@Scout If I want to set up a Dagster pipeline that takes data from a file in an SFTP location and puts it into a snowflake database, what would the resources, assets and ops be in this pipeline? I don't understand the difference.

In a Dagster pipeline, the concepts of resources, assets, and ops (operations) play distinct roles: - Resources are objects that provide capabilities to your pipeline, such as connections to external systems or services. They are typically long-lived and can be shared across different parts of your pipeline. For example, a resource could be a connection to a Snowflake database or an SFTP client for file transfer. - Assets represent the data that your pipeline produces or consumes. They are often the output of one step of your pipeline and the input to another. In the context of Dagster, assets are often defined using the

@asset

decorator, and they can be thought of as software-defined representations of your data. - Ops (formerly known as solids) are the functional units of computation in a Dagster pipeline. An op takes inputs, performs some computation or action, and produces outputs. Ops are the building blocks that you connect together to form a pipeline. For your specific use case, here's how you might think about setting up a Dagster pipeline: 1. SFTP Resource: You would define a resource that handles the connection to the SFTP server. This resource would be responsible for fetching the file from the SFTP location. 2. Snowflake Resource: Similarly, you would define a resource for connecting to your Snowflake database. This resource would handle the credentials and connection logic required to insert data into Snowflake. 3. File to Snowflake Op: You would write an op that uses the SFTP resource to download the file and then uses the Snowflake resource to load the data into the database. This op would be the core computation step of your pipeline. 4. Assets: If you want to track the data as it moves through your pipeline, you could define assets for both the file from the SFTP server and the data in the Snowflake database. These assets would be updated each time the pipeline runs and would provide lineage and observability for your data. Here's a very high-level example of what this might look like in code, based on the concepts from the documentation: ```python from dagster import job, op, ResourceDefinition # Define your SFTP resource class SFTPClient: def __init__(self, host, username, password): # Initialize connection to SFTP pass def get_file(self, file_path): # Logic to download file from SFTP pass sftp_resource = ResourceDefinition.hardcoded_resource(SFTPClient(...)) # Define your Snowflake resource class SnowflakeConnector: def __init__(self, user, password, account): # Initialize connection to Snowflake pass def put_data(self, data): # Logic to insert data into Snowflake pass snowflake_resource = ResourceDefinitio

@Scout so an asset would not be required in this context? What would adding an asset to this pipeline look like?

In Dagster, assets are not strictly required to build a pipeline, but they are useful for representing and tracking the data produced and consumed by your pipeline. Adding assets to your pipeline would allow you to define and track the state of your data over time, providing observability and lineage. Here's how you could incorporate assets into the pipeline: 1. Source Asset: This could represent the file on the SFTP server. You might not need to define an explicit asset for this if it's just an external source, but if you want to track metadata about the file (like when it was last updated), you could define a source asset. 2. Output Asset: This would represent the data that has been loaded into Snowflake. You would define this asset in Dagster and update it each time the pipeline runs and successfully loads data into Snowflake. Here's an example of how you might modify the previous code snippet to include an asset for the data in Snowflake:

python from dagster import asset, op, ResourceDefinition, Out, Output  # Resources definitions remain the same...  # Define your op that uses both resources and produces an output @op(required_resource_keys={'sftp', 'snowflake'}, out=Out()) def file_to_snowflake(context):     file_data = context.resources.sftp.get_file('/path/to/file')     context.resources.snowflake.put_data(file_data)     return file_data  # Return the data as an output of the op  # Define your asset that represents the data in Snowflake @asset def snowflake_data(context, file_data):     # Here, you could perform additional transformations or simply pass the data through     # You could also log metadata or perform checks     <http://context.log.info|context.log.info>("Data loaded into Snowflake")     return file_data  # Define your job that ties everything together @job(resource_defs={'sftp': sftp_resource, 'snowflake': snowflake_resource}) def sftp_to_snowflake_job():     snowflake_data(file_to_snowflake())

In this example, the

file_to_snowflake

op now returns the file data, which is then passed to the

snowflake_data

asset. The asset function could be used to perform additional transformations or simply act as a pass-through. By defining the

snowflake_data

asset, you create a software-defined representation of your data in Snowflake, which Dagster can track over time. This setup allows you to leverage Dagster's asset catalog, where you can see the history of updates to the

snowflake_data

asset, dependencies between assets, and other metadata that you might log during the pipeline execution.

@Scout When, if ever, would you want to put logic in an asset instead of an op?

In Dagster, the decision to put logic in an asset instead of an op can depend on several factors, including the desired granularity of tracking, the need for data lineage, and the structure of your pipeline. Here are some scenarios where you might want to put logic in an asset: 1. Data Lineage and Observability: If you want to track the lineage of your data and have observability over how data is transformed at each step, you might encapsulate that transformation logic within an asset. Assets in Dagster are designed to represent and track the state of data over time, making it easier to understand the flow and dependencies of data within your system. 2. Asset Materialization: When you want to materialize the result of a computation as a persistent object in storage (like a database table or a file), you can use an asset to represent that object. The logic that computes the contents of the asset can be included in the asset definition, allowing Dagster to manage the materialization process. 3. Direct Operation: If you want to directly operate on an asset (e.g., recompute it, backfill it, or view its upstream and downstream dependencies), it can be beneficial to define the computation logic within the asset. This allows you to use Dagster's tools for asset management, such as the Dagit UI, to interact with the asset directly. 4. Improved Code Ergonomics: When the logic is closely tied to the creation or transformation of a particular data asset, defining it within the asset can lead to cleaner and more maintainable code. This is because the asset definition can encapsulate both the computation and the metadata about the data, providing a clear and self-contained representation of the data object. 5. Simplified Dependency Declarations: Assets allow you to declare dependencies declaratively using the

deps

argument. This can simplify the process of defining how assets depend on one another, as opposed to managing dependencies through op inputs and outputs. 6. Graph-Backed Assets: In cases where an asset requires multiple computational steps, you might use a graph-backed asset. This allows you to define a series of ops (a graph) that computes the asset, while still tracking the asset as a single entity in Dagster. It's important to note that assets and ops are not mutually exclusive. You can use ops within assets to define the computation logic, and you can have assets that depend on other assets or ops. The choice between using an op or an asset for a particular piece of logic often comes down to whether you want to track the data as a long-lived object with lineage and observability features that assets provide.