In Part 2 of this series on Data Management, we’ll talk about good data structure. If you missed Part 1, I recommend going back and reading it first.

A good data structure is a way of organizing and storing data that optimizes the efficiency and effectiveness of operations performed on that data.

Choosing the right data structure is crucial for achieving optimal performance in terms of memory usage, access speed, and ease of manipulation.

This is especially critical in Power BI if you don’t use an enterprise data warehouse (EDW) because Power BI is acting like the database that aggregates all of your disparate sources of data.

Here are some key characteristics of a good data structure:

Efficient Access

A good data structure allows for fast and easy access to the stored data. This means that retrieval, insertion, updating, and deletion operations can be performed quickly and with minimal computational overhead.

Optimized Memory Usage

The data structure should use memory efficiently, minimizing wastage and unnecessary storage overhead. This is especially important when dealing with large datasets.

Balanced Trade-offs

Different data structures excel in different types of operations. A good data structure strikes a balance between the types of operations that will be performed most frequently. For example, some data structures might be optimized for fast lookups, while others are better for insertion and deletion operations.

Scalability

A good data structure should be able to handle growing datasets without a significant drop in performance. This includes both the size of the data and the number of operations being performed.

Easy Maintenance

The data structure should be easy to understand, implement, and maintain. A complex data structure might provide excellent performance, but if it's difficult to work with or understand, it can lead to increased development time and potential errors.

Appropriate for the Task

Different tasks require different data structures. For example, if you need to maintain a collection of elements with no specific order, a hash table might be appropriate. If you need to keep elements sorted, a balanced binary search tree could be a better choice.

Flexibility

A versatile data structure can handle a variety of scenarios without major modifications. For instance, a dynamic array can grow and shrink as needed, making it suitable for situations where the size of the data is uncertain.

Compatibility with Algorithms

Certain algorithms work well with specific data structures. A good data structure complements the algorithms you plan to use, enhancing their efficiency.

Support for Required Operations

Consider the types of operations you'll frequently perform on the data, such as searching, sorting, filtering, and merging. Choose a data structure that aligns with these operations to ensure efficient processing.

Maintaining Data Integrity

In some cases, data structures need to ensure certain integrity rules are maintained, such as uniqueness or consistency. Choose a data structure that supports these requirements.

Support for Concurrency

If your application needs to support multiple users accessing and modifying data simultaneously, the data structure should provide mechanisms for managing concurrency safely.

Examples of good data structures include arrays (for fast indexing), linked lists (for easy insertions and deletions), hash tables (for fast lookups), binary search trees (for sorted data), and graphs (for representing complex relationships).

The choice of data structure depends on the specific requirements of your application and the types of operations you need to perform efficiently.

Now, onto Part 3: Bad Data Structure