Highlights
- Data warehouse modernization is required to meet the growing variety, velocity, volume, and veracity of modern data and to keep a business competitive.
- Moving to the cloud is, at present, one of the best approaches for data warehouse modernization.
“Data is the new oil” is a nice little catchphrase that has been doing the rounds off late, highlighting how data has become a valuable commodity.
Most businesses and industries are leveraging data to the maximum to make informed decisions.
Now you must be wondering, how much data is created every day? Though there is no definitive answer to this question, estimates show that roughly 2.5 quintillion bytes of data is created every day.
The data creation rate is expected to increase with the growing popularity of the Internet of Things (IoT). According to statistics, at the dawn of 2020, the amount of data in the world was estimated to be 44 zettabytes. By 2025, the amount of data generated each day is expected to reach 463 exabytes globally.
With people, devices, and data working in extension of each other, there has been a deluge of data. This has, in turn, led to the demand for cutting-edge data landscapes and analytics to allow businesses to make sense of unstructured data.
A sine-qua-non for deriving business outcomes is to be able to gain insights from data, and for that, data needs to be processed or refined and governed.
Is it not obvious that data warehousing infrastructure also needs to be updated intermittently to handle all this data? After all, traditional data warehouses are incapable to manage data from new sources or innovations such as Machine Learning or predictive analytics.
They are simply not equipped for such complex data loads and the need of the hour is, therefore, data warehouse modernization. There is a need for a complete overhaul which means that the fundamental architecture needs to be upgraded to grant organizations the flexibility required to support current workloads and prepare for future data-driven innovations.
What is Data Warehouse Modernization?
In a nutshell, data warehouse modernization is all about extending the data warehouse infrastructure to reap the benefits of new technologies. This means inducing speed and agility in data processes, meeting changing business requirements, and staying relevant in an age of big data.
Gone are the days when a daily report at the end of the day would suffice to meet business’ daily needs. Valuable insights on a real-time basis are the need of the hour. Legacy data warehouses cannot meet present-day demands as they were not designed such. Data warehouse modernization has, thus, become critical for an industry-cloud strategy.
Methods of Data Warehouse Modernization
Organizations are often confused about how they should go about implementing data warehouse modernization. They wonder if they should rip everything apart, start from scratch, or add to the existing infrastructure. Both approaches can work. Below we will discuss three different approaches to data warehouse modernization.
Moving to the cloud:
Moving your on-premise legacy systems to the cloud is, perhaps, one of the best approaches to data warehouse modernization. This can involve migrating an existing data warehouse, but according to experts, it is better to start with a use case that is not sufficiently supported by the current infrastructure.
The advantages of this approach are many. Some of them are as follows:
- The cloud’s pay-as-you-go model can help minimize costs significantly as you pay only for the storage and compute that you use.
- Since one can scale a data warehouse easily on the cloud as the volume of data increases, it offers greater elasticity.
- Maintenance and support costs are negligible.
- It’s easier and faster to integrate with other cloud-based services and applications.
Given the benefits, the cloud is the best option for businesses that aim to minimize high costs and do away with the complexity of maintaining on-premise infrastructure.
Extending the existing data warehouse:
Some companies sometimes want to keep their on-premises and legacy systems because of reasons such as compliance and security. In such cases, businesses can still enjoy the benefits of data warehousing modernization by extending their existing data warehouse to modernize the data ecosystem. In such cases, organizations have to integrate their legacy sources with modern tools and cloud platforms to enhance the scalability and agility of the data warehouse.
Here a few legacy components remain while other components get modernized. The advantages of this approach are as follows:
- With a modern cloud platform, processing power and storage capacity can be extended as and when needed, thus improving scalability and reducing costs for hardware upgrades.
- Company gets access to a more controlled environment for experimenting with the modern platforms, and the cloud as an existing data warehouse is already in place.
Starting from scratch:
A business can opt to implement an entirely new data warehousing initiative. This is a good idea if it plans to launch any new initiative with a view to modernization. Launching modern initiatives with legacy systems can prove to be challenging, but doing the same with modernized infrastructure can be beneficial. This is also an ideal approach for organizations that are yet to build a data warehouse or feel that their existing data warehouse will not be able to support their next analytics initiative.
The advantages of implementing a new data warehouse are:
- Modern tools and cloud platforms allow one to experiment, test, and evolve ideas without heavy investment.
- New data warehouse implementation can provide better support for data warehouse automation tools and solutions and allows organizations to scale and expand analytics with less effort.
How to achieve data warehouse modernization?
Now that we have discussed the broad methods, let’s know why data warehouse modernization is important and how it can be achieved.
Data integration: The first step towards modernizing the data warehouse involves collecting and merging all available data in real-time via data integration pipelines. This can be achieved via modern data warehouse tools that simplify the process of building complex integration pipelines. Simple drag-and-drop actions allow one to point data sources, configure the connection, apply any transformations you need and execute the ETL pipeline – all of this without the need for a single line of code. Isn’t it simple? It is.
Data modeling tools: Data models evolve in accordance with changes in business data requirements. New data models can be created, or existing ones can be modified/redeployed to provision the new piece of information.
For this, old legacy processes need to be replaced with a single zero-code data warehousing tool that’s capable of performing tasks at the basic and advanced levels. After integrating these tools, changes to the modern data warehouse can be easily made. No code or scripts is needed.
Data warehouse automation: Modernization is not a one-off effort but a continuing practice. It is, thus, essential to automate as many processes as possible. Data warehouse automation is all about an agile architecture with a turnaround time of hours or days instead of months. It allows smooth integration of the latest technologies and platforms to achieve desired results.
Conclusion
Data Warehouse modernization might look tedious, but the benefits are many. Modernizing the data landscape allows organizations to keep pace with changing business requirements and helps them meet the demand for business-driven analytics and remain relevant in the market for today and the future.
