Centralization of all log (application, docker, security, ...)

Centralization of log systems
Thierry GAYET - 09/2023

During the life of the container, it is important to follow the traces of
the containers during their execution for debugging purposes.
GOAL

INTRODUCTION There are several types of log centralization services similar to Datadog and Papertrail, designed to
collect, aggregate, store, and analyze logs from various sources, including applications, servers,
containers, cloud services, and more. Here are some popular types of log centralization services:
Elasticsearch, Logstash, Kibana (ELK Stack) : The ELK Stack is an open-source toolset consisting of
Elasticsearch (search and storage engine), Logstash (log collector), and Kibana (visualization interface).
It's often used for log collection and analysis as well as full-text search.
Fluentd : Fluentd is an open-source log collector that can collect, filter, and forward logs to various
destinations, including Elasticsearch, Fluent Bit, or other storage systems.
Graylog : Graylog is an open-source log management platform that allows you to collect, analyze, and
visualize logs. It offers search, dashboard, and alerting features.
Splunk : Splunk is a commercial data and log management solution with log collection, analysis, and
visualization capabilities. It's widely used in enterprises for system monitoring and security.
Sumo Logic : Sumo Logic is a cloud-based log management service that can collect, aggregate, and
analyze logs from various sources. It also offers threat detection and alerting features.
Loggly : Loggly is a cloud-based log management service that makes log collection, aggregation, and
searching easy. It's often used for application monitoring and troubleshooting.

INTRODUCTION New Relic Logs : New Relic Logs is a log management service designed to integrate with the New Relic suite,
providing application performance insights and log analysis in one place.
Logz.io : Logz.io is a cloud-based log management service based on the ELK Stack, offering log collection, analysis,
visualization, and security features.
AWS CloudWatch Logs : Amazon Web Services (AWS) provides AWS CloudWatch Logs, a log management service
that allows you to collect and store logs from AWS services and other sources.
Azure Monitor Logs (formerly Log Analytics) : Microsoft Azure offers Azure Monitor Logs, a log management
service that collects, analyzes, and visualizes logs from Azure services and other sources.
Datadog : Datadog is a cloud-based monitoring and analytics platform that provides real-time visibility
into the performance, health, and security of an organization's applications and infrastructure
Papertrail : Papertrail is a cloud-based log management and log analysis platform that helps
organizations centralize, search, and analyze log data from various sources, including applications,
servers, and cloud infrastructure.
Promtail +Grafana/Loki : Promtail, Grafana, and Loki are three components that are commonly used
together for log aggregation, storage, and visualization in a modern observability stack
These log centralization services offer various features and pricing options, making them suitable for different needs and budgets. The choice of a service depends on the

ELK Stack, which stands
for Elasticsearch,
Logstash, and Kibana
The ELK Stack, which stands for Elasticsearch, Logstash, and Kibana, is a powerful open-source trio of
tools commonly used for log and data analysis, monitoring, and visualization.
Here's an introduction to each component of the ELK Stack :
Elasticsearch:
● Purpose: Elasticsearch is a distributed, RESTful search and analytics engine designed for
horizontal scalability, speed, and real-time search capabilities. It is at the core of the ELK Stack
and provides the storage and indexing capabilities needed to search and analyze large volumes
of data quickly.
● Features:
● Full-Text Search : Elasticsearch excels in full-text search and can quickly retrieve results
from large datasets.
● Real-Time Data : It supports real-time data indexing, making it suitable for log analysis and
monitoring.
● Distributed and Scalable : Elasticsearch can be distributed across multiple nodes for high
availability, fault tolerance, and scalability.
● Schemaless JSON Documents : Data is stored in JSON format, and Elasticsearch is
schema-less, allowing flexibility in data types.

ELK Stack, which stands for
Elasticsearch, Logstash, and
Kibana
Logstash :
● Purpose : Logstash is a data processing pipeline that ingests, transforms, and enriches
data from various sources before sending it to Elasticsearch for storage and analysis.
It's used to parse, filter, and structure log data from different applications and systems.
● Features :
● Ingestion : Logstash can ingest data from sources like log files, message queues,
databases, and more.
● Data Transformation : It can transform data using filters and grok patterns to
extract structured information from unstructured logs.
● Enrichment : Logstash can enrich data by adding metadata, geolocation, or other
contextual information.
● Output to Elasticsearch : Logstash typically sends processed data to
Elasticsearch for indexing.

Kibana
Kibana:
● Purpose : Kibana is a web-based data visualization and exploration tool that works
in conjunction with Elasticsearch. It provides a user-friendly interface for querying,
analyzing, and visualizing data stored in Elasticsearch.
● Features :
● Data Visualization : Kibana offers various visualization options, including
line charts, bar charts, pie charts, maps, and more.
● Dashboards : Users can create custom dashboards that combine multiple
visualizations and provide a comprehensive view of data.
● Querying : Kibana includes a query language and search interface that
makes it easy to filter and explore data.
● Alerting : It supports alerting and notification based on defined criteria and
conditions.

Kibana
How ELK Stack Works Together:
● Logstash collects, processes, and enriches log data from various sources.
● Processed log data is sent to Elasticsearch for indexing and storage.
● Kibana connects to Elasticsearch to provide a web-based interface for querying,
analyzing, and visualizing the indexed data.
The ELK Stack is widely used for log and data analysis, application monitoring, and
troubleshooting in various industries and IT environments.
It's highly customizable and can handle a wide range of data sources, making it a popular
choice for observability and analytics needs.

Kibana

FLUENTD
Fluentd is an open-source data collection and streaming software tool that is used for collecting,
processing, and forwarding logs and event data. It is part of the Cloud Native Computing Foundation
(CNCF) and is widely used in cloud-native and containerized environments for log management,
monitoring, and data integration. Here's an introduction to Fluentd:
Key Features and Concepts :
Data Collection : Fluentd is designed to collect data from various sources, including log files,
application logs, system logs, IoT devices, and more. It supports a wide range of input plugins
that allow you to collect data from different sources and formats.
Data Processing : Fluentd can process and transform data in real-time using filters. Filters enable
data enrichment, parsing, and transformation before forwarding it to the desired destination.
Fluentd provides a wide variety of built-in filters, and you can also write custom filters to suit your
specific needs.
Data Forwarding : After processing, Fluentd can forward data to multiple output destinations. These
destinations can include storage systems, databases, messaging systems, and external services.
Fluentd supports numerous output plugins, making it flexible in terms of where you can send your
data.
Tagging and Routing : Fluentd uses a tagging mechanism to label and route data to different outputs.
Tags help in organizing data streams and applying different processing rules to different data
sources.

Reliability and Fault Tolerance : Fluentd is designed for reliability and fault tolerance. It can handle
large volumes of data and provides mechanisms for buffering and retrying data in case of
network or destination failures.
Extensibility : Fluentd has a plugin ecosystem that allows you to extend its functionality. You can
find a wide range of community-contributed input, output, and filter plugins to suit your needs.
Custom plugins can also be developed when necessary.
Scalability : Fluentd can be deployed in a distributed fashion to handle high data volumes. This is
achieved by setting up Fluentd agents on multiple nodes and using load balancing or fan-out
configurations.
Configuration : Fluentd's configuration is typically done using simple configuration files written in a
Ruby-like configuration language. You can specify inputs, filters, and outputs in the
configuration file.
FLUENTD

FLUENTD
Common Use Cases:
● Log Management : Fluentd is frequently used for collecting and centralizing logs from various
sources, including applications, servers, and containers, for easier monitoring and
troubleshooting.
● Data Integration : Fluentd can be used to integrate data from different systems, databases, and
services into a centralized data repository or analytics platform.
● Event Streaming : It's used for streaming events and data from IoT devices, sensors, and
applications to data processing pipelines or analytics platforms.
● Real-time Data Processing : Fluentd is employed in real-time data processing pipelines where data
needs to be transformed, enriched, and routed to different destinations in real-time.
● Container Orchestration : Fluentd is often used in containerized environments like Kubernetes to
collect and manage container logs.
Fluentd is known for its simplicity, flexibility, and wide adoption in the cloud-native ecosystem. It plays a
crucial role in modern data and log management architectures, making it a valuable tool for DevOps,
system administrators, and data engineers.

GRAYLOG
Graylog is an open-source log management and centralized logging platform that helps organizations
collect, store, analyze, and visualize log data from various sources. It's designed to provide insights
into system behavior, security events, and application performance. Here's an introduction to
Graylog:
Key Features and Concepts:
Log Collection : Graylog can collect log data from a wide range of sources, including application
logs, system logs, network devices, databases, and more. It supports various protocols like
syslog, GELF (Graylog Extended Log Format), and Beats, making it versatile in collecting data.
Log Storage : Graylog provides a scalable and efficient log storage backend where log data is
indexed and stored for later retrieval and analysis. Elasticsearch is often used as the storage
backend, providing fast and powerful full-text search capabilities.
Log Analysis and Search : Graylog offers a powerful search and query language that allows you to
search, filter, and analyze log data easily. You can create complex queries, set up alerts, and
create dashboards to monitor specific log data patterns.
Alerting : Graylog enables you to define alert conditions based on log data and trigger
notifications when those conditions are met. This is crucial for proactive monitoring and
responding to critical events.

GRAYLOG
Data Enrichment : You can enrich log data with additional context by using lookup tables,
data adapters, and pipelines. This helps in correlating events and understanding their
context.
Dashboards : Graylog allows you to create custom dashboards with widgets that display
log data in various formats, including charts, tables, and graphs. Dashboards help in
visualizing data trends and patterns.
Plugins and Integrations : Graylog has a plugin system that supports extensions for
various purposes, such as new data sources, alerting plugins, and third-party
integrations. This extensibility makes it adaptable to different use cases.
Role-Based Access Control : You can define roles and permissions to control who has
access to specific log data and features within Graylog. This helps in maintaining
security and ensuring data privacy.

GRAYLOG
Common Use Cases:
● Log Management: Graylog is primarily used for log management and centralizing logs from
applications, servers, firewalls, network devices, and more. It simplifies the process of
collecting, searching, and analyzing logs for troubleshooting and auditing purposes.
● Security Information and Event Management (SIEM): Organizations use Graylog as a SIEM
solution to detect and respond to security threats and incidents. It provides real-time alerts,
correlation rules, and threat detection capabilities.
● Application Performance Monitoring (APM): Graylog can be used for monitoring and
analyzing application performance by collecting and visualizing application logs and metrics.
● Compliance and Auditing: It helps organizations meet compliance requirements by storing
and auditing logs for security and regulatory purposes.
● IoT and Network Monitoring: Graylog is suitable for collecting and analyzing log data from
IoT devices, network equipment, and sensor systems.
Graylog's open-source nature, active community, and commercial offerings make it a popular choice
for log management and analysis. Whether you're running a small IT environment or managing a
large-scale infrastructure, Graylog can provide valuable insights into your log data, making it easier
to troubleshoot issues, enhance security, and optimize system performance.

SPLUNK Splunk is a leading software platform used for searching, monitoring, analyzing, and visualizing
machine-generated data, including logs, events, and metrics. It provides organizations with
powerful capabilities to gain insights into their data, troubleshoot issues, and make informed
decisions. Here's an introduction to Splunk:
Key Features and Concepts:
Data Collection : Splunk collects data from a wide range of sources, including log files,
databases, applications, network devices, cloud services, and more. It supports various
data ingestion methods, such as agents, APIs, and integrations.
Data Indexing : Splunk indexes ingested data, making it highly searchable and enabling fast
and efficient retrieval. The indexing process involves breaking data into events and
creating an index for each field, allowing for complex searches.
Search Language : Splunk Query Language (SPL) is a powerful search language that
allows you to search and analyze data using search operators, functions, and patterns.
SPL is designed for ad-hoc searching and real-time analysis.
Alerting and Monitoring : Splunk can set up alerts based on predefined conditions or
custom queries. It can notify users or trigger automated actions when specific events or
patterns are detected.

SPLUNK
Dashboards and Visualizations : Splunk offers a user-friendly interface for creating
interactive dashboards and visualizations. You can build custom dashboards to
monitor data in real-time and gain insights through charts, graphs, and maps.
Machine Learning and AI : Splunk provides machine learning and artificial
intelligence capabilities for anomaly detection, predictive analytics, and
automated insights generation.
Data Forwarding : Splunk can forward data to other systems and tools, allowing for
integration with third-party services, data lakes, and other data analysis
platforms.
Security and Compliance : Splunk is used for security information and event
management (SIEM) and compliance monitoring. It can detect and respond to
security threats, as well as help organizations meet regulatory requirements.

SPLUNK
Common Use Cases :
● Log Management : Splunk is widely used for log management and log analysis. It centralizes
logs from various sources and provides a unified view for troubleshooting and monitoring.
● Security and Compliance : Organizations use Splunk for security monitoring, threat detection,
and incident response. It helps in identifying and mitigating security threats in real-time.
● IT Operations : Splunk is valuable for IT operations teams to monitor the performance and
health of systems and applications, helping with root cause analysis and capacity planning.
● Business Analytics : Splunk can analyze business data and provide insights into customer
behavior, user trends, and operational efficiency, helping organizations make data-driven
decisions.
● Internet of Things (IoT) : Splunk can process and analyze data generated by IoT devices and
sensors, enabling predictive maintenance and operational insights.
● DevOps and Application Monitoring : Splunk is used for monitoring and troubleshooting
applications, microservices, and containerized environments in DevOps workflows.
Splunk offers both free and paid versions of its software, with the paid versions providing additional features and scalability
options. It's a versatile tool with a wide range of applications across industries, including IT, security, finance, healthcare, and
more. Splunk's flexibility, scalability, and extensive ecosystem of apps and add-ons make it a popular choice for organizations

SUMO LOGIC
Sumo Logic is a cloud-native, machine data analytics and log management platform designed to
help organizations gain insights, monitor, and troubleshoot their applications and infrastructure. It
provides a unified platform for collecting, indexing, analyzing, and visualizing data from various
sources, making it a valuable tool for IT operations, security, and business intelligence. Here's an
introduction to Sumo Logic:
Log Collection : Sumo Logic can collect log data and machine-generated data from a wide
range of sources, including applications, servers, cloud services, containers, network
devices, and more. It supports various data collection methods, including agents, APIs, and
integrations.
Real-Time Data Analysis : Sumo Logic offers real-time data analysis capabilities, allowing users
to search, query, and analyze data as it's ingested. This real-time approach is particularly
valuable for monitoring and responding to events as they occur.
Search and Query Language : Sumo Logic provides a powerful search and query language that
enables users to explore and analyze data using a wide range of operators, functions, and
patterns. Users can create custom queries to extract insights from their data.
Alerting and Notifications : Users can set up alerts based on query results or specific
conditions. Sumo Logic can send notifications via email, Slack, PagerDuty, or other
integrations when defined thresholds or patterns are met.

SUMO LOGIC
Dashboards and Visualizations : Sumo Logic allows users to create custom dashboards
and visualizations to monitor data trends and anomalies. Dashboards can include
charts, graphs, and widgets that provide real-time insights into data.
Compliance and Security : Sumo Logic provides features for compliance monitoring and
security information and event management (SIEM). It can help organizations meet
regulatory requirements and detect and respond to security threats.
Log Retention and Archiving: Sumo Logic offers customizable log retention policies,
allowing users to retain data for the required duration. Archived data can be stored
in low-cost storage for compliance and historical analysis.
Integrations and Ecosystem : Sumo Logic has a rich ecosystem of integrations with
third-party tools and services, making it adaptable to various environments and use
cases.

SUMO LOGIC
Common Use Cases:
● Log Management : Sumo Logic is commonly used for log management, centralizing
logs from applications, servers, and cloud services for troubleshooting and analysis.
● IT Operations : IT teams use Sumo Logic for infrastructure monitoring, performance
optimization, and root cause analysis.
● Security Monitoring : Sumo Logic can help organizations detect and respond to
security incidents, as well as provide threat intelligence and compliance reporting.
● DevOps and Continuous Delivery : Sumo Logic is used in DevOps workflows for
monitoring applications, containers, and microservices, as well as tracking release
deployments.
● Business Analytics: Organizations can use Sumo Logic to analyze business data,
customer behavior, and operational metrics to inform decision-making.
Sumo Logic operates as a cloud-native platform, which means it leverages the scalability and elasticity of cloud infrastructure to
handle large volumes of data efficiently. Users can access Sumo Logic's features through a web-based interface, making it
accessible to both technical and non-technical users.
Sumo Logic offers both free and paid subscription plans, with the paid plans providing additional features, retention, and support
options. Its flexibility and cloud-native architecture make it a popular choice for organizations looking to manage and analyze their

SUMO LOGIC
https://d1.awsstatic.com/Marketplace/solutions-center/downloads/AWSMP_Datasheet_SumoLogic.pdf

LOGGLY
Loggly is a cloud-based log management and log analysis platform that helps organizations
collect, store, search, and analyze log data generated by their applications, servers, and
systems. It provides valuable insights into system behavior, application performance, and
security, making it easier to troubleshoot issues and monitor the health of your environment.
Here's an introduction to Loggly:
Log Collection : Loggly can collect log data from a wide range of sources, including servers,
applications, cloud services, containers, and network devices. It supports various data
collection methods, including agents, APIs, and integrations.
Real-Time Data Analysis : Loggly offers real-time log analysis capabilities, allowing users to
search, query, and analyze log data as it's ingested. This real-time approach is valuable
for identifying and responding to issues promptly.
Search and Query Language : Loggly provides a powerful search and query language that
allows users to explore and analyze log data using search operators, filters, and
patterns. You can create custom queries to extract insights from your logs.
Alerting and Notifications : Users can set up alerts based on specific log events, patterns, or
conditions. Loggly can send notifications via email, Slack, PagerDuty, or other
integrations when predefined thresholds are met.

LOGGLY
Dashboards and Visualizations : Loggly allows users to create custom dashboards
and visualizations to monitor log data trends and anomalies. Dashboards can
include charts, graphs, and widgets for real-time data visualization.
Compliance and Security : Loggly supports compliance monitoring and security use
cases. It helps organizations meet regulatory requirements and provides tools for
detecting and responding to security incidents.
Log Retention : Loggly offers customizable log retention policies, allowing users to
retain log data for the required duration. Archived logs can be stored for
compliance and historical analysis.
Integrations : Loggly has integrations with various third-party tools and services,
making it adaptable to different environments and use cases.

LOGGLY
Common Use Cases :
● Log Management : Loggly is frequently used for log management, centralizing logs from
various sources for troubleshooting, debugging, and monitoring.
● Application Performance Monitoring (APM) : Organizations use Loggly to monitor
application logs and metrics, helping identify performance bottlenecks and issues.
● Security Information and Event Management (SIEM) : Loggly can be employed as a SIEM
tool for security monitoring, threat detection, and incident response.
● DevOps and Continuous Delivery : Loggly is used in DevOps workflows to monitor
applications, microservices, and containers, as well as track deployments and releases.
● Business Analytics : Loggly can analyze business data, customer behavior, and operational
metrics to inform decision-making.
Loggly operates as a cloud-native platform, leveraging the scalability and flexibility of cloud infrastructure to handle large volumes of
log data efficiently. Users can access Loggly's features through a web-based interface, making it accessible to both technical and
non-technical users.
Loggly offers different subscription plans, including free and paid tiers, with the paid plans offering enhanced features, retention, and
support options. Its ease of use and cloud-based architecture make it a popular choice for organizations seeking to manage and

NEW RELIC LOGS New Relic Logs, also known as New Relic Logs in Context, is a cloud-based log management and log
analysis platform offered by New Relic, a well-known provider of application performance monitoring
(APM) and observability solutions. New Relic Logs is designed to help organizations collect, analyze,
and visualize log data from their applications and infrastructure to gain insights into system behavior,
troubleshoot issues, and improve application performance. Here's an introduction to New Relic Logs:
Log Collection : New Relic Logs allows you to collect log data from various sources, including
applications, servers, cloud services, containers, and more. It supports multiple data ingestion
methods, including agents, APIs, and integrations with popular logging libraries.
Real-Time Log Analysis : New Relic Logs provides real-time log analysis capabilities, enabling
users to search, query, and analyze log data as it's ingested. This real-time approach is
valuable for identifying and addressing issues promptly.
Search and Query Language : The platform offers a powerful search and query language that
allows users to explore log data using search operators, filters, and patterns. Custom queries
can be created to extract insights from logs.
Alerting and Notifications : Users can configure alerts based on specific log events, patterns,
or conditions. New Relic Logs can send notifications via email, Slack, or other integrations
when predefined thresholds are met.

NEW RELIC LOGS
Log Parsing and Enrichment : New Relic Logs can parse structured log data, making it
easier to extract valuable information. You can also enrich log data with additional
context by adding metadata and custom attributes.
Dashboards and Visualizations : New Relic Logs enables users to create custom
dashboards and visualizations to monitor log data trends and anomalies. Dashboards
can include charts, graphs, and widgets for real-time data visualization.
Compliance and Security : New Relic Logs supports compliance monitoring and security
use cases. It helps organizations meet regulatory requirements and provides tools for
detecting and responding to security incidents.
Log Retention : The platform offers configurable log retention policies, allowing users to
retain log data for the required duration. Archived logs can be stored for compliance
and historical analysis.

NEW RELIC LOGS
Common Use Cases:
● Log Management : New Relic Logs is commonly used for log management, centralizing logs
from various sources for troubleshooting, debugging, and monitoring.
● Application Performance Monitoring (APM) : It complements New Relic's APM solutions by
providing log data alongside performance metrics, helping identify issues affecting application
performance.
● Security Information and Event Management (SIEM) : New Relic Logs can be used for security
monitoring, threat detection, and incident response in conjunction with other security tools.
● DevOps and Continuous Delivery : New Relic Logs is used in DevOps workflows to monitor
● Business Analytics : Organizations can use New Relic Logs to analyze business data, customer
behavior, and operational metrics to inform decision-making.
New Relic Logs operates as part of New Relic's broader observability platform, allowing users to correlate log data with performance
metrics and traces, providing comprehensive insights into application and infrastructure health.
New Relic offers different subscription plans for its observability platform, which includes New Relic Logs, with varying features and
support options. Its integration with other New Relic products and its cloud-native architecture make it a popular choice for

LOGZ.IO
Logz.io is a cloud-based observability platform that specializes in log management, log analysis, and
monitoring solutions for modern, cloud-native environments. It is designed to help organizations
collect, centralize, analyze, and visualize log and machine-generated data to gain insights into their
applications, systems, and infrastructure. Logz.io is known for its scalability, ease of use, and strong
focus on open-source technologies. Here's an introduction to Logz.io:
Log Collection : Logz.io supports the collection of log data from various sources, including
applications, servers, containers, cloud services, and more. It provides multiple data ingestion
methods, including agents, APIs, and integrations.
Real-Time Log Analysis : Logz.io offers real-time log analysis capabilities, allowing users to
search, query, and analyze log data as it's ingested. This real-time approach is valuable for
monitoring and troubleshooting issues in real-time.
Alerting and Notifications : Users can set up alerts based on specific log events, patterns, or
conditions. Logz.io can send notifications via email, Slack, PagerDuty, and other integrations
when predefined thresholds are met.
Log Parsing and Enrichment : Logz.io can parse structured log data, making it easier to extract
valuable information. Users can also enrich log data with additional context by adding
metadata and custom attributes.

Dashboards and Visualizations : Logz.io allows users to create custom dashboards and
visualizations to monitor log data trends and anomalies. Dashboards can include charts,
graphs, and widgets for real-time data visualization.
Compliance and Security : Logz.io supports compliance monitoring and security use cases. It
helps organizations meet regulatory requirements and provides tools for detecting and
responding to security incidents.
Log Retention and Archiving : The platform offers customizable log retention policies,
allowing users to retain log data for the required duration. Archived logs can be stored for
compliance and historical analysis.
LOGZ.IO

LOGZ.IO
Common Use Cases :
● Log Management : Logz.io is commonly used for log management, centralizing logs from various
sources for troubleshooting, debugging, and monitoring.
● Application Performance Monitoring (APM) : Organizations use Logz.io to monitor application logs
and metrics, helping identify performance bottlenecks and issues.
● Infrastructure Monitoring : It can monitor the health and performance of infrastructure components,
including servers, containers, databases, and cloud services.
● Security Information and Event Management (SIEM) : Logz.io can be used as a SIEM tool for security
monitoring, threat detection, and incident response.
● DevOps and Continuous Delivery : Logz.io is used in DevOps workflows to monitor applications,
microservices, and containers, as well as track deployments and releases.
● Business Analytics : Organizations can use Logz.io to analyze business data, customer behavior, and
operational metrics to inform decision-making.
Logz.io also provides pre-built integrations with popular open-source logging and observability tools such as Elasticsearch, Fluentd,
and Kibana (EFK stack), making it easy for organizations to get started with log management and analysis.
Logz.io offers different subscription plans, with the paid plans providing additional features, scalability, retention, and support
options. Its cloud-native architecture and strong focus on open-source technologies make it a popular choice for organizations

AWS
CLOUDWATCH
LOGS
AWS CloudWatch Logs is a managed log management and monitoring service offered by
Amazon Web Services (AWS). It enables organizations to centralize and analyze log data from
various AWS resources, applications, and custom sources, making it easier to monitor and
troubleshoot issues, gain insights into system behavior, and ensure the health and security of
their AWS-based environments.
Log Collection : AWS CloudWatch Logs allows you to collect log data from a wide range of
AWS resources, including Amazon EC2 instances, Lambda functions, Amazon RDS
databases, and more. You can also send custom logs from applications and services
using the CloudWatch Logs API or SDKs.
Log Group and Log Stream : Logs are organized into log groups, which represent a collection
of log streams. Log streams represent a sequence of log events from a single source,
such as an EC2 instance or Lambda function.
Real-Time Log Analysis : CloudWatch Logs provides real-time log analysis capabilities,
allowing you to search, query, and analyze log data as it's ingested. You can use simple
text searches or create custom queries using CloudWatch Logs Insights.
Alerting and Notifications : Users can set up CloudWatch Alarms to trigger notifications or
automated actions based on specific log events, thresholds, or patterns. Notifications can
be sent via email, SMS, or integrations with AWS services like SNS and Lambda.

Dashboards and Visualization : CloudWatch Logs can be integrated with Amazon CloudWatch
Dashboards, allowing you to create custom dashboards with log data visualizations, charts,
and graphs.
Log Retention and Storage : You can configure log retention policies, specifying how long log data
should be stored. CloudWatch Logs offers long-term storage options for archived logs.
Access Control: Access to log data and CloudWatch Logs resources can be controlled using AWS
Identity and Access Management (IAM) policies, ensuring secure access and compliance with
data protection requirements.
AWS
CLOUDWATCH
LOGS

AWS
CLOUDWATCH
LOGS
Common Use Cases :
● Application and Infrastructure Monitoring : CloudWatch Logs is used for monitoring
applications and infrastructure within AWS environments, including tracking errors,
performance issues, and resource utilization.
● Troubleshooting and Debugging : Developers and operations teams use CloudWatch Logs to
troubleshoot issues by analyzing log data and identifying the root causes of problems.
● Security and Compliance : CloudWatch Logs can be used for security monitoring,
compliance auditing, and detecting unauthorized access or unusual activity within AWS
resources.
● Serverless Application Monitoring : For AWS Lambda functions, CloudWatch Logs provides
insights into function execution, errors, and performance, helping to optimize serverless
applications.
● Audit and Compliance : Organizations can use CloudWatch Logs to collect and retain logs
for auditing purposes, ensuring compliance with regulatory requirements.
AWS CloudWatch Logs seamlessly integrates with other AWS services, making it a fundamental part of AWS's overall monitoring
and observability ecosystem. It is available as a pay-as-you-go service, and pricing is based on the volume of log data ingested and

AZURE
MONITOR
LOGS
Azure Monitor Logs, formerly known as Azure Log Analytics, is a cloud-based log management and
monitoring service provided by Microsoft Azure. It allows organizations to collect, analyze, and gain
insights from log and telemetry data generated by their applications and Azure resources. Azure Monitor
Logs is a crucial component of the Azure Monitor suite, offering advanced observability and diagnostic
capabilities for Azure-based environments.
Data Collection : Azure Monitor Logs can collect log and telemetry data from a wide range of sources,
including Azure services, virtual machines, containers, applications, and custom sources. It
supports various ingestion methods, including agents, APIs, and direct integrations.
Log Queries : Azure Monitor Logs provides a powerful query language known as Kusto Query
Language (KQL). With KQL, users can query and analyze log data, perform complex searches, and
create custom queries to extract meaningful insights.
Real-Time Analysis : Azure Monitor Logs offers real-time log analysis capabilities, enabling users to
view log data as it's ingested. Real-time data visualization and dashboards help in monitoring and
troubleshooting issues promptly.
Alerting and Notifications : Users can set up alerts based on specific log events, thresholds, or query
results. Azure Monitor Logs can send alerts through various channels, including email, SMS, Azure
Monitor Action Groups, and third-party integrations.

AZURE
MONITOR
LOGS
Log Analytics Workspaces : Log data is organized into Log Analytics workspaces, which serve as
logical containers for data storage, query execution, and resource organization. Workspaces
can be associated with specific Azure resources or used centrally for multi-resource
monitoring.
Dashboards and Visualization : Azure Monitor Logs supports the creation of custom dashboards
and visualizations to track data trends and anomalies. Dashboards can include charts,
graphs, and widgets for data representation.
Data Retention and Archiving : Organizations can configure log data retention policies, specifying
how long log data should be retained. Azure Monitor Logs offers options for archiving data
for longer-term storage and compliance requirements.

AZURE
MONITOR
LOGS
Common Use Cases:
● Application Performance Monitoring (APM) : Azure Monitor Logs is used to monitor application logs,
metrics, and traces, helping identify performance issues and bottlenecks.
● Infrastructure Monitoring : It monitors the health and performance of Azure infrastructure resources,
virtual machines, databases, and cloud services.
● DevOps and Continuous Integration/Continuous Deployment (CI/CD) : Azure Monitor Logs plays a vital
role in DevOps workflows, enabling teams to monitor deployments, track code changes, and
troubleshoot issues in real-time.
● Security Information and Event Management (SIEM) : Organizations use Azure Monitor Logs for security
monitoring, threat detection, and incident response, correlating security events with log data.
● Compliance and Audit : Azure Monitor Logs is used to maintain compliance with regulatory
requirements by collecting and retaining logs for auditing purposes.
Azure Monitor Logs integrates seamlessly with other Azure services, making it an essential tool for managing, monitoring, and
securing Azure-based applications and resources. It is billed based on data ingestion and retention, offering flexibility and scalability

DATADOG
Datadog is a cloud-based monitoring and analytics platform that provides organizations with
comprehensive observability into the performance, health, and security of their applications, systems, and
infrastructure. Datadog is widely used by IT and DevOps teams to monitor, troubleshoot, and optimize
digital environments, making it easier to ensure the reliability and efficiency of their systems.
Data Collection : Datadog collects data from various sources, including application metrics, traces,
logs, and infrastructure events. It supports integrations with a wide range of technologies and
services, making it versatile for data collection.
Real-Time Monitoring : Datadog offers real-time monitoring capabilities, enabling users to track the
performance of their applications and infrastructure in real-time. This includes real-time
dashboards, alerts, and anomaly detection.
Alerting and Notifications : Users can set up custom alerts based on predefined conditions, thresholds,
or complex query patterns. Datadog can notify users via email, Slack, PagerDuty, and other
integrations when specific conditions are met.
Tracing and APM (Application Performance Monitoring) : Datadog provides distributed tracing and
APM features, allowing users to analyze the performance of their applications and microservices.
This helps identify bottlenecks and optimize code.
Log Management : Datadog integrates with various logging solutions and provides log management
features, including log collection, parsing, searching, and visualization. It allows for centralized log
analysis.

DATADOG
Infrastructure Monitoring : Datadog can monitor the health and performance of infrastructure
components, including servers, containers, databases, and cloud services. It provides
insights into resource utilization and capacity planning.
Security Monitoring : Datadog includes security monitoring features to help organizations detect
and respond to security threats and vulnerabilities. It can correlate security events with
performance data.
Dashboards and Visualization : Datadog allows users to create custom dashboards with charts,
graphs, and widgets to visualize data trends and anomalies. These dashboards help in
monitoring and troubleshooting.

DATADOG Common Use Cases :
● Cloud and Hybrid Cloud Monitoring : Datadog is used to monitor cloud infrastructure, including
AWS, Azure, and Google Cloud Platform, as well as on-premises and hybrid environments.
● Application Monitoring : Organizations use Datadog to monitor the performance of web
applications, APIs, and microservices to ensure they meet user expectations.
● Infrastructure Optimization : Datadog helps in optimizing infrastructure resource usage and
scaling based on actual demand.
● Security and Compliance : Datadog is used for security monitoring, threat detection, and incident
response, helping organizations maintain a secure and compliant environment.
● DevOps and Site Reliability Engineering (SRE) : Datadog is valuable for DevOps and SRE teams to
ensure the reliability and availability of services and applications.
● Business Intelligence : Datadog's analytics capabilities can be used to gain insights into business
metrics and KPIs.
Datadog's cloud-native architecture, extensive integration capabilities, and user-friendly interface make it a popular choice among organizations
looking to gain full-stack observability and actionable insights into their digital systems. It is suitable for businesses of all sizes, from startups to

PAPERTRAIL
Papertrail is a cloud-based log management and log analysis platform that helps organizations
collect, centralize, search, and analyze log data from various sources, including applications,
servers, and cloud infrastructure. It provides a streamlined solution for managing and gaining
insights from log data, making it easier to troubleshoot issues, monitor system health, and
maintain compliance with regulatory requirements.
Log Collection : Papertrail enables users to collect log data from different sources and
formats. It supports log ingestion through agents, Syslog, remote syslog, log forwarding,
and other methods.
Real-Time Log Streaming : Papertrail provides real-time log streaming and searching
capabilities, allowing users to view log events as they are generated. This is valuable for
monitoring and troubleshooting issues as they occur.
Search and Query Language : The platform offers a powerful search and query language that
enables users to explore log events using keywords, filters, and regular expressions.
Custom queries can be created to extract insights from log data.
Alerting and Notifications : Users can configure alerts based on specific log events or
patterns. Papertrail can send notifications via email, Slack, PagerDuty, and other
integrations when defined conditions are met.

PAPERTRAIL Dashboards and Visualizations : Papertrail allows users to create custom dashboards
and visualizations to monitor log data trends and anomalies. Dashboards can include
charts, graphs, and widgets for real-time data visualization.
Log Parsing and Enrichment : Papertrail can parse structured log data, making it easier to
extract valuable information. Users can also enrich log data with additional context
by adding metadata and custom attributes.
Log Retention and Archiving : The platform offers configurable log retention policies,
allowing users to retain log data for the required duration. Archived logs can be
stored for compliance and historical analysis.

Common Use Cases:
● Log Management: Papertrail is commonly used for log management, centralizing logs from
various sources for troubleshooting, debugging, and monitoring.
● Application Performance Monitoring (APM): Organizations use Papertrail to monitor application
logs and metrics, helping identify performance bottlenecks and issues.
● Infrastructure Monitoring: It can monitor the health and performance of infrastructure
components, including servers, containers, databases, and cloud services.
● Security and Compliance: Papertrail supports security monitoring and compliance use cases,
helping organizations meet regulatory requirements and detect security incidents.
● DevOps and Continuous Delivery: Papertrail is used in DevOps workflows to monitor
● Business Analytics: Organizations can use Papertrail to analyze business data, customer
behavior, and operational metrics to inform decision-making.
PAPERTRAIL
Papertrail is known for its simplicity and ease of use, making it accessible to both technical and non-technical users. It offers various
subscription plans, including free and paid tiers, with the paid plans providing enhanced features, retention, and support options. Its
cloud-native architecture makes it a convenient choice for organizations looking to manage and analyze log data in a straightforward

PROMTAIL +
GRAFANA/LOKI
Promtail, Grafana, and Loki are three components that are commonly used together for log
aggregation, storage, and visualization in a modern observability stack. Here's an overview of
each component and how they work together:
Promtail:
● Purpose: Promtail is a log shipper and collector. It is part of the Prometheus
ecosystem and is designed to scrape logs from different sources, enrich them, and
send them to Loki for storage and retrieval.
● Features:
● Tail log files: Promtail can tail log files in real-time, ensuring that the latest
log entries are collected.
● Labels and enrichment: It can add labels and metadata to log entries, making
it easier to query and filter logs in Loki.
● Relabeling: Promtail supports relabeling of log entries, allowing you to
modify log labels on the fly before they are sent to Loki.
● Agent configuration: Promtail can be configured to collect logs from various
sources, including log files, system journals, and Docker container logs.
FREE !
https://www.datree.io/helm-chart/promtail-truecharts

PROMTAIL +
GRAFANA/LOKI
Loki:
● Purpose: Loki is a log aggregation and storage system. It is designed to store logs in a
highly efficient and cost-effective manner, making it well-suited for large-scale log storage.
● Features:
● Index-free storage: Loki uses a unique storage engine that doesn't rely on
traditional indexing, which reduces storage costs and query latencies.
● LogQL: Loki includes a query language called LogQL, which allows you to query
logs efficiently using labels and regular expressions.
● High availability: Loki can be configured for high availability and can store logs
across multiple instances for redundancy.
● Scalability: Loki is horizontally scalable, making it suitable for ingesting and
querying large volumes of logs.

PROMTAIL +
GRAFANA/LOKI
Grafana:
● Purpose: Grafana is an open-source observability platform that provides visualization
and alerting capabilities for various data sources, including logs, metrics, and traces.
● Features:
● Log visualization: Grafana can visualize log data from Loki and other log sources
using log panels and explore features.
● Dashboards: Users can create custom dashboards that combine logs, metrics,
and other data for comprehensive observability.
● Alerts: Grafana supports alerting based on log data, enabling you to set up
notifications when specific log conditions are met.
● Plugins and extensions: Grafana has a rich ecosystem of plugins and extensions
that enhance its functionality, including support for various data sources and
visualization options.
How They Work Together:
● Promtail collects logs, adds labels and metadata, and sends them to Loki for storage.
● Loki stores logs efficiently and provides a query interface using LogQL.
● Grafana connects to Loki as a data source, allowing users to build dashboards and alerts based on log data.
This combination of Promtail, Loki, and Grafana provides a scalable and cost-effective solution for log collection, storage, querying,
and visualization, making it a popular choice for observability in modern IT environments.
https://github.com/grafana/grafana

PROMTAIL +
GRAFANA/LOKI
https://grafana.com/grafana/dashboards/13186-loki-dashboard/

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