Data Lake and Data Warehouse might seem alike, but are different concepts. In this post, we will understand the key differences between Data Lake VS Data warehouse.
Key Difference between Data Lake VS Data Warehouse
- The data lake is a new concept, whereas the Data warehouse is older than the Data Lake.
- Data lakes can contain all data and data types, whereas Data warehouses can provide insights into pre-defined questions for pre-defined data types.
- Data warehouse uses a traditional ETL (Extract Transform Load) process and Data Lakes use the ELT (Extract Load Transform) process.
- Data lakes can retain all data and In the data warehouse development process, significant time is spent on analyzing various data sources.
What is Data Lake?
A data lake is a storage system that stores significant volumes of data. It can store a broad range of datasets that can be later analyzed.
Data lakes have become popular because it allows business users to store data in its raw form rather than process it. This unprocessed data can be used to detect patterns and make predictions. They are designed with no boundaries, meaning you can use any tool of your choice to manipulate and analyze the data.
What is Data Warehouse?
The data warehouse is a database that is used to store data and prepare it for analysis. A data warehouse typically stores different types of information, including transactional and analytical data.
The data in a data warehouse may originate from one or more systems. It is a centralized depository that holds all the company's business activities and transactions, which can then be analyzed by business professionals to make decisions and predict future behaviors.
Difference between Data Lake VS Data Warehouse in Detail
Most important differences between data lake vs data warehouse:
||In the data lake, Data is kept in its raw form. It is only transformed when it is ready to be used.
||A data warehouse will consist of data that is extracted from transactional systems or data which consists of quantitative metrics with their attributes. The data is cleaned and transformed.
||Captures semi-structured and unstructured in their original form from source systems.
||Captures structured information and organizes them in schemas as defined for data warehouse purposes
||Data lakes allow users to access data before it has been transformed, cleansed, and structured. Thus, it is fast compares to the traditional data warehouse.
||Data warehouses offer insights into pre-defined questions for pre-defined data types. So, any changes to the data warehouse needed more time.
||The data lake is a new concept
||The Data warehouse is older than Data Lake.
||Data lakes can retain all data.
||In the data warehouse development process, significant time is spent on analyzing various data sources.
||Data Lake is relatively inexpensive than storing data in a data warehouse.
||Storing data in a Data warehouse is costlier and more time-consuming.
||Data lakes can contain all data and data types
||Data warehouses can provide insights into pre-defined questions for pre-defined data types.
|Position of Schema
||Typically, the schema is defined after data is stored. This offers high agility and ease of data capture, but requires work at the end of the process.
||Typically, schema is defined before data is stored. Requires work at the start of the process, but offers performance, security, and integration.
||Data Lakes use the ELT (Extract Load Transform) process.
||The Data warehouse uses a traditional ETL (Extract Transform Load) process.
||Data is kept in its raw form. It is only transformed when it is ready to be used.
||The chief complaint against data warehouses is the inability, or the problem faced, when trying to make changes in them.
1. Do you need a data warehouse with a data lake?
Depending on the requirements, an Organization might need both a data warehouse and a data lake, as both of these are used for different scenarios. Storing data in a data warehouse is more expensive than in a data lake. It depends on the organization, which one they should use.
2. Can data lake replace data warehouse?
Both of these are used for different use cases, and data lake cannot directly replace data warehouse. Most organizations use a data lake along with a data warehouse too.
3. Is Hadoop a data lake or a data warehouse?
A Hadoop data lake is a data management platform comprising one or more Hadoop clusters. It is one of the important elements that is used to make data lakes. Hadoop is popular in data lake architecture, and it is open source too.
4. Is Snowflake a data lake or warehouse?
Snowflake is a data lake, but it provides the advantages of both a data lake and a data warehouse. Snowflake is a rational database that is used to make data warehouses and can be built on cloud platforms like AWS, Azure, and Google cloud Platform.
We learned that a data lake is a storage system and a data warehouse is a database, they seem similar, but they are not. Both of these are used for different use cases. I explained the concept of data lake and data warehouse along with the key differences between Data Lake and Data Warehouse in this article.
Thanks for reading this article, I hope you find it useful.