The difference between a data warehouse and a data lake

Is a data lake going to replace the data warehousing system in the near future? Whether to use a data warehouse or a data lake or both? These are some of the common queries raised by business users. Businesses should understand the concept of both data lake and data warehouse, most importantly when and how to implement them.

A data lake is a repository that stores mountains of raw data. It remains in its native format and transformed only when needed. It stores all types of data irrespective of the fact that whether they are structured, semi-structured or unstructured.

On the other hand, a data warehouse is a storage repository that stores data that are extracted, transformed and loaded into the files and folders. A data warehouse only stores structured data from one or more disparate sources that are processed later for business users. Data extracted from a data warehouse helps the users to make business decisions.

What is right for your company- A Data Lake or a Data Warehouse or both?

Organizations, nowadays, generate a huge amount of data and access the huge number of disparate datasets. It makes the gathering, storing and analyzing data more complicated. Therefore, these are the factors to choose data management solutions– for data gathering and storing and later analyzing them for competitive advantages. Here’s where data lakes and data warehouses help the business users in their own way. Data Lakes can be used to store a massive amount of structured and unstructured data that comes with high agility -can be configured and reconfigured when needed. The data warehouse system as a central repository helps business users to generate one source of truth. It needs IT help whenever you use the data warehouse to set up new queries or data reports. Some data, which is incapable of providing answers to any particular query/request, is removed in the development phase of a data warehouse for optimization.

Classifications give Clarifications

Let’s explore and classify a few points to present some key differences between the Data Lake and Data warehouse:

  1. Data: Data Lakes embrace and retain all types of data, regardless of whether they are texts, images, sensor data, relevant or irrelevant, structured or unstructured, etc… Unlike a data lake, data warehouses are quite picky and the only store structured processed data. When the data warehouse is in its development stage, decisions are made on the grounds of which business processes are important and which data sources are to be used. A data Lake allows business users to experiment with different types of data transformations and data model before a data warehouse gets equipped with the new schema.
  2. User: Data lakes are useful for those users who are looking for data to access the report and quickly analyzing it for developing actionable insights. It allows users like data scientists who do an in-depth analysis of data by mashing up different types of data, extracted from different sources- to generate new answers to the queries. A data warehouse, on the contrary, supports only a few business professionals who can use it as a source and then access the source system for data analysis. A Data warehouse is appropriate for predefined business needs.
  3. Storage: Cost is another key consideration when it comes to the storage of data. Storing data in a data lake is comparatively cheaper than in a data warehouse. A data warehouse deals with data of high volume and variety, thus, it is designed for high-cost storage.
  4. Agility: A data warehouse is highly structured, therefore, comes with low agility. The data lakes, on the other hand, requires to technically change the data structure from time to time as it lacks a defined structure that helps developers and data scientists to easily configure queries and data model when the need arises.


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The difference between a Data Warehouse & a Data Lake

Basis of Differences Data Warehouse Data Lake
Types of data Stores data in the files & folders Stores raw data (Structured/Unstructured/Semi-Structured) in its native format.
Data retention Do not retain data Retains all the data
Data absorption Stores transaction system or quantitative metrics Stores data irrespective of volume and variety
User Non-cosmopolitan like the business professionals Cosmopolitan-the Data scientists
Processing Schema-on-write, meaning- cleansed data, structured Schema-on-Read, raw data which only transforms when needed
Agility Needs fixed configuration-less agile Configuration and reconfiguration are done when required-Highly agile
Reporting and analysis Slow and expensive Low storage, economical

In the concluding lines, it is quite tempting to say, “go with your current requirements” but let me advocate you here that if you have an operative data warehouse just go for implementing a data lake for your enterprise. Alongside, your data warehouse, the data lake will operate using new data sources you may want to fill it up with. You can also use the data lake as an archive storage and like never before, let your business users access the stored data. Finally, when your data warehouse starts to age you can either continue it by using the hybrid approach or probably move it to your data lake.

Looking to learn more about Azure Data Lake, Azure Data Warehouse, Machine Learning, Advanced Analytics, and other BI tools. Schedule time with our data modernization assessment experts.

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