Author : Wriddhi Majumder
Data Warehouse as the Core of Analytics
The technology landscape is transforming at an exponential rate. This is not only impacting business but bringing a paradigm shift in the way business has been carried out. Ability to handle a huge volume of data, faster calculations, ability to apply complex algorithms to generate near real-time results, have opened up new horizons for the organizations in terms of data-driven decision making. Nowadays terms like Big Data, Machine Learning, Real-Time Analytics,Predictive Analytics, Prescriptive Analytics, are getting used on a regular basis in the business arena. The core of all these analytical solutions is nothing but Data.
We all know how structured, as well as unstructured data, is getting generated at a rapid pace across multiple systems. This huge volume of data has enormous potential that needs to be tapped to get essential business insights. In order to do so, data from multiple sources (internal or external; structured or unstructured) need to be accumulated in an OLAP (Online Analytical Processing) Database, where it can be cleaned, transformed, and massaged to get the necessary insights. These OLAP databases, or as they are popularly known as Data Warehouses, are one of the essential building blocks in the Data Analytics Ecosystem
Existing Challenges in Traditional Data Warehouses
Although the concept of a data warehouse is there from decades, there have been several challenges in these traditional data warehouses. Following are some of the key challenges
- Rigidity in Structure:- The traditional data warehouses are built for handling structured datasets. Also, the rigid structure is a challenge in the modern-day business scenario, where things are transforming fast, and insights are required on-demand. With a traditional data warehouse, the data models take months to be created; and any change to that model again takes time.
- Data Redundancy:- Often the same datasets are used by multiple departments of an organization for their individual Use Cases. In such cases, multiple Data Marts are created which has lots of common and redundant datasets. This results in an increase in data storage and hence the data warehouse management and maintenance cost.
- Performance:- With more and more data ingested in the data warehouse, the business expects more output from Data in terms of generating insights. However, with the increase in data volume, the performance gets impacted. This can only be improved with an increase in compute power. However, everything comes at a cost. An increase in compute power elevates the overall data warehouse cost, which often makes the entire proposition inefficient and unsustainable.
- Technology:- In the modern era, a huge volume of data is unstructured and semi-structured in nature. The traditional data warehouses are not capable of handling those datasets, thus missing out on lots of information. Similarly, in many cases, the other technology components such as memory, storage, processors, networking are outdated and hence becomes a barrier in a fast-paced competitive ecosystem.
- Maintenance Overhead:- Continuous maintenance is required for any on-premise data warehouse to utilize it to its fullest potential. Regular maintenance activities such as capacity management, refining of data models, data warehouse administration, data process management, etc. workload allocations, etc. need to be done.
Breaking the Barriers of Traditional Data warehouse
Thus, it is imperative that the organizations are looking for modernized solutions which can break the above-mentioned barriers. Fortunately, with the advancement of technology, data warehouse concepts and propositions are also transforming. Few benefits are provided below:
- The modernized data platforms such as Snowflake, Redshift, Azure SQL Data Warehouse etc. come as cloud solutions, which provides the necessary flexibility to the end-users.
- A cloud offering means minimum capital expenditure. No infrastructure is required as one can use cloud-based data warehouses. This is a game-changer as the entry point to adopt the data warehouse has come down drastically
- The storage, computer, etc. are scalable in nature, which can be scaled up and down as per the usage. Especially, the cloud data platform of Snowflake has managed to decouple storage and compute, which gives even more flexibility to the end-users. Now, one can select the storage and compute separately.
- The costing is also done based on the usage, which makes the proposition cost-effective and efficient in nature. This means the users have to pay only what they are using.
- The upgrading and downgrading of commute can be done on the fly. Thus, if the customer wants to use a certain high compute workload for 1 hour (peak) per month, they need to incur costs for high compute only for that period.
- Since these modernized offerings are on the cloud, the maintenance overhead is almost zero. This can save a huge amount of maintenance costs.
- Features such as integrability, scalability, high availability, etc. are readily available in these warehouses.
- The concept of Virtual Warehouse (especially in Snowflake) means one has to create one copy of data. The virtual warehouses are just images of the actual data which can be used independently without replication of physical data.
- The modernized data warehouses boast enhanced security which ensures the sensitive data to be stored securely in a cloud environment