Key Distinctions between Data & App Pipelines
I have spent a great chunk of my career building customer-facing applications. This would frequently involve building UIs in Angular, Ember, and backend APIs in Java+Spring, Express. And then deploying them to various environments/prod using CI/CD pipelines (mostly GitLab CI and Jenkins).
More recently (circa 2021), I switched my domain from application development to data. Which, also involves extensive use of pipelines for deployments. But there are a few key differences I have learned over time between the more well-known app deployment pipelines, and data pipelines.
In this segment, I will try to share those key differences between data and app pipelines at a high-level.
Data pipelines are frequently designed to ingest, transform, and analyze large volumes of data.
They typically consist of several stages, each responsible for a specific data processing task.
Common architectural patterns for data processing pipelines include batch processing and stream processing (real-time or near real-time).
Data Pipeline Example
Consider a batch processing pipeline that analyzes customer sales data.
The pipeline could include the following stages:
a) Data Ingestion: Retrieve data from various sources (ex: databases, files) and load it into a data storage system (ex: Hadoop Distributed File System).
b) Data Transformation: Perform data cleaning, aggregation, and enrichment operations to prepare the data for analysis.
c) Data Analysis: Apply statistical algorithms or machine learning models to gain insights from the processed data.
d) Results Storage: Store the processed results in a suitable format (ex: database, data warehouse) for further consumption or reporting.
Code Example (using Apache Beam)
import apache_beam as beam
# Data ingestion stage
sales_data = (
| 'ReadData' >> beam.io.ReadFromText('sales.csv')
# Additional preprocessing steps...
# Data transformation stage
transformed_data = (
| 'TransformData' >> beam.Map(transform_function)
# Additional transformation steps...
# Data analysis stage
analysis_results = (
| 'AnalyzeData' >> beam.Map(analysis_function)
# Any additional analysis steps...
# Results storage stage
analysis_results | 'WriteResults' >> beam.io.WriteToText('results.txt')
Visualized: Data Pipeline
Application Deployment Pipelines
Application pipelines focus more so on automating the process of deploying software applications, such as APIs and web apps.
They facilitate stages like building, testing, and deploying the application code to various environments, such as development, staging, and production.
App Pipeline Example
Let’s consider a pipeline that automates the deployment of a web application.
The pipeline might include the following stages:
a) Code compilation/build: Compile and build the application source code into an executable format.
b) Unit Testing: Execute automated tests to verify the correctness of the application’s individual units.
c) Integration / End-to-End Testing: Perform tests to ensure that the application integrates seamlessly with other components or services.
d) Containerization: Package the application and its dependencies into a container (ex: Docker image) for portability.
e) Deployment: Deploy the containerized application to the target test/prod environment.
Code Example (using Jenkins)
// Code compilation stage
sh 'make build'
// Unit testing stage
sh 'make test'
// Integration testing stage
sh 'make integration-test'
// Containerization stage
sh 'docker build -t myapp .'
// Deployment stage
sh 'docker run -d myapp'
Visualized: App Pipeline
Closing thoughts 👏
Pretty much, the common aspect pipelines play for both app and data products is in automating and streamlining development.
These are the few key distinctions between data and app pipelines.
Data pipelines focus more so on ingesting, transforming, and analyzing large volumes of data, while app pipelines automate the process of building, testing, and deploying apps.
Thanks for reading along! Feel free to add questions/inputs below 👇