Revolutionizing data pipelines with dynamic blocks in Mage AI

• What are dynamic blocks?

• How dynamic blocks work

• A tutorial: Implementing dynamic blocks

• Step 1: Create a dynamic data loader

• Step 2: Create a transformer block

• Step 3: Create a second transformer block

• Step 4: Add a data exporter

• Step 5: Run the pipeline

• Advanced features of dynamic blocks

• Reduce output

• Dynamic SQL blocks

• Real-world applications of dynamic blocks

• Best practices for using dynamic blocks

• Conclusion

In the fast paced world of data engineering, the ability to create flexible, scalable, and efficient data pipelines is crucial. Some data pipelines may need to run jobs in parallel to handle large volumes of data, process data from multiple sources simultaneously, or adapt to changing business requirements. Mage AI approaches parallelization using their dynamic blocks feature. This article will discuss dynamic blocks in detail, show their benefits, and provide a tutorial for practical application.

Dynamic blocks in Mage are a special type of block that can create multiple downstream blocks at runtime. This feature allows for incredible flexibility in pipeline design, enabling data engineers to create workflows that adapt to the data they’re processing. The power of dynamic blocks lies in their ability to generate a variable number of blocks based on the output of an upstream block. This means your pipeline can scale and adjust itself depending on the data it receives, without requiring manual intervention or redesign. Dynamic blocks run in parallel, reducing the processing time and improving the efficiency of your data pipelines.

Let’s break down the mechanics of dynamic blocks:

1. Output Structure

   : A dynamic block must return a list of two lists of dictionaries. The first list contains the data that will be passed to downstream blocks, while the second list contains metadata for each dynamically created block.

2. Downstream Block Creation

   : The number of downstream blocks created is equal to the number of items in the output data multiplied by the number of direct downstream blocks.

3. Data Flow

   : Each dynamically created block receives a portion of the data from the dynamic block, allowing for parallel processing of different data subsets.

4. Metadata

   : The metadata provided by the dynamic block is used to uniquely identify each dynamically created block, ensuring proper data routing and execution.

Let’s walk through a simple example to illustrate how to implement dynamic blocks in your Mage AI pipeline. We’ll create a data pipeline that

First, we’ll create a data loader block that will serve as our dynamic block by completing the following steps:

• Add the code from this block into your Mage data loader block and run the code.

• After running the code, select the “More actions” (three dots) in the top right of the Mage block’s UI.

• From the dropdown select “Select block as dynamic”

This block generates data for three users and corresponding metadata. The output structure adheres to the dynamic block requirements

Next, let’s create a transformer block that will process each user’s data:

• Select the “Base template (generic)” block from the transformer, Python list

• From the “More actions” list select “Reduce output”

• Type in the code below and run the block

This transformer block multiplies each user’s ID by 100 and reduces the output into one return.

Let’s add another transformer to demonstrate the flexibility of dynamic blocks:

• Select the “Base template (generic)” block from the transformer, Python list

• Break the connection to the transformer block by clicking the connection and selecting “remove connection”

• Connect the block to the data loader block

• From the “More actions” list select “Reduce output”

• Type in the code below and run the block

This transformer adds a unique token to each user’s data and reduces the output into a single return.

Finally, let’s add a data exporter to see the results:

• Select the “Base template (generic)” from the Data exporter, Python list

• Type in the code below and run the block

• The data exporter should only be connected to the transform_dynamic_data_test_2 block

This returns a list of dictionaries containing user data from the dynamic children.

Create a trigger by clicking the “run@once” button. When you run this pipeline, here’s what happens:

1. The data loader creates data for three users.

2. Three instances of the first transformer are created, each processing one user’s data.

3. The second transformer, with “Reduce output” enabled, combines the results into a single list.

4. The data exporter receives and prints the final processed data.

Mage AI’s dynamic blocks offer a range of advanced features that enable data engineers to build flexible, scalable, and efficient data pipelines. From consolidating the results of multiple dynamically created blocks to the flexibility of dynamic SQL blocks, these powerful capabilities are redefining the way we approach data engineering.

The “Reduce output” feature is particularly powerful when you want to consolidate the results of multiple dynamically created blocks. When enabled on a dynamically created block, it combines the outputs of all instances of that block into a single list, which is then passed to downstream blocks.

This feature is invaluable when you need to perform operations on the entire dataset after individual processing, such as aggregations or summary statistics.

Mage AI also supports dynamic SQL blocks, allowing you to create flexible database queries that adapt to your data. For example:

In this case,

df_1

could be dynamically populated based on upstream block outputs, allowing for adaptive SQL queries.

Dynamic blocks can be applied under a plethora of applications. Here are a few scenarios where they shine:

1. ETL Processes

   : When extracting data from multiple sources or tables, dynamic blocks can create separate transformation pipelines for each source.

2. Parallel Processing

   : Split large datasets into smaller chunks for parallel processing, then recombine the results.

3. A/B Testing

   : Dynamically create different processing paths for different experimental groups.

4. Multi-tenant Systems

   : Process data for multiple clients or tenants in parallel, with customized logic for each.

Source: GIPHY

While dynamic blocks offer great flexibility, it’s important to use them judiciously:

1. Error Handling

   : Implement robust error handling in your dynamic blocks to manage unexpected data or processing failures.

2. Metadata Management

   : Use clear and consistent naming conventions in your metadata to ensure traceability and ease of management.

3. Testing

   : Thoroughly test your dynamic blocks with various input scenarios to ensure they behave correctly under different conditions.

4. Documentation

   : Clearly document the purpose and behavior of your dynamic blocks, especially if they create complex downstream structures.

Dynamic blocks in Mage AI represent a significant leap forward in data pipeline design. By allowing for adaptive, data-driven pipeline structures, they enable data engineers to create more flexible, efficient, and scalable data workflows.

As data continues to grow in volume and complexity, tools like Mage AI and features like dynamic blocks will become increasingly crucial in managing and deriving value from our data ecosystems. Whether you’re dealing with multi-tenant systems, variable data sources, or complex transformation logic, dynamic blocks offer a powerful solution for modern data engineering challenges.

By mastering dynamic blocks, you’ll be well-equipped to tackle a wide range of data processing scenarios, ultimately leading to more robust and adaptable data pipelines. As you integrate this powerful feature into your workflows, you’ll find new levels of efficiency and flexibility in your data engineering projects.

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