Data Warehouse Architecture: On-Prem Vs Cloud

When it comes to Enterprise Business or Data-driven business approach, very significantly Data warehousing approach comes into the picture.

As a definition,

A Data Warehouse is a system that pulls together data from heterogeneous sources within an organization for reporting and analysis. The reports created from those derived queries within a data warehouse are used to make business decisions.

In more comprehensive terms, a data warehouse is either a physical or logical data repository collected from various systems. The primary focus of a data warehouse is to build a correlation between data from existing systems, like, product inventory stored in one system purchase orders for a specific customer, stored in another system.

In a nutshell, here is a quick brief on on-premise and cloud data warehouses function differently from each other and the benefits each can provide to business so the organization can arrive at the right solution.

 How on-premise data warehouses work:

In a traditional configuration, on-prem data warehouse servers located 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 a team to manage it all.

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 Extraction, 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 Extraction, Transform, and Load (ETL) process.

 How cloud data warehouses work:

In this era, 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 the infras.

Each cloud warehouse has its own way of functioning. For example, Amazon Redshift tries to reiterate 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) 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 according to business restrictions. The freer your business is, the more freely your data can flow through cloud-based integration. Otherwise, if restrictions are a concern, then an on-premise approach may bring more peace of mind since all security remains 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. In a  cloud warehouse, uptime and reliability are guaranteed through your provider’s SLA.

• Cost: Obviously 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 be the optimum decision to choose it: Cloud or On-Prem:

According to 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.

Here are a few points,

• 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.

This is just a brief idea about the two approaches of Data Warehousing concept. Forthcoming articles would be a detailed level analysis of each component of warehousing tools and its functionalities towards various business approaches.