What you'll get
- 16+ Hours
- 5 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Elasticsearch Fundamentals: Build a strong foundation in Elasticsearch by learning its essential principles, such as structuring data, executing searches, expanding storage through indexing, and scaling systems. This knowledge supports faster processing, better organization, and precise data access.
- Logstash Integration: Uncover Logstash's role in consolidating information by collecting, refining, modifying, and enhancing incoming data. Learn how it connects smoothly with Elasticsearch to support unified log storage, improved processing, and deeper system-level evaluation.
- Kibana Visualization: Develop proficiency in Kibana's interface to transform complex data sets into meaningful visual outputs. Create interactive panels, analytical charts, performance trackers, and custom dashboards that deliver clear observations and support real-time system oversight.
- AWS Elastic Beanstalk Deployment: Discover the process of launching and overseeing applications using AWS Elastic Beanstalk. Understand how automated capacity adjustment, balanced traffic distribution, and integrated AWS infrastructure help maintain resilient, scalable, and dependable application environments.
- Real-world Projects and Case Studies: Put theory into practice by working on industry-relevant scenarios, such as aviation-based tracking systems and end-to-end application rollout. These practical exercises help strengthen execution skills and enable you to apply Elastic Stack tools to meet diverse business and technical needs.
- Set up and optimize Elasticsearch within a distributed group of interconnected servers.
- Define index layouts, assign fields, and configure data blueprints for structured storage.
- Retrieve detailed text-based and categorized data using varied search approaches.
- Transfer and load data into Elasticsearch through multiple import pathways
- Link Elasticsearch with external platforms, including real-time processors (Spark), event streamers (Kafka), cloud storage (S3, S3 buckets), relational systems, and more
- Summarize and compute organized information using segmentation containers and statistical measures
- Stream log records into Elasticsearch by configuring Logstash inside the ELK framework
- Capture high-velocity, real-time data feeds at scale using Filebeat and Elastic ecosystem tools
- Interpret and present Elasticsearch outputs visually with Kibana's dashboard and reporting modules
- Oversee daily functionality, stability, and improvements on active Elasticsearch systems
- Operate and support business-critical Elasticsearch distributed environments effectively
- By the end of this course, you will be fully prepared to operate Elasticsearch, Logstash, and Kibana while deploying applications using AWS Elastic Beanstalk. You will gain the ability to organize large-scale data systems, extract valuable patterns, interpret findings, and deploy applications securely across both private server setups and cloud-based infrastructures with confidence.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Elasticsearch Tutorials Module #1 - Queries | 10h 42m | ✔ | View Curriculum |
| Elasticsearch Tutorials Module #2 - Elastic Relations | 3h 38m | ✔ | View Curriculum |
| Project on Elasticsearch: Flight Monitoring During COVID-19 Pandemic | 37m | ✔ | View Curriculum |
| Project on AWS Elastic Beanstalk: Speeding Up The Application Deployment Process | 1h 47m | ✔ | View Curriculum |
| Project on Elastic Beanstalk: Application Creation and Launching | 55m | ✔ | View Curriculum |
Description
This end-to-end training program is built to equip participants with the core capabilities and expert-level insight needed to confidently work with the Elastic Stack, covering Elasticsearch, Logstash, Kibana, and AWS Elastic Beanstalk. The curriculum balances conceptual learning with guided lab work and industry-based use cases, helping learners understand how these high-performance platforms are applied in multiple environments, from large-scale log processing to visual analytics and cloud-hosted application deployment. The course opens with a detailed walkthrough of Elasticsearch, where learners study how data is organized, stored, indexed, and retrieved. Participants also learn key techniques to enhance cluster efficiency and apply proven practices for designing data relationships and running optimized searches, ensuring they are well-prepared for the modules that follow.
Elasticsearch Elastic Relations: This module introduces learners to Elasticsearch from the ground up, covering its primary components and operational abilities. Participants explore how data is structured within documents, how indices function, and the role of search-driven architecture in large distributed systems. The focus extends to designing interconnected data models and retrieving related information efficiently from within documents. Learners also study performance enhancement strategies and recommended development standards. Interactive assignments and scenario-based examples help reinforce practical understanding, enabling participants to apply their skills in real business situations.
Project on Elasticsearch – Flight Monitoring: In this application-focused training unit, participants build real-time Elasticsearch clusters optimized to handle live aviation data feeds. Learners work on capturing, indexing, and interpreting continuous streams of flight information while using Elasticsearch search logic to extract key trends and operational signals. They also design responsive monitoring interfaces in Kibana, producing interactive dashboards for live flight tracking. In addition, participants configure query-based triggers to deliver alerts and system updates, giving them first-hand exposure to implementing search-powered monitoring solutions in active environments.
AWS Elastic Beanstalk Case Study – Application Deployment: This unit transitions into AWS Elastic Beanstalk, where participants study its design model, advantages, and automated hosting approach. Using a structured case review, learners walk through deploying a reference application, managing environmental presets, and modifying scaling configurations to support changing workloads. The module further covers evaluating system stability and tracking service-level performance indicators directly from the AWS Console, helping participants gain practical deployment experience and manage cloud applications efficiently.
Project on Elastic Beanstalk – Application Deployment: In this guided lab, learners deploy a personalized application using AWS Elastic Beanstalk, focusing on key deployment requirements, including environment configuration, external database integration, and managing system inputs via environment variables. Participants also practice maintaining multiple app releases, executing phased update cycles, and diagnosing common deployment failures. Hands-on tasks ensure learners gain real experience launching and refining applications within AWS Elastic Beanstalk environments.
Elasticsearch with Logstash and Kibana: The closing module focuses on the Elastic Stack's interoperability, where learners configure Logstash to collect, transform, and stream incoming data into Elasticsearch. Participants build centralized ingestion channels using Logstash pipelines, integrate structured log routing into Elasticsearch, and work extensively with Kibana to interpret and visualize indexed data. Learners also explore sophisticated Elastic Stack add-ons, including security layers, irregularity pattern detection, and machine learning extensions, allowing them to expand their ability to run deeper analytics and dashboard-based insights. Practical assignments and business-oriented examples help finalize a full-stack working knowledge of Elastic Stack tools across different application landscapes.
Each unit in the program is deliberately structured to unite conceptual clarity with guided implementation, helping learners develop a strong command of Elasticsearch, Logstash, Kibana, and AWS Elastic Beanstalk. The inclusion of industry-aligned case reviews and live project experience ensures participants gain meaningful execution exposure, positioning them to apply Elastic Stack solutions with confidence and deliver high-impact outcomes in their roles.
Requirements
- Basic Understanding of Data Management: Having prior experience with how data is stored, accessed, updated, and processed will help you quickly adapt to Elasticsearch indexing and Logstash data workflows.
- Fundamental Knowledge of Cloud Computing: General awareness of cloud fundamentals, including virtual environments, network structure, and data hosting models, will support your ability to understand AWS Elastic Beanstalk deployment concepts more effectively.
- Proficiency in Command Line Interface (CLI): Confidence in working with terminal-based commands, switching between folders, and running system-level instructions will make it easier to execute practical exercises and build hands-on project deliverables.
- Basic Programming Skills: An introductory-level understanding of coding logic, especially in languages like Python or Java, will assist you in working with scripts, making platform customizations, and automating tasks across Elasticsearch, Logstash, and AWS environments.
- Familiarity with Web Technologies: Knowledge of core web communication standards, API interactions, and structured formats like HTTP, REST, and JSON will help you manage search requests in Elasticsearch and deploy web-based services through AWS Elastic Beanstalk.
- Curiosity and Eagerness to Learn: An enthusiasm for discovering modern tools, actively participating in lab exercises, and applying concepts through experimentation will accelerate your ability to work confidently with the Elastic Stack and AWS integrations.
- A Windows, macOS, or Ubuntu computer with at least 20GB of unused storage is required.
- Prior exposure to REST-based web services will be an added advantage
- Basic experience with Linux systems can help during installation and configuration tasks
- Familiarity with JSON-structured data will support smoother interaction with Elastic tools
Target Audience
- Data Engineers and Analysts: Specialists working with high-volume data sets who want to strengthen their ability to store, structure, transform, and present data using Elasticsearch, Logstash, and Kibana for faster analysis and clearer reporting.
- DevOps Engineers: Professionals handling application releases and infrastructure automation who aim to learn cloud deployment using AWS Elastic Beanstalk, including workload distribution, automatic scaling, and environment management.
- System Administrators: IT infrastructure owners looking to unify system logs and operational records through centralized pipelines using Elasticsearch and Logstash, while building monitoring views and log-based insights through Kibana dashboards.
- Software Developers: Programmers seeking to embed advanced search, structured logging, and interactive analytics into software products by integrating Elastic Stack components directly within application ecosystems.
- Cloud Architects: Solution designers focused on building cloud-first systems who want to operate AWS services like Elastic Beanstalk for reliable application hosting, paired with Elastic Stack for system tracking, performance assessment, and data-driven monitoring.
- Students and Aspiring IT Professionals: Career-driven learners preparing for data-centric, DevOps, or cloud engineering roles who want to build practical experience with Elastic Stack tools and AWS-based application deployment models.
- Elastic Beginners & App Monitoring Enthusiasts: Designed for Elastic first-timers and professionals exploring application monitoring. Learn to evaluate system performance, interpret logs, and create live dashboards using Elasticsearch, Logstash, and Kibana for faster, smarter insights.
