Businesses today generate data almost every moment. 

Data is not only generated in internal business systems, but also from external systems such as social media platforms, IoT devices, public data, etc.

Hence, Data management is mission-critical for every organization in today’s fast-paced business environment. 

A solid data management practice is essential not only for adding business competitiveness but also for other purposes – like regulatory compliances, creating an operational cadence, cost/time savings, and so on.

It’s up to each business to make use of these data, and turn them into valuable, insight-driven business decisions making decisions, which in turn generates positive results for the business.

Data Warehouses and Data Lakes are two different ways of storing, processing, and analyzing data, that contribute to a strong data management practice when used together.

In this blog post, we’ll take a quick look at:

1. What is a Data Warehouse? →
2. What is a Data Lake? →
3. The critical differences between a Data Warehouse and Data Lake →

What is a Data Warehouse?

Data Warehouse put in simple words, is simply a digital warehouse where your organization’s data is structured, formatted, and stored.

A data warehouse is a rigid asset, where the structure/format of the tables, and the relationships between them are foreseen and created in accordance with your existing business needs.

It’s difficult to maintain a data warehouse when your business needs to change. Though not impossible, accommodating the changes for evolving business needs is a daunting task and requires a huge effort.

Data Warehouses were an on-premise thing a few years back mainly due to security concerns. However, businesses are adopting to cloud data warehouses rapidly today, primarily to overcome hardware limitations, cut down on the Total Cost of Ownership (TCO), all while enjoying a flexible, and scalable-friendly solution.

When it comes to the sequence of Extract, Load and Transform, ETL and ELT are the two different ways the data gets stored in a Data Warehouse. Though ETL is widely used in on-premise data warehouses, most cloud data warehouse solutions support ELT also.

The Data Warehouse interacts with all critical applications in order to execute the day-to-day operations, while also acting as a single source of truth for analytics purposes.

What is a Data Lake?

A Data Lake, on the contrary, can store any data regardless of its structure or format. In fact, a Data Lake is used to store all data an organization owns (or legally has the right to store), in its raw, unstructured format.

You can store audio, video, images, and everything in a data lake. There’s no limitation on the type or format of data stored.

The use of the data stored is unforeseen when it is stored in a Data Lake. Some data could even remain unused forever. However, it gives the organizations a sense of mental relief that none of their data is unsaved.

Data Lakes could also connect and save data from a Data Warehouse, apart from storing other data in their raw formats. Data Lakes are also used for storing data from IoT devices, and social media streams. 

An on-premise setting is not preferred for Data Lakes, due to the ever-expanding data volume. Hence, the cloud is the best choice.

The Data Lakes doesn’t cater to the operational needs but are widely used for running AI/ML models by data scientists to derive insights for strategic business decisions.

The Critical Differences Between Data Warehouse and Data Lake

Here’s a quick snapshot of the critical differences between a Data Warehouse and a Data Lake:

Factor	Data Warehouse	Data Lake
Schema	Rigid	Flexible
Scalability	Limited scalability if on-premiseUnlimited in cloud	Unlimited
Hosting	On-prem/Cloud	Cloud is the best choice
End-Users	Management executives for insights, and app users for storing/retrieving data	Data scientists
Purpose	Operational	Analytics, running AI & ML models
Maintenance	Difficult. Small changes also require big efforts	Easy

Schema

In terms of the data stored, the Data Warehouse has specific conditions, in terms of data types or data formats stored in each column. Therefore, the raw data must be processed to meet the conditions set in the Data Warehouse, in order to store it successfully. Therefore, Data Warehouses have a rigid schema.

In the case of a Data Lake, there’s no need for such data processing, as data lakes are capable of storing data of any type, in its raw format. Data Lakes has a flexible schema.

Scalability

The Data Warehouses, though scalable, is not scalability friendly, especially if it’s hosted On-prem. When scaling up, the process includes purchasing new hardware and setting them up, and maintaining them, adding to the TCO. 

The Data Lakes, on the contrary, is designed to be scalable, as it is capable of storing all your organization’s data (including data streams) in the raw format.

Hosting

Most businesses have already switched over from an on-premise Data Warehouse to a Cloud Data Warehouse. However, a few businesses still stick with the on-premise option. The on-premise Data Warehouse demands the pain of managing the hardware infrastructure needs. 

On the other hand, Data Lakes are almost impossible to manage if it’s not hosted in the cloud. The cloud solution provider takes care of the hardware infrastructure, that businesses can create and operate a data lake with a simple browser.

End-Users

The Data Warehouse is primarily used by the users interacting with applications such as a CRM, accounting system, HRMS, etc, for the purpose of storing or retrieving data either directly or indirectly. On top of that, your business users also consume data from the Data Warehouse for business decision-making purposes.

Data Lakes are primarily used by Data Scientists to run ML models and extract hidden insights from historical data. As Data Lakes stores the raw data, data scientists use sophisticated tools to clean the data before performing analysis. 

Purpose

Data Warehouses mainly serve the business’s operational and analytical needs. The business systems involved are tightly coupled with each other via the data warehouse, which acts as a hub for enabling seamless communication between these systems. On the other front, Data Warehouses are used as a single source of truth for uncovering business insights.

Data Lakes serves the purpose of storing historical data in all forms. Businesses can make strategic decisions, using the insights uncovered from the large dataset in a Data lake, in order to achieve the long-term goals.

Maintenance

In Data Warehouses it is extremely difficult to accommodate the evolving business needs. Even adding a single column or an entire table would require you to create relationships, make a call to fill up the empty rows, and estimate (and take required actions) the impact in the applications involved, or analytics.

In the case of Data Lakes, you’re not going to worry about the maintenance from the technology standpoint, as it is designed to save all data in their raw format. However, maintenance efforts may be required from time to time to save on the storage space, to minimize the cost, etc.

Conclusion

Do you still have the million-dollar question – “Do I need a Data Warehouse or a Data Lake?”.

Well, here’s the quick answer! Businesses need both Data Lake and Data Warehouse as they both cater to different needs, and are not a replacement for each other.