Data Warehousing – Traditional Vs. Cloud

Data Warehousing
Share on facebook
Share on twitter
Share on pinterest
Share on linkedin
Share on email

The upfront costs for configuring, installing, and purchasing the infrastructure, hardware, and software have been inclining organizations towards modern data warehousing. The modern paradigm involves analytics in the cloud for big data. Approximately 60% data & analytics leaders, according to Gartner, are investing in data warehouses. Modern data management must be dynamic, for supporting a variety of use cases, enabling new connections, and enhancing architectural patterns. 

While the data industry has changed significantly, with huge transformations in the way data is being stored and processed, businesses are eyeing online data storage. They are moving away from traditional data warehousing options for 

  • Eliminating costs of buying physical hardware. 
  • Removing complexities of setting up the data architecture. 
  • Saving on capital and operational expenditure. 
  • Performing complicated analytical queries with agility. 

The Differences Between Traditional and Cloud-based Data Warehousing 

Traditional Data Warehouses 

As the data captured and stored by organizations continued to spiral up, organizations were in the need for replacing the online analytical processing. This was because of the difficulties and inefficiencies in processing voluminous data. Organizations came up with a solution, which is currently perceived as traditional data warehousing. A multi-tiered array of applications, data stores, and servers, traditional data warehouses saw several advancements over the past decade. 

With the advent of digital transformation and cloud computing, organizations were able to see how managing their traditional data warehouses was relatively challenging. This was particularly true, when it came to the technological interoperability, system orchestration, and use of diver software. Traditional data warehousing, which is typically an on-premises solutions, compelled organizations to manage and update their data with additional layers of support overhead. 

Cloud Data Warehouses 

Cloud data warehouses removed the challenges faced with their traditional counterparts, applying the cloud’s benefits of scalability, availability, performance, and scalability. This brought a huge processing advancement for all types and sizes of data teams. The cloud service providers take the responsibility for managing the infrastructure availability, hardware, and software updates.  

The cloud data warehouses proved to be a viable solution for organizations, as they are capable of scaling simultaneously with growing need for business analytics. The cloud-hosted data warehouses are more accessible, enable easy integrations with existing applications of organizations. Additionally, they empower organizations with reduced cost of ownership and overhead, enabling them to focus their resources to work on mission-critical objectives. 

Cloud Data Warehouses On-Premises Data Warehouses
Cloud Data Warehouses can be instantly scaled up or down based on business needs On-premises scalability is possible, but it is time consuming and resource-intensive, requiring hours of reconfiguring the hardware, software, and infrastructure
Availability of cloud data warehouses depends on the service level specified by a cloud provider. For Microsoft, Amazon & Google Guarantee 99.9% – 99.9% uptime. With On-Premises the availability depends on the quality of available hardware, software and the competence of an in-house IT team.
As far as security is concerned, the cloud provider ensures that its infrastructure is secure, as well as the data it is entrusted with On-Premises security is again dependent upon the competency of your in-house IT staff
It is well suited to serve multiple geographic areas. Perfect for queries measured in seconds. Unsuitable for queries measured in milliseconds. Shows excellent query performance (including the one measured in milliseconds), but only if the scalability problem is resolved.

How Blazeclan Helps Organizations in Cloud-native Data Warehousing? 

Agility has become vital as organizations prepare for modernizing their data. It is imperative to reimagine approaches for an accelerated data modernization and to reduce efforts and costs. Blazeclan helps organizations move to the cloud-based data warehouse with a thorough migration assessment of existing infrastructure, data, and code. With our unique approach to data warehousing integrated with our data modernization accelerator, IntelliSpout, we help organizations achieve 

  • Seamless workflow scheduling 
  • Maximum automation 
  • Data replication and archival 
  • Metadata management 
  • Security 
  • Data tuning and monitoring 

Our Approach 

Blazeclan’s data warehouse migration and modernization on the cloud empowers organizations to remove silos of data, increase time-to-insights, mitigate inefficiencies across traditional systems, improve compliance posture, and eliminate recurring costs. Data warehousing on the cloud has had a profound influence on the data analytics capabilities of organizations, allowing them to deploy virtual data warehouses with the modern analytics suite. Key factors behind cloud-native data warehousing’s impacts on data science and analytics are reduced costs, elastic scaling, the convenience of usage, and physical location. 

To Sum Up 

Automated data warehousing on the cloud is critical for organizations to achieve faster time-to-market and business agility. Data visualization and consolidation architecture integrated with analytics and machine learning and artificial intelligence  gives technology leaders practical solutions for their data management journey.