Over the last decade, machine data has grown exponentially. It is a result of the increasing number of devices in IT infrastructure and the increased use of Social Media and the Internet of Things (IoT). Therefore, it is important to search, index, and report on this machine data quickly and easily since it contains lots of valuable information for driving efficiency, productivity, and visibility for the business. This is where Data Model Splunk comes in.
The Splunk platform is used to index and search log files. Therefore, defining a Data Model for Splunk to index and search data is necessary. Splunk was founded in 2003 with one goal in mind: making sense of machine-generated log data, and the need for Splunk expertise has increased ever since. This article will explain what Splunk and its Data Model are, how to create a Data Model Splunk, etc.
Table of Contents
What is Splunk?
The Splunk software platform searches, analyses, and visualizes machine-generated data generated by your websites, applications, sensors, and other devices that comprise your business’s IT infrastructure.
Suppose you have a machine that generates data continuously, and you want to analyze the machine’s state in real-time. How would you do this? Is Splunk a good choice? Absolutely! It can be used. Splunk collects data in the following way, as shown in the image below.
Storage devices have been getting better and better over the years, and processors have also become more efficient with each passing year, but not data movement. Unfortunately, this technique has not been improved, which is the bottleneck in many corporate processes. This is why real-time processing is considered to be Splunk’s most significant selling point.
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Data Model Splunk: The Significance
A Data Model is a way to represent the semantic knowledge contained in a collection of datasets in a structured, hierarchical manner. It has the details needed to facilitate searches of dataset information. For example, it is typical for datasets to be organized into parent and child datasets within a Data Model. As a result of this sorting, users can more easily find specific parts of a dataset by searching for them.
Splunk uses Data Models and search queries to generate pivot reports for users. A pivot report is a visualization, table, or chart displaying information gathered from a dataset search. A pivot report can also be created by using Splunk’s pivot tool. According to the data they want to work with, Pivot users select the Data Model Splunk to use. This model allows them to choose the dataset that is specific to the data they wish to report. With a Data Model Splunk, users can then generate charts, statistics tables, and visualizations based on the row and column configurations selected.
The datasets that make up a Data Model Splunk are crucial to understanding them:
- Event Datasets: A root event dataset represents a type of event.
- Search Datasets: All kinds of searches can be conducted on root search datasets.
- Transaction Datasets: These are constructed from groups of related transactions spanning time. Grouping is done by using an existing object in our data hierarchy. In other words, they cannot be created directly, and you would need an event or a search dataset before you could make one.
- Child Datasets: Child objects are a way of limiting or narrowing down events in the objects above them in the hierarchy.
Event datasets and search datasets differ significantly in terms of search complexity. You cannot specify a pipelined search when defining an event dataset, and you can specify conditions. In search datasets, any search can be specified.
In Splunk, inherited fields are created by default when a dataset is defined with constraints, such as host, source, and source type.
There is a parental relationship between root datasets and their child datasets. Normally, boolean operators “AND” and “OR” connect root and child objects. In addition to inheriting constraints from the parent dataset, child datasets can also add more constraints.
You can also add fields from the drop-down menu for any dataset you want to create. However, there will be no searchable fields in the rest of the fields since these fields are not in the dataset.
- Auto-Extracted: List of fields retrieved from the data by Splunk. Because these fields are inherited from the root, they cannot be added to child datasets.
- Eval Expressions: They are created by evaluating an eval expression against a field.
- Lookup: This is done by using a lookup table.
- Regular Expressions: Regular expressions are used to create fields.
- GeoIP: The event’s geo IP data generates Geo IP. Additionally, the field is added to latitude, longitude, country, etc.
Why should you use Data Models?
Data Models offer the advantage of combining multiple source types into a single model using field aliases. Even though vendors like Cisco, Juniper, and Palo Alto produce similar products, their logging formats differ. Splunk Data Models use common field names in their Common Information Model (CIM) to search for events regardless of the original format or vendor. In Splunk Add-ons for proprietary log formats, field aliases and tags are used to comply with the CIM. A simple Splunk query with the appropriate tags allows the CIM Data Models to pull in the data from multiple vendors and sources.
Database Data Models can be accessed through the
cim_Databases_indexes macro of the Common Information Model. These events are tagged as “databases” and stored in indices.
When particular Splunk reports are frequently used, report acceleration can improve report loading time and reduce duplicate indexer activity. The same is true for specific data sets that may be frequently used in reporting. In a similar manner to Report Acceleration, Data Model Acceleration speeds up your search performance while reducing duplicate indexing. In addition, the accelerated Data Model allows you to generate reports without pulling all the results from your raw log files every time.
Some users may find Splunk searches hard to use because they require learning the Splunk Processing Language (SPL). Users benefit from Pivot searches when using Data Models. Through Splunk’s Pivot search, you can search without using an SPL query. Results can be tabled using either a column or row split or statistics functions like sum or average.
Now you can convert this pivot search into many of the standard Splunk visualizations, including a column chart.
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Creating a Data Model
You must understand your Data Sources and semantics before creating a Data Model Splunk. Then, when defining how datasets are ordered within your model, you must apply this understanding.
It is important to determine the data types and data sources included in the Data Model Splunk. For example, if the data is from system logs, you must create several root datasets, such as searches, events, and transactions. Alternatively, you can create a flat, single root dataset if your data comes from a table-based format, such as a CSV file. You can then generate child sets from the root dataset.
To create a Data Model, the first step is to identify the root event and root data set. All data necessary for any report against the Data Model Splunk are part of the root data set. For instance, the Web Data Model.
It is also possible to define child data sets, so a smaller subset of your data can be searched. You can, for example, search the “proxy” child dataset of the Web Data Model.
Your Data Model Splunk can then be expanded with fields after creating one or more datasets. Splunk indexes contain raw events in their entirety, but Data Models only store the fields you specify. A field can be added based on an eval expression, a lookup, a regular expression, or a field extraction by an automatic process. Each child dataset inherits the fields of its parents, along with optional fields of its own.
Tips for Designing Data Models
It can be challenging to design effective Splunk Data Models, and it may take multiple attempts to succeed. So, consider these tips when building and refining models. You can get started faster and refine your models more quickly by following them.
- Pivot models should be built around users’ needs. Creating models and then trying to adapt them to user needs is not efficient. Instead, start with what your users are trying to achieve and then work your way there.
- Models should be built based on existing searches and dashboards. You should incorporate this data into models first because it is the most relevant information for you. Additionally, dashboards often require less maintenance when built using pivot reports.
- Streaming commands with the root event dataset and the root search dataset enables you to accelerate the Splunk Data Model.
- Better accuracy is achieved when indexes are included in the definitions of constraints and searches for accelerated root datasets. Your model will search all the offered indexes by default if no indexes are specified.
- Minimize the depth of the hierarchy by adding only the required layers. The more layers you add, the less efficient it is to filter by constraints.
- Field flags should be used selectively. You can hide or reveal fields in a dataset using field flags. With this feature, you can limit Pivot users’ visibility of fields, making reporting more straightforward.
It is essential to estimate your storage requirements to ensure billing efficiency. Unfortunately, the Splunk storage calculator does not automatically calculate your storage requirements, but you can create your calculations.
That’s it! This article provides an overview of the Data Model Splunk and how it can improve Splunk efficiency. First, create your Data Model after defining your root data, child data, and fields. This sets you up to understand your data from an entirely new perspective.
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Share your experience of understanding Data Model Splunk in the comments section below.