Are you new to the concepts of data warehousing? Do you want to know how your enterprise can benefit from using a data warehouse to streamline your business, better serve your customers and create new market opportunities? If so, this blog is for you. Let’s cover the basics.
It begins with production data
Day-to-day, nearly all enterprise-class companies process data as part of core business operations. Banks process debit and credit transactions for account holders. Brick-and-mortar and online retailers process in-store and website purchases, respectively. Insurance companies maintain and update customer profile and insurance policy information for policyholders.
The nature of these production systems is transactional and require databases that can capture, write, update or delete information at the pace of business operations. The systems behind these transactions are online transaction processing (OLTP) databases. For example, OLTP databases for investment firms must operate at lightning speed to keep up with high-volume stock and bond trading activity that occur in fractions of a second.
The need for a data warehouse solution
In addition to capturing transactions, another aspect of business operations is to understand what’s happening, or what has happened, based on the information captured with OLTP databases. By this, I mean companies must not only know how much revenue is coming in, they must know where revenue is coming from, the profile of customers making the purchases, business trends (up or down), the products and services being purchased and when those transactions are taking place. And, certainly businesses need to know what it will take for customers to remain loyal and buy more. Answers and insights to these questions are necessary to develop strategic business plans and develop new products that will keep businesses growing.
Why transactional (OLTP) systems are not optimized for data warehousing
Acquiring these insights requires accumulating, synthesizing and analyzing the influx of data from OLTP databases. The aggregation of all this data results in very large data sets for analytics. In contrast, when OLTP systems capture and update data, the amount of data transacted upon is actually very small. However, OLTP systems will execute thousands upon thousands of small transactions at a time. This is what OLTP systems are optimized to do; however, OLTP systems are not optimized for the analysis of large to extremely large data sets.
This is why data warehousing solutions emerged. Data warehouse solutions will hold a copy of data stored in OLTP databases. In addition, data warehouses also hold exponentially larger amounts of data accessed by enterprises, thanks to the enormous amount of Internet and cloud-born data. Ideally, data warehouses should be optimized to handle analytics on data sets of any size. A typical data warehouse will have two primary components: One, a database (or a collection of databases) to store all of the data copied from the production system; and two, a query engine, which will enable a user, a program or an application to ask questions of the data and present an answer.
Benefits of deploying a data warehouse
As previously stated, with a data warehouse, you ask and find answers to questions such as:
- What’s the revenue?
- Who’s buying?
- What’s the profile of customers?
- What pages did they visit on our website?
- What caught their attention?
- Which customers are buying which products?
With native language processing and other deep learning capabilities gaining popularity, you can even develop insights about the sentiment of prospects and potential customers as they journey towards your enterprise.
Benefits of data warehousing… in the cloud
Many data warehouses deployed today were developed during the 1980s and were built for on-premises data centers typical of the time. These solutions still exist, including availability of “cloud-washed” versions. Both options typically involve upfront licensing charges to buy and to maintain these legacy data warehouses. Yet, neither legacy data warehouses (0r current generation data lakes based on Hadoop) can elastically scale up, down, or suspend as needed to meet the continuously varying demands of today’s enterprises.
As result, these types of solutions require a lot attention on low-level infrastructure tasks that divert IT and data science teams from truly strategic analytics projects that advance the business.
With modern, cloud-built data warehouse technology now available, such as Snowflake, you can gather even more data from a multitude of data sources and instantly and elastically scale to support virtually unlimited users and workloads.
All of this is accomplished while ensuring the integrity and consistency of a single source of truth without a fight for computing resources. This includes a mix of data varieties, such as structured data and semi-structured data. As a modern cloud service, you can have any number of users query data easily, in a fully relational manner using familiar tools, all with better security, performance, data protection and ease-of-use that are built-in.
For these reasons, you can expect enterprises to turn to companies like Snowflake to help propel insights from your data in new directions and at new speeds, regardless the size of the business or industry in which you compete.