WEBVTT

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In this section,

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we will take a look at

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data sources in Terraform.

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We know by now that

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Terraform makes use of configuration files

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along with the state file

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to provision infrastructure resources,

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but as we saw earlier,

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Terraform is just one of the infrastructure

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as a code tool

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that can be used for provisioning.

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Infrastructure can be

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provisioned using other tools, such as

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Puppet CloudFormation,

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SaltStack,

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Ansible, et cetera.

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Not to mention ad-hoc scripts

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and manually provisioned infrastructure

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or even resources that are created

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by Terraform from another configuration directory.

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For instance,

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let us assume that a database instance

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was provisioned manually in the AWS cloud.

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Although Terraform does not

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manage this resource,

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it can read attributes

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such as the database name,

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host address,

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or the DB user,

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and use it to provision an application resource

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that is managed by Terraform.

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Let's take a simpler example.

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We have a local file resource called

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"Pet" created with the contents

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"We love pets."

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Once this resource is provisioned,

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the file is created in the /root directory,

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and the information about this file

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is also stored in the Terraform state file.

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Now,

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let's create a new file using a simple

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shell script like this.

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Quite evidently,

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this file is outside the control

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and management or Terraform at this point in time.

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The local file resource that Terraform

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is in charge of

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is Petstore TXT

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under the /root directory

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and has no relationship with the local

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file called dogs.TXT,

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which is also created under the /root directory.

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The dogs.TXT has

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a single line

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that says, "Dogs are awesome."

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We would like Terraform to use this file

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as a data source

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and use its data as contents of

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our existing file called Petstore TXT.

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If you want to make use of the attributes

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of this new file

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that is created by the bar script,

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we can make use of data sources.

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Data sources allow Terraform

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to read attributes from resources

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which are provisioned outside its control.

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For example,

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to read the attributes from the local file

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called dogs.TXT,

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we can define a data block within

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the configuration file, like this.

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As you may have noticed,

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the data block is

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quite similar to the resource block.

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Instead of the keyword called resource,

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we define a data source block

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with the keyword called "data."

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This is followed by the type of

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resource which we are trying to read.

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In this example,

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it's a local file.

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This can be any valid resource type

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for any provider supported by Terraform.

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Next comes a logical resource name

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into which the attributes

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for a resource will be read.

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Within the block, we have arguments

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just like we have in a normal resource block.

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The data source block

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consists of specific arguments for a data source,

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and to know which argument is expected,

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we can look up the provider documentation

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in Terraform registry.

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For the local file data source,

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we just have one argument that should be used,

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which is the file name to be read.

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The data read from a data source

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is then available under the

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"data object" in Terraform.

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So,

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To use this data

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in the resource called "Pet",

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we could simply use

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data.local_file.dog.content.

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These details are of course,

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available in the Terraform documentation,

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under "Data sources."

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Within the documentation

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and under the attributes exported,

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we can see that the data source

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for a local file expose two attributes,

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the content

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and the B64 encoded version of the content.

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To distinguish between a resource

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and data sources,

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let's do a quick comparison.

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The resources are created with the resource block,

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and data sources are created with the

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data block.

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Resources in Terraform are used to create,

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update and destroy infrastructure,

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whereas a data source

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is used to read information from a specific resource.

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Regular resources are also called

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"managed resources"

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as it's an extension which is managed by Terraform.

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Data sources are also called data resources.

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That's it for this lecture.

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Now let's head over to the hands-on labs

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and practice working with data sources in Terraform.