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When you’re managing a large amount of data, it’s vital to have an SQL database model that makes sense for data analysts. Otherwise, they won’t be able to make sense of the complex datasets and get the insights they need.

With SQL, you can optimize your data models for readability, easy debugging, and effective data management. However, there are a few common problems that crop up, leading to future tech debt and more difficult debugging. 

In this post, we’ll look at some of the main issues you might encounter with the SQL data modeling process, and show you the best practices for fixing them.

What is data modeling with SQL?

Data modeling is the process of creating a visual representation of an information system, and SQL is the standard universal language for handling data. Most analytics engineers use it for everything from data ingestion to visualization.

Using SQL for data modeling involves organizing information into multiple tables, giving structure to the data and making it possible to analyze. Data modelers are responsible for turning complicated, raw data into something usable. 

For example, a VoIP phone provider might have gathered data about all of its subscribers, from their email addresses to the date of their first order. They want to email all of their lapsed subscribers with a new offer to tempt them back. In order to do this, they need to be able to sort through the data to find the right targets. Without a data modeler’s input, this would be incredibly challenging. With it, it’s a simple question of using the right query.

Data models are also the language of REST APIs, which facilitate data-sharing and interaction between apps and servers. These won’t be effective unless you take a consistent approach to data model formatting and validation—which has implications for API examples such as secure data transfers and supply chain visibility.

How to Solve Common SQL Data Modeling Problems

No matter where you get your data from – whether it’s via delta streams, manual input, or purchasing pre-populated data bases – you may encounter issues when it comes to modeling. To avoid problems, follow these best practices:

Create base models for raw data

Ideally, you should never be working directly with raw data. That way, if something goes wrong with your data model, the actual data will remain unaffected.

One way to do this is through using dbt. This open-source data transformation tool makes it easier to implement a modular structure, which allows you to use multiple layers. Its documentation recommends using three models: base, staging (otherwise known as ‘intermediate’), and core.

The base model is a view that sits on top of your raw data table and references it directly. It can include key SQL functions like renaming columns. 

Use the correct joins

In data modeling, SQL joins are how you merge two tables on a common field. If you don’t use the correct one, your code will run slowly – and you might create null fields, making your data messy and more difficult to use.

There are four main methods of joining:

  • Left join: this selects all the data from the left table but only the matching records from the right.
  • Right join: the opposite of a left join—they select all of the data from the right table and the matching records from the left
  • Inner join: this selects only the data that matches in both tables (like the middle section of a venn diagram)
  • Full outer join: this selects all the data from both tables

Because the majority of languages are read left-to-right, it’s easier to understand left joins. So, when modeling with SQL, it’s best to use this instead of right joins. Inner joins are ideal if you don’t want null values in your resulting table, because they select only records that are present in both tables.

Full outer joins should be avoided unless absolutely necessary, due to their tendency to introduce null columns.

Imagine you’ve got a fully digital recruitment process. The left table features candidate IDs and their contact details, and the right has their IDs and associated application form. If you do a left join, you’ll ensure that you have contact details associated with every application form.

A full outer join, however, might leave you with applications that aren’t associated with contact details, and vice versa.

Standardize naming of columns and models

Web scraping is a valuable source of data for SQL data modeling. When incorporating web-scraped data into your models, it’s essential to consider the unique challenges it presents. Web-scraped data often contains diverse information, such as business names, CEO names, and contact numbers. To maintain the integrity and clarity of your data models, it’s crucial to apply consistent naming conventions

Using random names and abbreviations may make sense to you, but the data models will be hard to understand when someone else is debugging or rewriting. 

Imagine you’re dealing with data you’ve obtained. It features business names, CEO names, and contact numbers. If you name these columns ‘bname’ ‘cname’ and ‘number’, subsequent users will find it difficult to follow. Instead, choose names that accurately describe the data they contain – for instance, ‘business_name’, ‘CEO_name’, and ‘business_phone_number’. The same applies to model names – they should accurately reflect the function they perform. 

Additionally, make sure to have a clear style guide in order to encourage consistency. Use standardized capitalization, tenses, and date formatting in order to avoid future tech debt.

Minimize duplicates and null values early

You’ll also hamper the performance of your data models by not replacing null values and removing duplicates early in the process. It’s far better to do this before you join the two tables, as otherwise it will slow it down. 

SQL can help you to replace null values when bringing in data columns from source tables. Use the COALESCE() function to scan a list and return the first non-null value, or IFNULL() to replace null values in a column with a pre-determined default. Another option is to filter out rows with null values for certain columns, instead of replacing them.

You can use DISTINCT to remove duplicates, or find duplicate rows with the GROUP BY column HAVING COUNT(*) >1 query. The ROW_NUMBER() window function assigns a combination of column values to a number, before filtering the numbers for only a row number of one.
 

Use CTEs, not subqueries

We’ve already mentioned the importance of readability for subsequent users. One way to ensure this is through avoiding subqueries and relying on CTEs instead. A CTE (common table expression) involves stringing together a list of multiple queries, each reliant on the one before it. This makes it easier to follow and understand exactly what is being asked of the data.

By contrast, a subquery performs the same function but reads from statements rather than table names. This can make it more difficult to understand, and increases the risk of misunderstanding and future errors.

Use the right tools

You can enhance SQL data modeling tasks with the right tools, such as those that simplify data streaming. Spark Structured Streaming unlocks data streaming on the Databricks Lakehouse Platform, and allows data teams to build streaming data workloads with the languages and tools they already know—including SQL. 

Staying updated with the latest developments in SQL and data modeling through relevant I.T training courses can provide you with valuable insights and skills to tackle complex challenges effectively.

Furthermore, consider incorporating infrastructure automation solutions to streamline the management and scaling of your SQL data modeling infrastructure. This can help you automate routine tasks, ensure resource efficiency, and reduce the risk of human error in maintaining your data modeling environment.

Key takeaway

Successful SQL data models are fast, efficient, and easy for data analysts to read and understand.

A key part of any data science pipeline, it’s vital to avoid problems by using base models, the right type of join, and the right tools. Ensure the code is easy to understand by giving your models and columns standardized names and using CTEs instead of subqueries.

If you keep readability and performance in mind when writing SQL code, you’ll be able to create high-quality SQL data models for anything from data governance to business intelligence.