Data Warehouse VS Data Lake

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 in business operations. 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 data analytics 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 low cost data storage. Though both are different in terms of functionality data structure, they have one thing in common: They allow data storage without compromising on security.

1. What is a data warehouse?

A data warehouse is a centralised system where data is extracted from various external data 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, semi structured data and unstructured data/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 key 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 big data and analytics approach which results in the refusal of unwanted files. In a big data analytics 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 of data visualization, 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 stores data 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 the business professionals data engineers and analysts.

Whereas on the other hand, a data lake vs other lakes store raw data or 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 of data stored 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 types and 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 processed data mine 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 business users 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.

6. Frequently Asked Questions

1. What is a data warehouse?

A data warehouse is a centralized system designed to extract, transform, and store data from various external sources for strategic business use. It shapes raw data into valuable insights, enhancing data quality, saving time, and increasing data security.

2. What is a data lake?

A data lake is a storage repository capable of holding vast amounts of structured, semi-structured, or unstructured data in its native format. Unlike a data warehouse, it does not mandate defining schema during data capture. Data lakes support data democratization, provide schema flexibility, and accommodate all data formats.

3. What are the major differences between Data Warehouse and Data Lake?

3.1 Data Retention

In a data warehouse, structured data models are created, and unwanted files are discarded to proceed with analysis. Data lakes retain all data regardless of its time of use, offering immense storage capacity at lower costs compared to data warehouses.

3.2 Users

Data warehouses are preferred by IT professionals and businesses with clear use cases. They store highly structured data suitable for analysis. Data warehouses store and lakes cater to data scientists or developers, as they store a wide range of data requiring expertise to clean and fill missing values.

3.3 Data Capturing Format

Data in a warehouse is filtered, cleansed, and present in a structured format with pre-defined schemas. Data lakes support various formats, from relational data to audio files, providing a low-cost storage solution for businesses.

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

Choosing between data warehouse and data lake depends on the business's existing infrastructure. If a data lake definition a warehouse is already in use, it's advisable to continue with it. For new implementations, a combination of both can be beneficial, using data lakes for storing unstructured data and data warehouses for efficient analysis with structured data models.

5. What is the primary function of a data warehouse?

A data warehouse serves as a centralized system for extracting, transforming, and storing data from external sources for strategic business use, providing valuable insights and business intelligence.

6. How does a data lake differ from a data warehouse in terms of data retention?

Data warehouses require structured data models, leading to the rejection of unwanted files. In contrast, data lakes retain all data, regardless of its time of use, offering substantial more storage space and capacity at lower costs.

7. Who are the primary users of a data warehouse?

Data warehouses are generally used by IT professionals and businesses with clear use cases, storing highly structured data suitable for analysis by other business analysts, professionals and analysts.

8. Who are the primary users of a data lake?

Data lakes are preferred by data scientists or developers due to their ability to store a wide range of data, including structured, semi-structured, and unstructured formats, requiring expertise to clean and process.

9. How does data capturing format differ between data warehouse and data lake?

In a data warehouse, data is filtered, cleansed, and stored in a structured format with predefined schemas. Data lakes, on the other hand, support various formats, including relational data, audio files, and images, providing a low-cost storage solution.

10. Can a data warehouse store unstructured data?

Data warehouses primarily store structured data for efficient analysis by data analytics tools and may discard unstructured data during the modelling process.

11. What is the role of a data lake in supporting data democratization?

Data lakes support data democratization by storing data in its native format, providing schema flexibility, and accommodating all data formats, making it accessible for various analysis purposes.

12. Should a business with an existing enterprise data warehouse consider implementing a data lake?

For businesses with no data warehouses cost an established data warehouse, continuing with the current infrastructure is advisable. However, a combination of both data lake and data warehouse may be beneficial for new implementations.

13. How does data security differ between data warehouse and data lake?

Both data warehouses and data lakes prioritize data security. Data warehouses offer security by managing structured data, while data lakes ensure security with their low-cost storage solution for diverse data formats.

14. In what scenarios is the combination of a centralized repository, a data warehouse and a data lake recommended?

The combination of a data warehouse and a data lake is recommended for new implementations, allowing businesses to leverage the strengths of both technologies. Data lakes can store unstructured data efficiently, while data warehouses can move data pipelines facilitate structured data analysis.

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