Splunk Data Ingestion Methods: Made Easy 101


Splunk Data Ingestion Methods_FI

Splunk is a software platform widely used for monitoring, searching, analyzing, and visualizing machine-generated data in real-time. It performs capturing, indexing, and correlating the real-time data in a searchable container and produces graphs, alerts, dashboards, and visualizations. Splunk provides easy to access data over the whole organization for easy diagnostics and solutions to various business problems.

In this article, you will gain information about Splunk Data Ingestion Methods. You will also gain a holistic understanding of Splunk, its key features, data ingestion, input types and data sources supported by Splunk, the best Splunk Data Ingestion Methods, and a demo that showcases an example on the Splunk Data Ingestion Methods.

Read along to find out in-depth information about Splunk Data Ingestion Methods.

Table of Contents

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What is Data Ingestion?

There is a massive amount of data coming from various sources, including your website, mobile application, REST Services, external queues, and even your own business systems. Data must be collected and stored securely, with no data loss and as little latency as possible. This is where Data Ingestion enters the picture.

The process of collecting and storing mostly unstructured sets of data from multiple Data Sources for further analysis is referred to as data ingestion. In simple terms, it is a process by which data is transferred from one point of origin to another, where it can then be stored and analyzed. The data transferred during the data ingestion process could be from any format, such as DBMS, RDBMS, files such as CSVs, and so on. It is preferable to clean and munge the data before analyzing it; otherwise, the data will not make sense.

This data can be accessed in real-time or in batches. When real-time data arrives, it is ingested immediately, whereas batch data is ingested in chunks at regular intervals.

There are basically 3 different layers of Data Ingestion.

  • Data Collection Layer: This layer of the Data Ingestion process decides how the data is collected from resources to build the Data Pipeline.
  • Data Processing Layer: This layer of the Data Ingestion process decides how the data is getting processed which further helps in building a complete Data Pipeline.
  • Data Storage Layer: The primary focus of the Data Storage Layer is on how to store the data. This layer is mainly used to store huge amounts of real-time data which is already getting processed from the Data Processing Layer.

Input Types & Data Sources Supported by Splunk

Data Sources

The different data sources supported by Splunk are as follows.

1) Web and Cloud Services

Apache and Microsoft IIS are the most widely used web servers. All Linux-based web services are hosted on Apache servers, while all Windows-based web services are hosted on IIS servers. Log files generated by Linux web servers are simple plain text files, whereas log files generated by Microsoft IIS can be in a W3C-extended log file format or stored in a database in the ODBC log file format.

Cloud services such as Amazon AWS, S3, and Microsoft Azure can be directly connected and configured on Splunk Enterprise based on the forwarded data. Many technology add-ons are available in the Splunk app store that can be used to create data inputs to send data from cloud services to Splunk Enterprise.

So, when uploading log files from web services, such as Apache, Splunk provides a preconfigured source type that parses data in the best format for it to be available for visualization.

2) IT operations and network security

Splunk Enterprise has many applications on the Splunk app store that specifically target IT operations and network security. Splunk is a widely accepted tool for intrusion detection, network and information security, fraud and theft detection, and user behavior analytics and compliance. 

A Splunk Enterprise application provides inbuilt support for the Cisco Adaptive Security Appliance (ASA) firewall, Cisco SYSLOG, Call Detail Records (CDR) logs, and one of the most popular intrusion detection applications, Snort. The Splunk app store has many technology add-ons to get data from various security devices such as firewalls, routers, DMZ, and others. The app store also has the Splunk application that shows graphical insights and analytics over the data uploaded from various IT and security devices.

3) Databases

Splunk Enterprise includes support for databases such as MySQL, Oracle Syslog, and IBM DB2. Aside from that, there are technology add-ons on the Splunk app store that allow you to retrieve data from the Oracle and MySQL databases. These technology add-ons can be used to retrieve, parse, and upload data from a database to the Splunk Enterprise server.

Data of various types may be available from a single source. There may even be a wide range of data generated from the same source. As a result, Splunk supports all types of data generated by a source.

4) Application and Operating system data

Splunk has a built-in configuration for Linux dmesg, syslog, security logs, and various other logs available from the Linux operating system. Splunk, in addition to the Linux operating system, provides configuration settings for data input of logs from Windows and iOS systems. It also includes default Log4j-based logging settings for Java, PHP, and.NET enterprise applications. Splunk also supports data from a variety of other applications, including Ruby on Rails, Catalina, WebSphere, and others.

Splunk Enterprise offers predefined configurations for various applications, databases, operating systems, cloud, and virtual environments, and cloud and virtual environments to enrich the respective data with better parsing and breaking into events, resulting in better insight from the available data. The sources of applications whose settings are not available in Splunk Enterprise can instead have apps or add-ons on the app store.

Input Methods

Splunk offers tools for configuring various kinds of data inputs, including those unique to application needs. Splunk also provides the tools to configure input forms of any arbitrary data. In general, Splunk inputs can be defined as follows:

1) Files and directories

Splunk Enterprise offers a simple interface for uploading data via files and directories. Files can be uploaded manually from the Splunk web interface, or you can configure Splunk to monitor the file for changes in content and upload new data to Splunk whenever it is written in the file.

Splunk can also be configured to upload multiple files by either uploading all of the files at once or monitoring the directory for new files and indexing the data on Splunk as it arrives. You can use files and directories to track input processors in order to get data from them.

2) Network events

Splunk accepts data from network sources via TCP and UDP. It can scan any network port for incoming data and index it in Splunk. For increased reliability, you can use TCP whenever possible.

Splunk Enterprise can also accept and catalog SNMP events. In general, when sending data from network sources to Splunk, it is recommended that you use a Universal forwarder, as the Universal forwarder buffers the data in case of any issues on the Splunk server thus preventing data loss.

3) Windows sources

Splunk Cloud and Splunk Enterprise Windows support a wide range of Windows-specific inputs. Splunk Enterprise allows for direct data access from a Windows system. It can handle both local and remote collections of various types and sources from a Windows system. Splunk Web allows us to configure the following Windows-specific input forms:

  • Windows Event Log data
  • Windows Registry data
  • Active Directory data
  • WMI data
  • Active Directory data
  • Performance monitoring data

Splunk includes predefined input methods and settings for parsing event logs, performance monitoring reports, registry information, hosts, networks, and print monitoring of both local and remote Windows systems.

To search and index Windows data on a non-Windows instance of Splunk Enterprise, you must first gather the data on a Windows instance.

4) Other input types

Splunk software also supports different kinds of data sources. For example:

  • Metrics
  • First-in, first-out (FIFO) queues
  • Scripted inputs
  • Modular inputs
  • The HTTP Event Collector endpoints

Best Splunk Data Ingestion Methods

In Splunk, data is ingested by selecting the “Add Data” option. This is the second option available on the welcome screen or the default dashboard, as shown in the image below.

Splunk Data Ingestion Methods: Add Data | Hevo Data
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This option allows you to import or forward data into Splunk. It can be used to extract the data’s essential features after it has been added.

The Add Data window appears on the screen after you click the “Add Data” button. You can then select the type of data to send to the Splunk platform. These options are:

1) Splunk Data Ingestion Methods: Upload

The Upload option  is used to upload the data from an external source into our system. Through this option you can upload data in a variety of file formats in your systems. The following image illustrates the different file formats supported by the Upload option. 

Splunk Data Ingestion Methods: File Options | Hevo Data
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2) Splunk Data Ingestion Methods: Monitor

If you have the need to monitor data from any outer source such as any website, app, etc. in the Splunk platform, then in that case you can use the monitor option. For example, HTTP, WMI, TCP/UDP, etc.

3) Splunk Data Ingestion Methods: Forward

You can get the incoming data and visualize it in Splunk Forwarder by using the forward option.

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Demo : Splunk Data Ingestion Methods

In order to understand the steps involved for data ingestion in Splunk, you can consider the following example. This is a step-by-step guide to ingest a data file in the Spunk dashboard.

  • Step 1: Go to the Splunk CLI, and start the Splunk server.
Splunk Data Ingestion Methods: CLI | Hevo Data
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  • Step 2: The login page appears. You can log in with your Splunk credentials.
Splunk Data Ingestion Methods: Log in | Hevo Data
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  • Step 3: After successfully logging in, you will be navigated to the Splunk dashboard. On the top bar, click on the “Settings” tab.
Splunk Data Ingestion Methods: Dashboard | Hevo Data
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  • Step 4: Now, select the “Add Data” option.
Splunk Data Ingestion Methods: Add Data | Hevo Data
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  • Step 5: In the next window that appears, select the “Upload” option.
Splunk Data Ingestion Methods: Upload | Hevo Data
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  • Step 6: In the Select Source window that appears, select the file that is to be uploaded. In this example, this sample data file has been uploaded. https://docs.splunk.com/Documentation/Splunk/8.0.1/SearchTutorial/Systemrequirements#Download_the_tutorial_data_files
Splunk Data Ingestion Methods: Select Source | Hevo Data
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  • Step 7: It allows you to configure the data input settings so that data is indexed according to the settings you specify.
Splunk Data Ingestion Methods: Input Settings | Hevo Data
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  • Step 8: The Review Page appears. You can check through all the actions. Then, click on the “Submit” button.
Splunk Data Ingestion Methods: Review | Hevo Data
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The file will now be uploaded. 

Splunk Data Ingestion Methods: Uploading data | Hevo Data
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Splunk Data Ingestion Methods: Successful | Hevo Data
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For further information on Splunk Data Ingestion Methods, you can visit here.


In this article, you have learned about Splunk Data Ingestion Methods. This article also provided information on Splunk, its key features, data ingestion, input types and data sources supported by Splunk, 3 Splunk Data Ingestion Methods and a demo that showcases an example on the Splunk Data Ingestion Methods.

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Share your experience of understanding Splunk Data Ingestion Methods in the comment section below! We would love to hear your thoughts on Splunk Data Ingestion Methods.

Manisha Jena
Research Analyst, Hevo Data

Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has hadns on experience in using data analytics stack for various problem solving through analysis. Manisha has written more than 100 articles on diverse topics related to data industry. Her quest for creative problem solving through technical content writing and the chance to help data practitioners with their day to day challenges keep her write more.

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