Modern enterprises face unprecedented challenges in handling the ever-increasing volumes of data generated across their networks. Traditional centralized data processing methods often struggle with performance bottlenecks, network congestion, and rising storage costs. As more devices and systems are added to enterprise environments, data processing requirements grow exponentially, pushing existing infrastructure to its limits. Splunk’s Edge Processor offers a solution to these challenges by bringing data processing capabilities directly to the network edge, fundamentally changing how organizations collect, process, and analyze their machine data.
Today, we‘ll explore Splunk Edge Processor: a powerful solution that enables data processing at the network edge and give a step-by-step guide to implementing it in your environment. We will cover everything from installation to creating data pipelines.
What is Edge Processor and Why Use it?
Splunk Edge Processor is a distributed data processing solution that operates at the edge of your network, close to where data originates. It functions as an intermediary layer between your data sources and destination platforms like Splunk Cloud, Splunk Enterprise, or Amazon S3.
When data is generated at various points across your network, it is routed through an Edge Processor before it reaches your main Splunk infrastructure. It then applies user-defined processing rules to parse, filter, transform, or enrich the data.
Key Benefits & Capabilities
- Data Processing Before Transit: Filter, mask, and transform data before it leaves your network
- Reduced Data Volume: Decrease storage costs through granular elimination of unnecessary data
- Enhanced Security: Prevent sensitive information from leaving your network through data masking
- Centralized Management: Configure and monitor all Edge Processors through Splunk Cloud
- SPL2 Processing: Leverage SQL-like syntax for creating data processing pipelines
- Data Routing: Process and route data to multiple targets including Splunk Cloud, Splunk Enterprise, and AWS S3
The Splunk Edge Processor solution is particularly valuable for organizations that want to implement a more robust data pipeline management solution without incurring additional licensing costs, as it’s included with existing Splunk Cloud Platform subscriptions. By processing data at the edge before transmission, it not only enhances security and performance but also reduces overall data storage expenses by filtering out unnecessary information before it reaches your central Splunk environment.
Integration with Existing Systems
Before implementing Edge Processor, it’s important to evaluate how it will integrate with your existing Splunk environment and other third-party systems. Whether you are using Splunk Cloud, Splunk Enterprise, or Amazon S3, ensure that Edge Processor is properly configured to forward data to the correct destinations.
Consider integration with existing security tools, data storage systems, and analytics platforms to ensure a seamless flow of data across your infrastructure.
Key Actions
- Plan for integration with your existing Splunk environment and other relevant platforms
- Ensure that data to and from Edge Processor flows seamlessly to your centralized storage and analytics systems
- Collaborate with internal teams to align the deployment of Edge Processor with overall infrastructure goals
Accessing the Cloud Environment
Before installing Edge Processor, you’ll need to access the Edge Processor service in Splunk Cloud to obtain the specific installation commands for your environment:
#1. Access the Edge Processor Service:
- Navigate to https://px.scs.splunk.com/<tenant>, replacing <tenant> with your Splunk Cloud deployment name
- Log in using Splunk Cloud credentials with admin_all_objects capability
#2. Access Installation Commands:
- In the Edge Processor service interface, navigate to “Edge Processors”
- Look for the installation commands section, which provides a customized script specific to your environment
- The commands include configuration details like your group ID, tenant name, and authentication token
Installation
One of the first steps in deploying a Splunk Edge Processor is evaluating your existing infrastructure and network setup. The solution is designed to be lightweight and scalable, but it’s important to ensure that the hardware and network environment where it will be deployed meet the minimum requirements.
System Requirements:
- Linux-based operating system
- 1GB RAM minimum (4GB recommended)
- 20GB available storage
- Network connectivity to both data sources and Splunk Cloud
Organizations should assess network bandwidth to ensure that data can be transmitted efficiently from the edge to your central systems. In environments with large data flows or geographically distributed locations, the network configuration should support low-latency connections for optimal performance.
Installation Steps
#1. Copy the provided installation commands from the Edge Processor service interface, which looks like this:
#2. Paste and execute these commands on your Linux system where you want to deploy Edge Processor
#3. Verify Installation:
- Return to the Edge Processor service interface
- Confirm your Edge Processor appears with “Connected” status
- Review the system metrics to ensure proper operation
NOTE: The Edge Processor uses a TLS 1.2+ authentication like any other connection to the Cloud using a token as part of the installation commands given to you. If you want to secure connections between your data sources and Edge Processor using mTLS, you’ll need to obtain and configure TLS certificates yourself as a separate process. See the Splunk documentation at:
Setting Up a Data Pipeline
One of the most powerful features of Splunk Edge Processor is its ability to create sophisticated data processing pipelines using SPL2 (Search Processing Language 2), Splunk’s SQL-like query language specifically designed for data transformation. These pipelines define exactly how your data should be collected, processed, filtered, transformed, and routed to its destination.
Unlike traditional Splunk forwarders that rely on props.conf and transforms.conf files for data manipulation, Edge Processor uses an approach that allows for complex data transformations with simple, readable syntax. Pipelines provide real-time feedback into how your data would be processed. Allowing you to immediately test and validate your processing logic.
We’ll walk through creating a basic pipeline, exploring the SPL2 syntax, and demonstrating how to deploy your pipeline to an Edge Processor instance. Let’s start by creating a pipeline that processes web access logs and forwards them to Splunk Cloud:
-
#1 Access Pipeline Configuration:
In the Edge Processor service, navigate to “Pipelines”. Click “Create Pipeline”.
#2 Define the Data Source and Destination using the GUI:
In the right panel under “Partition”, set conditions for data processing. For example, set “sourcetype equals nixlog“ and “host equals arch-robert-1“ as shown above. These settings determine what data will be processed by your pipeline. You will also be able to select your destination. If you have only set it up to your Splunk Cloud instance, then only one option will appear called “default_splunk_cloud_destination“.
#3 Start Your Pipeline with the Source Reference:
| from $source
This references the source defined in the Partition section of the GUI.
#4 Add Processing Logic:
| rex field=_raw /(?<ip>\d+\.\d+\.\d+\.\d+) - - \[(?<timestamp>.*?)\] \" (?<method>\w+) (?<url>.*?) HTTP/.*?\" (?<status>\d+) (?<bytes>\d+)/
| fields timestamp, ip, method, status, bytes, url
This extracts fields from the raw data and selects specific fields to include.
#5 Configure Destination in the Pipeline:
| into $destination
This references the destination defined in the Actions section of the GUI.
#6 Complete Pipeline:
The full pipeline would look like:
$pipeline =
| from $source
| rex field=_raw /(?<ip>\d+\.\d+\.\d+\.\d+) - - \[(?<timestamp>.*?)\] \"(?<method>\w+) (?<url>.*?) HTTP/.*?\" (?<status>\d+) (?<bytes>\d+)/
| into $destination;
- #7 Deploy the Pipeline:
- Save the pipeline with a descriptive name
- Select your Edge Processor from the list
- Click “Apply” to deploy the pipeline
Parsing Data Using the Pipeline
Once your pipeline is configured, you can implement advanced data parsing techniques:
Field Extraction with Rex:
$pipeline = | from $source
| rex field=_raw /^(?P<ip>\d+\.\d+\.\d+\.\d+) - (?P<username>[\w\d]+|-) \[(?P<timestamp>\d+\/\w+\/\d+:\d+:\d+:\d+ [+-]\d+)\] "(?P<method>GET|POST|PUT|PATCH|HEAD|DELETE) (?P<path>\/(?:[\w\d\-\/]|%[0-9A-Fa-f]{2})*) (?P<http_version>HTTP\/\d\.\d)" (?P<status_code>\d{3}) (?P<bytes>\d+)$/
| into $destination;
This is a larger version of the same rex command above. In this case, the pipeline extracts IP addresses, usernames, timestamps, method, path, HTTP version, status codes, and byte size from the logs.
Data Masking for PII:
$pipeline = | from $source
| eval masked_ssn=replace(_raw, /(\d{3})-(\d{2})-(\d{4})/, "XXX-XX-$3")
| eval masked_cc=replace(masked_ssn, /(\d{4})-(\d{4})-(\d{4})-(\d{4})/, "XXXX-XXXX-XXXX-$4")
| into $destination;
This pipeline masks Social Security Numbers and credit card information before forwarding the data.
Real-Time Testing:
Edge Processor provides a real-time testing feature that allows you to validate your pipeline with sample data:
- Either add sample data when creating the pipeline or click “Inserted Sample” at the top right of the Pipeline Editor page (see image above)
- Upload or add your data in raw or CSV format
- Press the blue play button at the top right to parse your sample data with your current pipeline
- At the bottom, review the results in table or list format to see how your data is processed
- Adjust your pipeline as needed before final deployment
Conclusion
Splunk Edge Processor transforms data management by processing information at the network edge rather than in central systems. This approach delivers three immediate benefits: reduced data volume and storage costs, enhanced data security through pre-transit filtering, and improved performance by decreasing network traffic.
As a component included with Splunk Cloud Platform subscriptions, Edge Processor requires minimal resources while providing sophisticated data transformation capabilities through its intuitive SPL2 pipeline interface. Organizations looking to optimize their data infrastructure, control costs, and improve processing efficiency will find Edge Processor a valuable addition to their Splunk environment.
To access more Splunk searches, check out Atlas Search Library, which is part of the Atlas Platform. Specifically, Atlas Search Library offers a curated list of optimized searches. These searches empower Splunk users without requiring SPL knowledge. Furthermore, you can create, customize, and maintain your own search library. By doing so, you ensure your users get the most from using Splunk.
