Data is a transformative force in today’s business. And overwhelmingly, business leaders have prioritized data management and analysis as part of their business strategy, with surveys showing that over 80% of executives are satisfied with the success of their Big Data investments.
As someone tasked with making the decision, you're hoping for successful big data projects as you work to further optimize your data collection, storage, and analysis. Whether it’s to gain more insight into your customers’ preferences, uncover untapped market opportunities, or assess how to improve internal processes for greater productivity at lower costs, you want to continue moving your business forward with the litany of big data tools at your disposal.
But with so much information out there about the various cloud and on-premises data warehousing options, some of your more important — existential, even — questions may go unanswered. What if the cloud — while clearly the way of the future — doesn’t serve your needs just yet? Is an on-premises solution too hard to manage? And how will you know which direction to take when it’s time to take the plunge and modernize your data management strategies?
Here’s how on-premise and cloud data warehouses function differently from each other and the benefits each can provide to your business so you can arrive at the right solution.
How on-premise data warehouses work
In a traditional configuration, on-prem data warehouse servers located onsite at your organization collect, store, and analyze your data. These data warehouses often require extensive investment — buying all the hardware you’ll need up front, regardless of how long before you can use it — and staffing a team to manage it all.
You extract the data at the database level from many different sources, then standardize data to prep it for querying and analysis. Next, make it available to business users through various tools so they can mine, analyze, and report on it as their roles demand.
Data can be pulled from separate databases and queried together, from specific business units such as sales or marketing, or from the organization as a whole. And it can either be funneled directly into a central repository where the data is then converted into a usable form through Extract, Load, and Transform (ELT) processes, or get sent to a temporary database where it’s converted into a preferred format before going into the central repository via an Extract, Transform, and Load (ETL) process.
How cloud data warehouses work
As cloud technologies continue to command more attention and market share, cloud-based data warehouses have become an attractive option for storing data because of their inherent flexibility and cost-effectiveness. With cloud data warehouses, data is collected, stored, queried, and analyzed in a cloud environment, without the need for upfront investments in hardware or IT teams and the extra time needed to configure and maintain the infrastructure.
Rather than following a prescribed structure, each cloud warehouse has its own. For example, Amazon Redshift mimics the structure of a traditional data warehouse, while Google BigQuery doesn’t use servers at all, but instead allows users to query and share data without having to set up and pay for storage.
The unique capabilities of cloud data warehouses allow organizations to more easily and quickly adapt to changing markets and trends, increase productivity and efficiency, and find new paths to revenue through shared data insights.
Key differences in benefits
Differences in structure and functionality are not the only factors. How your business can benefit from a cloud or on-premise solution matters when it comes to adequately dealing with growth, reducing costs, and increasing efficiency.
Speed. For time-to-insight, on-premise data warehouses generally deliver more speed than their cloud counterparts because they aren’t as susceptible to latency issues. Unlike cloud solutions that send queries out to servers in other regions and have to wait for the responses to come back, local servers onsite minimize trip time so you can get the answers you need faster. However, if your business is spread across multiple geographic locations, then a cloud solution that also offers multiple-location redundancy can still meet your needs — delivering data in seconds rather than milliseconds.
Scalability. As your business changes, you’ll likely have to purchase new software or hardware to accommodate large-scale growth if you have an on-premise warehouse. But a cloud warehouse eliminates that need entirely, making scaling up (adding throughput or storage) or down much easier.
Integrations. A cloud data warehouse also makes it easier to connect to and integrate with other cloud services to help you better manipulate your data — but only if your business isn’t predicated on tight industry restrictions. The freer your business is, the more freely your data can flow through cloud-based integrations. Otherwise, if restrictions are a concern, then an on-premise approach may bring more peace of mind since all security remains squarely under your IT team’s control.
Reliability. Both on-premise and cloud data warehouses can offer the highest uptime and reliability, but on-premise has an added variable: the level of uptime and reliability are solely dependent on the human resources and equipment you have at hand. Without the best team or the best equipment, any issues with reliability are on you. With a cloud warehouse, uptime and reliability are guaranteed through your provider’s SLA.
Cost. You probably guessed it: a cloud data warehouse costs significantly less upfront since it doesn’t require hardware, human resources, or server rooms to purchase, hire, train, or maintain.
What will you choose?
According to Rightscale’s 2018 cloud computing report: 96% of respondents now use the cloud, indicating the vast majority of organizations recognize that some form of cloud computing is necessary for doing business today.
So while you may still opt for an on-premise solution, especially if you’re in a single geographic location with a solid IT team you trust, there may be some specific use cases in which a cloud solution could bridge where your organization is today with where it’s headed tomorrow.
For example, with cloud-based data warehousing, you can:
Create reports pulling anything you want to know from disparate data sources through ad-hoc analysis.
Identify business trends, get to the root of data relationships, and make future predictions through the kind of statistical algorithms you get with machine learning, AI, and data science.
Monitor business units and teams by continuously querying key performance indicators (KPIs) in real time with operational analytics.
Track data and performance across your entire organization with mixed-load analytics that combine any or all analysis and algorithms.
There always comes a time to modernize a business, so when you’re ready to modernize yours, it’s important to have an expert on hand to assist. Alooma makes it easy to move your data warehousing to the cloud. Now, you can connect data warehousing resources directly with your raw data to extract the actionable intelligence you need to improve data analysis, enable advanced technologies like machine learning, or simply take a deeper dive into your internal performance.