A cloud data warehouse is a core element of any data analytics infrastructure within which a cloud provider allocates the space required to store your data, and computing power needed to process it.
According to the 2019 Analytical Data Infrastructure Market Study, cloud deployment is currently the highest priority for companies (more than 50% of respondents indicated it as either critical or very important).
The research findings are in line with what ScienceSoft’s customers expect from their data analytics solutions. When turning to us for data warehouse services, our customers mainly need either in-cloud deployment, or migration from on-premises to the cloud.
In response to such interest to cloud data warehousing, our data analytics team has prepared a complete guide to the topic, including the overview of the leading cloud data warehouse solutions, as well as how-to-mitigate-the-risk recommendations.
We compare cloud-based data warehouses with traditional (read ‘on-premises’) ones on 5 important features to explain the growing popularity of cloud DWHs. In our data analytics consulting practice, we consider the chosen features as key criteria for recommending technology for designing and implementing a data warehouse.
Based on the comparison results, cloud data warehouses are easier to scale and more cost-effective. An extra advantage is service availability and data warehouse security that a cloud provider promises.
Now, let’s take the main features from the table above and look at 3 market leaders in data warehousing solutions: Microsoft Azure SQL Data Warehouse, Amazon Redshift, and Google BigQuery.
According to the comparison table, the leaders offer almost identical functionality: they are all highly scalable, available, and secure. This means that the final decision on what cloud provider to choose will strongly depend on architectural peculiarities that may make some cloud platform substantially better, brand preferences (maybe you already run a traditional data warehouse on Microsoft SQL Server and want to migrate to Microsoft Azure SQL Data Warehouse), and the pricing model.
Here, we share our experience on how to mitigate the common risks for both companies planning to deploy their cloud data warehouses from scratch and those planning to migrate from on-premises to the cloud.
Vendor lock-in risks
No company wishes to tolerate technologies that come along with their data warehouse just because they were an integral part of the cloud provider’s product kit. To avoid this unpleasant situation, you should:
- Have a long-term strategy defined, and envisage all the components of your data analytics solution.
- Scrutinize the technologies that a cloud provider offers for each component of your solution-to-be (either proprietary or compatible).
- Design a high-level architecture for your analytics solution based on the best mix of technologies.
Data migration risks
Not to be disappointed with the results of data migration, such as having incomplete or redundant data, we recommend the following:
- Define use cases that your future data analytics infrastructure is to satisfy and formalize available and potential sources of traditional and big data, as well as workloads that these use cases require. This will help you understand what data you actually need in the warehouse and migrate only it. In addition to process optimization, this will help to optimize both storage and compute costs.
- Address data quality before migrating data to the cloud. Do all data management activities, such as data profiling and data cleaning, i.e., removing duplicate and erroneous records, before moving your data.
Time to start your journey to cloud data warehousing
If you have plans to migrate your on-premises data warehouse or start with a brand-new cloud DWH, it seems like the perfect time. ScienceSoft definitely sees (both from our real-life projects and an overall market situation) the growing adoption of cloud data warehousing. Compared to traditional DWHs, cloud ones win in terms of scalability and availability. Even security concerns that for a long time have been hindering the adoption rate of cloud DWHs seem to disappear.
Going cloud means that many tasks (like providing storage and compute resources, ensuring the security) lay upon a cloud provider. Still, you need to develop your business intelligence strategy and roadmap, run all the data management activities and set up proper ETL (extract, transform, and load) processes to ensure flawless integration of data from disparate sources. If you require any assistance with the strategy development, planning, or implementation part, you are welcome to turn to our team of professional consultants and implementation specialists with 14 years of experience in data warehousing.