DATA LAKE VS. DATA WAREHOUSE: 5 DIFFERENCES

In this article, we will explain data lake and data warehouse terms which are also considered as ‘buzzwords’ when it comes to storing big data. 

The reason you need to know about the differences between them and the current data trend is that companies work with a huge volume of data every day and need to figure out which data storing method should be adopted based on the type of data they have. For example, visitor activities on your app, website, sensor, or other things need to be collected, stored, and then process it in order to make reliable data-driven business decisions and increase performance. Deciding which data storage concept is more suitable for your business is the first and probably the most crucial step in unleashing the data’s power. That is why to be aware of the difference between data lake and data warehouse is quite important. Let’s get dive in…

Simply put, a data lake refers to storing a large amount of all structured, unstructured, and other data resources. Whereas, a data warehouse is a database that usually used for business insights. In other words, the data warehouse is focusing on business activities to enhance the organization’s performance. In a data warehouse, the data is usually structured which is historical data. However, it may contain unstructured data as well. 

Let’s highlight some of the key differences between the two data storing types.

  1. Data Types

    In the data lake, as we mentioned above, all structured, unstructured, and the other various data inputs are stored.
    In the data warehouse, historical data is stored to create reports for business analytics. This type of data is called structured data.
  2. Purpose

    The data lake’s purpose is to provide cost-effective big data storage, whereas the data warehouse’s aim is to analyze the data to make data-driven business decisions.
  3. User Profile

    A data lake is suitable for data scientists and engineers. Since a data warehouse is more business-focused, the most suitable user profiles are business analysts and data analysts.
  4. Tasks

    With the data lake, you can store big data, I literally mean a BIG DATA, and also apply Deep Learning and other ML models since data lake has a huge amount of unsorted data that gives flexibility and makes it suitable for Machine Learning as well as massive data analysis. Whereas, with the data warehouse, you can aggregate and summarize the data. The purpose of the data warehouse is to allow analysts to explore more insights from the sorted historical data.
  5. Size (Amount of Data)

    In a data lake, all types of data are stored just in case those data might be needed. You can think of like a black hole, it contains everything regardless of what type of that data is. In a data warehouse, only relevant data is stored for analysis. If the business is not analyzing a data attribution or source, then it won’t be included.
 

 

  Data Lake Data Warehouse
Data Types All data types including structured and unstructured Historical data which is structured data.
Purpose Lower Cost for Big Data storage Data-driven business decisions through data analysis
User Profile Data engineers and scientists Business and data analysts
Tasks ML, Deep Learning, and other Big Data Analysis  Summarize and aggregate data 
Size(Amount of Data) Since it can store any type of data, its storage capacity can go up to petabytes Only the data is worth analyzing and making business decisions based on that stored data.

 

After making the differences clear, let’s talk about the data warehouse concept a bit more since it is directly related to business operations and performance. As we mentioned these differences between the two concepts of storing data, data warehouse stores structured data (usually!) which means a pre-defined data and ready to analyze straightforwardly.

In a warehouse, there are important steps that should be followed if we want to make the most out of data analysis to evaluate our business performance as well as make business decisions afterwards. Those are; data extraction, cleaning, transformation, and loading-refreshing respectively.

As a business owner, if you want to adapt the data warehouse concept to make an analysis of your company in order to get actionable insights, CloudMile can help you with its professional cloud architect team. CloudMile is a Google Cloud Premier Partner and can offer you ‘BigQuery’ which is one of the Google Cloud services that CloudMile is entitled to offer. 

Using BigQuery can help you in several ways such as; gaining insights through real-time and predictive analysis, high data protection, easy access…etc. 

Long story short, BigQuery is a modern version of the data warehouse concept provided by CloudMile. You can apply BigQuery to your business with the help of CloudMile.

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