How can you create a data warehousing project plan?

Few more things to consider at this stage around a well architectured design 1. Develop a robust data architecture for scalability and flexibility 2. Define Ingestion Strategy 3. Define data massaging and transformation procedures 4. Choose an appropriate technology stack 5. Differentiate between Data Mesh and Data Lakes 6. Define a purpose-driven data access strategy 7. Implement robust authorization and performance mechanisms 8. Establish monitoring and observability 9. Define guardrails and streaming solutions for real-time data processing and analysis 10. Structure data to meet regulatory requirements such as GDPR 11. Implement storage solutions and multi-cloud strategy 12. Develop policies for data archiving, purge, and hydration

I think every organization should have this. Most organizations begin with analytics, but they might not realize the benefits of having a data warehouse. A useful data warehouse can handle various business situations without being limited by a specific use case. A good data warehouse is flexible and can solve different problems.

I would like to add below point help in good business requirements: 1- Stakeholder Analysis: Identify key stakeholders to understand diverse requirements. 2- Data Governance Policies: Propose policies for data quality, security, and compliance with regulations. 3- Performance Metrics: Suggest specific KPIs the data warehouse should track. 4- Scalability and Future-Proofing: Ensure design accommodates future growth and technological advancements. 5- Integration with Existing Systems: Stress the importance of seamless integration for data consistency. Prototype Development: Advocate for a prototype to validate requirements and test feasibility. 6- Prototype Development: Advocate for a prototype to validate requirements and test feasibility.

I want to add a few things apart from what's already listed, we must keep the following factors in mind: - Is the given set of requirements even eligible for data warehouse - what's the level of granularity for the data model that will help you identify the model to be a star, snowflake, or galaxy schema as per the complexity of the system - Do you need SummaryFact for pre-calculated columns - Use the data storage optimization techniques as you won't get enough chance to reindex or index the millions of rows at a later stage - Remember one thing, HISTORY CAN'T BE UPDATED OR DELETED and Data warehouse is all about HISTORICAL DATA

We have so much data around us and there can be data driven decision making for how are we doing ? What can be improved and what can be acheived . Datawarehouse is required to make analytics by OLAP and OLTP from structured and unstructured data . Datawarehouse does this process of converting unstructured to structured data required for analystics . Lakehouse is a key requirements and then ETL process comes into picture.

This is probably the most important step for any data project- getting the data model right at the beginning is 80% of the battle won!

One thing that really helps is breaking down the data lake/DWH requirements into product areas, lines of business so that data models and data integration deliveries can be in iterations.

Date modeling is the pillar of any Data related project. How you define your table structure, your schema and keys to relate and connect the tables are very important not only from your query performance perspective but also from data storage , cost purpose. Data models are the stepping stones towards a successful data warehouse and helps you with an optimized warehouse solutions .

For good data model below some points better to add on our mind: 1- Data Normalization: Focus on data normalization for consistency and reduced redundancy. 2- Historical Data Strategy: Plan for effective management of historical data for trends and forecasting. 3- Efficient ETL Processes: Implement robust ETL processes for data integration and transformation. 4- Agile Approach: Use an agile modeling method for iterative development and flexibility. 5- Data Security Considerations: Incorporate data security and privacy measures in the model. 6- Comprehensive Metadata Management: Ensure thorough metadata management for data understanding and use.

Data modelling covers the logical data model which is based on the functional relationship between the different entities and the atomicity of the data. Data modelling should also be designed flexible enough to accommodate functional changes and reasonable addition of data elements in the future. Avoid orphaned tables.

Key points of data integration is related in: - How mapping data from origin sources to standardized data. - How define standardized data to support enterprises business requirements. - What data quality, accountable, privacy and security rules apply in standardized data. - How is stored in data modelings.

I would begin with understanding the file formats, frequency of updates, data inserting vs data updates, methodologies used to update dimensional data type 0, type 1, type 2 etc. Requirements to artificially maintain history etc. ETL tool should be picked based on the ease of use as well as the familiarity your team has with various programming languages and tools. On premises vs cloud will also effect the flexibility we have. Realtime data such as streaming data vs relational data be non relational or big data will also impact the ETL and reporting tool used.

For data warehousing project delivery most important is to design the Extract, Transform, Load (ETL) process to collect, cleanse, and transform source data. Also, choose the most suitable tools and frameworks to support the ETL process and develop scripts or workflows for efficient data extraction and transformation.

In my experience, this is the most challenging process in setting up data ware house. Legacy systems often use outdated or varied data formats, making integration into a modern data warehouse (potentially in cloud) challenging. This complexity can lead to unexpected challenges like out of support versions, increased costs and extended timelines. Migrating data from legacy systems to a new data warehouse may have issues like data loss, corruption, or compatibility problems.

Data governance rules are also best established and clearly documented at this phase of the design so the precedents can be correctly set and followed by the entire team as the database grows in both scale and complexity. This helps maintain a cohesive data warehousing environment as a whole.

Estimation and cost analysis is really important as far as meeting the timelines of a data warehouse project is concerned. We need to properly understand the business requirements , number of components required in the project and estimate the time and effort required for a project so that you can do your cost analysis. Detail level report with charts and detailed data on the exact ask of a project helps you to develop close to accurate estimates. Project not meeting the delivery dates is a common problem in any industry and that essentially is to do with wrong estimation or under estimating the requirements of the project.

Understanding the technical skills and roles required is critical. This includes having skilled database administrators, ETL developers, data architects, and other technical experts who can navigate the intricacies of data warehousing technologies. Assessing the availability and proficiency of these technical resources contributes to realistic estimations. In terms of financial resources, a detailed breakdown of costs associated with hardware procurement, software licensing, and salaries must be meticulously examined. Additionally, accounting for potential scalability needs and technology upgrades is crucial to avoid unforeseen costs during project execution

Estimate resources and costs in follows stages: - Understanding business requirements (requirements gathering) - Mapping origin sources (requirements gathering) - Defining and standardizing of business unified data (design) - Designing and modeling architecture components (design) - Implementation of architecture components (build) - Test of data architecture operations (test) - Validation with analytics users about enviroment (validation) you need to be closest as possible to final general costs.

I would go a step further and say this is not enough. You need to implement a full FinOps program to be successful. This will span more than just a single data warehousing project, but will really drive accountability across all of your IT estate.

Other than what has been shared above, there has to be a plan as how do we migrate data from various other sources as datawarehouse will have a lot of historical data that has to be made available.

Assess potential vulnerabilities in data storage, transmission, and access, ensuring robust measures are in place to safeguard sensitive information. Additionally, evaluate the impact of evolving technology trends, such as emerging data formats or storage solutions, on the project's technical landscape. Adapting to these advancements proactively can prevent obsolescence and enhance the project's technical longevity. Furthermore, scrutinize regulatory compliance risks, ensuring that the technical infrastructure aligns with data protection and privacy regulations, such as GDPR or HIPAA.

Allocating resources for exploring new technologies and implementing innovative solutions to improve data warehousing operations and efficiency. Be flexible and adapt the data warehouse to evolving business requirements and ensure its long-term value and relevance.

Keep the following points in mind: - How data warehouse system can improve all business areas from data products. - Why each step of project lifecycle is needed to achieve goals. - How each step affect business areas positivily. this points must be clear when you communicate with stakeholder all time: these are reasons to support the project.

When communicating the project plan always ask the question "how will this data architecture potentially change in the next 3-5 years?". By answering this question you will have the ability to prepare for the potential data scalability down the road. It is inevitable that new or more robust data will be integrated in the environment depending on how much the business grows. So its important to work with internal and external stakeholders to really communicate the project plan in advance. Doing the work in the beginning allows the organization to be prepared in the near future for any drastic changes.

One of the key improvements in data warehousing project delivery is to populate the data warehouse manually so that both ELT and BI teams can work simultaneously, thus shorten the project duration. Traditionally, in the first few months of the project, the BI team is unable to work, waiting for the ELT team to finish their work and provide them with data. Another way to boost productivity is to shred the work into stripes, giving each stripe to the E, DQ, T, L and BI person sequentially like a production line in the factory.

High level data warehousing can be expanded into further development be it either with creating data marts first and forecasting on counting the enterprise data warehouse dimension confirmations over usage which is usually the Kimball approach or be the other way round with construction of EDW firstly and then spinning the data marts from the single EDW models which usually takes the Inmon route. Dependency of the route is usually as per the companies data strategy plans, the user bandwidth and finally the overall execution process & timelines depending on the historical data that needs to be processed for the datawarehousing plan.

Another thing to consider is a use of chat, and the infrastructure of an LLM in today’s world is not that difficult to create them even from open source, and when you look at automating data governance and stewardship, as well as the users layer, critical and friendly, very simple, compared to the processes Required for complete data governance ,conversational data governance the AI community will soon faith and realize that without the artifacts that are created as an essential part of the governance process you can not create an effective chat, application or LLM from just transactional data in order to support effective prompts and quality answers you’ll need both

Extending Carlos’s point above as a Data Warehouse is always growing and expanding with the organization’s data needs it’s less of a point system and more of an ongoing process. That said there must be a conscious enterprise decision to fund and support the data warehouse including expansion and ongoing maintenance. Non-IT stakeholders are often shocked by the resources and commitment required for Data Management and must be completely bought-in before an Enterprise DW is initiated.

Define evolutionary plan to add, change things and remove things related business requirements: your data warehouse system is live system because it's about processes, goals, systems and teams needs.