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Vladyslav K, Senior AWS DevOps Engineer

Vinnitsa, Ukraine
Last Updated: 4 Jul 2023

- 4 years in IT field as a DevOps Engineer. Strong scripting skills (Python, Bash, Groovy). - Good understanding of Software Development processes. Practical experience and professional competence in CI/CD (Jenkins, Azure DevOps), infrastructure as code (Terraform, CloudFormation), monitoring (ELK, Zabbix, Grafana), cluster management (Kubernetes, Kubespray, Helm), configuration management (Ansible) and computer network. Proficiency in Amazon Web Services. Extensive background in UNIX-like Operating Systems administration and maintenance. - Upper-Intermediate English

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Amazon Web Services (AWS)

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Mugzar A., .Net Developer

Last Updated: 4 Jul 2023

- 10+ years of experience in IT - Upper intermediate English - Available ASAP

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Bohdan R., DevOps Engineer

Last Updated: 4 Jul 2023

- Have good hands-on experience in implementing core DevOps concepts such as virtualization, version control, cloud computing, load balancing, etc. by using a wide variety of technologies. - I am a team player, communicable, hardworking, and open-minded person. I like getting new skills and knowledge. - Upper-Intermediate English. - Available ASAP.

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Amazon Web Services (AWS)

Amazon Web Services (AWS)

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Serhii B, Cloud Solutions Architect/DevOps

Last Updated: 16 Nov 2023

- Highly skilled and experienced Software Engineer with a strong background in computer science and automated control systems. - Proficient in a wide range of programming languages and technologies, including Java, Python, Ruby, and Node JS. - Extensive experience in designing and implementing software architectures, with a focus on cloud solutions and DevOps practices. - Strong knowledge of methodologies and frameworks such as TOGAF, Agile, and SCRUM. - Experienced in working with cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Compute Platform. - Skilled in data analysis, data modeling, and data integration, with expertise in Big Data and IoT solutions. - Demonstrated ability to lead and manage teams, conduct architecture assessments, and provide improvement recommendations.

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Logstash   3 yr.

Amazon Web Services (AWS)

Amazon Web Services (AWS)   8 yr.

Azure DevOps

Azure DevOps   8 yr.

Google Compute Engine (GCE)

Google Compute Engine (GCE)   3 yr.

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Antony S., DevOps/SecOps/Cloud Security Engineer

Sivakasi, India
Last Updated: 25 Jan 2024

- A technocrat with over 22+ years of experience in IT Infrastructure Architect, Design, Develop, Implement, Document.- 18 years+ working with US timezone and European timezone teams. - Expertise in Infrastructure as Code (IaC) by automating with Terraform and administrative tasks with Puppet, Ansible configuration management including deployment of emergency security vulnerability fixes.- Working on high-load platforms and corporate customers (e.g IBM, ATT, CenturyLink etc). - CIS Benchmarks, Global Security Compliance- Providing mechanisms for deployment, maintenance, and application scaling within OpenShift Container Platform, Kubernetes Cluster, and Docker Containers Technologies.- Upper-intermediate English- Has experience and is ready for a full-time workload in the European timezone (e.g +2 GMT)- Available ASAP

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Amazon Web Services (AWS)

Amazon Web Services (AWS)

CIS Benchmarks

CIS Benchmarks

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AI tools and expert human reviewers in the vetting process are combined with a track record and historically collected feedback from clients and teammates. On average, we save over 50 hours for client teams in interviewing Logstash candidates for each job position. We are fueled by a passion for technical expertise, drawn from our deep understanding of the industry.

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Our journey starts with a 30-minute discovery call to explore your project challenges, technical needs, and team diversity. Meet Carefully Matched Logstash Talents. Within 1-3 days, we’ll share profiles and connect you with the right talents for your project. Schedule a call to meet engineers in person. Validate Your Choice. Bring a new Logstash developer on board with a trial period to confirm that you’ve hired the right one. There are no termination fees or hidden costs.

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Upstaff Managers conduct an introductory round with potential candidates to assess their soft skills. Additionally, the talent’s hard skills are evaluated through testing or verification by a qualified developer during a technical interview. The Upstaff Staffing Platform stores data on past and present Logstash candidates. Upstaff managers also assess talent and facilitate rapid work and scalability, offering clients valuable insights into their talent pipeline. Additionally, we have a matching system within the platform that operates in real-time, facilitating efficient pairing of candidates with suitable positions.

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Table of Contents

TOP 14 Facts about Logstash

  • Logstash is an open-source data processing tool that allows for the collection, parsing, and enrichment of data from various sources.
  • It is part of the Elastic Stack, which also includes Elasticsearch, Kibana, and Beats.
  • Logstash is written in Ruby and offers a wide range of plugins to support different data sources and transformations.
  • One of Logstash’s key features is its ability to ingest data from a variety of sources, including logs, databases, message queues, and more.
  • It supports a wide range of input plugins, such as file, syslog, TCP, UDP, and HTTP, making it flexible for different data collection needs.
  • Logstash also provides a set of filters that allow users to parse, transform, and enhance the data before sending it to the output.
  • With its powerful filter plugins, Logstash can handle complex data parsing tasks, including pattern matching, data enrichment, and conditional processing.
  • Logstash supports multiple output plugins, allowing users to send processed data to various destinations, such as Elasticsearch, databases, message queues, and external systems.
  • It has built-in support for Elasticsearch, making it seamless to ingest data into Elasticsearch for further analysis and visualization using Kibana.
  • Logstash provides robust error handling and retry mechanisms, ensuring data reliability and fault tolerance.
  • It offers a web-based management interface called the Logstash Monitoring API, which provides insights into the health and performance of Logstash instances.
  • Logstash can scale horizontally by setting up multiple instances in a cluster, distributing the data processing workload across the nodes.
  • It is highly extensible, allowing users to develop custom plugins to integrate with their specific data sources or processing requirements.
  • Logstash has a vibrant community and active development, with regular updates and new features being added to enhance its functionality.

What are top Logstash instruments and tools?

  • Logstash: Logstash is an open-source data processing pipeline that ingests, transforms, and loads data from various sources into a centralized repository. It was created by Elasticsearch in 2010 and has since become an integral part of the Elastic Stack. Logstash offers a wide range of input, filter, and output plugins, making it a versatile tool for log parsing, data enrichment, and data transformation.
  • Elasticsearch: Although Elasticsearch is primarily a search and analytics engine, it works seamlessly with Logstash to store and index the processed data. It provides powerful search capabilities and real-time data analysis, making it an ideal choice for applications requiring fast and efficient data retrieval.
  • Kibana: Kibana is a data visualization and exploration tool that complements Logstash and Elasticsearch. It allows users to create interactive dashboards and visualizations to gain insights from the ingested data. Kibana’s intuitive interface and extensive set of visualization options make it a popular choice for monitoring and analyzing log data.
  • Beats: Beats are lightweight data shippers that serve as agents for collecting and sending data to Logstash or Elasticsearch directly. They come in different flavors, such as Filebeat for log files, Metricbeat for system metrics, and Packetbeat for network data. Beats simplify the process of data collection and can be easily deployed on various platforms.
  • Grok: Grok is a powerful pattern-matching engine used in Logstash to parse unstructured log data into structured fields. It enables Logstash to extract meaningful information from log lines by matching them against predefined patterns. Grok patterns are highly customizable and can be extended to handle different log formats, making it an essential tool for log parsing and analysis.
  • Logstash Forwarder (now deprecated): Logstash Forwarder, also known as Lumberjack, was a lightweight tool used to ship log files securely to a Logstash server. However, it has been deprecated in favor of Beats, which offer more features and better performance.
  • X-Pack: X-Pack is an optional commercial extension for the Elastic Stack that provides additional security, monitoring, alerting, and machine learning capabilities. It can be integrated with Logstash to enhance data security and enable advanced monitoring and alerting functionalities.

Soft skills of a Logstash Developer

Soft skills are equally important for Logstash Developers as they are for any other professional in the tech industry. These skills enable developers to effectively communicate, collaborate, and contribute to their teams and projects. Here are the soft skills required for Logstash Developers at different levels:


  • Problem-solving: Ability to analyze and solve issues efficiently to ensure smooth data processing.
  • Attention to detail: Paying close attention to configurations and mappings to avoid errors and ensure accurate data transformation.
  • Time management: Prioritizing tasks and meeting deadlines in a fast-paced environment.
  • Adaptability: Being open to learning new technologies and adjusting to changing project requirements.
  • Teamwork: Collaborating with team members to troubleshoot and optimize data pipelines.


  • Data analysis: Proficiency in analyzing data patterns and identifying areas for optimization.
  • Communication: Effectively communicating with stakeholders to understand data requirements and provide updates.
  • Leadership: Taking ownership of projects and guiding junior developers in implementing Logstash solutions.
  • Critical thinking: Evaluating complex data scenarios to design efficient Logstash configurations.
  • Problem-solving: Troubleshooting and debugging complex issues in data pipelines.
  • Attention to detail: Ensuring data integrity and accuracy through meticulous configuration management.
  • Collaboration: Collaborating with cross-functional teams to integrate Logstash pipelines with other systems.


  • Mentorship: Mentoring and coaching junior and middle developers to enhance their Logstash skills.
  • Project management: Overseeing multiple Logstash projects, ensuring timely delivery and quality.
  • Strategic thinking: Aligning Logstash implementations with long-term business objectives.
  • Problem-solving: Resolving complex data transformation challenges and optimizing performance.
  • Decision-making: Making informed decisions on architecture and tool selection for Logstash projects.
  • Conflict resolution: Effectively resolving conflicts within the team and facilitating productive discussions.
  • Continuous learning: Staying up-to-date with Logstash advancements and industry best practices.
  • Collaboration: Collaborating with stakeholders to gather requirements and provide technical guidance.

Expert/Team Lead

  • Strategic planning: Defining Logstash strategies and roadmaps aligned with organizational goals.
  • Team management: Leading and managing Logstash development teams, ensuring optimal performance.
  • Innovation: Driving innovation in Logstash implementations and exploring new technologies.
  • Technical expertise: Deep understanding of Logstash internals and advanced data transformation techniques.
  • Project coordination: Coordinating Logstash projects across multiple teams and departments.
  • Quality assurance: Implementing quality control measures to ensure data accuracy and reliability.
  • Presentation skills: Presenting Logstash solutions and strategies to stakeholders and executives.
  • Continuous improvement: Identifying areas for improvement and implementing process enhancements.
  • Risk management: Mitigating risks and proactively addressing potential issues in Logstash projects.
  • Collaboration: Collaborating with other teams to integrate Logstash pipelines into the overall data ecosystem.
  • Technical leadership: Guiding the team in resolving complex technical challenges and providing technical direction.

Let’s consider Difference between Junior, Middle, Senior, Expert/Team Lead developer roles.

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior0-2 yearsAssisting in coding, debugging, and testing software applications. Learning and gaining experience in various programming languages and frameworks. Collaborating with team members and following instructions from senior developers. Participating in code reviews and contributing to the overall development process.$50,000 – $70,000
Middle2-5 yearsDeveloping software applications independently. Designing and implementing new features. Collaborating with cross-functional teams to gather requirements and ensure project success. Mentoring and assisting junior developers. Participating in code reviews and ensuring code quality and performance.$70,000 – $100,000
Senior5-10 yearsLeading and driving the development of complex software applications. Providing technical guidance and expertise to the team. Designing and architecting software solutions. Mentoring and coaching junior and mid-level developers. Collaborating with stakeholders to define project requirements and deliver high-quality software products.$100,000 – $150,000
Expert/Team Lead10+ yearsLeading and managing a team of developers. Setting technical direction and defining best practices. Making critical technical decisions. Collaborating with stakeholders to align business goals with technical solutions. Providing leadership and guidance on complex projects. Ensuring high-quality deliverables and driving continuous improvement.$150,000 – $200,000+

Pros & cons of Logstash

9 Pros of Logstash

  • Scalability: Logstash is designed to handle large volumes of data and can scale horizontally to accommodate increasing data loads.
  • Flexibility: Logstash supports a wide range of data inputs, outputs, and filters, allowing you to process and transform data in various formats.
  • Easy Integration: Logstash seamlessly integrates with other components of the Elastic Stack, such as Elasticsearch and Kibana, providing a comprehensive data processing and visualization solution.
  • Real-time Processing: Logstash enables real-time data ingestion and processing, allowing you to monitor and analyze data as it arrives.
  • Community Support: Logstash has a large and active community of users, which means there are plenty of resources, tutorials, and plugins available for support and customization.
  • Extensibility: Logstash can be extended with custom plugins to meet specific data processing requirements, providing a highly flexible and customizable solution.
  • Reliability: Logstash is built on a reliable and robust architecture, ensuring data integrity and minimal data loss.
  • Security: Logstash provides built-in security features, including authentication and encryption, to protect your data during transit and at rest.
  • Monitoring and Alerting: Logstash offers monitoring and alerting capabilities, allowing you to track the health and performance of your data pipelines and receive notifications when issues arise.

9 Cons of Logstash

  • Resource Intensive: Logstash can consume significant system resources, especially when processing large volumes of data, which may require careful resource allocation and monitoring.
  • Configuration Complexity: The configuration of Logstash can be complex, especially for complex data processing pipelines, requiring a good understanding of its configuration syntax.
  • Learning Curve: Logstash has a learning curve, especially for users who are new to the Elastic Stack or data processing pipelines in general.
  • Performance Overhead: The additional processing and transformation steps performed by Logstash may introduce some overhead, which can impact the overall performance of your data processing pipeline.
  • Debugging Challenges: Troubleshooting and debugging issues in Logstash pipelines can be challenging, especially when dealing with complex data transformations or plugin interactions.
  • Dependency on JVM: Logstash is built on Java and requires a Java Virtual Machine (JVM) to run, which adds an additional layer of dependency and potential complexity.
  • Version Compatibility: Ensuring compatibility between different versions of Logstash, Elasticsearch, and Kibana can be crucial to maintaining a stable and functional data processing pipeline.
  • Documentation Gaps: Some users may find gaps or inconsistencies in the Logstash documentation, requiring additional research or community support to resolve specific issues.
  • Plugin Compatibility: While Logstash provides a wide range of plugins, compatibility issues between plugins or with specific versions of Logstash may arise, requiring careful plugin selection and testing.

How and where is Logstash used?

Case NameCase Description
1. Centralized Log ManagementLogstash is widely used for centralized log management. It helps collect, parse, and store logs from various sources in a central location. By consolidating logs, organizations can easily monitor and analyze their system’s health, troubleshoot issues, and identify potential security threats.
2. Real-time Data ProcessingLogstash is designed to handle real-time data processing. It can ingest large volumes of data from multiple sources, apply filters and transformations, and deliver processed data to various destinations. This capability makes Logstash a valuable tool for streaming analytics, real-time monitoring, and data-driven decision-making.
3. System and Application MonitoringLogstash can be used for system and application monitoring by collecting and analyzing logs generated by servers, applications, and network devices. It enables proactive monitoring, alerting, and performance analysis, helping organizations ensure the smooth operation of their IT infrastructure.
4. Security Event ManagementLogstash plays a crucial role in security event management. By collecting and analyzing security logs from various sources such as firewalls, intrusion detection systems, and authentication servers, Logstash helps identify and respond to security incidents in real-time. It can also integrate with security information and event management (SIEM) systems for comprehensive threat detection and response.
5. Data Integration and ETLLogstash is often used for data integration and extract, transform, load (ETL) processes. It can connect to multiple data sources, transform data formats, enrich data, and deliver it to target systems or databases. This makes Logstash a versatile tool for data integration, data migration, and data synchronization across different platforms.
6. Application Performance MonitoringLogstash can be utilized for application performance monitoring by collecting and analyzing application logs. It helps identify performance bottlenecks, track user behavior, and troubleshoot issues affecting application performance. By correlating log data with performance metrics, Logstash enables organizations to optimize their applications and enhance user experience.
7. IoT Data ProcessingWith the rise of the Internet of Things (IoT), Logstash has become a valuable tool for processing and analyzing IoT data. It can ingest data from IoT devices, apply filters and transformations, and route the processed data to appropriate destinations. Logstash’s scalability and real-time capabilities make it suitable for handling the high volume and velocity of IoT data.
8. Compliance and AuditingLogstash can assist in compliance and auditing processes by collecting and analyzing logs that capture important events and transactions. It enables organizations to maintain an audit trail, detect anomalies or suspicious activities, and meet regulatory requirements. Logstash’s ability to handle diverse log formats and integrate with other compliance tools makes it valuable for ensuring data integrity and security.
9. Data Visualization and ReportingLogstash can extract valuable insights from log data and generate visualizations and reports. By aggregating and analyzing logs, it helps organizations gain a deeper understanding of their systems, identify patterns, and generate actionable insights. Logstash can integrate with visualization tools like Kibana to create interactive dashboards and reports for data-driven decision-making.
10. Log Analysis for DevOpsLogstash is widely used in DevOps environments for log analysis. It helps DevOps teams monitor and troubleshoot applications, infrastructure, and deployments by collecting and analyzing logs from various components. Logstash’s ability to handle logs in different formats and its integration with other DevOps tools make it an essential component of the DevOps toolkit.

TOP 10 Logstash Related Technologies

  • Java

    Java is a popular programming language for Logstash software development. It is known for its robustness, scalability, and cross-platform compatibility. With a vast ecosystem of libraries and frameworks, Java enables developers to build high-performance Logstash applications.

  • Elasticsearch

    Elasticsearch is a distributed search and analytics engine that plays a significant role in Logstash software development. It provides real-time data indexing, searching, and analysis capabilities, making it ideal for handling large volumes of log data efficiently.

  • Python

    Python is another widely used language for Logstash software development. Its simplicity, readability, and extensive libraries make it a preferred choice for many developers. Python’s versatility enables seamless integration with Logstash and facilitates data processing and analysis.

  • JSON

    JSON (JavaScript Object Notation) is a lightweight data interchange format commonly used in Logstash software development. It provides a human-readable and easy-to-parse structure, making it perfect for exchanging data between Logstash and other systems.

  • Grok

    Grok is a powerful pattern-matching plugin used in Logstash for log parsing and analysis. It allows developers to extract structured data from unstructured logs quickly. Grok patterns define the log’s structure, making it easier to process and extract relevant information.

  • Kibana

    Kibana is a data visualization and exploration tool that works seamlessly with Logstash. It provides a user-friendly interface for exploring, analyzing, and visualizing log data stored in Elasticsearch. Kibana’s interactive dashboards and visualizations help in gaining insights from log data.

  • Beats

    Beats is a lightweight data shipper that can send log files or system metrics from various sources to Logstash. It simplifies the process of collecting and forwarding data to Logstash, enabling efficient log management and analysis.

Cases when Logstash does not work

  1. Insufficient system resources: Logstash requires a certain amount of memory and CPU resources to run efficiently. If the system on which Logstash is installed does not meet the minimum requirements, it may not function properly or may crash frequently.
  2. Incorrect configuration: Logstash relies on configuration files to determine how to process and manipulate data. If these configuration files are not set up correctly or contain errors, Logstash may fail to start or process data incorrectly.
  3. Network connectivity issues: Logstash often interacts with other components in the data pipeline, such as Elasticsearch or Kafka. If there are network connectivity issues between Logstash and these components, data may not be transmitted correctly, resulting in Logstash not working as expected.
  4. Unsupported plugins or versions: Logstash supports a wide range of plugins for various data sources and outputs. However, using unsupported or outdated plugins can cause compatibility issues and lead to Logstash failures.
  5. Insufficient disk space: Logstash may generate a significant amount of output data, especially when dealing with large volumes of logs. If the disk space on which Logstash is installed becomes full, it can cause Logstash to stop working or fail to process new data.
  6. Firewall or security restrictions: Logstash may need to communicate with external systems or services, such as fetching data from a remote server or sending data to a cloud-based storage solution. If there are firewall rules or security restrictions in place that prevent Logstash from establishing these connections, it may not function correctly.
  7. Incompatible operating system: Logstash is designed to run on various operating systems, including Linux, Windows, and macOS. However, certain versions or distributions of these operating systems may have compatibility issues with Logstash, leading to malfunctions or failures.
  8. Hardware failures: Like any other software running on a physical server, Logstash can be affected by hardware failures such as disk failures, network interface failures, or power outages. These failures can cause Logstash to stop working until the underlying hardware issues are resolved.

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