It sounds daunting when you’re going about building a cloud data warehouse for your organization because the scalability and agility benefits it offers will outweigh any negatives. Denodo conducted a study recently, which found that 56% of businesses deploy data warehouse technology in the cloud that allows them to benefit from vendor lock-in capabilities and efficient workload management. However, it can be difficult to understand where to start when venturing into this technology for the first time. Mistakes will cost you a lot in terms of money and time. So, what should organizations do to minimize risk and ensure maximum reward? We’re going to be highlighting the steps you must take to build your first cloud data warehouse.

1.    Start with Baby Steps

When creating your own cloud data warehouse, it’s best to minimize risk if mistakes do happen by keeping your ambitions low from the start. You must acquire as much experience as possible, and a small project allows you to expand your project slowly instead of being overwhelmed by the scale of the task. The best thing about the cloud is that it offers you scalability and elasticity, which ensures that you learn as you build your data warehouse. You can move onto the next step without making costly mistakes that set your business back. Once you have understood how the system works and what changes you require, you can take the next step without worrying about messing up the entire cloud structure.

2.    Skill Up Your Workforce

One of the most important things you must focus on is ensuring that your workforce’s skills match with the requirements, and they are prepared for the transition. An important aspect of data warehouse technology in the cloud is that it continuously offers big data, which is beneficial for monitoring customer behavior. Its potential can’t be reached if your employees don’t have the skills and don’t know how to leverage it correctly. You can easily leverage cloud providers such as AWS and Microsoft Azure to allocate hardware resources for your data analytics needs. However, dealing with big data requires serious upskilling as a sound understanding of data handling, along with standard network engineering, will add another bow into the skills of IT professionals.

3.    Planning New Architecture

Apart from starting small, it’s imperative that businesses practice patience by planning out their cloud data warehouse architecture. You should be aware of the myths surrounding cloud technology during your journey, but you can’t throw your data in the cloud without first analyzing the architecture or design that is required. An analytics environment must be planned and architected in a manner that allows everyone to understand it and use it. You should look at this as an opportunity to revamp your old data warehouse by removing inefficient processes and wasted space from unused assets.

4.    Establish Data Governance

It’s imperative that you don’t forget about data governance as you need to ensure that company data is managed in a manner that doesn’t produce siloed or duplicate data. The biggest challenge you’re likely to face with implementing data warehousing will be in collecting, curating, and aggregating multiples copies of the same data. Most businesses have several data silos, and if they are integrated into a data warehouse, it can result in redundancy. That’s the reason why so much importance is given to establishing a proper data governance strategy because this will allow businesses to identify silos before it is implemented with the data warehouse.

5.    Use an Existing Model

One way to take some of the pressure off your shoulders is when you’re moving to a data warehouse in the cloud is by taking cues from existing architectures that require minor improvements. That gives you the advantage of better data governance, and this migration can be turned into an opportunity to revise existing on-premise data warehouses. All you must do is identify what data sources and assets can be augmented, added, or revised, and then come up with an incremental strategy for migration. Your goal should be to achieve a cohesive cloud data warehouse platform with proper oversight and governance.

6.    Consider Serverless Technology

You should also think about serverless technology and how it can help you. Most serverless relational databases are commonly chosen for business intelligence applications, and for publishing data. They are popular because they offer scale, performance, and SQL-based access to prepared data. Numerous vendors offer serverless technology, which includes the likes of Azure SQL Data Warehouse, AWS Redshift, and Google BigQuery. These are all great choices for moderately-sized and simple data structures. However, for high performance and complex data models, it’s best to choose massively parallel processing (MPP) databases. MPP databases store large volumes of data in-memory and are super-fast but can be expensive.

7.    Seek Expertise and Research

It’s important that you understand exactly what you are looking for because different platforms offer different benefits for types of data, processing, and analysis. For instance, a business may find a multi-cloud service to be a better fit for them, but just because you have one cloud service, it doesn’t mean the provider is also the best option for your other cloud needs. You must conduct your research and engage with experts who have developed frameworks and have experience in this area. That will minimize any risk or challenges in adopting a cloud data warehouse and ensure that your business is in the best place to take complete advantage of the benefits it will offer.

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