Data Warehouse vs Data Lake

The interest in Big data has been trending up for several years, and many organizations are looking to use their collected data to create valuable business insights. The terms both Data Lake and Data Warehouse are the two popular options for storing big data.

This post highlights the differences between a Data Lake and a Data Warehouse to help you make an informed decision on how to manage your data. But before comparing Data Warehouse vs Data Lake, let us first learn what Data Lake and data warehouse are?

Data Lake vs Data Warehouse - What Is The Difference?

Following is the list of comparison elements that we are going to discuss in this Data Lake vs Data Warehouse blog:

• What is a Data Lake?
• What is a Data Warehouse?
• Data Lake vs Data Warehouse - Which is Better?

So let us get started with this Data Lake vs Data Warehouse article,

What is a Data Lake?

A Data Lake is a central storage repository that allows you to store large amounts of structured, unstructured, and semi-structured data. You can store any type of data in its native format without any fixed limits on account size or file.

A Data Lake is like a vast pool of data, the purpose of which is not defined yet. When storing data, Data Lakes are associated with metadata tags and identifiers for faster retrieval.

A Data Lake is just like a large container which is very similar to an actual lake and rivers, wherein lakes have multiple tributaries coming in, but Data Lakes have structured data, machine to machine, unstructured data, and logs flowing through in real-time. It offers a wide variety of analytic capabilities.

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What is a Data Warehouse?

A data warehouse is a blend of technologies and components for the strategic use of data. It collects and manages data from various sources to give meaningful business insights. It is used to connect and analyze business data from multiple sources. It's a process of transforming data into information.

Data Warehouse stores data in folders or files that help you organize and use data to make crucial decisions. It gives a multi-dimensional view of atomic and summary data. The essential functions performed are data extraction, data cleaning, data transformation, data loading, and refreshing. 

Next, let's highlight critical differences between the Data Lake and data warehouses approach.

[ Related Article: Overview of Data Warehouse ]

Data Lake vs Data Warehouse - Which is Better?

Let's look at the differences between the Data Lake and Data Warehouse in crucial areas

#1. Data Structure

In Data Lakes, data is stored in its raw form and is transformed only when it is ready to be used. Thus, they require a much larger storage capacity and can quickly be analyzed for any purpose. In Data Lakes, the risk of raw data sometimes becomes data swamps without appropriate data governance and data quality measures in place. 

Whereas Data warehouses store processed and refined data. They save pricey storage space, and the processed data is defined by a larger audience. 

#2. Data Types Support

Data Lakes embrace a non-traditional data types approach. It stores all types of data irrespective of its source and structure. The data is kept in raw form and can be transformed only when it's ready for use.

Data is extracted from transactional systems in data warehouses and includes qualitative metrics and attributes that define them. Non-traditional data sources such as sensor data, text and images, web server logs, social media activity, etc., are largely ignored. New uses for these data types to evolve, but storage and consumption are pretty tricky and expensive. 

#3. Data Capturing

Data Lakes capture all data and structures, semi-structured and unstructured only in their native form from source systems.

Data Warehouses captures structured information and arranges them in schemas.

#4. Data Timeline

Data Lakes retains all data. This includes the data that is used and the data that it might use in the future. Also, the data is kept for all time, to go back in time to any point and do analysis. 

[ Related Article: Tools of Data Warehouse ]

In the data warehouse development process, a significant amount of time is spent analyzing various data sources, knowing business processes, and profiling data. That results in a highly structured data model for reporting.  

#5. Purpose 

In Data Lakes, the purpose of individual data pieces is not fixed. The raw data flows into Data Lakes, sometimes with a particular future usage in mind and sometimes just to have on hand. It indicates Data Lakes have less filtration of data compared to their counterpart.

The processed data is raw data that has been put to a specific use. Since data warehouses contain only processed data, all of the data has been used for a particular purpose within the organization. This means data storage space is not wasted on data that may never be used.

#6. Users

Data Lakes are often difficult for those who are unfamiliar with unprocessed data. It is ideal for users who are involved in deep analysis. Such users include data scientists who need specialized tools and capabilities like predictive modeling and statistical analysis.

The data warehouse is ideal for operational users because it is well structured, easy to use and understand.

#7. Accessibility

Accessibility and ease of use refer to data repository use as a whole, not the data within them. Since Data Lake architecture has no structure and is therefore easy to access and change. Additionally, any changes made to the data can be done quickly, as Data Lakes have very few limitations.

Data Lakes increase agility and give more opportunities for data exploration and proof of concept activities, including self-service business intelligence, within your privacy and security settings. 

By design, data warehouses are more structured. Data warehouse architecture's primary advantage is that data processing and structuring make data itself easier to decipher. The limitations of structure make data warehouses complex and costly to manipulate. These changes will undoubtedly consume developer resources and take more time.

#8. Schema

Data Lakes use Schema-on-read. Users can store any data in Data Lakes without the necessity of a single schema. They can discover schema later while reading the data. This means various teams can store their data in the same place without relying on others to query data.

This offers higher agility and ease of data capture but needs work at the end of the process. Once the schema is developed, it can be kept for future use or discarded when no longer needed.

On the other hand, data warehouses use schema-on-write. This requires upfront data modeling to define the schema for the data. Before performing data storage, it has to be transformed and presented for application in analytics and reporting.

The purpose you are using the data should be known prior to import it into the data warehouse. As you unearth new requirements, you may have to reevaluate the models that were defined earlier.

This improves performance, security, and integration.

#9. Cost

One of the essential things about big data technologies is that data storage cost is relatively inexpensive than storing data in a data warehouse. This is because the data technologies are mostly open-source, so the licensing and community support is free. 

Data Lakes are designed for low-cost storage purposes. On the other hand, storing data in data warehouses is costlier and time-consuming.

#10. Security

Data warehouses have been around and in use for decades compared to big data technologies. They are much more mature and secure than Data Lakes. Data warehouses store extremely sensitive data for reporting purposes.

Big data technologies, which incorporate Data Lakes, are relatively new, and so the ability to secure data is still challenging. As discussed above, a Data Lake is created using open-source technologies; securing data is not great as that of data warehouses. 

#11. Agility

A data warehouse is a highly structured data house with a fixed configuration and little agility. Transforming the structure isn't too difficult, but doing so is time-consuming when you account for all the business processes tied to the data warehouse.

Furthermore, databases are less agile to configure because of their structured nature. Data Lakes lack structure, making it easy for data scientists and data developers to easily configure and reconfigure data models, queries, and applications.

#12. Technology

The Hadoop ecosystem is well-aligned to the Data Lake approach because of its agility. It can scale large volumes and handle any data structure easily.

Data warehouse apps use relational database technologies because this supports quick queries against structured data.

Conclusion

Both data warehouses vs Data Lake play a significant role in modern data architecture. A Data Lake is usually a starting point from where company-wide data is onboarded. It's also the phase where the data warehouse structures its data.

We hope the above-listed differences will help you determine which one would be better for your needs and help your organization grow.