Data Lakes vs. Data Warehouses: Debunking the Top 3 Myths
Data storage isn’t as simple as it once seemed. Intricate machines and technologies now collect an incredible breadth of data — over 2.5 quintillion bytes every day! — from equipment sensors, logs, users, consumers, and elsewhere. All of that data must go somewhere and be stored in a way that allows businesses to leverage it.
Considering the volume and variety of data available today, quite a few misconceptions exist about the ways in which data can be stored. Today we’ll tackle common myths about two popular types of data storage: data lakes and data warehouses. And don’t miss our infographic below demystifying the differences between data lakes and data warehouses.
Myth #1: You Only Need One Or The Other
Nowadays you often hear people talk about data lakes and data warehouses as if businesses must choose one or the other. But the reality is that data lakes and data warehouses serve different purposes. While both provide storage for data, they do so using a different structure, support different formats, and are optimized for different uses. Often, a company may benefit from using a data warehouse as well as a data lake.
Data warehouses best serve businesses looking to analyze operational systems data for business intelligence. Data warehouses work well for this because the stored data is structured, cleaned, and prepped for analysis. Alternatively, data lakes allow businesses to store data in any format for virtually any use, including machine learning (ML) models and big data analysis.
Myth #2: Data Lakes Are Niche; Data Warehouses Aren’t
Artificial intelligence (AI) and ML represent some of the fastest-growing cloud workloads, and organizations are increasingly turning to data lakes to help ensure the success of these projects. Because data lakes allow you to store virtually any type of data (structured and unstructured) without first prepping or cleansing, you’re able to retain as much potential value as possible for future, unspecified use. This setup is ideal for more complex workloads like machine learning models where the specific data types and uses have yet to be determined.
Data warehouses may be the more well-known of the two options, but data lakes (and similar types of storage infrastructure) are likely to continue rising in popularity in conjunction with data workload trends. Data warehouses work well for certain types of workloads and use cases, and data lakes represent another option that serves other types of workloads.
Myth #3: Data Warehouses Are Easy To Use, While Data Lakes Are Complex
It’s true that data lakes require the specific skills of data engineers and data scientists (or experts with similar skill sets) to sort and make use of the data stored within. The unstructured nature of the data makes it less readily accessible to those without a full understanding of how the data lake works.
However, once data scientists and data engineers build data models or pipelines, business users can often leverage integrations (custom or pre-built) with popular business tools to explore the data. Likewise, most business users access data stored within data warehouses through connected business intelligence (BI) tools like Tableau and Looker. With the help of third-party BI tools, business users should be able to access and analyze data, whether that data is stored in a data warehouse or a data lake.
Learn all about the differences between data lakes and data warehouses in the infographic below.
Plus: Check out our free eBook on Operationalizing the Data Lake for information on how to maximize the value of your data lake.