It can be a minefield to deal with big data! The volume of data generated on a daily basis is rising exponentially and the preservation and security of this information is of utmost importance as our customers are only too aware. As more businesses find themselves accruing large quantities of data, it is something that must be seriously considered to figure out the most business-appropriate way to store their information.
It can be a minefield to deal with big data! The volume of data generated on a daily basis is rising exponentially and the preservation and security of this information is of utmost importance as our customers are only too aware. As more businesses find themselves accruing large quantities of data, it is something that must be seriously considered to figure out the most business-appropriate way to store their information.
An centralised repository of storage is a Data Warehouse:
Data sources, business processes and inclusion/exclusion protocols must be defined as part of the initial set-up of a data warehouse. As a general rule, only if a need has been established can data be included in the warehouse.
The data is stored, archived and organised in a pre-defined way inside a data warehouse.
Advantages:
- All the data has a particular function, which is established during the setup.
- Permissions may be set on a pre-agreed role by role basis when setting up a data warehouse. This is perfect for facilitating various levels of access to information and ensures that individual business users will be able to report, interpret and extract information from the information as appropriate.
- The capacity of a data warehouse to have a scalable multi-layered security setup
Disadvantages:
- The business processes associated with the creation and setup of a data warehouse mean that it is not an easy task to make any changes to the structure (once it is live).
- For data scientists who may need to go deeper when researching and collecting detailed information, data warehouses are typically too restrictive.
A Data Lake is an unstructured, single-store repository.
In comparison to a data warehouse, the data is loaded unstructured and unorganised inside a data lake. Until it reaches the repository, it is not evaluated or processed; it can be loaded in its roughest state. In a data lake, there can be information that is never used because data can be accepted from all sources and in all formats.
The configuration (creation of the schema) takes place as and when the data is required within a date lake.
Advantages:
- The lack of structure means that a data lake can make modifications to models and queries easier. This versatility makes data lakes attractive to many. As needed, they can be configured and reconfigured.
- Deep analysis, which is useful for data scientists, is possible.
- All users can be provided by data lakes and are open to all.
- A data lake can hold all information until it is required.
- In order to make auditing and compliance simpler, there is only one store to handle.
Disadvantages:
- With all the information contained in one repository, there is a concern that the information might be more fragile.
- If a Data Lake is not adequately managed, the possibility of it being a data swamp is present. This occurs when the knowledge deteriorates or becomes useless and unavailable to users inside the lake.
The Data Swamp Avoided:
If a Data Lake is not adequately managed, the possibility of it being a data swamp is present. This occurs when the knowledge is lost or useless and unavailable to the users inside the lake. It is important to have a plan, vision and target for the data lake.
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