Most businesses collect and store massive amounts of data on a daily basis and are always on the lookout for the best way to store this important information. Data lakes and data warehouses are two of the most common ways to store data and both options support the same goals, but do so in their own way. This may leave you wondering which one is right for your business. Keep reading to understand the differences between a data lake and a data warehouse and which one you should choose to get the most out of your data.
What Is a Data Lake?
A data lake is a large, open repository for all your data. It’s a place where you can store data in any format, without much planning and without too much concern for pre-processing or preparation. The key characteristics of a data lake include:
- Data is stored as-is
- Data is not standardized or pre-processed
- There is no schema or schema evolution
- Data has no timestamp
- Data is not segmented or aggregated
- Data is not processed or cleaned.
- Data is stored in any format
Data lakes allow for faster query results using low-cost storage and enable analytics such as machine learning, predictive analytics, data discovery, and profiling. One thing to keep in mind with data lakes is that since data is raw and unstructured, you’ll want to have a strong cataloging procedure in place so that users can more easily find what they’re looking for.
What Is a Data Warehouse?
Unlike a data lake, a data warehouse is organized and structured. It’s a specialized data store that holds metadata and cleans, standardizes, and processes data as it is being stored. The key characteristics of a data warehouse include:
- Data is stored in a standardized format
- Data is pre-processed, prepared, and cleaned
- There is a schema that is enforced during ingestion
- There is a data model and metadata
- Data is rolled up, segmented, and aggregated
- Data has a timestamp
- Data is stored in a table structure
Data Lake or Data Warehouse?
Now that you have a better understanding of what differentiates a data lake from a data warehouse, you may still be wondering which one your business should use. But the reality is, you can, and should, use both. In fact, as organizations that use data warehouses see the benefits of data lakes, many of them are evolving their data warehouses to include data lakes to enable diverse query capabilities, data science use-cases, and advanced capabilities.
This means that rather than choosing one over the other, you can store your structured data in a data warehouse and your unstructured data in a data lake. And, you can use a data lake and data warehouse together to accomplish more than one goal. For example, you can use your data warehouse to store data that needs to be rolled up, aggregated, and normalized, and you can analyze it over time. Then, you can use your data lake to analyze untimely data—data that doesn’t need to be rolled up and doesn’t change over time. Ideally, using both a data lake and a data warehouse can help your business get the most out of its data.
The Right Choice
Data is the lifeblood of every organization. With it, you can make smart, informed business decisions. Without it, everything you do will be based on educated guesses or instinct.
Choosing the right data storage solution for your business is key to getting the most out of your data and it’s important that you can store, analyze, and access it easily. Using a combination of a data lake and a data warehouse can enable your business to be agile and flexible in managing all types of data.
If you’re looking to better understand what your data is telling you, reach out to Gemini Data. We can help you solve your biggest data challenges, enabling you to understand and share data stories, and get from data to insights faster.
