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You’re in charge of a data warehousing project. How can you ensure that deadlines are met?

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Data warehousing projects can be complex and challenging, especially when you have to deal with multiple sources, transformations, models, and reports. As a project manager, you want to deliver a high-quality data warehouse that meets the expectations and needs of your stakeholders, while also staying within the budget and timeline. How can you ensure that deadlines are met without compromising the quality and value of your data warehouse? Here are some tips to help you plan, execute, and monitor your data warehousing project effectively.

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Ulhas Patil

Managing the Services for a global leading SAAS Based Talent Management Solution Company in Japan
Shreyas Shukla

I Can Help You learn AI || IIT Kharagpur Alum || Microsoft Azure Certified Data Engineer Associate || 3 × Azure ||…

1 Define the scope and objectives

The first step in any data warehousing project is to define the scope and objectives clearly. What are the business problems that you are trying to solve with your data warehouse? What are the key performance indicators (KPIs) that you want to measure and analyze? What are the data sources, formats, and quality requirements that you need to consider? What are the expected deliverables, milestones, and dependencies that you need to follow? By answering these questions, you can establish a clear vision and scope for your project, and communicate it to your stakeholders and team members. This will help you avoid scope creep, manage expectations, and align your project with the business goals.

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Ulhas Patil

Managing the Services for a global leading SAAS Based Talent Management Solution Company in Japan
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Amongst many best practices and do`s & dont`s, below are the most critical ones to ensure that deadlines are met.... 1> Don`t over commit and do the change management diligently. 2> Define scope and objective clearly. 3> Consider stake holders, data sources, existing and expected quality of data while planning and estimations. 4> Manage and monitor dependencies and risks timely. 5> Define and control the critical path assiduously. 6> Ensure all stake holders are in sync always. 7> To do this effectively ---> Plan <-> Monitor <-> Communicate. Regards, Ulhas

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Shreyas Shukla

I Can Help You learn AI || IIT Kharagpur Alum || Microsoft Azure Certified Data Engineer Associate || 3 × Azure || Python || SQL || 5?SQL HackerRank
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In a data warehousing project, we need to outline the boundaries of the project, including the data sources, technologies, and functionalities to be included. We also have to define the specific goals to be achieved through the project, such as improving data accessibility, enabling better analytics, or supporting decision-making processes. These objectives should align with the overall goals of the organization and provide measurable outcomes for success.

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AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
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Define clear and quantifiable goals to facilitate effective monitoring and evaluation. Conduct a risk analysis to identify potential challenges and obstacles. Plan for educational sessions to familiarize users with the data warehouse interface and functionalities.

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2 Design the architecture and model

The next step is to design the architecture and model of your data warehouse. This involves choosing the best approach and tools for your data integration, storage, processing, and presentation layers. For example, you might decide to use a cloud-based or on-premise platform, a dimensional or relational model, an ETL or ELT process, a star or snowflake schema, and a BI or self-service tool. You should also consider the scalability, performance, security, and governance aspects of your data warehouse. By designing a robust and flexible architecture and model, you can ensure that your data warehouse can handle the current and future data needs of your organization.

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AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
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Consider dimensional modeling for analytical queries and relational modeling for transactional systems. Define the Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes. Consider storage optimization techniques to manage costs and performance. Develop a robust backup and recovery strategy for your data warehouse. Provide resources and support to help users effectively leverage the data warehouse for analysis.

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3 Develop and test the components

The third step is to develop and test the components of your data warehouse. This involves writing the code, scripts, queries, and reports that will extract, transform, load, and analyze your data. You should also perform various tests to ensure that your components are working correctly and efficiently. For example, you might conduct unit tests, integration tests, regression tests, performance tests, and user acceptance tests. You should also document your code, logic, and assumptions, and follow the best practices and standards for your data warehousing environment. By developing and testing your components thoroughly, you can ensure that your data warehouse is reliable and accurate.

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Shreyas Shukla

I Can Help You learn AI || IIT Kharagpur Alum || Microsoft Azure Certified Data Engineer Associate || 3 × Azure || Python || SQL || 5?SQL HackerRank
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So as to make data warehouse efficient, I always believe in rigorous testing of various components of data warehouse in order to avoid any potential malfunction. This involves unit test where we test procedure, SQL script, UNIX shell etc. as a separate component, Integration test in which we test various modules of Data Warehouse taken together and lastly system testing in which entire Data Warehouse application as a single entity is put to testing and checked for efficiency and proper functioning. In all of the testing process, it is important that best practices, documentation and standards are maintained.

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AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
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Implement data quality validation processes to ensure accuracy and consistency. Collaborate with end-users to conduct user acceptance testing. Implement regression testing to identify and address unintended consequences of code changes.

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4 Deploy and maintain the data warehouse

The final step is to deploy and maintain your data warehouse. This involves moving your components from the development to the production environment, and ensuring that they run smoothly and securely. You should also monitor and troubleshoot any issues that might arise, and provide support and training to your end users. You should also review and update your data warehouse regularly, and implement any changes or enhancements that might be required. By deploying and maintaining your data warehouse properly, you can ensure that it delivers value and insights to your organization.

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Shreyas Shukla

I Can Help You learn AI || IIT Kharagpur Alum || Microsoft Azure Certified Data Engineer Associate || 3 × Azure || Python || SQL || 5?SQL HackerRank
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Optimum deployment and maintenance is perhaps the most crucial part in a data warehousing project. We have to regularly review and continuously monitor the working of data warehouse for any potential improvement or updates . Smooth and secure running of data warehouses is paramount for efficient functioning and timely availability of data in an organisation.

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AAMIR P

Senior Software Engineer at Tiger Analytics | Padma Shri Award nominee for the year 2023 | Author of 25+ books | Badminton Player | Udemy Instructor | Public Speaker | Podcaster | Chess Player | Coder | Yoga Volunteer |
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Validate data integrity during the migration process. Regularly review and update access controls, encryption protocols, and other security configurations. Identify opportunities for enhancements based on changing requirements or emerging trends.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You’re in charge of a data warehousing project. How can you ensure that deadlines are met?

1

2

3

4

5

1 Define the scope and objectives

2 Design the architecture and model

3 Develop and test the components

4 Deploy and maintain the data warehouse

5 Here’s what else to consider

Data Warehousing

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You’re in charge of a data warehousing project. How can you ensure that deadlines are met?

1

2

3

4

5

1 Define the scope and objectives

2 Design the architecture and model

3 Develop and test the components

4 Deploy and maintain the data warehouse

5 Here’s what else to consider

Data Warehousing

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