What do you do if you need to choose new technology for data warehousing?

You have to define the implementation approach ( data strategy ) as step 1 , if your choice is event driven data warehouse then you can design the right architecture if your approach is top down then other type of architecture , only then you can evaluate what elements should remain on prem or move to the cloud , as you see there are no recipes , you have to create ones

When choosing a new technology for data warehousing, consider the following points: ?? Requirements: Identify your business needs and problems to solve. ?? Scalability: Choose a technology that can handle data growth. ?? Performance: It should handle complex queries quickly. ?? Security: Look for robust security features. ?? Cost: Consider initial and ongoing costs. ?? Integration: It should integrate with existing systems. ?? Support: Choose a technology with strong support and documentation. ?? Vendor Reputation: Consider the vendor's reputation. ?? Future Proof: Choose a regularly updated technology. ?? Ease of Use: The technology should be user-friendly.

Understanding why the change is needed is a great place to start. Answering questions like why do we need to make the change, what are we trying to achieve, what are the constraints and comparing the "as is" and the "to be" scenarios will help drive the change.

First of all is The Need, then the cost and ROI on adopting the same. If the need is genuine and there is no other choice, then we need to take up the technology, set up and build knowledge around that to achieve the work. Anything New is not a problem but before jumping into the sea we should know the OKRs and then decide accordingly thru a proper actionable plan rather in force or impulse to work with new technology.

Tread lightly. The level of risk is extremely high. New means current staff have no experience, product has little known history. Do you feel comfortable with that level of risk?

Research available data warehousing technologies and solutions in the market. Explore both traditional on-premises solutions and cloud-based options. Consider factors such as features, functionality, ease of use, vendor reputation, and customer reviews.

Evaluating the alternatives that best suit the needs will help make a more informed decision. Do we need a solution as a service or something where we own the platform?

Consider the scalability and flexibility of each technology to accommodate changing business needs and data growth over time. Look for features such as elastic scaling, multi-cloud support, and pay-as-you-go pricing models.

I Agree with all that but also new technologies includes new inventions and revolutionary technologies so we must take a breath first and do next. searching the existing Technologies to adjust the channels that make Combatants and connections between variant technologies for all types of users . also stepping on all the assets and skills Assessed to make sure that all bugs and risks will be elemenated testing methods and abilities also maintenance chanses and usability. set a good frame of data management, safety, flows, and database management services. searching costs not only financial but also time, efforts, and evolution time frame and roadmaps. finaly discovering a new eras and open universe for more options and features.

Evaluate the total cost of ownership (TCO) for each data warehousing technology option, including initial implementation costs, licensing fees, ongoing maintenance costs, and any additional infrastructure or support expenses. Consider both short-term and long-term costs to make an informed decision.

Costs are important, costs related to maintenance and support, licensing and storage. Understanding the expense in short term and long term will help keep a healthy budget.

Consider factors such as data transfer fees, storage costs, and compute charges associated with cloud services versus the upfront investment required for on-premises infrastructure.

Understanding the migration effort will help drive a transition project that is completed within time and budget. Not only considering the technologies but also the size of the data, can the new solution grow with the data?

Ensure that the selected data warehousing technology is compatible with your existing infrastructure, systems, and tools. Consider factors such as compatibility with databases, data integration tools, analytics platforms, and business intelligence (BI) solutions.

Assess the scalability of the data warehousing technology to accommodate future growth in data volume and user demands. Consider factors such as scalability features, performance scalability, and the ability to seamlessly scale up or out as needed without significant disruption.

Assess the performance impact of scaling operations on each data warehousing technology. Evaluate the efficiency of data sharding and partitioning algorithms in distributing workload evenly and optimizing query performance in a distributed environment.

To evaluate scalability of data warehouse technologies for your organization, consider: Performance Testing conduct stress tests to measure response times under heavy loads. Data Distribution examines how evenly data is distributed across nodes to prevent bottlenecks. Query Optimization ensures efficient query processing even with large datasets by analyzing optimization features. Concurrency Handling evaluates how well the technology manages multiple simultaneous queries or users. Resource Management sssess mechanisms for allocating resources dynamically based on workload demands. Consider the cost implications of scaling up with the chosen technology. Look for vendor documentation and support for scaling strategies and best practices.

My Approach to thoroughly test data warehouse technologies for selection: Outline specific needs, like scalability, performance, integration capabilities, and analytics support. Conduct tests on large datasets to assess speed, query execution, and concurrency handling. Verify compatibility with existing tools, databases, and ETL processes. Evaluate encryption, access controls, and compliance features to ensure data protection. Test failover mechanisms and recovery processes to ensure data reliability. Consider licensing, deployment, and maintenance expenses for long-term viability. Assess how well each technology adapts to evolving data needs and industry trends.

When faced with the task of selecting new technology for data warehousing, it's crucial to begin by clearly identifying the organization's requirements and objectives. Thorough research should be conducted on available options, considering factors like scalability, performance, integration capabilities, security, and compliance needs. Evaluating features, assessing scalability, and considering integration with existing infrastructure are key steps. Additionally, evaluating security, total cost of ownership, vendor support, and future trends can help make an informed decision. Prototyping or conducting proof of concept tests can validate the technology's suitability before finalizing the choice.

Delimit your purpose and perspectives concerning the goal of Data Warehouse. Analyze the tools available for Data Warehouses and the advantages of each technology, considering its suitability for your business and purpose. Define a budget, as adopting a technology is an investment that requires consideration of costs and how it will affect your project budget. Choose a technology that aligns with your company's development strategy.

In addition to the aforementioned requirements, consider factors such as data security, compliance with laws and regulations, support and maintenance, and the reputation and track record of the supplier. Choosing the best technology for your data warehousing requirements can also be greatly aided by speaking with professionals and getting referrals from peers.

Apart from the given points , we can also consider - technology’s capabilities in terms of data governance and security. -vendor support has to be evaluated and should be in aligned - user training and easy adoption should be considere as it is time rich - disaster recovery and high availability ( most of cloud solutions have good options) -ease of migration of existing data, this can sometimes make or break things -exit strategy if the product is not suitable