Section 1.1 Establishing a Strong Foundation

The first step is to consolidate all data pipelines and transformations in a single location. Just as we establish a solid foundation in relationships through shared experiences, we must identify where our data pipelines are and what data they are pulling. Understanding these initial components will provide a robust base for working with data.

I’ve often encountered data pipelines scattered across various locations—some on outdated systems, others on cloud platforms, and some on virtual machines. Each instance had different code versions, making it difficult to ascertain the most reliable source. By centralizing all code in GitHub, we created a single source of truth and could effectively manage version control.

Section 1.2 Creating a Comfortable Environment

The second step is to create a controlled environment where we can experiment before venturing into unknown territory. When meeting someone new, I prefer to choose familiar settings to ease the interaction. In data terms, this means establishing a staging area—a replica of the production environment that limits exposure to potential issues before they reach end-users.

This precaution minimizes errors resulting from data transformations, thus enhancing our relationship with data.

Section 1.3 Finding the Right Balance

The third step is to limit the number of transformations in database queries. Relationships thrive on focus and intention; excessive actions can lead to misunderstandings and strain.

As your data relationship matures and you set up an initial data warehouse, it’s crucial to manage the balance between transformations and queries. If your data grows, the complexity of transformations can overwhelm your database and lead to failures.

To maintain this balance, consider using tools like Spark for larger datasets, while Python can handle smaller ones effectively. I transitioned transformations from SQL to Python within AWS Redshift and Tableau, containerizing the pipelines and running them continuously via AWS Fargate.

Section 1.4 Analyzing Your Data Model

The fourth step involves assessing your data model. What do you hope to achieve from this relationship? Are there gaps? What questions can you ask to deepen your understanding?

This analysis is akin to examining your data model, which defines the semantics and structure of the data. A well-structured model should be intuitive and tailored to the needs of its users, reflecting business requirements.

Signs that your data model may need revision include lengthy SQL queries or those that take too long to execute. Regularly reassessing your model is essential as your understanding of data evolves.

Section 1.5 Regular Check-ins

The fifth and final step is to regularly evaluate your data. Just as we check in with friends to see how they are doing, we should do the same with our data. This step is the most time-consuming but crucial for maintaining a healthy relationship.

Start by profiling your data, which is relatively straightforward for batch data. You can track metrics like null values and averages, storing them in a repository for future reference.

Analyzing these metrics against historical data can help identify anomalies. Tools like Flyte or Prefect can automate this process, sending notifications for unusual values.