How can you build a scalable and extensible dimensional model for data governance?

If you prioritize simplicity, performance, and ease of maintenance, I recommend choosing the star schema for your dimensional modeling approach. It offers better scalability and extensibility, making it well-suited for data governance and accommodating more facts and dimensions without adding significant complexity or affecting performance.

Data modeling schemas are crucial for structuring and organizing data in SQL (Star and Snowflake schemas) and NoSQL databases. Here's a breakdown of the differences and factors to consider when choosing one: 1) SQL Schemas (Star and Snowflake): 1.1) Star Schema: Simple, denormalized structure for better query performance in data warehousing. 1.2) Snowflake Schema: Normalized structure for data integrity and storage efficiency. 2) NoSQL Database Schemas: 2.1) Document Store: Schema flexibility for evolving data, semi-structured formats (e.g., MongoDB). 2.2) Column-Family Store: Scalable, high-throughput design for write-heavy workloads (e.g., Cassandra). 2.3) Graph Database: Optimized for relationship-heavy data (e.g., Neo4j).

Fact Constellation Schema Fact Constellation is a schema for representing multidimensional model. It is a collection of multiple fact tables having some common dimension tables. It can be viewed as a collection of several star schemas and hence, also known as Galaxy schema. It is one of the widely used schema for Data warehouse designing and it is much more complex than star and snowflake schema. For complex systems, we require fact constellations.

Dimensional modeling offers two main strategies: star and snowflake schemas. A star schema is straightforward, with a central fact table connecting to dimension tables. It's fast and easy to manage but can lead to data redundancy. In contrast, a snowflake schema normalizes some dimensions, reducing redundancy but increasing complexity and query time. Choosing between them depends on your data's nature. Star schemas are often preferred for scalability, while snowflake schemas suit complex hierarchies and data quality concerns. Balancing performance, simplicity, and data complexity guides your choice. The key in dim modeling is finding the schema that aligns best with your data's unique characteristics & your organization's analytical needs.

Few Principles that enhance data models performance Use Conformed Dimensions: If multiple fact tables reuse a dimension (like Date), ensure they all use the same structure and content. This ensures consistency and scalability. Consider Slowly Changing Dimensions (SCDs): Understand how you want to track historical changes. There are various SCD types, including type 1 (overwrite), type 2 (create a new row), and type 3 (add a new column). This is essential for data governance as you track changes in dimensions over time.

When designing meaningful and consistent dimensions data models, one can consider the below points:- 1. Use descriptive names for dimensions and attributes. This will make the data model more understandable for users and easier to maintain. 2. Implement surrogate keys in dimension tables. This will improve data management and performance. 3. Maintain consistent attributes within dimensions. This will improve clarity and accuracy. 4. Use indexes for dimension tables and partitions for fact tables. This can improve query performance. 5. Implement aggregations for fact tables. This can improve query performance for complex queries. 6. Document the data model thoroughly. This will help users to understand and use the data model effectively.

Having consistency in the column naming convention is really important. Have same attributed named different across different database table causes problems.

Not all dimensions are created equally. Consider a date dimension there are several calendar variations that may be required depending on the use case required, I.e. - Gregorian Calendar - ISO Calendar - Fiscal Calendar - other Calendar reprentations The basis of each can still be aligned to the same date grain, but rolls up differently across weeks, months / periods. This scenario can also apply when considering the localisation aspect of some dimensions for various stakeholders I.e. how a Telephone number is formatted or an address is referenced, these cultural considerations impact upon how conformed a dimension can be across variety of use cases with different locations or cultural norms, some effort should be taken to align the grain.

While creating data model we also need to consider about the data we would receive from the sources and utilisation of the data in the downstream systems. So in case of facts dedupe is a must to reduce duplication and we should maintain history vs main tables using strategy to merge update or merge insert based on the analysis of data profiled .

To design meaningful and consistent dimensions, I follow the below approach: Firstly, clear naming conventions and standardized attributes should be established to ensure uniformity and understanding across the organization. Secondly, it's crucial to maintain consistency in attributes, including data types, formats, and units of measurement, to support accurate analysis. Thirdly, the use of primary keys, foreign keys, and surrogate keys should be well-defined to establish data integrity and relationships within the dimension. Lastly, subdimensions can be employed when dealing with complex hierarchies or attributes that require further categorization, enhancing the dimension's comprehensiveness and usability.

Consider the significance of Data Lineage and Metadata collection in bolstering your security protocols. Data Lineage: This involves monitoring each data item's origin. It offers insights into data's journey - its source, transformations, and final destination. This is crucial for pinpointing source errors, gauging the effects of upstream data changes, and maintaining transparency. Familiarity with each data element's journey ensures trust in its authenticity. Metadata Management or the maintenance of a data dictionary is essential for multiple reasons Clear Understanding Uniformity Streamlined Operations Data Integrity

Applying top-notch data quality and security measures in data governance is non-negotiable. They ensure our insights are spot-on, build user trust, keep us compliant, shield our business secrets, and ensure operations run without hitches. It's like having a strong security system with a meticulous housekeeper; nothing gets past them, and everything stays pristine! With ever-evolving data regulations (like GDPR, CCPA), ensuring data security is not just best practice but often a legal mandate. Secure data safeguards an organization from potential legal repercussions and financial penalties.

The best practices I follow to ensure effective data quality and data security in dimensional modeling are: Data Quality Measures: Implement data validation and cleansing rules in the data pipeline. Monitor data quality using indicators and metrics (completeness, validity, consistency, timeliness, accuracy). Data Security Measures: Define data security roles and permissions based on data sensitivity. Use encryption and masking techniques to protect sensitive or personal data. These best practices have helped me maintain reliable, accurate, and secure data, ensuring compliance with governance policies and standards.

In preparing dimensional modeling, think of the customer and prevent reputational risk by removing any PII data from customer surveys using Qualtrics XM and collect information that is fit for purpose for the goal of the customer survey. You may use AWS Glue or AWS Glue Studio to detect sensitive data such as PII before it is written into a data lake as part of the data integration process. You may utilise AWS Glue Data Quality create rules to monitor the quality of the data to check the health of the data as some surveys might include contact numbers and postal addresses. By performing data quality checks and scheduling or running batch jobs you will receive a data quality score. For data security, define IAM roles for the project team.

Few Principles that can enhance models performance Use Conformed Dimensions: If multiple fact tables reuse a dimension (like Date), ensure they all use the same structure and content. This ensures consistency and scalability. Consider Slowly Changing Dimensions (SCDs): Understand how you want to track historical changes. There are various SCD types, including type 1 (overwrite), type 2 (create a new row), and type 3 (add a new column). This is essential for data governance as you track changes in dimensions over time. Implement Efficient Indexing: Use indexing techniques like star-join optimizations, bitmap indexes, and partitioning for scalability.

Horizontal partitioning, also known as sharding, stores table rows in different data‐ base clusters. Each partition has the same schema and columns but contains different rows of data, it allows an otherwise too-large database the ability to scale out horizontally (Not applicable on Snowflake). Vertical partitioning is undertaken by creating tables with fewer columns and storing the remaining columns in additional tables; a practice commonly known as row splitting. Vertical partitioning is a physical optimization technique, but it can be combined with a conceptual optimization technique known as normalization.

Elaborating Partitioning, a relational database will result in large tables divided into many smaller parts, or partitions. Databases are most often partitioned to improve scalability, assist in backup and recovery, improve security, and improve query performance. There are two traditional methods by which relational databases can be partitioned: horizontal and vertical.

This not only boosts data usability and analysis but also supports scalability and adaptability to evolving data needs. Additionally, it is key for scalability and extensibility.

The best practices that I follow to optimize a dimensional model for performance are: Indexes for Dimension Tables: Use indexes to improve query speed and efficiency for dimension tables. Partitions for Fact Tables: Divide data into smaller chunks based on criteria like date or region to enhance data loading and updating processes for fact tables. Aggregations for Fact Tables: Pre-calculate common calculations (e.g., sums, averages) to improve query performance for large or complex fact tables.

We can use DLT framework of databricks with S3+alation as good governance pipeline .Since DQ and data governance has been priority while designing the data model we need to consider schema specifically for PII and client usable one.

- One of the most important things to keep in mind when building a scalable and extensible dimensional model is to design the model with flexibility in mind. This means avoiding hard-coding any business rules or assumptions into the model. Instead, you should design the model in a way that makes it easy to add new dimensions and facts to the model, as well as to change the existing dimensions and facts in the model. - It is important to document your dimensional model thoroughly. The documentation should include a description of the different dimensions and facts in the model and the relationships between them.

To build a scalable and extensible dimensional model for data governance, consider business needs, employ both top-down and bottom-up approaches, and use star schema or snowflake designs based on data complexity. Include confirmed dimensions, incorporate compliance and business rules, ensure data privacy and security, implement access policies, scheduling, and monitoring for data quality. Prioritize extensibility, thorough documentation, collaboration, and alignment with business goals. Continuously evaluate and adapt the model to meet evolving business requirements while maintaining data quality and security.

I would add that efficient indexing is also fundamental for optimizing query performance in a dimensional model. Techniques like star join optimizations, bitmap indexes, and partitioning can significantly enhance scalability and query speed. Ensure that indexing strategies are aligned with your specific data governance needs, as they can impact data retrieval and updates.