Data Lake vs Data Warehouse – Which to Pick in 2022?

September 2, 2021 Kevin Dabrowski

In the late 1980s, IBM researchers developed the idea of the “business data warehouse”. Since then, data warehouses (DWHs) rose into an increasingly important data repository great at storing processed data with a clear purpose. Yet, the market also offers alternatives – like the data lake. In this blog post, we’re going to compare these two systems to help you pick the one better suited to your needs.





Data Lake vs Data Warehouse – Overview


In 2021, data lakes and data warehouses are the two most popular options for big data storage. However, their goals are very different.

Starting by the type of data they store and their overall purpose, going through the users that can access the data within them, and finishing on the tasks they can perform, they stand on somewhat two extreme opposites.

To compare them, we’re going to describe both of them first and summarize their differences afterwards.

First, let’s take a look at data warehouses.



What is a Data Warehouse (DWH)?


Citing AWS, data warehouses are central information repositories. The data stored within such a repository can come from various sources, like transactional systems or relational databases. However, not everybody can use this information seamlessly. To access the data, one has to use BI tools, SQL clients or other analytics applications.  As a result, working with the information within data warehouses is usually reserved for professionals like business analysts, data engineers, data scientists, and other technologically aware decision-makers.

What’s more, DWHs are not useful in every business scenario. To get a deep understanding of how (and when) data warehouses can deliver value, it’s important to understand how they work.

And again, AWS describes the specifics well.

Data warehouses contain multiple databases. Within each of them, information is organized into tables and columns. Within each column, you can define a description of the information, like integer, data field, or string. You can organize tables inside schemas, which are something like data warehouse folders. After the data is added to the data warehouse, it’s stored in various tables described by the schema. Finally, the schema enables query tools to establish which data tables to access and analyze.

And this process depicts the core trait of DWHs – that they store only processed data. For example, they can store the output of ETL workflows. As a result, the data within data warehouses can be analyzed to empower precise data-driven decision making (DDDM).


Read more about ETL workflows: What is Data Engineering? | PGS Software


However, sometimes you may also need to store raw data – and perform some operations on it. And that’s where data lakes can shine bright.



What is a Data Lake?


Compared to DWHs, data lakes are relatively new. The term itself dates back to 2010. Upon their introduction, they received somewhat mixed reviews. A Forbes article even called them evil. However, in 2021, their perception is different. According to Business Wire, the Data Lakes Market was valued at USD 3.74 billion in 2020. And by 2026, is expected to reach USD 17.60 billion.

As we’ve mentioned in our previous article in the data series, data lakes were introduced as a solution to recurring data storage limitations. The new concept aimed at bringing together the data from multiple business applications and data systems to one place in a raw form for future structuring and processing (which is useful for operating data pipelines). Thus, data lakes strived to make the dreams about fast-tracking structured and unstructured data into a one-stop repository shop for business insights reality.

And whereas the popular DWHs force enterprises into narrow data paradigms and silos, data lakes offer a more expansive and holistic view of analytics. Data lakes emerged to fill the need for a scalable, low-cost data repository that would enable companies to store all data types easily, regardless of their source, and then make it possible to analyze this data for evidence-based decision making.


To get more data lake insights, read our previous article: How is the Data Lake Doing in 2021? | PGS Software


Difference Between Data Lake and Data Warehouse


As you can tell by now, the first major difference between these two data repositories is the type of data they store. Data warehouses only store historical, structured data, adjusted to fit a relational database schema. On the other hand, data lakes are not so picky and will accept nearly anything, structured or not. So much so, that we’ve even previously compared data lakes to black holes (however, recklessness in data storage also comes with the threat of turning your data lake into a data swamp, so be careful).

Now, the goal of data warehouses is to offer information in a read-only mode for analysts. Since the data within DWHs is already processed, cleaned, and structured, there’s (mostly) no need to update it. In opposition to that, data lakes can store any incoming data, which, in some cases, makes them better for big data analysis. As DataCamp puts it, this is especially true for deep learning since it requires scalability in the increasing amount of training data. There are also dedicated tools for big data analysis on data lakes – for example, Apache Spark.

Obviously, data lakes will be much “larger” than data warehouses, since the latter are limited to structured, processed data, “hand-picked” for specific analytical purposes. To give you a perspective – data lakes often store thousands of… terabytes (!) of data.

Clearly, data lakes are automatically more cost-effective for storage, while DWHs are the go-to spot for decision making with a clear idea behind it.



Data Lakes and Data Warehouses are Intertwined 


Finally, it’s important to remember that DWHs and data lakes aren’t competitors. Often, they can be part of the same data pipeline. However, they enter the scene at different stages of the process. Data lakes can be there right from the start, as soon as unprocessed data starts flowing in. Serving as storage for ETL workflows, they contain the data until it’s processed and structured. At this point, the information can be ingested into a data warehouse, as part of the data access stage.



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