Want to Survive in Data Science? Master the Basics of Relational Databases
As a data scientist, having a solid understanding of relational databases is crucial. Much of the data you will work with in the real world is stored in relational databases, so being able to effectively query and extract insights from these data stores is an essential skill.
In this guide, we‘ll cover all the foundational concepts you need to know about relational databases to thrive in data science. Whether you‘re just getting started or looking for a refresher, read on for an in-depth look at this important topic from the perspective of an AI and machine learning expert.
Why Relational Databases Matter for Data Science and AI/ML
Relational databases play a crucial role in data science and AI/ML workflows. Here‘s why:
Data quality and consistency – The structured nature of relational databases enforces data consistency. This is crucial for machine learning models, where data quality issues can lead to poor model performance. Features like constraints, unique indexes, and foreign keys help maintain data integrity.
Large data volumes – Relational databases are designed to efficiently store and query large volumes of structured data. This scalability is important as ML models often require large training datasets. According to a 2022 Kaggle survey, 65.5% of data scientists work with datasets over 1GB in size, with 14.3% working with over 100GB [1].
SQL for data preparation – SQL is an essential tool for data preparation and feature engineering. Complex queries can be used to join data from multiple tables, aggregate data, and calculate new features. Proficiency in SQL is consistently ranked as one of the top skills for data science roles.
Integration with other tools – Relational databases integrate well with other data science tools and platforms. Python libraries like SQLAlchemy and pandas make it easy to query databases and load data into data frames for further analysis. Many data visualization tools like Tableau and PowerBI can also directly query relational databases.
Designing Relational Databases for Data Science
Proper database design is key to supporting data science workflows. When designing a relational database for data science, consider these best practices:
Use a star schema for data warehousing – A star schema organizes data into fact and dimension tables. Fact tables store quantitative metrics, while dimension tables store attributes about the data. This structure optimizes the database for analytic queries common in data science. Here‘s an example star schema for analyzing sales data:
+--------------+
| Sales |
| (Fact Table)|
+--------------+
|
+--------+--------+
| | |
+------------+ | +-------------+
| Date | | | Product |
| Dimension | | | Dimension |
+------------+ | +-------------+
|
+--------+
| Store |
|Dimension|
+--------+Normalize for data integrity, denormalize for performance – Normalization reduces data redundancy and inconsistency by dividing data into multiple tables. However, heavily normalized schemas can negatively impact query performance. In data science, it‘s often beneficial to denormalize data by consolidating tables or adding redundant columns to improve query speed. The right balance depends on the specific use case.
Partition large tables – For very large tables, partitioning can significantly improve query performance. Partitioning divides a table into smaller subsets based on a partition key, allowing queries to scan less data. Common partition keys for data science include date, region, or category columns.
Use appropriate data types and indexing – Choosing the right data type for each column conserves space and improves query performance. For example, use DATE or DATETIME types for date columns rather than VARCHAR. Additionally, indexing columns used frequently in WHERE clauses or joins speeds up queries. However, overusing indexes can slow down inserts and updates, so index judiciously.
Leveraging SQL for Data Science
SQL is an indispensable tool for data scientists working with relational databases. Beyond basic queries, SQL provides powerful features for data preparation and analysis. Here are some key SQL skills to master:
Window functions – Window functions perform calculations across a set of rows related to the current row. They‘re useful for calculating running totals, rankings, and moving averages. Here‘s an example query that calculates a running total of sales by date:
SELECT date, sales,
SUM(sales) OVER (ORDER BY date) AS running_total
FROM daily_sales;Common table expressions (CTEs) – CTEs allow you to define temporary result sets that can be referenced in a later part of the query. They make complex queries more readable by breaking them into manageable chunks. CTEs are also helpful for recursive queries. Here‘s an example CTE that calculates the percentage of total sales for each product category:
WITH category_sales AS (
SELECT category, SUM(sales) AS total_sales
FROM product_sales
GROUP BY category
)
SELECT category,
total_sales,
(total_sales / (SELECT SUM(total_sales) FROM category_sales)) * 100 AS pct_of_total
FROM category_sales;User-defined functions – Most SQL dialects support user-defined functions (UDFs) for encapsulating custom logic. UDFs are useful for applying complex transformations or business rules to your data. Here‘s an example UDF in PostgreSQL that calculates the Euclidean distance between two points:
CREATE FUNCTION euclidean_distance(x1 float, y1 float, x2 float, y2 float)
RETURNS float AS $$
BEGIN
RETURN sqrt(power(x1 - x2, 2) + power(y1 - y2, 2));
END;
$$ LANGUAGE plpgsql;The Role of Relational Databases in MLOps
Relational databases also play a key role in MLOps, the practice of collaboration between data science and IT teams to deploy and maintain ML models in production. Here are a few ways relational databases support MLOps:
Model metadata storage – Relational databases are commonly used to store metadata about trained models, such as model version, hyperparameters, performance metrics, and training data version. This information is crucial for model governance and reproducibility.
Feature stores – A feature store is a central repository for storing curated features used in ML models. Relational databases are often used to implement feature stores due to their support for ACID transactions and ability to handle large data volumes. Popular open-source feature stores like Feast and Hopsworks use databases like PostgreSQL and MySQL under the hood.
Experiment tracking – Many MLOps platforms use relational databases to log experiments and their results. Each experiment can be stored as a row in an "experiments" table, with columns for hyperparameters, metrics, artifacts, and more. This allows for easy comparison and reproducibility of experiments.
Advancing Your Database Skills as a Data Scientist
To get the most out of relational databases in your data science career, continually deepen your knowledge and stay current with the latest trends. Here are some tips:
Master advanced SQL concepts – In addition to the techniques covered earlier, dive into topics like query optimization, indexing strategies, and transaction management. Many online courses and books can help you level up your SQL skills.
Explore database extensions for machine learning – Several databases offer extensions that allow in-database machine learning. For example, PostgreSQL‘s MADlib extension provides a suite of ML algorithms that run inside the database. With the rise of GPU-accelerated databases, we can expect more powerful in-database ML capabilities in the future.
Complement SQL with other data skills – While SQL is essential, data scientists should also be proficient in statistical programming languages like Python and R. Skills in data visualization, data wrangling, and using machine learning libraries like scikit-learn and TensorFlow are also valuable. Seek out projects and learning opportunities that allow you to practice integrating SQL with these other tools.
Stay informed about database trends – The database landscape is constantly evolving. Keep an eye on trends like the convergence of transactional and analytical databases (HTAP), serverless and managed database offerings, and the growth of cloud data warehouses. Understanding these developments will help you make informed decisions about database architectures and stay competitive in the job market.
Conclusion
Relational databases are a foundational technology for data science and AI/ML. By designing schemas optimized for analytics, leveraging powerful SQL techniques, and staying up-to-date with the latest database advancements, data scientists can extract maximum value from their data.
Remember, becoming an expert in relational databases takes practice and continuous learning. Take on projects that challenge you to apply your database skills in new ways, and don‘t hesitate to experiment with different database technologies. With dedication and curiosity, you‘ll be well-equipped to harness the full potential of relational databases in your data science career.
