The growth of data is without a doubt one of the greatest technological problems that companies must face. Terabytes, petabytes, and exabytes are well-known terms. These phrases are used in practically every industry nowadays, especially when discussing storage capacity.
One thing is certain: the advent of new technologies on the Internet has resulted in an overabundance of access and storage of information from both potential and own customers. It’s also important to have a system like Data Lake to keep all of this data safe. A company that can develop business value from its data, you’ll be ahead of the competition.
5 advantages of a data lake
Among the main benefits of a Data Lake are the following:
- It enables for the centralization of all data, regardless of its source, in one area. They can be processed with Big Data tools once they’ve been placed in their respective information silos. Because of this, it’s likely that some information needs to be treated differently in terms of security, but it’s a problem that can be solved with this approach.
- Even though the original source is no longer available, its content may still be useful for analysis. If you have this system, you’ll be able to access the data.
- Normalization and enrichment are possible for all data that enters the system.
- Preparation of the data is based on the demands of the moment, which minimises both expenses.
- This allows the business to more readily obtain the data it needs to make choices because any authorized user can access and enrich this information from anywhere.
Data Lake vs. Data Warehouse
When discussing Data Lake, another term generally comes up: Data Warehouse. As the name implies, it’s a database that’s optimized for processing relational data from transactional.
Perhaps, even if both technologies are based on data storage, there are some clear differences them:
- Data structure: Only structured data is stored in a data warehouse; unstructured and structured data can be stored in a Data Lake.
- Purpose of the data: A Data Lake may or may not have the purpose of data, even while with a Data Warehouse, there is no possibility for improvised solutions.
- Flexibility: There are fewer restrictions on what can be changed in a Data Lake due to the absence of any formal structure; however, this is more difficult in a Data Warehouse.
- Schema: When it comes to Data Lakes, On-Read schemas are used whereas On-Write schemas are used.
- Users: Unlike a Data Lake, a Data Warehouse allows any person with access to manage the data.
- Accessibility: In contrast to a Data Lake, which has fantastic and quick access, a Data Warehouse has a more expensive and difficult section for this.
- Storage: Compared to a Data Warehouse, a Data Lake is less expensive and has the potential for cloud expansion, while a Data Warehouse expansion is more costly.
As you can see, the data lakes are way better than the Data Warehouses in terms of price, and data flexibility. With the help of India Snowflake Consultants, you can get the support of the fastest-growing cloud technology, Snowflake and its data lake benefits.
