Throughout the training, key concepts for index configuration, cluster management, and query optimization are covered, enabling efficient and scalable work with large volumes of information. The course places special emphasis on improving performance, search relevance, and system availability.
The training offers a practical vision of how to use Elasticsearch in real scenarios, both for advanced search and for real-time data analysis, helping organizations to make the most of their data within modern architectures.
Advantages of ElasticSearch Pro
Fast and scalable search
Elasticsearch allows advanced searches with very low response times, even on large volumes of data.
Real-time data analysis
Facilitates immediate analysis of information, ideal for use cases such as monitoring, logs, metrics, and events.
High availability and security
Designed for distributed environments, it guarantees fault tolerance, high availability, and integrated security mechanisms.
Scalability and flexibility
The engine easily adapts to the growth of data volume and different architectural needs.
Powerful analysis and visualizations
Integrated with the Elastic ecosystem, it allows the creation of dashboards and visualizations that facilitate data-driven decision making.
Prerequisites
- Basic knowledge of computer science and operating systems.
- Familiarity with databases (relational or non-relational) is essential for a big data developer.
- Basic knowledge of JSON (data format used in Elasticsearch) and its configuration in the ELK stack.
- Previous experience in development environments or system administration may be useful, but is not mandatory.
Training Objectives. What will I learn?
- Understand the Elastic Stack: Become familiar with core components like Elasticsearch, Kibana, Beats, and Logstash.
- Data Indexing: Learn to index documents and manage data within Elasticsearch.
- Analysis and Visualization: Use Kibana to analyze and create dashboards, perform queries, and visualize data.
- Optimization and Scalability: Learn best practices for improving search performance and scaling clusters in the ELK stack.
- Practical Applications: Develop skills to implement real search, analysis, and monitoring solutions.
Who is the ElasticSearch Pro course for?
This proposal is aimed at professionals in areas such as computer engineering, data analysis, systems administration, and software development who seek to strengthen their skills in managing and exploiting large volumes of information.
It is also relevant for technical staff in technology departments, managers of digital transformation projects, and business intelligence specialists who need to integrate Elastic Search into their solutions.
The activities and content are also useful for those who, without prior experience with the tool, wish to get started in the use of advanced search engines and data visualization techniques. The program has been designed to adapt to both those with basic knowledge and those who aspire to delve deeper into the architecture, optimization, and scalability of Elastic Search in enterprise environments.
ElasticSearch Pro Course Content - Syllabus
Below is a linearly organized training program, unit by unit, that guides participants through the essential concepts and practical skills necessary for mastering ElasticSearch in professional environments.
Unit 1: Architecture: nodes, shards, replicas - Index Creation - Index Templates (static/dynamic) - Data Streams - ILM Policies
- Design a log index with 3 shards, 2 replicas, compression, mapping for timestamp, message, level.
- Create an index template for logs-app-YYYY.MM.dd in the context of the ELK stack.
- Define a dynamic template that changes the analyzer based on the log type.
- Configure ILM with rollover, 30 active days, archiving, and deletion after 90 days.
- Create an index template that generates a Data Stream for sensors.
Unit 2: Term, phrase, and multi-field queries - Boolean queries and filters - Asynchronous search - Metric and bucket aggregations - Sub-aggregations - Runtime fields - Cross-cluster search
- Write a query with an exact phrase + a single term using must and must_not.
- Add filters by date range and numerical values (e.g., latency > 200 ms).
- Execute asynchronous search and retrieve results.
- Formulate metric aggregations (avg, sum, min, max) on response times.
- Create buckets by log level ("info", "warn", "error").
- Implement sub-aggregation: % of errors per service within a latency range.
- Use a runtime field to calculate normalized latency and filter on the Elastic Stack platform.
- Configure and execute a multi-cluster search in an Elasticsearch cluster.
Unit 3: Highlighting - Result Sorting - Advanced Pagination - Index Aliases - Search Templates
- Perform a query with highlighting in text fields.
- Sort results by level (error>warn>info) and descending timestamp.
- Implement pagination with from/size and then search_after.
- Create an index alias (logs-current) and move it during rollover without downtime.
- Define a parameterized search template with variables (term, date, level).
Unit 4: Advanced Mappings - Custom Analyzers - Multi-fields - Reindexing and update_by_query - Ingestion pipelines - Painless - Runtime fields
- Define mapping with title, text, tags, date, geo in the Elasticsearch configuration.
- Configure a multi-field in the title (analyzed text + keyword).
- Create an analyzer with stop-words, lowercase, and n-grams.
- Pipeline for raw logs: parse timestamp, extract level, clean fields.
- Painless script to assign numerical severity based on level.
- Use update_by_query to add a "severity" field to old documents.
- Reindex to a new index with updated mapping.
- Define a runtime field that calculates the difference between timestamps or a normalized value.
Unit 5: Diagnosing and repairing shards - Backups and snapshots - Searchable snapshots - Cross-cluster search - Cross-cluster replication - Updating and monitoring in an Elasticsearch cluster.
- Simulate a shard in "red" state and diagnose with _cluster/health and _cat/shards.
- Recover a damaged shard.
- Create a snapshot of critical indices and partially restore one using the Elasticsearch API.
- Searchable configurator snapshot.
- Configure cross-cluster search between 2 clusters and execute a remote query.
- Implement replication between clusters.
- Plan a rolling version upgrade.
- Configure cluster monitoring: JVM metrics, disk, latency, Kibana dashboards.
Final Project: Implement a complete log system with ingestion, ILM, advanced queries, aliases, backups, and monitoring.
Do you want to take this course? Request information now
If you want to take this course virtually, you can purchase it at the top of the product page. If you have any questions, please contact us.
If you want to take this course in in-person or telepresence mode, please contact us:
Frequently Asked Questions – Elasticsearch Pro Course
What is Elasticsearch and what is it used for?
Elasticsearch is a distributed search and analysis engine that allows you to index, query, and analyze large volumes of data in real time. It is widely used in advanced search, analytics, logging, monitoring, and observability projects.
Does the course include real-world practice with Elasticsearch?
Yes, the course includes the use of Kibana for data analysis. The ElasticSearch Pro course includes practical exercises to learn Elasticsearch using Kibana and Logstash. for:
- Data indexing
- Complex queries and searches
- Aggregations and analysis
- Real-world use cases of Elasticsearch in professional environments implementing the ELK stack.
Is data visualization learned with Elasticsearch?
Yes. The course covers Elasticsearch configuration and the use of Kibana and Logstash. Data visualization using ecosystem tools, such as Kibana and Logstash, for efficient and agile management of large volumes of data. Elastic Stack, allowing information and results to be analyzed clearly and structured.
Is Elasticsearch useful for big data and observability projects?
Yes. Elasticsearch is widely used in:
- Big Data
- Log management
- Observability
-
Real-time analysis The course shows how to apply Elasticsearch in these common business and technology scenarios.
Is this course suitable for enterprise use?
Yes. ElasticSearch Pro is geared towards enterprise environments, helping to implement scalable search, analysis, and monitoring solutions in organizations.
