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Data Warehouse vs. Data Lake – What’s the Difference?

Data Warehouse vs. Data Lake – What’s the Difference?

Published by Programme B

Data integration is one of the most important aspects of data management. But what does data integration entail? Well, essentially, it’s all about consolidating data from multiple sources to provide a central source where consumers can access it for analysis to informed decision-making. 

There are two main data integration models that companies use, often simultaneously, to facilitate data movement from several sources to target systems: data lakes and data warehouses. Data lakes accept all kinds of raw data whose purpose is unknown at the capture point. In contrast, data warehouses store structured data with a schema and whose purpose is defined beforehand. 

While they may be used together or one at a time, they have key differences that set them apart, two of which have been hinted at in the definitions. An excellent example of where the duo is used concurrently is in top-rated online gaming platforms such as online casino to boost player experiences. Read on for a better understanding of the differences between data lakes and data warehouses. 

Structured vs. Unstructured Data

Data warehouses store structured data; data that has been processed by being extracted from the source, cleaned, transformed into a logical format and then loaded into the system. On the other hand, data lakes store raw data from all sources without filtering through them. Hence, they store structured, semi-structured and unstructured data without adhering to a stipulated format. This makes data lakes scalable as there are no limits on how the data is supposed to look or its source when it is being stored, and the transformation happens after it has already been loaded.

Purpose of the Data

In a data warehouse, each single data set that is inputted has a predetermined use, whereas the data in a data lake may have immediate use in mind or may simply be inputted in the hope that it will come in handy someday. As you must have guessed, the storage space for a data warehouse becomes much less since we are talking about data set aside for a specific purpose and not an unspecified amount and type of data from diverse sources.

Intended Users

Dealing with the data in data warehouses requires less analytical skills as compared to data lakes since the format in which it’s stored makes it easily queryable. In addition, the level of detail in data stored at a data warehouse will heavily depend on the agenda driving the company that needs to use it, making it subject-oriented and therefore ideal for business intelligence professionals.

Since the data stored in a data lake comes raw and unfiltered, the potential to extract much more information from it tremendously increases, and so does the need for specialized tools for data mining and extraction. Data scientists and engineers are better equipped to handle raw data and do deeper analyses of the data sets, leading to even deeper insights into the subject in question.

Accessibility and Scalability

While data warehouses are easier to interpret given their ready-to-use structure, they are complicated to manipulate and access due to security constraints. The options increase a lot more when it comes to data lakes since it’s easier to access the data and change or update it because the number of consumers requesting the data is diverse.

Which One Is Better?

The amount of big data generated from mobile apps, social media, transaction processing systems, documents, emails, clickstream data and many more sources just keeps increasing at lightning speed, and so does the need to process it on demand. The question as to which is a better option for a company’s needs depends on the agenda at the time, although most companies have opted to capitalize on both. 

Why? It is vital to note that even though the data warehouses require less storage space, they are still much more expensive to maintain and set up. This leads to a conundrum when a company needs to transfer just a portion of data from a data lake for a specific purpose without incurring insane costs and being forced to bite off more than can be chewed. 

All in all, one fact remains: different sectors will tend to lean on either side based on the type of data being processed by them. So, while both data warehouses and data lakes come with their merits, whether both are used, or one is preferred over the other, solely depends on the business. What’s your take on the difference between data warehouses and data lakes? Feel free to share your thoughts right here in the comments section!

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