A data lake and data warehouse is a group of technology tools that use large amounts of disparate data in analytics to aid decision-making within an organization. This type of database is characterized by the compatibility of its internal structure with what the user needs in terms of indicators and axes of analysis.
Data lakes and data warehouses are widely used for storing big data, but they are not interchangeable terms. A data lake is a large set of raw data, the purpose of which has not yet been determined. A data warehouse is a repository of organized and filtered data that has already been processed for a specific purpose. There is also an emerging trend of data lake management architecture, which combines the flexibility of a data lake with the data management capabilities of a data warehouse.
A data lake stores large amounts of structured, semi-structured, and unstructured data in its native format.
The difference between a data lake and a data warehouse
Data Lake stores all data regardless of the source and its structure while a data warehouse stores data in quantitative scales with their attributes.
Data Lake is a repository that stores structured, semi-structured and unstructured data while Data Warehouse blends technologies and components that allow for strategic use of data.
Data Lake defines the schema after the data is stored while Data Warehouse defines the schema before the data is stored.
Data Lake uses the ELT (Load Transformation Extraction) process while the Data Warehouse uses the ETL (Load Transformation Extraction) process.
Comparing Data Lake vs. Warehouse, Data Lake is ideal for those who want in-depth analysis while Data Warehouse is ideal for operational users.
should I use a data lake or data warehouse?
The “data lake vs. data warehouse” conversation is probably just getting started, but the key differences in structure, process, users, and overall agility make each model unique. Depending on your company's needs, developing the right data lake or data warehouse will be beneficial for growth.
What are the main differences between a database, a data warehouse, and a data lake?
Databases, data stores, and data lakes are used to store data. So what's the difference
The main differences between database, data warehouse, and data lake are:
- The database stores the current data required to run the application.
- A data warehouse stores current and historical data from one or more systems in a pre-defined and static schema, allowing business analysts and data scientists to easily analyze the data.
- A data lake stores current and historical data from one or more systems in its raw form, allowing business analysts and data scientists to easily analyze the data.
Data Warehouse Engineering
A data warehouse architecture uses dimensional models to determine the best technique for extracting meaningful information from raw data and translating it into an easily understandable architecture. However, you should keep in mind three main types of engineering when designing a real-time data warehouse at a business level.
- single layer architecture
- Two-level architecture
- Three layers architecture