Getting Started with Splunk: An In-Depth Guide for Beginners

As someone who has used Splunk for several years, I can attest to its usefulness in many different contexts. Whether you’re a security analyst, a data scientist, or just someone who needs to search through large volumes of data quickly, Splunk is an excellent tool to have in your arsenal. In this comprehensive guide, I’ll take you through the basics of Splunk and give you some tips for using it effectively.

Introduction to Splunk

At its core, Splunk is a tool for searching, analyzing, and visualizing machine-generated data. This can include log files, network traffic, system metrics, and more. Splunk takes this data and allows you to search through it quickly, create reports and dashboards, and set up alerts based on specific events or patterns.

Why use Splunk?

There are many different reasons why you might choose to use Splunk. Here are just a few:

• Centralized logging: If you have multiple servers or devices generating log files, Splunk can help you consolidate them into a single location for easier searching and analysis.
• Real-time monitoring: Splunk can ingest data in real-time, allowing you to monitor for specific events or patterns as they happen.
• Custom dashboards and reports: With Splunk, you can create custom dashboards and reports to visualize your data in a way that makes sense to you.
• Alerting and automation: Splunk can be configured to send alerts or trigger automated actions based on specific events or patterns.

Splunk Enterprise and its features

Splunk Enterprise is the full version of Splunk, and it includes a number of features beyond the basic search and analysis capabilities. Here are a few of the key features you might use:

• Indexing and searching: This is the core functionality of Splunk, allowing you to ingest data and search through it quickly.
• Alerting: You can set up alerts based on specific events or patterns in your data. These alerts can be sent via email, SMS, or other methods.
• Dashboards and reports: Splunk includes a number of built-in dashboards and reports, and you can also create your own custom ones.
• Data inputs: Splunk can ingest data from a variety of sources, including log files, APIs, and databases.
• Data forwarding: If you have multiple Splunk instances, you can forward data between them to create a distributed search architecture.
• User management: Splunk includes a robust user management system, allowing you to control access to your data and dashboards.

How to download and install Splunk

To get started with Splunk, you’ll need to download and install it on your system. Here are the basic steps:

• Download the installer: You can download the installer for your operating system from the Splunk website.
• Install Splunk: Run the installer and follow the prompts to install Splunk on your system.
• Start Splunk: Once Splunk is installed, you can start it from the command line or via the GUI interface.
• Access the web interface: Splunk runs a web interface on port 8000 by default. You can access this interface by opening a web browser and navigating to http://localhost:8000

Ingesting Data into Splunk

Here are the key steps and options for ingesting data into Splunk:

1. Choose an ingestion method:

• Upload: Manually upload files through the Splunk web interface 
• Monitor: Configure Splunk to monitor files, directories, network ports, etc. for new data 
• Forward: Use Splunk forwarders to send data from source systems to Splunk 

2. For the Upload method:

• Go to Settings > Add Data in the Splunk web interface
• Select “Upload” and choose the file to upload
• Configure indexing settings and submit

3. For the Monitor method:

• Configure Splunk to monitor specific data sources like files, directories, network events, Windows sources, etc.

4. For the Forward method (most common for production):

• Install Splunk Universal Forwarder on source systems
• Configure inputs on the forwarder to collect desired data
• Configure outputs to send data to Splunk indexers

5. Additional options:

• Use Splunk apps and add-ons for specific data sources 
• Configure Ingest Actions to filter, mask, or route data at ingestion 
• Use HEC (HTTP Event Collector) for sending data via HTTP/HTTPS
• Use Splunk Cloud specific options like the Cloud Universal Forwarder 

6. Best practices:

• Create appropriate indexes for your data
• Use source types to categorize data
• Follow Splunk’s data onboarding workflow guidelines 

The Universal Forwarder method is recommended for most production use cases as it provides reliable, secure, and scalable data collection. The specific steps may vary based on your Splunk deployment (on-prem vs cloud) and data sources.

Splunk Forwarders

Splunk forwarders are lightweight agents installed on data sources (such as servers, devices, or applications) that collect and send data to a Splunk indexer or to another forwarder.

Types of Splunk Forwarders:

1. a) Universal Forwarder (UF):
   • The most commonly used type Lightweight with minimal resource footprintPrimarily used for forwarding dataCannot index data locally or run custom applications
   b) Heavy Forwarder (HF):
   • Full Splunk installation configured to forward data
   • Can perform parsing, indexing, and data manipulation before forwarding
   • Useful for preprocessing data or handling complex data sources
2. Key Functions:
   • Data Collection: Gather logs, metrics, and other machine data from various sources
   • Data Forwarding: Send collected data to Splunk indexers or other forwarders
   • Load Balancing: Distribute data across multiple indexers for better performance
   • Data Filtering: Optionally filter out unnecessary data before forwarding
   • Data Compression: Compress data to reduce network bandwidth usage
   • Data Encryption: Secure data transmission using SSL/TLS encryption
3. Deployment Scenarios:
   • Direct to Indexer: Forwarders send data directly to Splunk indexers
   • Tiered Deployment: Forwarders send data to intermediate forwarders, which then forward to indexers
   • Heavy Forwarder as Gateway: Use heavy forwarders to preprocess data before sending to indexers
4. Configuration:
   • inputs.conf: Defines data inputs and sources
   • outputs.conf: Specifies where and how to send data
   • props.conf and transforms.conf: Used for data parsing and transformation (mainly in heavy forwarders)
5. Advantages:
   • Efficient Data Collection: Streamlines the process of gathering data from multiple sources
   • Reduced Network Impact: Compression and filtering help minimize network traffic
   • Scalability: Easily add or remove forwarders as your infrastructure changes
   • Security: Provides options for secure data transmission
6. Best Practices:
   • Use Universal Forwarders when possible for better performance
   • Implement load balancing for high-volume environments
   • Regularly update forwarders to benefit from the latest features and security patches
   • Monitor forwarder performance to ensure efficient data collection and forwarding
7. Monitoring Forwarders:
   • Use Splunk’s Monitoring Console to track forwarder status and performance
   • Set up alerts for forwarder issues or data flow problems
8. Troubleshooting:
   • Check forwarder logs for errors or warnings
   • Verify network connectivity between forwarders and receivers
   • Ensure proper configuration of inputs and outputs

Understanding and effectively utilizing Splunk forwarders is crucial for building a robust and efficient data.

Splunk Indexer

An indexer is a core component of the Splunk architecture. Its primary function is to process incoming data and store it as events in indexes. Indexes are repositories for data, similar to databases, but optimized for fast search and retrieval of time-series data.

Splunk Indexer Architecture:

1. Data Input:
   • Indexers receive data from various sources, such as forwarders, network ports, or scripts.
   • Data can be in different formats like logs, metrics, or JSON.
2. Parsing:
   • The indexer breaks the incoming data into individual events.
   • It identifies timestamps and extracts key-value pairs.
3. Indexing:
   • Events are processed and stored in index files on disk.
   • The data is compressed and optimized for fast searching.
4. Search:
   • When a search is performed, indexers quickly locate and retrieve relevant events.
   • They can perform this task in parallel across multiple indexers for improved performance.
5. Clustering:
   • In larger deployments, indexers can be configured in clusters for high availability and data redundancy.
   • This setup includes a cluster master and multiple peer nodes.

Key Components of Indexer Architecture:

1. Raw Data: The original, unprocessed data received by the indexer.
2. Index Files:
   • Hot buckets: Active, writable buckets for recent data.
   • Warm buckets: Read-only buckets for less recent data.
   • Cold buckets: Compressed, less frequently accessed data.
   • Frozen buckets: Archived data, typically moved to cheaper storage.
3. Index Directory: Where all index files are stored on disk.
4. Metadata: Information about the indexed data, facilitating faster searches.
5. Bloom Filters: Probabilistic data structures that help quickly determine if data exists in an index.

Benefits of Splunk Indexer Architecture:

1. Scalability: Can handle massive amounts of data by adding more indexers.
2. Performance: Optimized for fast data ingestion and searching.
3. Flexibility: Can process various data types and sources.
4. Reliability: Clustering provides high availability and data redundancy.

Understanding the Splunk indexer architecture is crucial for efficiently managing and scaling a Splunk deployment. It forms the backbone of Splunk’s ability to handle large volumes of data and provide rapid search capabilities across diverse data sources.

Visual Example of Splunk architecture:

Understanding SPL basics

Now that we’ve learned the architecture and how Splunk works, lets learn how to actually use it. SPL (Search Processing Language) is the primary language used in Splunk for searching and analyzing data. Here are a few key concepts to understand:

• Indexes: Splunk stores data in indexes, which are essentially collections of data with a specific schema.
• Searches: Searches are the primary way to interact with data in Splunk. You can run searches from the GUI or via the command line.
• Search commands: SPL includes a number of search commands, which allow you to filter, transform, and analyze your data in various ways.
• Pipes: You can chain search commands together using pipes (|) to create more complex search queries.

SPL language and syntax

SPL syntax can be a bit overwhelming at first, but it’s fairly easy to pick up with a bit of practice. Here are a few additional key concepts to understand:

• Fields: Fields are key-value pairs that represent individual pieces of data in your logs or other data sources.
• Operators: SPL includes a number of operators that allow you to compare, combine, and manipulate fields in various ways.
• Functions: SPL includes a number of built-in functions that allow you to perform more complex operations on your data.
• Regular expressions: Regular expressions (regex) are a powerful tool for pattern matching in SPL.

Here are some examples of useful Splunk SPL searches:

Search for errors in the last 24 hours:

error OR failed OR failure OR exception 
| stats count by sourcetype, host

This search looks for common error terms across all data in the last 24 hours and provides a count grouped by sourcetype and host.

Find top 10 IP addresses with most failed login attempts:

sourcetype=*access* action=failure 
| top limit=10 src_ip

This search analyzes access logs to find the IP addresses with the most failed login attempts.

Chart CPU usage over time:

sourcetype=perfmon metric_name=CPU* 
| timechart avg(Value) by host

This creates a time-based chart of average CPU usage across different hosts.

Identify rare events:

sourcetype=* 
| rare limit=20 sourcetype

This finds uncommon sourcetypes that may indicate unusual activity.

Calculate average response time by URL:

sourcetype=access_combined
| stats avg(response_time) as avg_response_time by uri_path 
| sort - avg_response_time

This calculates the average response time for each URL path and sorts from slowest to fastest.

Find long-running processes:

sourcetype=process_monitoring duration>300
| table process_name, duration, host
| sort - duration

This identifies processes running longer than 5 minutes and displays relevant details.

More from Splunk on SPL – Here

Splunk for cybersecurity and security investigations

Splunk is widely used in the cybersecurity industry for threat detection and incident response. Here are a few ways you can use Splunk for security investigations:

• Log analysis: Splunk can help you analyze logs from various security devices, such as firewalls, IDS/IPS systems, and endpoint protection.
• Threat intelligence: Splunk can be used to ingest threat intelligence feeds and correlate that data with your own logs to identify potential threats.
• Incident response: Splunk can help you quickly identify and respond to security incidents by alerting you to suspicious activity, and providing central collection for historic log analysis during incident investigations.

Splunk Rex – Regular Expression

Regular expressions, or regex, are a powerful tool for pattern matching in SPL. Splunk includes a number of regex functions, but one of the most powerful is the rex command. Here are a few things you can do with rex:

• Extract fields: You can use rex to extract fields from your data using regex patterns.
• Replace text: You can use rex to replace text in your data using regex patterns.
• Create new fields: You can use rex to create new fields based on regex matches in your data.

Practical Examples

1. Extracting IP addresses from log files:

Let’s say you have log entries that contain IP addresses, and you want to extract them into a new field called “client_ip”. Here’s how you can use the rex command:

index=web_logs | rex field=_raw "(?<client_ip>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})"

In this example:

• We’re searching the index “web_logs”
• The rex command looks at the “_raw” field (which contains the full log entry)
• The regular expression \d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3} matches an IP address pattern
• The extracted IP address is stored in a new field called “client_ip”

This is useful for quickly identifying and analyzing client IP addresses in your web logs.

2. Extracting HTTP status codes and URLs from web server logs:

If you’re working with web server logs and want to extract both the HTTP status code and the requested URL, you can use rex like this:

index=web_logs | rex field=_raw "(?<status_code>\d{3})\s+(?<requested_url>https?://\S+)"

In this example:

• We’re again searching the “web_logs” index
• The rex command extracts two pieces of information:
  1. (?<status_code>\d{3}) captures a 3-digit HTTP status code
  2. (?<requested_url>https?://\S+) captures the full URL (starting with http:// or https:// )
• These are stored in new fields called “status_code” and “requested_url” respectively

This extraction is particularly useful for analyzing HTTP response codes and the most frequently requested URLs in your web traffic.

These examples demonstrate how the rex command can be used to extract specific patterns from your log data, creating new fields that can be used for further analysis, filtering, or visualization in Splunk. The power of rex lies in its ability to use regular expressions to match and extract complex patterns from your data.

Timechart in Splunk

The timechart command is a powerful tool for visualizing time-series data in Splunk. Here are a few things you can do with timechart:

• Create charts: You can use timechart to create line charts, column charts, and other types of charts to visualize your data.
• Calculate metrics: You can use timechart to calculate various metrics over time, such as average response time or number of requests.
• Group data: You can use timechart to group your data by different time intervals, such as minutes, hours, or days.

Practical Examples

1. Monitoring CPU Usage Over Time:

Let’s say you want to monitor the CPU usage of multiple servers over a 24-hour period, with data points every 15 minutes:

index=os sourcetype=cpu_metrics 
| timechart span=15m avg(cpu_usage) by host

This command does the following:

• Searches for CPU metrics in the “os” index
• Uses timechart to create a time-based chart
• Sets the time span to 15 minutes (span=15m)
• Calculates the average CPU usage for each time span
• Groups the results by host, so you can see each server’s CPU usage separately

The resulting chart would show lines for each host, allowing you to compare CPU usage across different servers over time.

2. Tracking Web Traffic by Source:

For a web application, you might want to see how many hits you’re getting from different sources throughout the day:

index=web sourcetype=access_combined
| timechart span=1h count by referer_domain
| fields - NULL

This command:

• Searches for web access logs in the “web” index
• Creates a timechart with 1-hour intervals (span=1h)
• Counts the number of hits, grouped by the referring domain
• Removes the “NULL” field to clean up the results

The resulting chart would show how traffic from different sources (like search engines, social media, or direct visits) varies over the course of the day.

These examples demonstrate how the timechart command can be used to visualize system performance and user behavior over time. The timechart command is versatile and can be applied to many different types of time-based data in Splunk, making it a valuable tool for monitoring and analysis.

Datamodel searching in Splunk

Datamodels are structured representations of data that help in organizing and categorizing the information in a more meaningful manner. They provide a standardized way of defining and describing the relationships between various data elements, making it easier for users to search and analyze the data efficiently. Splunk supports the creation and use of datamodels through its powerful search processing language (SPL), which allows users to perform complex searches and analytics on their datasets.

The process of datamodel searching with Splunk involves performing searches on the underlying data using SPL queries. These queries enable users to filter, aggregate, and analyze the data based on specific attributes or criteria. One of the main advantages of using datamodels in Splunk is that they allow users to perform searches at a higher level of abstraction, without having to deal with the complexities of raw event data. This not only simplifies the search process but also allows for faster and more accurate results.

Furthermore, datamodel searching with Splunk offers several benefits over traditional search methods. Firstly, it enables users to perform more advanced analytics by leveraging the structure and relationships defined in the datamodels. This allows for deeper insights into the data and better decision-making capabilities. Secondly, datamodels provide a more efficient way of storing and accessing data, as they can be accelerated to improve search performance. This is particularly useful for organizations dealing with large volumes of data, where search speed and efficiency are critical factors.

Datamodel searching with Splunk represents a significant advancement in the field of data analytics. By enabling users to search and analyze their data using structured representations, it makes the process of extracting meaningful insights from complex datasets much more efficient and effective. Organizations that leverage the power of datamodels in Splunk can gain a competitive advantage by making better-informed decisions based on a deeper understanding of their data. As the demand for advanced analytics continues to grow, it is imperative for businesses to embrace tools and techniques like Splunk and datamodel searching to stay ahead in the digital age.

Practical Examples

Example 1: Searching for Failed Authentication Attempts

Let’s assume you have a datamodel called “Authentication” that captures login events across your systems. Here’s how you might use it to search for failed authentication attempts:

| datamodel Authentication Authentication search
| search action=failure
| stats count by src_ip, user
| sort - count

This search does the following:

1. It uses the “Authentication” datamodel and its “Authentication” dataset.
2. It filters for failed authentication attempts (action=failure).
3. It counts the occurrences grouped by source IP address and username.
4. It sorts the results in descending order based on the count.

This search is useful for identifying potential brute-force attacks or compromised user accounts.

Example 2: Analyzing Network Traffic by Application

Let’s say you have a datamodel called “Network_Traffic” that captures network flow data. Here’s an example of how you might use it to analyze traffic by application:

| datamodel Network_Traffic All_Traffic search
| stats sum(bytes) as total_bytes by app
| eval total_MB = round(total_bytes/1024/1024, 2)
| sort - total_MB
| head 10

This search does the following:

1. It uses the “Network_Traffic” datamodel and its “All_Traffic” dataset.
2. It calculates the sum of bytes for each application.
3. It converts the total bytes to megabytes for easier reading.
4. It sorts the results in descending order based on total megabytes.
5. It shows only the top 10 results.

This search is useful for identifying which applications are consuming the most bandwidth on your network, which can help with capacity planning and identifying potential performance issues.

These examples demonstrate how datamodels can simplify complex searches and make it easier to extract meaningful insights from your data. Datamodels abstract away the underlying complexity of your raw data, allowing you to focus on the analysis rather than the intricacies of your log formats.

Tips for using Splunk effectively

Here are a few tips for getting the most out of Splunk:

• Learn and practice using SPL: The more you understand SPL syntax and operators, the more powerful your searches will be.
• Use saved searches: Saved searches allow you to run complex queries on a regular basis and get notified when specific conditions are met.
• Take advantage of apps and add-ons: Splunkbase has a wealth of apps and add-ons that can add new functionality to Splunk.
• Learn and Use regex: Regular expressions can be intimidating at first, but they’re a powerful tool for pattern matching in SPL.
• Learn to use the GUI: While SPL is powerful, the GUI interface can be a great way to get started with Splunk.

Troubleshooting common issues in Splunk

Here are a few common issues you might run into when using Splunk, and how to troubleshoot them:

• Performance issues: If your searches are running slowly, you might need to adjust your search syntax or optimize your data inputs.
• Data ingestion issues: If you’re having trouble ingesting data into Splunk, check your data inputs and make sure they’re configured correctly.
• Dashboard issues: If your dashboards aren’t displaying correctly, check your searches manually and make sure they’re returning the correct data.
• Authentication issues: If you’re having trouble logging in to Splunk, make sure your user account is configured correctly and has the correct permissions.

Splunkbase and its features

Splunkbase is a repository of apps and add-ons for Splunk, created by both Splunk and third-party developers. Here are a few things you can do with Splunkbase:

• Download apps: You can download apps from Splunkbase to add new functionality to Splunk.
• Browse add-ons: Add-ons are pre-built integrations with other systems, such as AWS, Azure, or ServiceNow.
• Contribute to the community: If you’re a developer, you can contribute your own apps and add-ons to Splunkbase.

Splunk certification and training

If you’re interested in becoming a Splunk expert, below is all of the available certifications from Splunk:

Certification		Skills	Related Products
	Splunk Core Certified User	Perform searchesUse fields and lookupsCreate alerts, basic reports and dashboards	Splunk Enterprise Splunk Cloud
	Splunk Core Certified Power User	Understand SPL commandsCreate knowledge objects, workflow actions, data modelsUse field aliases, calculated fields, macrosNormalize data	Splunk Enterprise Splunk Cloud
	Splunk Core Certified Advanced Power User	Author complex searches and reportsImplement advanced knowledge object use casesUnderstand best practices for building dashboards	Splunk Enterprise Splunk Cloud
	Splunk Cloud Certified Admin	Monitor Splunk Cloud on a daily basisConfigure data inputs and forwarders, manage user accounts and learn to isolate problems	Splunk Cloud
	Splunk Enterprise Certified Admin	Manage Splunk Enterprise on a daily basisGain expertise in license management, indexers and search heads, configuration, monitoring and data ingest	Splunk Enterprise
	Splunk Enterprise Certified Architect	Understand best practices for planning, data collection and sizing deploymentsManage and troubleshoot a distributed deployment with indexer and search head clustering	Splunk Enterprise
	Splunk Core Certified Consultant	Understand how to deploy and implement large Splunk installationsGain expert knowledge of multi-tier Splunk architectures, clustering and scalability	Splunk Enterprise
	Splunk Enterprise Security Certified Admin	Manage Splunk Enterprise Security environmentUnderstand event processing deployment requirements, technology add-ons, risk analysis settings, threat and protocol intelligence and customizations	Splunk Enterprise Security
	Splunk IT Service Intelligence Certified Admin	Install and configure Splunk IT Service Intelligence (ITSI)Gain insight into architecture, deployment planning, design and implementation and developing glass tables and deep dives	Splunk ITSI
	Splunk SOAR Certified Automation Developer	Install, configure and use SOAR serversPlan, design, create and debug basic playbooksUnderstand complex SOAR solution development and integration including custom coding and REST API	Splunk SOAR
	Splunk O11y Cloud Certified Metrics User	Monitor and visualize metrics using Splunk Observability CloudDeploy and configure the OpenTelemetry Collector to send in metricsSet up alerts to monitor development environments in real time	Splunk Observability Cloud Splunk Infrastructure Monitoring
	Splunk Certified Cybersecurity Defense Analyst	Detect, analyze and combat cyber threatsUse tools for continual monitoring as a security analystMitigate risk while managing vulnerabilities and threats using common types of cyber defense systems	Splunk Enterprise Splunk Enterprise Se

Conclusion

Splunk is a powerful tool for searching, analyzing, and visualizing machine-generated data. Whether you’re a security analyst, a data scientist, or just someone who needs to search through large volumes of data quickly during an incident, Splunk is an excellent tool to know and have in your arsenal. With the tips and tricks in this guide, you should be well on your way to becoming a Splunk expert!