The exponential growth in structured, semi-structured and unstructured data has brought about a paradigm shift in the process and infrastructure used for gaining business intelligence. Organizations are constantly searching for the right data infrastructure which can facilitate well-informed business decisions. While data warehouses have been in practice for decades, data lakes have recently gained traction in the business community. Data Lake is sometimes presumed to be an incarnation of data warehouse but these two are very different types of data storage repositories.
Let’s look at some of the significant differences between the Data warehouse and Data Lake:
In a data warehouse, the data sources which will be used are selected in the development phase. The data sources which don’t support the need of a selected business process are excluded from the warehouse. This is known as the “schema on write” approach for data storage, whereas a data lake is a repository of all sorts of data in its native form whether or not it’s relevant. A Data Lake maintains data in its raw form and which will be transformed only when it is to be analyzed. This is known as the “schema on read” approach.
Types of Data
Data warehouses usually support transactional system data or quantitative metrics and they don’t support unstructured data, whereas a Data Lake can support all types of data including non-traditional data types such as texts, images, social media content, as well as, web server logs. Data Lake is economical to scale which aids to its ability to hold large volumes of data, irrespective of the source and structure.
Types of Users
Data warehouses are well structured, thus are easy to use and understand. They hold data pertaining to a specific business process/ use case, which makes it ideal for a limited set of users. Data warehouse caters better to operational users who wish to get reports in form of spreadsheets. On the other hand, a Data Lake supports all sorts of users, as they hold a wide variety and large volumes of raw data. A data scientist can leverage Data Lake and use it for statistical analysis or predictive modeling.
Adaptability to Change
Data warehouses are not configured to rapidly change as they require a considerable amount of time and resource to incorporate structural changes. The complexity of data loading process further delays the implementation of any changes. While data lakes act as a repository of data in raw form, the users can always explore data going beyond the structure of the warehouse. The automation and reusability of data can be implemented in a data lake if a data is required repeatedly. Data Lake does not require any development resources to support business needs.
The processing, cleaning and transforming of data for creating a data warehouse takes time which delays the process of uncovering actionable insights. In a data lake, users have instant access to all data which reside in a single repository that needs to be analyzed. The data can be quickly configured, reconfigured and explored for ad-hoc purposes. This implies that data lake can be used to derive faster insights.
Data warehouse is expensive to maintain in case of large data volumes, whereas data lakes are designed to provide low-cost storage. The off-the-shelf servers, combined with low-cost storage facilitates, aids the scaling of data lakes to suit business requirements. Data lakes can accommodate large data volumes, be it structured, semi-structured and unstructured data, at an affordable cost.
While data warehouses are useful for storing data fetched from traditional sources, data lakes can store data from non-traditional sources such as social media. Data Lake acts as a centralized repository for all organizational data that’s structured, semi-structured or unstructured, either internal or external. A Data Lake enables business analysts and data scientists to mine all organizational data scattered across various sources. Data lakes support predictive and prescriptive analytics to improve decision making.