When it comes to big data storage in the cloud, you may be unsure whether it’s better to utilise a data lake or a data warehouse. Are data lake solutions better for meeting your data requirements? Or should you stick with the traditional solution and implement a data warehouse?
In this article, we will explore the differences between the two solutions, their key benefits and the potential for both to be used in tandem to complement each other.
What is a data warehouse?
A data warehouse is a data management system that collects and stores business data. It can only handle structured and unified data that is then further processed and organised into sections called data marts.
The key benefits of data warehousing
The key benefits of data lake solutions include:
- Leverages very structured data makes it quick to run a query
- Mature solution with more than 30 years of development
- Provides a centralised repository for big volumes of historical data
- Enables actionable insights for informed business decisions
- Improves data quality
- Ideal for generating repeatable reporting and KPIs.
Data warehouse use cases
Some examples of how data warehousing can be used in the real world include:
- Banking companies can use data warehousing to provide access to data across the company for generating reports
- Food and beverage companies that operate on a large scale can use data warehouse systems to help run operations from one place
- Healthcare companies can store large amounts of historical healthcare data to help them manage operations and reach business goals
- Retail companies can use a data warehouse to store and analyse transactional and customer information data to inform their purchasing and marketing decisions.
Also Read: Data warehousing explained
What is a data lake?
A data lake is a repository that can store your organisation’s structured and unstructured data in a single storage pool. It can handle large volumes of raw data without the need for it to be structured on entry.
The key benefits of data lake solutions
Some of the key benefits include:
- Capable of handling raw, unstructured data
- Can be broken down into simple components for quick set-up
- Easier to manage with scalability in the cloud
- More flexible as it can store a range of file formats
- Data can be analysed to gain new insights
- Typically low storage costs
- Artificial intelligence (AI) and machine learning (ML) tools can be applied to unstructured data types, such as image recognition or natural language processing
Data lake use cases
Data lakes provide data consistency and can be used to power big data analytics, ML and predictive analytics to unlock valuable insights that inform business decisions. Some examples of data lake use cases include:
- Financial organisations such as investment firms may use data lake solutions to collect and store real-time market data to help manage portfolio risks
- Subscription-based media streamers can use a data lake to gather insights on customer behaviour to improve their recommendation algorithms
- Healthcare organisations may use a data lake to help them improve patient care to enhance outcomes and reduce costs
- Data scientists and sales engineers can use a data lake to help them build predictive models to identify consumer behaviours.
What are the key differences between a data lake and a data warehouse?
There are six key differences between a data lake and a data warehouse. Our table below compares the two solutions.
| Data lake | Data warehouse | |
| Users | Ideal for data scientists and engineers who analyse raw data to glean new insights | Typically used by business managers as data is organised to provide insights on KPIs |
| Data Storage | Supports many file formats and can handle unstructured, raw data, which can be structured with a query | Can only handle structured data and then further processes it into categories for analysis |
| Processing | Predominantly uses extract, load, transform (ELT) to extract data from its source and only structures data when needed | Uses extract, transform, load (ETL) to extract data from its source and clean it ready for business-end analysis |
| Cost | Typically low storage costs and easier to manage, which can reduce operational costs | Typically more expensive storage costs and more labour-intensive to manage |
| Schema | Defined after the data is stored, making the capture and storing of data quicker | Defined before the data is stored, making it a longer process to capture and store data but ready for use across the company once complete |
| Analysis | Provides predictive analytics, ML, data visualisation, business intelligence and big data analytics | Business intelligence, data visualisation and data analytics |
In conclusion, it is crucial to remember that this is not an either-or situation. Rather than focusing on the industry, the conversation should focus on the right tool for the job. For example, within healthcare, a data lake is better at handling complex data such as medical records. However, a data warehouse is ideal for standard patient record data such as date of birth or next of kin, so a combination of both is needed to fully optimise the available data.
It is also worth noting that the two solutions may be used in tandem quite effectively. Where data may be inputted in inconsistent formats, a data lake can be used to land and standardise data for ingestion into a data warehouse.
Data lakes can also make for cost-effective archives, providing low cost storage to house infrequently accessed data. Utilising data lakes in this manner opens up the possibility for streamlined warehouses, reduced costs and increased performance.
Adopting a hybrid approach that includes both solutions working together to perform different tasks may be the most productive and cost-effective solution for your business.
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