Data analytics has emerged as one of the best techniques for businesses to be one step ahead of their competitors, and to serve this purpose data plays the most vital role. Nowadays when the whole world is dependent on this data we need to ensure its safety and integrity by protecting and preventing it from loss and damage. For this purpose, we need to effectively manage and store data in a safe and secure place. The amount of data that is present currently cannot be stored in the traditional file systems, so storing data online is the need of the hour for each and every business right now.

Data warehouses and Data lakes are a one-stop solution for storing enormous data in the easiest way possible. Though both are different in terms of functionality, they have one thing in common: They allow data storage without compromising on security.

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1. What is a data warehouse?

A data warehouse is a centralised system where data is extracted from various sources and stored for strategic use. It has some technologies and components using which we can turn raw data to derive some valuable business insights from it. To be precise it generally gives the raw data a shape and form and then transforms it into information. Data warehousing has emerged as one of the best techniques which provide countless benefits from saving time to enhancing data quality, not only this it also helps in increasing data security.

2. What is a data lake?

A data lake is a storage repository that is competent in storing a vast amount of data: be it structured, semi-structured, or unstructured. In a data lake, data is stored in its native format, and defining schema while capturing data is also not mandated. It is an emerging technology that has redefined data extraction, storage, and analysis. The main purpose of a data lake is to centrally store all the enterprise's immense unstructured/structured data to use it further for reporting, and visualisation to eventually dig deeper to get the best business understanding. The benefits provided by a data lake are data democratisation, provides schema flexibility but the most useful thing about it is that it supports all data formats.

As we all know that data warehouses and data lakes are somewhat alike but there are many differences in terms of their functionalities. Keep on reading this article to know about the main differences between these two.

3. Major Differences Between Data Warehouse and Data Lakes

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3.1 Data Retention 

During the creation of a data warehouse a lot of time is consumed in making a structured data model by following the data analytics approach which results in the refusal of unwanted files. In a data warehouse, the data needs to be in a structured form to proceed for further analysis, so for this purpose a lot of data is dumped. It is also considered good practice to discard unwanted data to keep data warehousing costs at bay.

In a data lake, all the data can be kept regardless of its time of use. For example, you can keep data in a data lake that can be used in the future. It gives huge data storage capacity with much lower costs as compared to a data warehouse.

3.2 Users 

Generally IT professionals or businesses who are clear with the use cases or are ready with the data models generally prefer to use a data warehouse. The data warehouse stores information and any data that is not crucial for the analysis process however is rejected. From this, we can infer that the data that sits in a data warehouse is highly structured and ready for analysis. So we can say that data warehouses are generally used by business analysts.

Whereas on the other hand, data lakes store a wide range of data (structured/semi-structured/unstructured) so to clean this raw data or fill in missing data values generally the expertise of a data scientist is required. So for a data lake mostly the users are data scientists or data developers.

3.3 Data capturing format

The data that sits in the data warehouse is highly filtered and cleansed, data is present in the structured format and the schema in a data warehouse is also pre-defined. Some audio files and log files are ignored in the case of a data warehouse, to maintain the consistency of the data model, and the removal of unnecessary data files is required in the case of a data warehouse to make it more cost-effective.

In the case of a data lake, it can keep everything from relational data to JSON documents to PDFs to audio files. All types of data formats are supported because of the low-cost storage solution provided to businesses to store their data.

4. Data Warehouse VS Data Lake: Which strategy should be picked?

Data warehouses and data lakes both are very flexible and can store massive amounts of data but when it comes to picking one out of the two, it can be really tricky. 

• If your business already has a data warehouse then you should consider this option only as if you decide to switch to a data lake it can be really hard for you to begin all over again from scratch. But if you are planning to start using one of these technologies then you should consider using both. 
• Data lakes can be a place to store all the unstructured data because of its low-cost storage benefit and using a data warehouse you can easily create data models to facilitate analysis efficiently.

5. Conclusion 

Data warehouses and data lakes have their own different purposes and importance. Mostly to analyse the current and historical data of an organisation these two can be a boon for you. Adapting to a data warehouse or a data lake purely depends on the company’s needs, so to choose which is right for you, you should first understand the functionalities provided by both of them.

TL;DR

• All data is retained in case of a data lake and only selective data that is structured is kept in the warehouse.
• For data lakes data scientists and data developers are the key users and in the case of data warehouses business analysts are the key users.
• In a data lake all the data regardless of its format is captured(audio files, images, logs, etc) which is not the case for the data warehouse.