As data has become increasingly central to business operations and results, it’s essential for organizations to manage all data activities. To keep up with the most recent era of cloud-based technology, more businesses are rapidly transitioning their data warehouse architectures to the cloud.
We’ve compiled all the basics about data warehouses, the differences between traditional and cloud-based solutions, and how you can reap the full benefits of a modern cloud data warehouse.
What is a Data Warehouse?
A data warehouse is a system of data management that supports business intelligence-related activities, particularly data analytics (to read more about data intelligence vs. data analytics, check out our blog). Typically, data warehouses contain large amounts of historical data and are used exclusively for querying and analysis.
Data stored in data warehouses can come from a variety of sources, including application log files, transaction applications, and more.
In general, data warehouses consist of:
- A relational database for storing and managing data
- An ELT (extraction, loading, and transformation) platform for preparing data
- Analysis tools for presenting data in a visual format
- Statistical analysis and reporting tools
- Data mining capabilities
Data warehouses tend to be subject-oriented, stable, highly integrated across data sources, and capable of providing analysis of how data changes over time.
A quality data warehouse will be capable of performing fast queries, delivering a high level of data output, and being configured for maximum flexibility to meet an organization’s unique needs. It should also be able to provide a ‘bird’s eye’ view of data, as well as granular analysis at a more detailed level.
Traditional Vs. Cloud Data Warehouse
What exactly is the difference between a traditional data warehouse and a modern cloud data warehouse? For starters, cloud data warehouses are becoming much more common while traditional data warehouses are being used less frequently. But there are other key distinctions that differentiate one from the other, both in the way they’re used and the features and structures they offer.
Traditional Data Warehouse
A traditional data warehouse is built based on a three-tier structure. On the bottom tier you’ll find the database server, which is used to pull data from a range of sources such as front-end transactional databases.
Next is the middle tier, which houses an OLAP (online analytical processing) server designed to transition the data from its raw form to a structure that is more accessible and interpretable for querying and analysis. The OLAP server can either serve as a relational database management system or as a multidimensional model that directly implements data and operations.
Finally, the third tier acts as the client interface layer. On this tier, high-level data analysis, querying, reporting, and data mining are all possible.
Traditional data warehouses can be virtually composed of separate databases or use a data mart model, wherein data is collected from a range of systems specific to lines of business, such as finance. There’s also the enterprise data warehouse model, wherein the data warehouse contains an aggregate of all data pertaining to the entire organization.
Cloud Data Warehouse
While the traditional data architecture was the de facto method for many years, more data warehouses are moving to the cloud. Not only do these modern cloud data warehouses not follow the structures outlined in the previous section, but each individual cloud data warehouse platform offers a completely different structure and feature set.
Some of the most common cloud data warehouse platforms are Amazon Redshift and Google BigQuery. Amazon Redshift is essentially a cloud-based interpretation of the structure with which traditional cloud warehouses are familiar. Google BigQuery, however, uses a serverless structure that automatically manages resource allocation behind the scenes.
Modern Cloud Data Warehouse Architecture
Modern cloud data warehouse architectures may look different from one platform to the next, but they’re all designed to meet the same core need — managing all types of data, unique workloads, and advanced analysis. Each component of the modern cloud data warehouse is designed to integrate closely to serve IT, analytics, data science, or engineering teams. And while they may differ in their overall designs, most modern cloud data warehouse architectures include a converged database, self-service ingestion services, support for machine learning, spatial processing, and SQL, and multiple options to simplify data analysis without having to move the data.
Benefits of a Cloud Data Warehouse
Wondering whether the transition from a traditional data warehouse to a modern cloud data warehouse is worthwhile for your organization? Low-overhead cloud-based solutions have made the transition process seamless, but there are five additional benefits of making the switch:
Expanded Data Access
Data stored in a modern cloud data warehouse can be accessed and analyzed from anywhere, providing data analysts and company stakeholders with real-time insights from the full range of data sources. In today’s business environment, fast analysis enables more streamlined decision-making and less waste.
Ease of Maintenance
Modern cloud-based data warehouses don’t require software upgrades, can be easily expanded or retracted based on evolving data needs, and are able to be monitored from anywhere. They don’t require physical maintenance or upkeep and aren’t beholden to on-premises storage spaces.
Since cloud data warehouses don’t require physical hardware onsite or constant software updates, and because they can be run remotely, they’re typically far less costly to setup and maintain than traditional data warehouses. Scaling is also extremely cost-effective, and the risk of irreversibly allocating too many resources to data storage is virtually eliminated.
A modern cloud data warehouse can be scaled much more quickly, simply, and inexpensively than a traditional data warehouse. Why? Modern cloud data warehouses don’t require organizations to acquire additional hardware in order to scale, like traditional, onsite data warehouses do. Additionally, scaling can be automatically adjusted based on a company’s unique and ever-changing needs.
Flexibility When Choosing Cloud Vendors
Maintaining a modern cloud data warehouse also means enjoying the ability to choose from a wide array of vendors in the cloud data management space. For example, Immuta’s data access control solution is designed to integrate seamlessly with your modern cloud data warehouse to provide universal access control at any scale. The ability to scale across platforms without vendor lock-in allows you to choose best-in-class solutions that are suited to your organization’s specific needs.
How to Migrate to a Cloud Warehouse
Looking to migrate your traditional cloud warehouse to a cloud-based solution? Immuta can help. Our cloud data access control solution provides universal cloud compatibility, so you can select the cloud data warehouse vendor of your choice, seamlessly integrate technologies, and ensure that your data – even the most sensitive data – is always kept secure and accessible only by those with authorization.
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