Data is the new oil. Businesses generate large amounts of data from internal and external sources daily. Monitoring, correlating, and comparing various aspects and parameters produce valuable insights. Advanced technologies and tools are utilized to store, manage and analyze the data.
This enables the extraction of insights in real-time and utilizes them at the right place in the business. Errors are eliminated, gaps are identified, mistakes are rectified, and the business process is enhanced constantly using these insights. This blog will discuss big data technologies – data lake and data warehouse.
What is a data lake?
A data lake provides a platform for businesses to store data in any form – structured, unstructured or semi-structured. It can process data of any size and variety. It allows data to be ingested from any system – Cloud, edge-computing systems, or on-premises at any speed.
What is a data warehouse?
Large amounts of data ingested from transaction applications and application log files are stored in the data warehouse. The primary purpose of storing data here is to enable analytics and business intelligence activities. Business analysts and data scientists draw insights using a data warehouse.
Data lake vs Data warehouse
Though both are Big data technologies, there are differences between the two. Let’s check out the differences in detail.
1. Structure of data
Data lakes mainly store unprocessed raw data, which is why it requires a large storage capacity compared to the data warehouse. Analysis can be performed quickly on unprocessed data, which makes it an ideal choice for ML.
Data storage in data warehouses is for processed and refined data. This makes information readily available to users.
2. Customer
Customer is a significant differentiator in Data lake vs Data warehouse. Business analysts and data scientists use advanced tools and techniques to derive information from raw data stored in data lakes.
Any working professional who can access spreadsheets, charts, tables, and other forms of pictorial presentation to interpret results can analyze and utilize the data stored in data warehouses.
3. Convenient
Data storage in a data lake calls for quick and easy access to make changes since it has hardly any restrictions.
Since the data warehouse stores structured data that is processed and in a readable format, manipulating the data is an expensive affair. Not to mention, it is highly time-consuming, which is why it is not a preferred method.
4. Usage of data
Since raw and unfiltered data flows into a data lake, large amounts of data are stored for future reference. There is no immediate task in hand for data storage. Therefore, there is no data filtration or organizing the data when it is stored.
Since only processed data is stored in a data warehouse, it is intended for a specific purpose. This means the data will be utilized sooner or later, and there will be no wastage of data storage.
5. Schema
Schema is defined in data after the data is stored in a data lake, whereas schema is defined in the data warehouse before the data is stored.
6. Usage in the market
Data warehouse has been the traditional way of storing data. Hence, it has been used by businesses worldwide for several decades. A data lake is a relatively new technology that is slowly being adopted by businesses that require it.
7. Advantages of the Big data technologies
You can integrate multiple types of data in a data lake. This will help you generate new questions for your users.
Key performance metrics and reports are the crucial outputs from the data warehouse by its users. The business benefits from the different types of reports generated. Action is taken based on the reports to improvise the business and deliver the optimum user experience.
Data lake vs data warehouse
Now that you know the critical differences between the two technologies of Big Data, let’s see the industries that have benefitted.
- A data lake is beneficial for sectors wherein a mix of structured and unstructured data is required to draw insights. For instance, the healthcare industry.
- The education industry carries large amounts of raw data. A data lake is utilized to predict potential issues, helping management prevent them before they occur. For instance, it can streamline billing.
- A data warehouse is best suited for finance companies because anyone in the team can access data at any time.
Summary
There is no one-size-fits-all approach when it comes to Big Data. You can opt for a data lake or data warehouse based on the nature and requirements of your business. You can also opt for combining the two to derive reports from the data as needed. Harness the power of Big Data and take your business to heights.
