Big Data: Data Lake vs Data Warehouse
Dealing with big data can be a minefield! The volume of data being created on a daily basis is growing exponentially and as our clients are only too aware, the storage and safety of this data is of paramount importance. As more companies find themselves accruing mass amounts of data, working out the most business-appropriate way to store their data is something that must be seriously considered.
Below we’ve written a high-level breakdown of the two main data repositories: Data Warehouse and Data Lake, both of which have a number of benefits and drawbacks.
Data Warehouse vs Data Lake: A high-level breakdown
A Data Warehouse is an organised storage repository.
As part of the initial set up of a data warehouse the data sources, business processes and inclusion/exclusion protocols must be set. As a general rule, data will only be included in the warehouse if a use has been identified.
The data within a data warehouse is stored, archived and ordered in a pre-defined way.
- All the data there has a specific purpose, which is defined during the set up
- When setting up a data warehouse, permissions can be set on a pre-agreed role by by role basis. This is great for allowing different levels of access to the information and means that particular business users will be able to report, analyse and extract information from the data as needed
- A data warehouse has the ability to provide a flexible multi-layered security set up
- The business processes attached to the development and set up of a data warehouse mean that making any changes to the structure (once it is live) is not an easy task
Data warehouses are usually too restrictive for data scientists who may need to go deeper when analysing and gleaning particular information
A Data Lake is a single-store, unstructured repository.
Unlike a data warehouse, the data within a data lake is loaded unstructured and unorganised. It is not analysed or processed before it enters the repository; it can be loaded in in its rawest form. There may be data that is never used within a data lake because data can be accepted from all sources and in all formats.
Configuration (schema creation) takes place as and when the data within a date lake is required.
- The lack of structure means that changes to models and queries can be made more easily with a data lake. This flexibility makes data lakes appealing to many – they can be configured and reconfigured as necessary
Deep analysis is possible, which is useful for data scientists
Data lakes can support all users and are available to all
A data lake can hold all data until it is needed
There is just one store to manage so auditing and compliance become easier
- With all the data stored in one repository, there is a concern that the data is potentially more vulnerable
- If a Data Lake isn’t properly maintained, there is a danger of it becoming a data swamp. This happens when the data within the lake deteriorates or becomes useless and inaccessible to the users
Avoiding the Data Swamp
If a Data Lake isn’t properly maintained, there is a danger of it becoming a data swamp. This happens when the data within the lake becomes deteriorated or useless and inaccessible to the users. Having a plan, vision and goal for your data lake is key.
Can we help you make sense out of your ever-growing data storage? Get in touch and find out more about our tailor-made storage solutions: email@example.com