3 Tips for Building a New Cloud Data Warehouse

Cloud data warehouse projects, and data modernization projects in general, are no small undertaking. But taking intentional, well-defined steps can make them more approachable. Whether you're planning to build a new data warehouse in the cloud or to migrate an existing one from on-premise to the cloud, you'll find these tips helpful.

Without further ado, here are 3 ways to improve your chances for a successful cloud data warehouse.

1. Define Your Success Criteria

A cloud data warehouse project is an investment, there are no two ways about it. It's important to understand the size of the investment and how will you measure success before you get started. Every organization has different reasons for undertaking a cloud data warehouse project. What's yours? Are you migrating to decrease costs, increase throughput, scale faster, or mitigate risk? The “why” behind the initiative might be a good place to start defining your success criteria.

Some success measures other companies have used include:

• Potential Cost Savings
• Increased Access To Data
• Increased Data Quality
• Ability To Cost Effectively Scale
• Increased Governance Of Your Data

The key is to define success criteria early on and measure to show results. Nobody wants to invest in a cloud data warehouse solution only to have the success of the project questioned after it's complete.

2. Consider Your Potential Migration Paths

There are several paths an organization may take to build out a new cloud data warehouse or to migrate from a legacy platform to the cloud. The term “lift and shift” is often used when describing an approach to the project, but it's not quite as easy as it sounds. Most organizations:

• Can't afford to completely replace their solution overnight, or
• Don't have all of the elements in place and may need to take a phased approach to their project, making them re-think the “lift and shift” approach

There are other ways to migrate your data besides using “lift and shift” like an incremental, agile approach. The key is to identify the data assets and the most efficient and impactful order of how to migrate the data (the “lifting” part of the equation).?In many cases, there's a way to leverage your existing ETL/ELT data pipelines by refactoring them in the short term to integrate with your new platform and build into the plan to eventually replace the ETL/ELT solutions over time (a.k.a. “the shift").

Also, the use of the right technology can both automate the process and determine more efficient ways to migrate your data. Whether that's data loading tools, automated ETL/ELT tools, or leveraging an automated data quality management tool to improve trust with data consumers.

So what's the best migration path for you? Here are some factors to keep in mind when deciding:

• When was my legacy system built?
• What shape is my data in today?
• Do I need to bring in new data sources?
• Do I have the budget to perform a true “lift and shift” or do I need to take a phased approach?
• Can I leverage technology to help?
• Do I have the expertise on my team to do it the right way?

3. Start with Strategy

It's key to start with strategy and have a well-thought-out plan to ensure success. Starting with strategy is the key to success.

Here are some areas to think about as you develop your strategy:

People & Processes

Ask yourself: Who are the people on your team? What's their expertise? Are they in the right role? Do you have critical gaps in expertise or capability? How does the current ETL/ELT process perform today? Is there room for improvement?

Data Models, Structure, & Catalog

Ask yourself: What shape is my data in today? How is it structured? When I move to the new platform will the current structure hinder our ability to scale? Is there a more efficient way to extract, load, and transform my data?

Data Architecture & Platforms

Ask yourself: Will my data architecture allow me to effectively and efficiently scale in the future? Will my data be accessible to the people that need it? Should I build a data lake? A data vault? What technology should I be leveraging to improve efficiencies and drive more automation?

Data Quality Management

Ask yourself: How do I ensure I improve the quality of my data? Can I increase transparency for my data consumers? How do I improve trust in my data? What should I be testing? Do I need to automate my data testing processes?

Data Governance

Ask yourself: Do I have a data governance program? Does it need to be updated? Who else from the organization must I involve in data governance design decisions? What regulatory risks do I need to consider? How do I design data governance elements into every aspect of my data warehouse migration solution?

There are many variables to consider when planning for and executing a data warehouse migration solution. You don’t have to do it alone. Our experts at Resultant can help with your migration to the cloud with a focus on project agility and quick analytics wins.

Learn more about our data warehouse services