A data lake acts as a repository or a huge storage space. Vast amount of raw data is stored in this storage repository for future use. A data lake does not use a hierarchical architecture unlike a data warehouse to store data; it uses a flat architecture instead.
Each data element in a vast data lake can be identified separately with the help of a unique data identifier. Also each data element is tagged with metadata tags. A relevant set of data elements can be used accordingly from the data lake as and when required. A data lake is being made easily accessible. It is located centrally so that it can be accessed equally by all the existing organizations connected with it and using it. A data lake can contain both structured and unstructured data.
When a data or a set of data is being stored in the storage repository of a data lake, they are not at all classified. When the data is being used for some purpose only then it is analysed, structured accordingly and then it is classified if necessary.
A data warehouse consists of an array of data containing corporate details, data obtained from other sources externally and data obtained from functioning systems. A data warehouse with using big data analytics services consolidates, and analyses data in various ways and helps in making various business decisions. Data is stored in a data warehouse by extorting, loading and altering data. A data warehouse stores data in a hierarchical fashion.
The concept of data warehouse emerged in the late 1980s when various companies felt the need of a huge data storage space which they are going to need in the near future. A data warehouse is designed to control data flow from operational systems that will provide and support various decision making mechanisms.
A data warehouse contains heterogeneous data which are obtained and extracted from various external sources. The obtained data is transformed according to the need of the organization. The main job of a data warehouse is to allow the decision makers of an organization analyse the raw data present in the warehouse and take decisions based upon the analysis of the datum.
So, it can be safely assumed that a data warehouse plays an important role in future organizations’ decision makings capabilities. Maintaining and controlling a data warehouse might sound quite costly and complex, but all the organizations have their own data warehouse because of its huge contribution.
If your organization already has a data warehouse then there is no point rejecting huge mass of data, days work and money. But there are some downsides of data warehouse viz. not being able to store all kinds of data, not being able to support different data types, a data warehouse is not always user friendly and so on. If you have suffered any of these then it is advisable to use a data lake alongside a data warehouse. With time as your data lake starts filling with new sets of data you can shift permanently to the new data lake or keep both.