What Is a Data Pipelining?
Data pipelining is the process of extracting, transforming, and loading (ETL) data from various sources into a centralized system, ensuring it is clean, consistent, and ready for analysis or application use. I help organizations streamline their data pipelines, ensuring data is ready to fuel advanced analytics and AI solutions.
Data Modeling
Effective data preparation begins with strong data modeling. Without it, integrating data into your AI app is like building a house without a blueprint. Using tools like dbt (Data Build Tool), I create structured, reusable models that transform raw data into clean, usable formats. This means tackling questions like: How should dates be standardized? How do we treat null values? And how do we design for compliance with regulations like GDPR or HIPAA?
For example, imagine you’re working with sales data across different regions, each with its own currency and date format. I would build a dbt model that standardizes currency to USD and formats all dates as ISO 8601 before any analysis or transformation occurs. This ensures consistency and eliminates confusion downstream. By implementing modular, easy-to-debug models, your data pipeline becomes a powerful tool for decision-making.
Pipeline Automation
Once the models are in place, we automate the extraction, transformation, and loading (ETL) processes. This means designing pipelines that ingest data from multiple sources—databases, APIs, even flat files—transforming it into a consistent format and loading it into a warehouse or application.
For example, let’s say your app relies on user activity data from Google Analytics, sales data from Shopify, and user feedback from Zendesk. I design and implement an automated pipeline that ingests, transforms, and aligns these datasets so they’re ready for analysis. Tools like Airflow or Azure Data Factory ensure the pipeline runs seamlessly, with fail-safes for error handling and retries.
Data Governance
Data governance is the backbone of a reliable pipeline. It’s not just about clean data; it’s about maintaining compliance and ensuring data integrity over time. This includes defining data quality checks, access controls, and audit trails.
For instance, if you’re running an e-commerce business, I’d establish rules that verify every transaction record has a valid customer ID, timestamp, and purchase amount. Any anomalies—like missing fields or outliers—are flagged automatically. Using dbt’s built-in testing capabilities, these checks are embedded directly into the pipeline, catching errors before they propagate.
Performance Optimization
Efficient pipelines don’t just process data—they optimize it. I analyze every step to eliminate bottlenecks, reduce latency, and improve scalability. Whether it’s tweaking SQL queries or implementing parallel processing, the goal is a pipeline that grows with your needs.
For example, let’s say your team complains that the daily data refresh takes hours. By profiling the pipeline, I might identify that a join operation is consuming most of the time. I could optimize the query or pre-aggregate data to cut processing time dramatically.
Portfolio
dbt
Because of the sheer number of redactions that would be required to share my dbt work with clients, I created a demo project using publicly available data in BigQuery. I demonstrate:
- A data source YAML file that dictates the tests (i.e., constraints) to run on select columns to ensure data integrity.
- Multiple models of varying complexity (i.e., staging, intermediate, and mart).
- The inclusion of a seed file to pull in country names (the data sources just had ID and who knew the country code for Austria was AT).
- Macros to merge and sum 20 columns using Pythonic logic in a Jinja template.
- The creation of dbt’s sexy lineage graph to visualize your data pipeline.
SQL
Below are a few samples of some of the SQL queries I’ve written over the years.
Note: I normally comment out my code like it’s my job, but Tableau chokes on SQL code with comments and then flips on its back and wants a belly rub. 🙄 I now spawn off tables that I set to refresh on a regular cadence and reference the table in my dashboards to avoid this issue, but I still need to go back and clean up my old code.
Let’s Chat 📞
If you’re interested in learning more, you can request a proposal or ask questions via the form below. Or grab some time to talk about your unique needs.
Image Credit: Felix Mittermeier