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Carrying out daily activities and assignments has been made easy thanks to technology. We have varieties of IT tools out there but in this article I will be adding 5 more Powerful IT tools to the 5 published in the other article. Click here to read the first part of this article. Do not skip, you might need these tools.

Below are some powerful new IT tools:

1.      GRAFANA:

Grafana is a widely used open-source platform for monitoring and observability, providing powerful visualisation and dash boarding capabilities. Grafana is often employed in conjunction with various data sources, including popular time-series databases and monitoring systems.


Here are key aspects of Grafana:

i.        Data Source Agnostic: Grafana is data source agnostic, meaning it supports a wide range of data sources, including time-series databases like Prometheus, InfluxDB, Graphite, and Elastic search, as well as relational databases, cloud monitoring services, and more.

ii.    Dash boarding: Grafana allows users to create interactive and customisation dashboards for visualising data. Dashboards can include panels with various types of visualisations, such as graphs, tables, heat maps, and gauges.

iii.  Plugins: Grafana has a rich ecosystem of plugins that extend its functionality. These plugins cover different data sources, panel types, and additional features, providing flexibility for users to customise their Grafana installations.

iv.   Alerting: Grafana includes alerting features that enable users to set up alerts based on data thresholds or conditions. Alerts can be sent via various channels, including email, slack, and other notification systems.

v.     Templating: Grafana supports templating, allowing users to create dynamic and reusable dashboards. Templating makes it easier to create dashboards that adapt to different environments or instances.

vi.   Annotations: Annotations in Grafana enable users to add context to their dashboards by marking events or points in time. Annotations can be used to correlate data with incidents, deployments, or other relevant events.

vii. User Permissions: Grafana provides user authentication and authorization features, allowing administrators to control access to dashboards and data sources. This is important for securing sensitive information and ensuring that users only have access to the data they need.

viii. Community and Community Dashboards: Grafana has an active and engaged community that contributes to the development of plugins, dashboards, and other extensions. The Grafana community site includes a repository of community-contributed dashboards that users can import and use.

ix.   Grafana Loki: Grafana Labs developed Loki, a log aggregation system that integrates seamlessly with Grafana. Loki allows users to explore, visualize, and analyse log data alongside other metrics in Grafana.

x.     Cross-Platform Support: Grafana can be installed on various operating systems, and it provides support for containerised deployments, making it flexible and adaptable to different infrastructure setups.


2.      JENKINS:

Jenkins is an open-source automation server widely used for building, testing, and deploying software. It enables the automation of various tasks, from code compilation to testing and deployment, providing a continuous integration and continuous delivery (CI/CD) solution.


           Here are key aspects of Jenkins:

i.                    Continuous Integration (CI): Jenkins supports continuous integration by automatically building and testing code changes whenever developers commit changes to version control systems like Git.

ii.                  Plugins: Jenkins has a rich ecosystem of plugins that extend its functionality. These plugins cover various aspects; including integrations with version control systems, build tools, testing frameworks, deployment platforms, and more.

iii.                Job Configuration: Jenkins jobs are defined through a web-based graphical user interface or by configuring jobs using Jenkins files (declarative or scripted pipelines as code). Jobs define the steps to be executed in the build, test, and deployment processes.

iv.                 Build Pipelines: Jenkins allows users to define complex build pipelines, consisting of multiple stages and jobs. Pipelines provide a way to model and visualize the entire software delivery process.

v.                   Distributed Builds: Jenkins supports the distribution of builds across multiple nodes, allowing for parallel and distributed builds. This is useful for handling large-scale projects or speeding up build times.

vi.                 Extensibility: Jenkins is highly extensible, and users can extend its functionality using plugins or by writing custom scripts. This extensibility allows Jenkins to integrate with a wide range of tools and services.

vii.               Monitoring and Logging: Jenkins provides monitoring and logging capabilities, allowing users to track the progress of builds, view build logs, and identify issues during the build process.

viii.             Integration with Source Control: Jenkins integrates seamlessly with version control systems such as Git, enabling automated builds and tests triggered by code changes.

ix.                 Authentication and Authorisation: Jenkins supports user authentication and authorization, allowing administrators to control access to Jenkins resources. It supports integration with LDAP and other authentication mechanisms.

x.                   Community and Documentation: Jenkins has a large and active community, contributing to its extensive documentation and providing support through forums and other channels. The community also plays a crucial role in the development of plugins.


3.      GitLab CI/CD:

GitLab CI/CD (Continuous Integration/Continuous Deployment) is an integrated part of the GitLab platform, providing a comprehensive set of tools for automating the software delivery process. GitLab CI/CD is used to build, test, and deploy applications efficiently.


Here are key aspects of GitLab CI/CD:

i.                    Integrated CI/CD Platform: GitLab CI/CD is tightly integrated into the GitLab platform, making it a single application for source code management, CI/CD, code review, and collaboration. This integration simplifies the development workflow.

ii.                  Pipeline Configuration with .gitlab-ci.yml: CI/CD pipelines in GitLab are defined using a file called .gitlab-ci.yml that is stored in the root of the project. This file specifies jobs, stages, and other configurations for the CI/CD process.

iii.                Jobs and Stages: CI/CD pipelines consist of jobs organised into stages. Jobs represent individual tasks, such as building, testing, or deploying, and stages group related jobs together. Pipelines automatically progress through stages based on the success of previous stages.

iv.                 Runners: GitLab CI/CD uses runners to execute jobs in CI/CD pipelines. Runners can be shared among projects and are responsible for executing the tasks defined in the .gitlab-ci.yml file.

v.                   Auto DevOps: GitLab provides an "Auto DevOps" feature that automates the entire CI/CD pipeline, from code commit to deployment. It includes predefined templates and best practices for common development scenarios.

vi.                 Docker Integration: GitLab CI/CD has strong integration with Docker, allowing users to build and publish Docker images as part of the CI/CD process. This makes it easy to package applications and their dependencies into containers.

vii.               Artifact Management: GitLab CI/CD allows for the storage and retrieval of build artifacts, such as compiled binaries or documentation. Artifacts can be shared between jobs and pipelines.

viii.             Manual and Automatic Deployments: GitLab CI/CD supports both manual and automatic deployments. Manual deployments provide control over when and where a release is deployed, while automatic deployments can be triggered based on predefined conditions.

ix.                 Environment and Review Apps: GitLab CI/CD allows users to define and manage different environments (e.g., production, staging) and supports the creation of Review Apps for each merge request, enabling developers to test changes in a production-like environment.

x.                   Security Scanning and Code Quality: GitLab CI/CD integrates with various security scanning tools for code quality, static code analysis, dependency scanning, and container scanning. Security checks can be configured to run automatically in the pipeline.



The Elastic Stack, commonly referred to as the ELK Stack, is a set of open-source tools for searching, analysing, and visualising data in real-time. The Elastic Stack is developed by Elastic and is widely used for log and event data analysis, monitoring, and observability. The ELK acronym stands for Elastic search, Log stash, and Kibana.


Here are key components of the Elastic Stack:

i.                    Elastic search:

Search and Analytics Engine: Elastic search is a distributed search and analytics engine that provides full-text search capabilities on structured and unstructured data.

Document Store: Data in Elastic search is stored as JSON documents, making it suitable for a wide range of use cases, including text search, log and event data analysis, and more.

Scalability: Elastic search is designed to be horizontally scalable, allowing users to add more nodes to a cluster to handle increased data and query loads.

ii.                  Log stash:

Data Collection and Processing: Log stash is a server-side data processing pipeline that ingests, transforms, and enriches data. It supports a wide range of input sources, including logs, metrics, and event streams.

Filters and Pipelines: Log stash provides a variety of filters and plugins that enable users to parse, transform, and enrich data before sending it to Elastic search.

iii.                Kibana:

Visualisation and Dash boarding: Kibana is a web-based user interface that allows users to interact with Elastic search data. It provides visualisation tools for creating charts, graphs, and dashboards.

Discover and Explore: Kibana's Discover feature allows users to explore and search        data in real-time, making it easy to investigate and analyse log and event data.

Security and User Management: Kibana offers features for user authentication, authorization, and role-based access control, ensuring secure access to data. 

iv.                Beats:

Lightweight Data Shippers: Beats are lightweight data shippers that can send data from various sources to Elastic search or Log stash. Examples include File beat for log files, Metric beat for system and application metrics, and more.

Agent-Based Approach: Beats are designed to be easy to install and configure,   providing a way to collect data from different sources without heavy resource requirements.

v.                  X-Pack (Now known as Elastic Stack Features):

Commercial Features: Previously known as X-Pack, Elastic Stack Features is a set of commercial extensions to the Elastic Stack. These include features for security, monitoring, alerting, machine learning, and more.

Security: Provides features for securing Elastic search and Kibana, including encryption, authentication, and access control.

vi.                Elastic Common Schema (ECS):

Standardised Data Model: ECS is a standardised data model for log and event data. It provides a common framework for organising data fields, making it easier to normalise and analyse data from different sources.



Visual Studio Code (VS Code) is a lightweight, free, and open-source source-code editor developed by Microsoft for Windows, macOS, and Linux. It has gained immense popularity among developers due to its extensibility, versatility, and robust set of features.


Here are key aspects of Visual Studio Code:

i.                    Cross-Platform Support: VS Code is available for Windows, macOS, and Linux, providing a consistent development experience across different operating systems.

ii.                  Intuitive User Interface: VS Code features a clean and intuitive user interface with a minimalistic design. It includes a file explorer, integrated terminal, and a side-by-side source code editor.

iii.                Extensibility: VS Code is highly extensible through a rich ecosystem of extensions. Extensions can add support for additional programming languages, integrate with version control systems, enhance debugging capabilities, and more.

iv.                 Integrated Terminal: VS Code includes a built-in terminal that allows developers to run commands, scripts, and other tasks directly within the editor. The terminal supports multiple shells and can be customised to suit the developer's preferences.

v.                   Language Support: VS Code provides out-of-the-box support for a wide range of programming languages, including popular ones like JavaScript, Typescript, Python, Java, and more. Language support is often enhanced with extensions for specific languages.

vi.                 Intelligent Code Editing: VS Code includes features like syntax highlighting, auto completion, and code formatting to enhance the coding experience. It also supports intelligent code navigation and provides suggestions for code refactoring.

vii.               Debugging Capabilities: VS Code has built-in debugging support for various languages and frameworks. It allows developers to set breakpoints, inspect variables, and step through code during the debugging process.

viii.             Version Control Integration: VS Code integrates seamlessly with version control systems such as Git. Developers can view and manage changes, commit code, and perform other version control operations directly within the editor.

ix.                 Customisation: VS Code can be customised to suit individual preferences. Users can choose from a variety of themes, customise keyboard shortcuts, and configure settings to create a personalised development environment.

x.                   Integrated Extensions Marketplace: The Visual Studio Code Marketplace offers a wide array of extensions contributed by the community. These extensions add functionality, language support, themes, and more to customise the editor to individual needs.

xi.                 Live Share: VS Code Live Share allows collaborative development by enabling multiple developers to work on the same codebase in real-time. It supports pair programming and remote collaboration.



Jupyter Notebooks are an open-source web application that allows you to create and share documents that contain live code, equations, visualisations, and narrative text. The name "Jupyter" is a combination of three core programming languages it supports: Julia, Python, and R. However, Jupyter Notebooks are not limited to these languages and support many others through various kernels.


Here are key aspects of Jupyter Notebooks:

i.                    Interactive Computing: Jupyter Notebooks support interactive computing, allowing users to execute code cells interactively. This is particularly useful for data analysis, exploration, and experimentation.

ii.                  Cell-Based Structure: Notebooks are organised into cells, which can contain code, Markdown text, equations (using LaTeX syntax), or visualisations. Cells can be executed individually or as a whole.

iii.                Language Agnostic: While initially designed to support Julia, Python, and R, Jupyter Notebooks have a flexible architecture that allows the use of various programming languages through different kernels. Kernels provide support for specific languages and handle the execution of code.

iv.                 Support for Various Programming Languages: Jupyter supports a wide range of programming languages beyond the initial trio, including but not limited to, C++, Java, Scala, and more. Each language is typically supported through its own Jupyter kernel.

v.                   Data Visualisation: Jupyter Notebooks provide integration with data visualisation libraries like Matplotlib, Plotly, and Seaborn, allowing users to create interactive charts and plots directly in the notebook.

vi.                 Integration with Libraries and Frameworks: Jupyter Notebooks seamlessly integrate with popular data science libraries and frameworks such as NumPy, Pandas, SciPy, TensorFlow, and scikit-learn, making it a powerful tool for data analysis and machine learning.

vii.               Exporting and Sharing: Notebooks can be easily exported to various formats, including HTML, PDF, and slideshows. This makes it convenient for sharing analyses and reports with others who may not have Jupyter installed.

viii.             Collaboration and JupyterHub: JupyterHub allows the deployment of Jupyter Notebooks in a multi-user environment, enabling collaborative work in academic, research, or business settings.

ix.                 Educational Use: Jupyter Notebooks are widely used in education for teaching and learning programming, data science, and scientific computing. They provide an interactive and visual way for students to experiment with code and concepts.

x.                JupyterLab: JupyterLab is an enhanced interface for Jupyter Notebooks that provides a more integrated and extensible environment. It offers features like a file explorer, a text editor, and a terminal alongside the traditional notebook interface.

xi.        Community and Extensions: The Jupyter community is active and has developed a variety of extensions to enhance the functionality of Jupyter Notebooks. These extensions cover areas such as themes, code snippets, and more.

Jupyter Notebooks have become a standard tool in data science, machine learning, and scientific research due to their versatility, interactivity, and ease of use.


7.      SPLUNK

Splunk is a platform for searching, monitoring, and analysing machine-generated data. It provides a comprehensive set of tools for ingesting, indexing, and visualising large volumes of data from diverse sources. Splunk is widely used for log analysis, security information and event management (SIEM), and operational intelligence.


Here are key aspects of Splunk:

i.                    Data Ingestion: Splunk can ingest and index data from a variety of sources, including logs, events, metrics, and more. It supports real-time streaming and batch processing of data.

ii.                  Splunk Search Processing Language (SPL): SPL is a powerful query language used in Splunk for searching and analysing data. It allows users to filter, transform, and aggregate data to derive insights.

iii.                Indexes and Data Models: Splunk uses indexes to organise and store data efficiently. Data models provide a structured way to represent and analyse data, making it easier to create meaningful reports and dashboards.

iv.                 Dashboards and Visualisations: Splunk provides a visualisation framework for creating dashboards and reports. Users can design custom dashboards with charts, tables, and other visualisations to monitor and analyse data.

v.                   Alerting and Monitoring: Splunk allows users to set up alerts based on specified conditions or thresholds. Alerts can be configured to trigger actions such as sending notifications or executing scripts.

vi.                 Correlation Searches: Correlation searches in Splunk help identify relationships between different events and detect patterns. This is particularly useful for security use cases to identify potential threats.

vii.               Splunk Apps and Add-ons: Splunk supports a wide range of apps and add-ons that extend its functionality. These can include pre-built dashboards, data inputs, and configurations tailored for specific use cases or industries.

viii.             Machine Learning Toolkit (MLTK): Splunk's Machine Learning Toolkit enables users to apply machine learning algorithms to their data for predictive analysis, anomaly detection, and clustering.

ix.                 Search Head Clustering and Indexer Clustering: Splunk supports clustering configurations to enhance scalability and fault tolerance. Search head clustering allows multiple search heads to work together, while indexer clustering improves data availability and reliability.

x.                   Security and Access Controls: Splunk provides robust security features, including role-based access controls (RBAC), SSL encryption, and auditing capabilities. These features ensure that data is secure and access is controlled.

xi.                 Community and Splunkbase: Splunk has an active community, and users can find a wealth of information, tutorials, and discussions on the Splunk community forums. Splunkbase is a marketplace for Splunk apps, add-ons, and content created by the community.

xii.               Splunk Cloud: Splunk Cloud is a cloud-based offering that provides the benefits of Splunk without the need for on-premises infrastructure. It is managed and maintained by Splunk.

Splunk is widely used across various industries for operational intelligence, security analysis, IT operations monitoring, and business analytics.


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