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Data Lake vs. Data Warehouse: Know the Difference

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The software development industry is handling more data than years ago. This data is often the key driver for decision making. For instance, the larger the volume of data you possess, the more information you have for better decision making. However, the main challenge that most software development companies face is how and where to store that volume of data. 

For instance, companies like Netflix deal with processing and storing a lot of data. They need this data to optimize the quality of their video streams. For example, they might need to collect users’ ratings, searches, and watch history so they can recommend the next watch to users. They’ll also need to collect credit information as well as payment and tax calculations so that they can bill users appropriately. These are different types of data, and since they have a lot of users, it’s different types of Big Data. 

The mannerism of handling different data determines the ease of gleaning useful information from it. Throughout the years, some storage architectures sprang up with the notion of mitigating this challenge. These storage architectures deal with how the data is stored, and how to retrieve useful information from the data. 

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In this article, we’ll be exploring two major data storage architectures: data lakes and data warehouses. We’ll discuss at length what they are, and how they function. Finally, we’ll highlight the basic similarities and differences between them. 

What Is a Data Lake?

A data lake is a data storage architecture with a highly flexible and centralized data storage repository. It’s useful in the storage of Big Data. A data lake is unique for its ability to accommodate large chunks of various formats of data. These formats of data range from structured data to unstructured data. A data lake stores and processes data in its raw state, therefore presenting the very original template of the data. 

To understand data lakes better, let’s consider a real-life lake of water around us. In a lake of water, numerous kinds of living things, ranging from different species of fish to plants to even crocodiles, coexist in their natural state. Likewise, a data lake is just a large pool of data that houses all kinds of data in its original state. 

We can also describe a data lake by expounding on some characteristics that it possesses. These include data processing, frequent users, frequent use cases, and many more. We’ll be exploring them in a subsequent section. However, at a glance, most users of data lakes are analysts for machine learning and deep predictive modeling and analysis. 

Key Benefits of a Data Lake

Data lakes are of the utmost importance to many software development companies in many ways. Here are some benefits of data lakes: 

  • They take advantage of the ability to store and present data in any format without the need for any form of preprocessing. This attribute helps analysts gain more insight into decision making since they’re looking at the data in its native state.
  • By allowing the storing of various kinds of data, the organization can have all its data eggs in one basket. Companies can now operate a centralized bank for all their data. Whether it’s structured, unstructured, or even semi-structured data, it’ll all be in one place.

Drawbacks of a Data Lake

Data lakes have also been criticized for the following points: 

  • Since they are handling a variety of data, they can become disorganized and messy, thereby becoming data swamps, a dumping ground for all kinds of data. This is a surefire way for a data lake to lose its relevance and make the process of getting useful data very difficult for analysts.
  • Security of the data in the data lake is also a matter of concern. For instance, fake or corrupt data in any format has the tendency to corrupt others in a ripple effect. This threatens the security and validity of data in the data lake.

What Is a Data Warehouse?

A data warehouse is also a large data storage repository for storing chunks of data. However, it only accepts data that is in a structured format. Data warehouses receive data from varied sources and pass it through some preprocessing before logging it into the warehousing system. 

An analogy for understanding a data warehouse is to view a data warehouse as a regular retail warehouse. In a retail warehouse, storekeepers arrange all goods within the specified sections that their goods fall under. Whenever new stock arrives, the storekeeper begins the real work of classification. The storekeeper picks each item and places it in the section of the warehouse it falls into, with groceries going into the groceries section, and so on. This is a great strategy, as it helps in easy access and subsequent retrieval of items. This is exactly how a data warehouse works. It’s great for business intelligence, as it aids business analysts in churning out great business insights in the shortest time possible because of the organized structure. 

Key Benefits of a Data Warehouse

In this section below, we’ll take a look at the ways a data warehouse helps organizations maximize operations: 

  • A data warehouse allows businesses the ease of retrieving data from an organized structure and accelerates the speed at which business insights can be drawn. This is because it’s easy to locate data from the category it falls into in the warehouse.
  • The organization in the data warehouse boosts the confidence of users. It makes them see the warehouse as a reliable source of data truth
  • Security of data within the warehouse is top-notch, as any incoming data is preprocessed and must fall into categories. Any corrupt data or data that falls outside the categories within the warehouse doesn’t get in.

Drawbacks of a Data Warehouse

  • A data warehouse accepting only structured data makes it difficult for businesses with unusual forms of data, thereby forcing companies already using them to invest in other data storage architectures in order to accommodate each of their specific needs.
  • It’s not dependable for machine learning and deep predictive analysis because of its type of data. The reason for this is that its data has been processed in a specific structured format.

Similarities Between a Data Lake and a Data Warehouse

The similarities between a data lake and a data warehouse are very generic and broad. They’re as follows: 

  • They’re both useful in the storage of Big Data.
  • Their content is for analysis, making them both geared toward great business decision-making.
  • Both can accept historical data as well as current data.

Differences Between a Data Lake and a Data Warehouse

In this section, we’ll explore the differences between a data lake and a data warehouse. 


Parameter

DATA LAKE

DATA WAREHOUSE
Purpose of DataThe purpose of the data stored is yet to be determined. It might be for future usage or current usage.The purpose of the data stored is predefined and for current and continuous usage.
Data StorageStores raw data in its original form.Stores processed structured data.
Data FormatCan receive data in a structured form (e.g. rows and columns), unstructured form (e.g. PDF and audio-visual files), or even semi-structured form (e.g. CSV files).Receives data strictly in a structured form.
UsesMostly used for machine learning and deep predictive analysis purposes.Mostly used for data analysis and business intelligence purposes.
UsersUsed mostly by data scientists and data engineers.Used mostly by business analysts, data analysts, and business professionals.
Schema FlexibilityThe schema is always defined after the data has been stored, resulting in a faster data capturing and storing process.The schema is defined before the data is ever stored, which slows the process of capturing data, but once captured, data is constantly ready.
ProcessingProcessing follows the ELT (Extract, Load, Transform) process. The data is gotten from its source and loaded into the lake but is only worked upon when it’s needed.Processing follows the ETL (Extract, Transform, Load) process. Data is extracted from its source and then worked upon before loading it into the category it falls under within the warehouse.
ToolsExamples of data lake platforms are Google Cloud Storage, AWS S3, and Azure DataLake.Examples of data warehouse platforms are Google BigQuery, Amazon Redshift, and Oracle.

Conclusion

Having gone through the entire article, one will agree with the fact that while data is essential to every business, possessing the desired data is one thing, but storing it in the best place is another. 

There’s no unbreakable rule on how to make a decision between employing either a data lake or a data warehouse. Just take a critical overview of your company and its peculiarities and make your choice. In particular, consider the users who will be accessing the data, and what purpose you have for storing and analyzing your data. 
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Ukpai Ugochi

Ukpai is a full-stack JavaScript developer (MEVN), and she contributes to FOSS in her free time. She loves to share knowledge about her transition from marine engineering to software development to encourage people who love software development and don't know where to begin.