A data lake is a centralized repository that can hold all of your structured and unstructured data at any scale. You may save your data without first structuring it and then run various sorts of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning—to help you make better decisions.
This may seem quite impractical at first because it could seem that a sizable collection of random, unsorted data is essentially meaningless. While it is true that data is not organized hierarchically, it is nonetheless simple to retrieve.
Data lakes utilize object-based storage, which marks each data item with metadata and an identifier to make it “unique.” This metadata includes vital information about each data item, such as its purpose and usage, distinguishing it from all other information in the same data pool.
Because of this, a traditional hierarchical structure becomes superfluous, since it becomes impossible to confuse two data bits because each has its own metadata and identification to act as its “fingerprint.” Consequently, data lakes may store both structured and unstructured data in a single location, making them a good option for a variety of technology stacks.
Data lake vs. data warehouse
Data warehouses are the polar opposite of data lakes. Data is sorted hierarchically with relational logic, just like in a real warehouse. Consider data warehouses to be the standard folder/subfolder/file storage structure.
Each piece of information is saved “cleanly” as it is processed during storage and linked to a specific, predefined use, ready to be queried. As a result, warehoused data performs admirably. Each item has a predetermined place and usage, making it simple for the end user to pull the information, regardless of warehouse size.
This form of organization is useful for operational analysis and transactional processing, which is why data warehouses are utilized in most traditional firms.
While it may appear to be advantageous to store data in a hierarchical manner, data warehouses have significant limitations:
- Lack of scalability – while data warehouses provide error-free data that is ready to be queried, the inflexible schema makes data warehouses significantly more difficult to scale.
- Resource-intensive – data warehouses process data upon entering, which necessitates a significant amount of computational power. If a result, as the warehouse expands, so will its resource requirements. This contrasts with data lakes, which process data only upon request, allowing you to conserve resources if the data bits are underutilized.
- Less versatility – Unlike data lakes, which may store information from social media, IoT devices, websites, and mobile apps in a single spot, data warehouses must be built for specific planned uses, making them significantly less versatile.
As you can see, data lakes and data warehouses are two unique approaches to maximizing the value of big data. Both have advantages – in a nutshell, data warehouses provide a classic, time-tested, but pricey method of organizing data, whilst data lakes are more flexible and less expensive but necessitate a more profound understanding of modern technologies.