It is well known that data generation and storage is growing at a great pace. Needless to say, enterprises are capturing and creating more and more data and using this data to help identify new revenue opportunities to help them grow and become more profitable. On the other hand, this trend does present the challenge of how best to store and manage all of this data. Cloud Data Warehouses have emerged as a more cost-effective means of storing and managing these ever increasing volumes of enterprise data. According to Marketwatch, the Cloud Data Warehouse market is predicted to grow at 31.4% annually over the next 5 years and will reach a value of $39.1 billion by 2026.
Ultimately this challenge boils down to a financial discussion. Yes, leveraging customer data can lead to increased sales, but there is always a balance to be struck between opportunity and investment. This means that as data capture and acquisition accelerates, it’s more important than ever for organizations to optimize their data storage strategies to ensure that their data works for them rather than the other way around. From the CFO’s perspective, significant areas of expense are prime targets in their quest to make the organization leaner and more profitable ...and data storage costs are no exception.
Leaders whose agenda it is to drive more value from data such as the CDO or even a CMO must understand how to demonstrate that the infrastructure used to house and run this data is as efficient and secure as possible so they remain agile and leverage the full potential of all their data assets
As a result, enterprises are changing their policy of storing all customer data in a premium data warehouse to a strategy in which data that is deemed a lower priority is housed in lower-cost storage solutions, such as Amazon’s Redshift and S3 products or Snowflake’s Cloud Data Warehouse (check out this insightful blog for more detail on the history of Data Warehousing and where it is going). This category of storage is perfect for data that is less likely to be needed for analytics, such as historical interaction and behavioral data, or data that must be captured and retained for compliance purposes. Tiering data in this way can result in large volumes of data being moved from costly storage environments, resulting in potentially dramatic reductions in overall cost of ownership. However, managing how to split out and distribute this data requires careful planning and the ability to execute automatically and at scale.
Best practices for leveraging Cloud Data Warehouses
Even if your organization has the tools and skills to achieve this transformation, it is natural to assume that architectural changes of this nature, wherein a premium provider is substituted for a more economical solution will inevitably lead to a decrease in availability, service levels and for want of a better term, ‘quality’.
However, this is not necessarily the case for two reasons:
- If data tiering is done correctly, only data which is lower priority will be housed in Cloud Data Warehouses, meaning this data will be required less often so that any decreases in performance has less of an impact.
- Some Enterprise Data Warehouse vendors have developed tools to enable the integration of data housed in third party cloud environments. These tools, such as Teradata Vantage’s Native Object Store enable data housed in external Cloud Data Warehouses to be queried within the Teradata solution by end users who will be effectively unaware that the data resides in an external database. These tools deliver a best-of-both-worlds style utopia in which enterprises benefit from dramatic cost savings combined with little-to-no decrease in performance, with the added advantage of complete consistency. Data Scientists can query the data from day 1 using the best-of-breed Teradata tools which they know so well, with no need to retrain or familiarize themselves with new operating environments.
Celebrus features a number of capabilities that have been developed specifically to meet the growing requirement for enterprises to connect Celebrus data to a range of cloud storage platforms. Our out-of-the-box connectors enable data to flow with ease into AWS S3 and Redshift, Microsoft Azure Synapse and Snowflake Cloud Data Warehouses.
The Celebrus data model remains completely consistent and is entirely compatible with these cloud storage platforms. In addition, our Teradata Native Object Store integration provides data loaders for AWS S3 and Microsoft Azure Synapse, which allow Teradata clients complete flexibility regarding their use of these cloud platforms, enabling the use of sophisticated cloud enabled storage strategies and architectures.
