Visão Geral

Curso Elastic Logstash Kibana Full Stake ELK. O Elasticsearch é um banco de dados NoSQL baseado no mecanismo de pesquisa Lucene. O Logstash é uma ferramenta de pipeline de log que aceita entradas de várias fontes, executa diferentes transformações e exporta os dados para diferentes destinos. Kibana é uma camada de visualização que funciona sobre o Elasticsearch. Esses três produtos de código aberto diferentes geralmente são usados ​​na análise de logs em ambientes de TI.

A DevOpsSchool oferece o Programa de Treinamento ELK stack exclusivo para vários níveis de profissionais de TI. Temos especialistas/instrutores experientes em pilha ELK para conduzir aulas online e offline para ajudar os candidatos a obter o conjunto de habilidades e sua capacidade que pode ser utilizada por eles.

Objetivo

Após realizar este Curso Elastic Logstash Kibana Full Stake ELK, você será capaz de:

• Começando
  
• Arquitetura do Elasticsearch
• Instalando o Elasticsearch e o Kibana
• Gerenciando Documentos
• Mapeamento
• Análises e analisadores
• Introdução à pesquisa
• Consultas em nível de termo
• Consultas de texto completo
• Adicionando lógica booleana às consultas

Informações Gerais

• Carga horaria 24h
• Se noturno este curso e ministrado de segunda-feira a sexta-feira das 19h às 23h, total de 6 encontros,
• Se aos sábados este curso e ministrado das 09h às 18h, total de 8 encontros,
• In-company

Formato de Entrega:

• 100% on-line ao vivo via Microsoft Teams na presença de um instrutor/consultor

Materiais

Conteúdo Programatico

Getting Started

1. Introduction to this course
2. Introduction to Elasticsearch
3. Overview of the Elastic Stack (ELK+)
4. Elastic Stack

Architecture of Elasticsearch

1. Introduction to this section
2. Nodes & Clusters
3. Nodes & Clusters
4. Indices & Documents
5. A word on types
6. Another word on types
7. Sharding
8. Sharding
9. 4 questions
10. Replication
11. Replication
12. 6 questions
13. Keeping replicas synchronized
14. Searching for data
15. Distributing documents across shards

Installing Elasticsearch & Kibana

1. Running Elasticsearch & Kibana in Elastic Cloud
2. Installing Elasticsearch on Mac/Linux
3. Using the MSI installer on Windows
4. Installing Elasticsearch on Windows
5. Configuring Elasticsearch
6. Installing Kibana on Mac/Linux
7. Installing Kibana on Windows
8. Configuring Kibana
9. Kibana now requires data to be available
10. Introduction to Kibana and dev tools

Managing Documents

1. Creating an index
2. Adding documents
3. Retrieving documents by ID
4. Replacing documents
5. Updating documents
6. Scripted updates
7. Upserts
8. Deleting documents
9. Deleting indices
10. Batch processing
11. Importing test data with cURL
12. Exploring the cluster

Mapping

1. Introduction to mapping
2. Dynamic mapping
3. Meta fields
4. Field data types
5. Adding mappings to existing indices
6. Changing existing mappings
7. Mapping parameters
8. Adding multi-fields mappings
9. Defining custom date formats
10. Picking up new fields without dynamic mapping

Analysis & Analyzers

1. Introduction to the analysis process
2. A closer look at analyzers
3. Using the Analyze API
4. Understanding the inverted index
5. Analyzers
6. Overview of character filters
7. Overview of tokenizers
8. Overview of token filters
9. Overview of built-in analyzers
10. Configuring built-in analyzers and token filters
11. Creating custom analyzers
12. Using analyzers in mappings
13. Adding analyzers to existing indices
14. A word on stop words

Introduction to Searching

1. Search methods
2. Searching with the request URI
3. Introducing the Query DSL
4. Understanding query results
5. Understanding relevance scores
6. Debugging unexpected search results
7. Query contexts
8. Full text queries vs term level queries
9. Basics of searching

Term Level Queries

1. Introduction to term level queries
2. Searching for a term
3. Searching for multiple terms
4. Retrieving documents based on IDs
5. Matching documents with range values
6. Working with relative dates (date math)
7. Matching documents with non-null values
8. Matching based on prefixes
9. Searching with wildcards
10. Searching with regular expressions
11. Term Level Queries

Full Text Queries

1. Introduction to full text queries
2. Flexible matching with the match query
3. Matching phrases
4. Searching multiple fields
5. Full Text Queries

Adding Boolean Logic to Queries

1. Introduction to compound queries
2. Querying with boolean logic
3. Debugging bool queries with named queries
4. How the “match” query works

Joining Queries

1. Introduction to this section
2. Querying nested objects
3. Nested inner hits
4. Mapping document relationships
5. Adding documents
6. Querying by parent ID
7. Querying child documents by parent
8. Querying parent by child documents
9. Multi-level relations
10. Parent/child inner hits
11. Terms lookup mechanism
12. Join limitations
13. Join field performance considerations

Controlling Query Results

1. Specifying the result format
2. Source filtering
3. Specifying the result size
4. Specifying an offset
5. Pagination
6. Sorting results
7. Sorting by multi-value fields
8. Filters

Aggregations

1. Introduction to aggregations
2. Metric aggregations
3. Introduction to bucket aggregations
4. Document counts are approximate
5. Nested aggregations
6. Filtering out documents
7. Defining bucket rules with filters
8. Range aggregations
9. Histograms
10. Global aggregation
11. Missing field values
12. Aggregating nested objects

Improving Search Results

1. Introduction to this section
2. Proximity searches
3. Affecting relevance scoring with proximity
4. Fuzzy match query (handling typos)
5. Fuzzy query
6. Adding synonyms
7. Adding synonyms from file
8. Highlighting matches in fields
9. Stemming

Building a Web Application Search Engine

1. A quick note
2. Introducing Application & Client Libraries
3. Adding a simple query
4. Paginating search results
5. Adding fuzziness
6. Aggregations & Filters
7. Adding product details page