What are the best data warehouse design practices for academic and industry sources?

In my experience datawarehouse architecture should be flexible enough to allow any future changes. Config/metadata driven is one of the robust design. Always plan for archival of old data (keep only latest max 5 years of data)

So now you have managed to wade through the very dry old school data warehouse approach think about how AI and LLMs now disrupt it all. I hv already seen products like #glean which can do most of the things covered here but without any of this infrastructure For anyone who doesn’t have this setup then I would recommend going straight to a LLM and making that your knowledge base

Clear Objectives: Define clear business objectives and data requirements. Schema Design: Choose a suitable schema (e.g., star or snowflake) based on analysis needs. Data Integration: Integrate data from diverse sources using ETL processes. Normalization: Normalize data to reduce redundancy. Data Quality: Ensure data quality through validation and cleansing. Historical Data: Capture historical data for trend analysis. Scalability: Design for scalability to accommodate growth. Performance: Optimize queries for fast retrieval. Security: Implement role-based access control and encryption. Metadata Management: Manage metadata for data lineage. Testing: Rigorously test data and queries. Version Control or schema changes.

It is always good to start from question of interest for Business. The designer should aim to get 2 major questions answered from user. 1. What depth of data will be queried? 2. On what perspectives the data is expected to be viewed? Once the answers are received the scope for building the MVP is ready. Design practice should include: - source system and interface agreements - Design data model: Build mix of detailed level of data as EDW layer & fact dimensional schema(consumption layer). This helps in fulfilling further consumptionrequirements faster. - Choose a portable ETL/ELT Technology. - Create a Bus Matrix of the fact and dimensions to have control on what data the business is exposed to. Non Functional aspects to be covered.

Creating a versatile data warehouse involves: 1. Data Profiling: Understand data types, formats, and volume. 2. ETL Implementation: Extract, transform, and load data into a standard format. 3. Data Modeling: Choose suitable models for analytical queries. 4. Data Quality Assurance: Ensure accuracy and detect anomalies. 5. Table Management: Enhance performance with partitioning and indexing. 6. Scalability and Optimization: Plan for growth and optimize queries. 7. Security and Access Control: Protect data with robust security. 8. Data Versioning and Auditing: Maintain versions and track changes. 9. Metadata Management and Backup: Document data and ensure backups. 10. Performance Monitoring and Tuning: Continuously optimize performance.

Two main approaches to data warehouse modeling are:- Dimensional modeling: Its a data modeling technique that is optimized for analytical queries. It organizes the data into facts and dimensions, where facts are the quantitative measures of interest and dimensions are the descriptive attributes that define the context of the facts. Dimensional models are used to perform complex analytical queries. Relational modeling: This is a data modeling technique that is optimized for data integrity and maintenance. It stores the data in normalized tables that reduce redundancy and ensure data integrity in 3NF schema. Eliminates any dependencies or anomalies and data accuracy.

IMO, a hybrid approach to be taken (Normalized for EDW Maintenance + Dimensional for Consumption). There should be a Normalized Layer at source level of detail (possibly not enforcing the integrity but handling via ETL/ELT). This layer can be built faster as most of the cues will be taken from Source structures. And the Consumption Layer to be restricted only on the fields of interest for Business. There are pros and Cons. Cons being additional layer to be processed in between; Pros includes time to market for solutions requiring attribute level changes, that can be quickly fetched from already built relational layer.

Improper "Data Warehouse Modeling" poses significant risks. - Redundant data storage, like storing unchanged datasets daily, bloats costs and affects performance. - Over-normalization complicates queries, while rigid schema designs hinder adaptability to evolving needs. - Inadequate granularity can skew analyses, and poor historical data handling erases valuable context. Legacy implementations often magnify these issues, being constrained by outdated designs and practices, leading to compounded inefficiencies and analytical inaccuracies.

I would suggest hybrid approach but it depends on the requirements and the underlying technology because performance shouldn't be compromised and cost should be under control. Build either a dimensional, relational or hybrid model, data should be materialised into summary tables for the most frequent queries to improve the performance. Historical picture models should be verified thoroughly. Create views for the consumables to manage access, performance and usage.

Various Layers:- 1. Dumping Layer (Source):- Dump the whole data. 2. Staging Layer (Basic transformations and column selection from tables) for both dimensions and facts. Like setting up the incremental and full load logic on the tables. Data Quality standards can be setup here. 3. Transformation Layer:- If any further advanced calculations/logic need to build and data gets stored in dimensions/facts. Forming of Surrogate keys and loading main tables is done in this layer. 4. Visualisation Layer:- Exposing the data from join of both dimension and fact by forming reporting views to and applying masking policies according to different roles (Data security). 5. Automate the whole flow with an orchestration tool (Airflow, Control M).

When designing a Data Warehouse, ETL considerations are pivotal: -> Extraction - Incremental vs. Full -> Optimality & Quality: Data Partitioning, Custom Transformations, Data Cleansing -> Error Handling & Validation: Anticipate inconsistencies, integrate robust error-catching mechanisms; confirm data integrity post-ETL, ensuring accurate and reliable analytics. -> Utilise Parallel Processing to adeptly manage vast data sets. -> Maintain mechanism for continuous log analysis and active monitoring to promptly identify and address potential bottlenecks. Strategic ETL choices ensure data warehouse efficiency and reliability.

ETL processing timing is important. Schedule loading of larger data sets for after hours to avoid resource conflicts during peak usage times for the end users. Smaller, frequently changed data should be loaded more frequently in manageable bursts.

Data warehouse architecture best practices involve designing a robust & efficient framework to store, manage, and retrieve data for analytical purposes. Understanding the specific analytical needs and long-term business goals & Align the architecture with these objectives. Some key points which I think is important: Scalability: Design for scalability to accommodate data growth. Consider cloud-based solutions that offer elasticity and easy scalability. Data Quality: Ensure data quality and consistency by implementing data cleansing, validation and enrichment processes. Monitor and address data quality issues proactively. Data Security: Implement robust security measures to protect sensitive data. Metadata Management and Backup and Recovery

As mentioned earlier, keeping data quality high in a Data Warehouse is crucial and a continuous task. - Start by getting to know your data with profiling. Deduplication at the early stages helps. - Clean and double-check it regularly through an audit mechanism. Keep track of all changes with audit trails for clarity. - Use metadata as your 'how-to guide', explaining data details. - And don’t forget to train your team, so everyone knows the importance of good data.

Some common practices that could apply to Datawarehouse for security and privacy include: a) Conducting a data classification and risk assessment to identify the data elements, sources, and destinations that need protection b) Applying the principle of least privilege and role-based access control to limit the access and permissions of users and applications to the data warehouse c) Implementing encryption, masking, or anonymization techniques to protect the data in transit and at rest d) Establishing data quality and integrity checks to ensure the accuracy, completeness and consistency e) Developing and enforcing data governance policies and procedures to define the roles, responsibilities and standards for data collection.

Ensuring data safety in a Data Warehouse is paramount. - Adopt role-based access to limit data visibility. - Use data encryption for protection during file transfer and storage. - Maintain audit trails to track activities. - Apply data masking for sensitive details. - Conduct regular security audits to spot vulnerabilities. - Have robust backup and recovery plans. - Follow strict data retention policies. Stay compliant with regulations like GDPR and HIPAA. - Educate users and strengthen network security for better protection.

Internal data sandboxing is important to ensure an "eyes only" approach to sensitive information across organizational departments.

an example that comes to mind, is tending to a data warehouse in a hospital, dealing with data that is used to measure efficiencies in the facility. as we all know, a few years ago COVID 19 was such a disruptive force, it also had a huge impact on data, as the epidemic was evolving in a rapid pace, data needed to be updated and ingested faster than ever, as well as some of the attributes. understanding the impact of such an event, and having data warehouse teams at the ready, was crucial to the success of getting the right information at the right time.

One major evolution while designing data warehouse, especially ETL process is "WHERE does the transformation happen and HOW to store raw data?" Traditionally transformation was applied as we extract data from the sources. So, Datawarehouse had to be designed in advance to support this approach. With modern approach, data storage being cheaper than ever and processing power on its rise, All the raw data(which can go up to 100's of TBs to PBs) are stored in cheap data storages/lakes and later data can be enriched/transformed in multiple stages by following the Medallion architecture.