Today’s data engineers and DevOps teams are tasked with assisting their organizations to make the most effective use of their data. In the past, this took the form of building data warehouses that enabled those companies to not only store and manage their historical data, but to draw insights from it to be used towards business growth. However, legacy data warehousing has already reached the next iteration – modern data warehousing.
There are several differences that distinguish a modern data warehouse from a traditional data warehouse. The purpose of this article is to help IT and DevOps managers understand those differences and the benefits of upgrading your infrastructure to modern warehousing.
Traditional data warehousing vs modern data warehousing
The core differences between a traditional data warehouse and a modern data warehouse all derive from the fact that modern data warehouses are built on top of cloud technology versus on-premises infrastructure, which allows much more flexible, agile, and scalable data processes and analytics from multiple data sources. The above-mentioned differences include but are not limited to:
|
Item |
Traditional Data Warehouse |
Modern Data Warehouse |
|---|---|---|
|
Location |
Mainly On-premises |
In the cloud |
|
Data source |
Operational and transactional databases |
All sources (sensor equipment, social media, etc) |
|
Data storage |
Limited by availability of resources at a given time |
No limit due to unlimited scalability and parallel processing |
|
Capability |
Provide data for business intelligence |
Use varied data sources and ML to create insights |
|
Performance |
High volume and complexity of queries increase server loading and weaken response time |
Maintains performance by splitting peak workloads dynamically and automatically between resources |
|
Interoperability |
Challenging to orchestrate separate systems |
Virtual interoperable layer allows simple integration of data from separate systems |
|
Cost |
On-prem infrastructure increases overhead costs of support and maintenance |
Maintenance and support costs all included in cost of cloud provider |
Figure: Differences between traditional data warehousing and modern data warehousing
The benefits of modern data warehouses
There are numerous compelling factors drive businesses to adopt a modern data warehouse like Google Cloud BigQuery:
Faster decision-making:
Data can be instantly refreshed and queried upon entry, requiring no additional processing overhead. Small, targeted queries can yield results in mere 10s to 100s of milliseconds. Quick mutations can be performed without extensive rewriting. This modern data warehouse significantly accelerates the decision-making process.
Data access for everyone:
Modern data warehousing enables everyone within an organization to access both recent and historical data, creating a cascade effect of improved efficiency across teams with proper data sharing capabilities.
Allows for customization:
Advanced analytics, machine learning, and data visualization allows businesses to respond to changing customer needs in real time, creating opportunities and enabling more personalized customer experiences.
Easier to scale:
Flexibility in the cloud is equivalent to scalability. Because resources are allocated virtually, you can adjust resource allocation quickly, making it simpler to handle larger workloads and incorporate more data as you discover new ways to utilize your data warehouse.
