Data is the lifeblood of any business. Data integrity, accuracy, and completeness are critical to making informed decisions, yet many business leaders feel stuck in place with less than ideal data outputs.
A traditional approach to deciphering (and more importantly, using) business intelligence is to use a data warehouse, which integrates all of your structured data into one place for instant access and analysis.
Using a modern data warehouse can give an organization limitless query power, as well as the ability to unlock insights from large amounts of information. Here, we’ll break down what a data warehouse is and show you how to implement modern data warehousing for your organization.
A data warehouse is a centralized repository for business-critical information. It's a common architecture for storing and analyzing large amounts of data from multiple sources over time.
A data warehouse is not a database or an application. It doesn't serve any specific business function or support any line of business. Instead, it provides cross-functional reporting and analysis capabilities for all stakeholders across the organization.
While data warehouses have existed since the 1980s, data warehousing architecture continues to evolve at a fast pace, thanks to the rise of big data and cloud computing. Old data warehouses were built using relational database management systems (RDBMSs) with row-based transaction processing engines.
A modern data warehouse can now support advanced analytical tools and data visualization.
Enterprises use data warehouses and data lakes to store, analyze and sort out collected data. They may have several overlaps and can complement each other for effective data management.
Data Lakes refer specifically to non-relational databases that can hold any type of structured or unstructured data in an atomic form. You can think of atoms being like individual rows. The data is stored in native format and processed later on-demand.
On the other hand, data warehouses store cleaned, structured, and organized data. The data warehouse “ETL” automation process, which stands for “extract, transform, and load,” provides significant advantages when analyzing multiple sets of information from your databases.
The reasons are numerous, and they boil down to the flexibility and agility that modern data warehouses can offer.
Modern data warehousing is more flexible than traditional approaches because it allows you to scale what you’re doing as your business needs change. It’s much easier to add new features or functionality if you have an agile platform in place.
Similarly, modern data warehousing gives you greater agility by allowing for the rapid deployment of new functions like machine learning models or AI algorithms. This way, you can quickly experiment with different strategies and implement those that work best for your company.
In addition to being flexible and agile, modern data warehouses also tend to be more efficient than previous generations, which means less time spent on maintenance and a higher capacity for handling larger volumes of information at once.
Moreover, modern data architecture takes advantage of new cloud-based services like Amazon Redshift and Azure SQL Data Warehouse through their APIs. These new cloud-based services offer lower costs than traditional on-premises solutions while still providing enterprise-grade reliability and support options.
A modern data warehouse is made up of these layers:
All data is accessed through this layer, which provides an interface with SQL and NoSQL databases.
The integration layer allows you to integrate with external sources such as cloud providers, RDBMSs, and other third-party applications. It also supports ETL tools like Informatic, Redshift, and Snowflake.
This layer provides query language support and tools that allow users to perform analytics on the transformed data from your source systems. The analytics platform also includes machine learning capabilities to build predictive models using historical data stored in your warehouse for future predictions.
A common option for this type of product is Amazon Athena or Microsoft Azure Analysis Services (Azure AS).
This layer provides visualizations based on people’s preferences, so they can easily gain insights without having to write complex queries or build their reports from scratch.
A modern data warehouse is more than just a collection of datasets. It embodies the following features, which are critical for companies looking to be truly data-driven:
Scalability refers to the ability to handle large volumes of structured, semi-structured, and unstructured data as the company grows. Choose a scalable modern data warehouse that can grow with your company's needs.
The reliability of your system is an important factor as it ensures you can keep up with your growing demands. Your organization's reputation depends on its ability to deliver accurate results every time and in real-time without any downtime or errors.
Performance refers to how quickly queries execute against queries that run against the database engine (e.g., SQL Server). The query performance directly impacts end users' experience when interacting with applications built on this database engine (such as Power BI).
Modern databases need some degree of flexibility to store their configuration settings locally within themselves and allow extensions via plugins to extend beyond their current capabilities. The ability to change your data warehouse architecture or design is critical so you can adapt as your business grows and evolves over time.
The data warehouse architecture you pick should have features that make it easy to integrate with solutions from different vendors and the different platforms necessary for data storage and deep analysis.
Security is a multi-faceted problem that requires solutions in the business, technical, people, operational, and compliance domains. In today's world of information privacy regulations and constant security threats, you should have policies and procedures to protect your data from unauthorized access and misuse.
It’s essential to have a data warehousing migration strategy that will allow for a secure, seamless transition.
The modern data warehouse is the solution to traditional data warehousing problems. It provides a cost-effective, scalable, and flexible architecture to enable organizations to access their business-critical data.
When establishing your modern data warehouse, you may need to focus on scalability, reliability, performance, flexibility, integrations, and security. If you wish to get more acquainted with modern data warehousing, download the Playbook for Modern Data Architectures.