Partition Strategy for Efficiency and Performance
In the Benefits of Partitioning section, you learned how partitioning helps with performance, scale, security, availability, and so on.
The following are some strategies to be kept in mind while designing for efficiency and performance:
- Partition datasets into smaller chunks that can be run with optimal parallelism for multiple queries.
- Partition the data so that queries don’t end up requiring too much data from other partitions—that is, minimize cross-partition data transfers.
- Design effective folder structures to improve the efficiency of data reads and writes. The following is the sample directory structure that you can create within ADLS Gen2 (Figure 2.4):
![Figure 2.4 - An organizational diagram illustrates the hierarchical directory structure within Azure Data Lake Storage Gen2 for sales data. At the top level is “Sales data,” which branches into four subdirectories: “Raw data” with a nested “Source files” subdirectory, “Staging area,” “Processed data” and “Reports.” The structure is depicted with clear lines indicating the relationship and flow between each directory level.](https://static.packt-cdn.com/products/9781805124689/graphics/image/B21126_02_04.jpg)
Figure 2.4 – The directory structure within ADLS Gen2
- Use descriptive names for files, reflecting their content, date, or purpose; for example, “sales_data_2024_q1.csv.” Also consider...