What Is A Data Lake?
The question is more of … what can I store in a data lake? That really is what makes a data lake unique in comparison to a data warehouse or a database or even a flat file type like a CSV or XLSX file.
So let’s take a deeper look. For one, a data lake is a data store that includes a very wide range of data types. Those include:
- Structured data
- Unstructured data
- Semi structured data
The data lake primary role is to capture important information that may be useful to the organization. For example, saving machine data and log files. Data lakes have become increasingly viable with lower cost cloud storage. Thus, whereas it used to be too expensive to capture things like machine data, it is now viable. This provides data scientists and data analysts important data sets to do analysis.
Understanding What Data Lakes Are and How They Differ From Data Warehouses
Companies generating massive amounts of both structured and raw data are served well by data lakes. These repositories, hosted on cloud-based data storage platforms, allow stakeholders access to vast quantities and types of data for management and analysis.
Perhaps the most notable feature of a data lake is its ability to store all types of data, including:
- All data is loaded from source systems. No data is turned away.
- Data is stored at the leaf level in an untransformed or nearly untransformed state.
- Data is transformed and schema is applied to fulfill the needs of analysis.
Why Use A Data Lake
The flexibility of data lakes gives companies one central source for their data management needs. Moreover, this type of repository is easy to scale—adding new data is simply a matter of expanding the size of the data lake, thereby avoiding data silos and streamlining data integration.
There are, however, some potential challenges to using data lakes, particularly when compared to their more structured peer: data warehouses.
The Difference Between Data Lakes And Data Warehouses
Unlike data lakes, data warehouses only accept structured data. This limits their size compared to data lakes and allows stakeholders to solely upload processed data meant for a specific purpose.
For business professionals, this highly organized approach to data management is a blessing. Employees working on a particular topic can easily access the relational databases and spreadsheets that make up a data warehouse, then analyze what they need for their given assignment.
In contrast, data lakes hold a wider array of data types and quantity, including unstructured data and semi-structured data, which accounts for as much as 90% of all enterprise data.
Data lakes hold a wider array of data types and quantity, including unstructured data and semi-structured data, which accounts for as much as 90% of all enterprise data.
To the average business analyst, unstructured data are arcane and unusable. For data scientists, raw data are key to building high-quality machine learning (ML) tools. The rising importance of AI has put data lakes into the spotlight, even though data warehouses provided wider accessibility to individuals without expertise in data management.
Choosing A Data Lake Vendor
Data lakes require no vendor lock-in, allowing companies to switch service providers with ease. With that in mind, the top data lake vendors compete on cost and quality of service, and include:
Next Up: How to Get Data Into a Data Lake
ETL tools are useful for expediting the process of data integration, no matter the data lake or data warehouse vendor. Request a demo of K3, the “powerful, yet elegant and easy to use” low code ETL solution for moving all types of structured data into data lakes or learn more about what ETL is and how it works in our next blog post.
