at a massive scale. If you ever thought that running Kafka is difficult, this talk may change your mind and provide you with valuable insights into how to configure a Kafka cluster efficiently, how to manage Kafka for enterprise customers and how to measure, monitor and maintain the Quality of Kafka Service. Our production Kafka cluster runs over 1500+ VMs, and serves over 10 GBPS data spread across hundreds of topics for multiple teams across Microsoft. We built a self-serve Kafka management service to make the process manageable and scalable across many teams. In this talk, I will also share insights about running Kafka in Private vs multi-tenant mode, supporting failover and disaster recovery requirements, and how to make Kafka Compliant with regulatory certifications such as ISO, SOC, FEDRAMP, etc. Presented by Nitin Kumar, Microsoft Systems Track

Big Data LDN 2017: Delivering Instant Experience with Redid EnterpriseMatt Stubbs

Elastic v5.0.0 Update uptoalpha3 v0.2 - 김종민NAVER D2

Les objets connectés : de nombreux cas d'usage Jedha Bootcamp

In-Memory Computing Webcast. Market Predictions 2017SingleStore

Séminaire Big Data Alter Way - Elasticsearch - octobre 2014ALTER WAY

What's New in 6.3 + Data On-BoardingSplunk

ParStream - Big Data for Business UsersParStream Inc.

Top 17 web scraping tools for data extraction in 2022Aparna Sharma

Big Datayasinzamani

Create Your Future with z Systems CloudCA Technologies

High-performance database technology for rock-solid IoT solutionsClusterpoint

HUG Ireland Event - HPCC Presentation SlidesJohn Mulhall

Big Data LDN 2018: DATABASE FOR THE INSTANT EXPERIENCEMatt Stubbs

ELK Solutions Enablement Session - 17th March'2020Ashnikbiz

Big Data Processing Beyond MapReduce by Dr. Flavio VillanustreHPCC Systems

Real-time big data analytics based on product recommendations case studydeep.bi

Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...Maya Lumbroso

Ronan Corkery, kdb+ developer at Kx Systems: “Kdb+: How Wall Street Tech can ...Dataconomy Media

MongoDB .local Houston 2019: Building an IoT Streaming Analytics Platform to ...MongoDB

Kafka Summit SF 2017 - Providing Reliability Guarantees in Kafka at One Trill...confluent

Recently uploaded (20)

Fort night presentation new0903 pdf.pdf.anuragmk56

Mathematical foundation machine learning.pdfTalhaShahid49

some basics electrical and electronics knowledgenguyentrungdo88

QA/QC Manager (Quality management Expert)rccbatchplant

ELectronics Boards & Product Testing_Shiju.pdfShiju Jacob

Oil-gas_Unconventional oil and gass_reseviours.pdfM7md3li2

Data Structures_Introduction to algorithms.pptxRushaliDeshmukh2

Concept of Problem Solving, Introduction to Algorithms, Characteristics of Algorithms, Introduction to Data Structure, Data Structure Classification (Linear and Non-linear, Static and Dynamic, Persistent and Ephemeral data structures), Time complexity and Space complexity, Asymptotic Notation - The Big-O, Omega and Theta notation, Algorithmic upper bounds, lower bounds, Best, Worst and Average case analysis of an Algorithm, Abstract Data Types (ADT)

introduction to machine learining for beginersJoydebSheet

Value Stream Mapping Worskshops for Intelligent Continuous SecurityMarc Hornbeek

new ppt artificial intelligence historyyyPianoPianist

Explainable-Artificial-Intelligence-XAI-A-Deep-Dive (1).pptxMahaveerVPandit

"Feed Water Heaters in Thermal Power Plants: Types, Working, and Efficiency G...Infopitaara

A feed water heater is a device used in power plants to preheat water before it enters the boiler. It plays a critical role in improving the overall efficiency of the power generation process, especially in thermal power plants. 🔧 Function of a Feed Water Heater: It uses steam extracted from the turbine to preheat the feed water. This reduces the fuel required to convert water into steam in the boiler. It supports Regenerative Rankine Cycle, increasing plant efficiency. 🔍 Types of Feed Water Heaters: Open Feed Water Heater (Direct Contact) Steam and water come into direct contact. Mixing occurs, and heat is transferred directly. Common in low-pressure stages. Closed Feed Water Heater (Surface Type) Steam and water are separated by tubes. Heat is transferred through tube walls. Common in high-pressure systems. ⚙️ Advantages: Improves thermal efficiency. Reduces fuel consumption. Lowers thermal stress on boiler components. Minimizes corrosion by removing dissolved gases.

theory-slides-for react for beginners.pptxsanchezvanessa7896

AI-assisted Software Testing (3-hours tutorial)Vəhid Gəruslu

Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different ...Journal of Soft Computing in Civil Engineering

Passenger car unit (PCU) of a vehicle type depends on vehicular characteristics, stream characteristics, roadway characteristics, environmental factors, climate conditions and control conditions. Keeping in view various factors affecting PCU, a model was developed taking a volume to capacity ratio and percentage share of particular vehicle type as independent parameters. A microscopic traffic simulation model VISSIM has been used in present study for generating traffic flow data which some time very difficult to obtain from field survey. A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for prediction of PCUs of different vehicle types. From the results observed that ANFIS model estimates were closer to the corresponding simulated PCU values compared to MLR and ANN models. It is concluded that the ANFIS model showed greater potential in predicting PCUs from v/c ratio and proportional share for all type of vehicles whereas MLR and ANN models did not perform well.

The Gaussian Process Modeling Module in UQLabJournal of Soft Computing in Civil Engineering

We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a standalone surrogate modeling tool. We first briefly present the key mathematical tools on the basis of GP modeling (a.k.a. Kriging), as well as the associated theoretical and computational framework. We then provide an extensive overview of the available features of the software and demonstrate its flexibility and user-friendliness. Finally, we showcase the usage and the performance of the software on several applications borrowed from different fields of engineering. These include a basic surrogate of a well-known analytical benchmark function; a hierarchical Kriging example applied to wind turbine aero-servo-elastic simulations and a more complex geotechnical example that requires a non-stationary, user-defined correlation function. The GP-module, like the rest of the scientific code that is shipped with UQLab, is open source (BSD license).

Lidar for Autonomous Driving, LiDAR Mapping for Driverless Cars.pptxRishavKumar530754

π0.5: a Vision-Language-Action Model with Open-World GeneralizationNABLAS株式会社

今回の資料「Transfusion / π0 / π0.5」は、画像・言語・アクションを統合するロボット基盤モデルについて紹介しています。拡散×自己回帰を融合したTransformerをベースに、π0.5ではオープンワールドでの推論・計画も可能に。 This presentation introduces robot foundation models that integrate vision, language, and action. Built on a Transformer combining diffusion and autoregression, π0.5 enables reasoning and planning in open-world settings.

Artificial Intelligence (AI) basics.pptxaditichinar

International Journal of Distributed and Parallel systems (IJDPS)samueljackson3773

The growth of Internet and other web technologies requires the development of new algorithms and architectures for parallel and distributed computing. International journal of Distributed and parallel systems is a bimonthly open access peer-reviewed journal aims to publish high quality scientific papers arising from original research and development from the international community in the areas of parallel and distributed systems. IJDPS serves as a platform for engineers and researchers to present new ideas and system technology, with an interactive and friendly, but strongly professional atmosphere.

Fort night presentation new0903 pdf.pdf.anuragmk56

Mathematical foundation machine learning.pdfTalhaShahid49

some basics electrical and electronics knowledgenguyentrungdo88

QA/QC Manager (Quality management Expert)rccbatchplant

ELectronics Boards & Product Testing_Shiju.pdfShiju Jacob

Oil-gas_Unconventional oil and gass_reseviours.pdfM7md3li2

Data Structures_Introduction to algorithms.pptxRushaliDeshmukh2

introduction to machine learining for beginersJoydebSheet

Value Stream Mapping Worskshops for Intelligent Continuous SecurityMarc Hornbeek

new ppt artificial intelligence historyyyPianoPianist

Explainable-Artificial-Intelligence-XAI-A-Deep-Dive (1).pptxMahaveerVPandit

"Feed Water Heaters in Thermal Power Plants: Types, Working, and Efficiency G...Infopitaara

theory-slides-for react for beginners.pptxsanchezvanessa7896

AI-assisted Software Testing (3-hours tutorial)Vəhid Gəruslu

Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different ...Journal of Soft Computing in Civil Engineering

The Gaussian Process Modeling Module in UQLabJournal of Soft Computing in Civil Engineering

Lidar for Autonomous Driving, LiDAR Mapping for Driverless Cars.pptxRishavKumar530754

π0.5: a Vision-Language-Action Model with Open-World GeneralizationNABLAS株式会社

Artificial Intelligence (AI) basics.pptxaditichinar

International Journal of Distributed and Parallel systems (IJDPS)samueljackson3773

Elk - An introduction

1. Elastic Stack, When and How? 1

2. @KeyvanIraji [email protected] @keyvaniraji @HosseinShemshadi Hossein.Shemshadi @gmail.com @H_Shemshadi

3. What is our problems? • Full text search on Persian messages. 3 • BI Dashboard • ETL • Search engine • Log Analyzer • Monitoring • Sensor Monitoring • CQRS – read side

4. 4

5. 5

6. Which companies uses ELK 6 Supporting e-commerce search for 60+ countries in 21+languages Generate Actionable value from game play data and server events Searching Across 800 million listings in sub seconds Powering the search for Interplanetary discovery Delivering a better help experience for over a billion users Providing search on azure and powering social dynamics

7. Which companies uses ELK 7 Supporting e-commerce search for 60+ countries in 21+languages Unlocking yesterday’s content for the future of media search Reducing system downtime at the basis of cisco’s cloud native Enhancing user experience by processing over a billion of events every day Aggregating business metrics to control critical marketplace behaviors And many others…

8. Specifications 8

9. Specifications 9

10. Specifications 10

11. Specifications 11

12. Specifications 12

13. Specifications 13

14. ElasticSearch vs Relational databases 14 ElasticSearch Relational databases Index Database Type Table Document Row Field Column Analyze Index _id Primary key Mapping Schema Shard Physical partition Route Logical partition Parent/child, nested Relational DSL Query SQL

15. How to use 15

16. Logstash 16

17. Logstash 17

18. Logstash 18

26. Search 26