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.
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
- Data tuning and monitoring
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.