Modern End-to-End Data Pipelines: From Data Sources to Deployed ML Models

Antonio Ercole De Luca
3 min readJun 4, 2023

In the ever-evolving era of big data, comprehending the modern tech stack to construct an efficient end-to-end data pipeline is pivotal. This pipeline extends from the foundational data sources to the deployment of machine learning models in production. Let’s navigate through this journey and explore the tools that aid in streamlining this process.

Data Ingestion

The journey of the data pipeline commences with data ingestion. The strategy is to ingest raw data directly into an advanced data warehouse like Snowflake, mandating only necessary transformations, such as casting data types and enforcing some predefined schema.

Adopting this approach confers dual benefits. Firstly, it simplifies the ETL (Extract, Transform, Load) process, thus minimizing the potential for errors during transformation. Secondly, it ensures the availability of raw data, providing an invaluable resource for troubleshooting, auditing, or subsequent analysis.

Tools like Airflow, python, or Fivetran can facilitate this process as they are equipped to manage data ingestion tasks without invoking transformations or group-by operations at this stage.

Data Transformation

Following data ingestion, the transformation phase can ensue directly using a tool like DBT (data build tool). DBT is adept at performing transformations using SQL, offering repeatability and seamlessly integrating into the overall flow.

Opting for SQL provides the advantage of executing computations directly where the data resides, leveraging the optimization techniques of the DBMS. For instances where some functionalities pose complexity for implementation using SQL, Snowflake’s Snowpark enables the execution of custom Python code directly into the node where the data resides, akin to user-defined functions from Spark.

Machine Learning

Once the data is ready in the data warehouse, the focus shifts to the machine learning process. Consider a challenging scenario: training a large-scale neural network using a library like PyTorch. Here, tools like Ray can be harnessed to distribute the computation across numerous instances, thereby substantially reducing the training and parameter search time. Tools like Optuna or Ray Tune can further optimize this process.

In this phase, iteration over various models and experiments in feature engineering is crucial. An iterative approach allows for model improvement and helps identify the best features to enhance the model’s performance. Therefore, tools that support quick experimentation and flexible model selection are essential.

Model Deployment

With the model ready, it’s time for deployment. A crucial aspect to ponder here is the establishment of a CI/CD pipeline to continually monitor the model’s performance. This pipeline can comprise performance tests that examine metrics over blind test sets and time performance, an aspect of paramount importance in real-time applications.

Tools like GitHub Actions can be seamlessly integrated for CI/CD. When a new pull request is submitted (ideally changing the model’s reference), GitHub Actions can trigger tests automatically and convey the results to an MLflow server. MLflow facilitates tracking the versioning of the system, maintaining a chronicle of performance metrics over time.

The Role of Snowflake

The role of Snowflake in this pipeline is profound. It offers a cloud-based solution that allows for scalability to an extensive degree, effectively alleviating worries about scalability and infrastructure. Consequently, engineers can save substantial time and effort typically spent on scalability and infrastructure issues, allowing them to focus more on the data and models.

In conclusion, the voyage from raw data to a production-ready machine learning model comprises various stages, each posing its unique challenges. The contemporary tech stack, featuring tools like Snowflake, DBT, Airflow, Python, Fivetran, PyTorch, Ray, Optuna, GitHub Actions, and MLflow, can notably simplify this process and foster a smooth, efficient data pipeline. By embracing these tools, you take a significant stride towards transforming your data into actionable insights and driving your business to newer heights.