Cloud data warehousing is all the rage in the tech sector. A cloud data warehouse is a system designed to gather data from a variety of different sources within a business. From there, employees and leaders can easily find and analyze that data to reveal trends and patterns that may have otherwise gone undiscovered. This also allows them to digest and report on historical data in a way that lets them generate more accurate forecasts and make better business decisions moving forward.
The Pioneers in the Field of Cloud Data Warehousing
Over the last few years, companies like Snowflake and Databricks have changed the approach to the traditional database and data warehousing with cloud offerings and have become behemoths as a result. Snowflake in particular is notable as it was designed as an elastic data warehouse that can run on all three major cloud platforms including Amazon Web Services, Microsoft Azure, and Google Cloud.
It offers storage with two to three times compression that, along with multi-cluster computing resources, offers users near-unlimited concurrency at all times. Snowflake also uses data encryption, dynamic data masking, and tokenization for increased security and user authentication. It is also in compliance with SOC2 Type 2, ISO/IEC 27001, PCI DSS, and many others. For most businesses, putting this type of solution together in-house would be next to impossible given all of the features they get for the price point.
Data bricks is another notable example of an innovator in the field, combining all of the business data, analytics, and artificial intelligence onto one Lake house platform. It combines the best of data warehouses and data lakes together as a simple way to unify everything onto one common platform for all data use cases.
Data bricks is also a viable opportunity to unify your data ecosystem with open source, standards and formats. But for most users, the biggest benefit has to do with the collaborative opportunities such a system creates. Data bricks helps businesses unify their data teams so that they can collaborate across the entire data and AI workflow.
Another example of a pioneer in the field of cloud data warehousing is Google’s BigQuery – a solution that brings with it exceptional performance and allows for SQL querying, even against very large data sets and with results delivered in a matter of seconds. Like Snowflake, Google BigQuery offers multi-cloud capabilities and support for technologies like natural language processing. It also offers built-in integrations with operational databases, a big data ecosystem and more – making it an ideal solution for environments that need support for data mining and various related workloads.
Cloud Data Warehouses vs. Older Solutions
One of the major reasons why cloud data warehouses are so popular when compared to the on-premises data warehouses that most people are used to have to do with the unparalleled scalability they bring with them. With an on-premises data warehouse, scaling is equal parts time and resource-intensive. Not only do you have to purchase new hardware, software, and infrastructure assets, but you also have to properly configure them as well. With cloud data warehousing, this isn’t an issue as you can enjoy instant up or downscale whenever you want.
Another major benefit of cloud data warehousing has to do with availability. With an on-premises data warehouse, availability largely comes down to the quality of the hardware and software you’re working with, coupled with the skills of your in-house IT team. With cloud data warehousing, 99.99% uptime is guaranteed with all leading cloud providers like Amazon, Microsoft, and Google.
Finally, most organizations find cloud data warehousing ideal when compared to on-premises data warehouses due to cost-effectiveness. With an on-premises data warehouse, whatever solution you build requires major upfront capital investments for not only hardware and software but to also hire and train your necessary IT staff members and more. With cloud data warehouses, you have no investment in terms of not only those factors but also in regard to solution maintenance and support as well. Most providers also offer a pay-as-you-go model, meaning that you’re only actually paying for the storage and computing resources that you’re actively utilizing.
The Risks and Rewards of Adopting Cloud Data Warehouse Solutions
Possibly the biggest reward of adopting cloud data warehousing is one of security. Reputable cloud data warehouse providers leverage all of the latest best practices for not only data protection but also factors like regulatory compliance as well. Your vendor will provide you with user authentication and authorization features, like the ability to restrict access to specific data based on the role someone has within your business. They will also use sophisticated data encryption methods to protect your information while it is both at rest and in transit.
One potential risk of adopting cloud data warehousing involves resource latency. If you’re running the type of business where literally every second count when it comes to decision-making, obviously the physical location of your data matters a great deal. The farther away you are from your data, the longer any action involving that data (like business analytics) will take to complete. In some industries fractions of a second matter a great deal. This may not necessarily be a problem depending on your use case, but if speed is crucial in terms of the types of transactions you conduct it is absolutely a factor you need to be aware of.
Cloud data warehouses also provide the agility you need to simplify your ability to deploy BI (business intelligence) and data analytics services. This not only makes it easier to start taking advantage of these technologies but far more cost-effective at the exact same time.
A Roadmap to Your Cloud Data Warehouse
In an effort to make sure that your own cloud data warehouse project goes off without issue, there are a few basic steps you’ll want to follow.
The first involves gathering all requirements, as you need to make sure that you understand the strategic direction of the project before it actually begins. By making an effort now to understand your business’ strengths and weaknesses, and by understanding exactly what challenges a cloud data warehouse is supposed to solve, you can help avoid a lot of the potential development issues companies face and deliver maximum value as well.
Next, you’ll want to assign those requirements scores based on A) the value they bring with them, and B) how complicated they will be to implement. This will help you prioritize the areas you need to focus on as your project continues. Anything on your list that will deliver high value to your business while also requiring minimum effort should be delivered first so that your cloud data warehouse can start showing benefits as early on in the process as possible.
As you continue to work through this list of requirements you will be left with a roadmap to follow for your cloud data warehouse project and you’ll know precisely what you need to focus on within the context of the business value it can generate. You’ll still need to think about things like resource availability and dependencies, but you’ll be left with a solid foundation from which to build from for the success of your project moving forward.