With the vast amount of data being collected today for various purposes, there is an increasing need to find the proper data storage, which also heavily depends on your specific analytical objectives. The good news is that new technological possibilities are being introduced to accommodate both the need for scalable storage and efficient analysis. This blog provides a comprehensive comparison of three such technologies: data warehouse vs data lake vs data lakehouse.
Before such an advancement, the data warehouse was the standard solution companies chose. To go “down this road”, the data should be structured, and its schema (name of columns, data types) should be defined. Although this approach is reliable, it lacks the flexibility introduced by the Lake and Lakehouse solutions, which can store and process data in an unstructured format. It is worth mentioning that some providers already offer those services as part of their database solutions, and migration is much more intuitive.
With that being pointed out, let’s summarize the questions you should ask yourself before making this decision:
- Is my data structured, semi-structured, or unstructured?
- Will my analysis benefit from having a pre-defined, fixed schema?
- Where is my data currently stored?
Let’s dive into their in-depth comparisons to inform you of your decision better.
Understanding Data Warehouse
A data warehouse is a central repository for storing business data in a structured format. A schema definition is required before writing it to the location. There are two options for the writing process – manually using SQL, or if the data source is an operational system -using the ETL (Extract, Transform, Load) process. It is not, however, to be confused with database systems designed for transactional data. The point of the data warehouse is to store data coming from different systems.
This solution offers high-quality and performant query results. It can be configured on-premises or on a cloud. Amazon Redshift, Azure SQL Data Warehouse, and Snowflake offer the latter.
As already mentioned, this option cannot store unstructured data.
Understanding Data Lake
The data lake offers a separate storage and processing layer. Data is generally processed with tools such as Athena or Spark. Azure is an example of such an offering owned by Microsoft.
The advantage of this type of solution is that it can handle both structured, semi-structured and raw data. The schema is on-read, meaning it is not explicitly defined when loaded to the Lake. A Data Scientist is required to process and use this data for predictive analytics or to feed it into a Machine Learning algorithm. That is counter-intuitive to using data from the warehouse, which is already structured and ready for business use.
The flexibility of the Data Lake solves many data warehouse challenges. Still, it exhibits a lack of quality of the data which brings to attention the need to think about better data organization to avoid data loss and stagnation.
Understanding Data Lakehouse
The novel data architecture called Data Lakehouse is here to offer us the best of both worlds – the combination of data warehouse and data lake. Its key features are ACID transaction, a perspective from the Warehouse, and low-cost storage like a Data Lake. It allows concurrent read-write operations, data governance schema support, and direct access to the source data. There is no need to duplicate data in multiple locations due to the possibility of ETL processing landing data directly to the Lakehouse. Therefore, the cost of de-duplication is also reduced.
Among its uniqueness lies the possibility of accommodating the needs of different business users by providing access or permissions to specific data and maintaining a clear data governance structure. If specific business users need the data further processed, many data processing technologies are generally available for that purpose and particular use case.
Querying speed is enhanced with techniques such as indexing and data compaction to again mirror the efficiency of the querying process in a warehouse environment. On the other hand, the ACID (Atomicity, Concurrency, Isolation, and Durability) support provides transaction consistency and data integrity, another inherited data warehouse property.
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Key Difference – Data Warehouse vs Data Lake
The data warehouse offers reliability and effectiveness in storing large amounts of data. The querying process is fast. To write data for this storage solution, the schema should be defined, and data should be structured.
On the other hand, Data Lake offers the innovative possibility to store semi-structured and raw data.
Below, you can see a tabular comparison of the key differences between these two data storage technologies:
Feature | Data Warehouse | Data Lake |
Storage | Reliable and effective for storing large amounts of data | Capable of storing semi-structured and raw data |
Querying Process | Fast querying process | Can query but might require additional processing |
Schema Definition | Schema must be defined before writing data | Schema can be defined after writing data |
Data Structure | Requires structured data | Can handle structured, semi-structured, and unstructured data |
Use Case | Ideal for business intelligence, reporting, and data analysis | Ideal for big data analytics, machine learning, and data discovery |
Data Ingestion | ETL (Extract, Transform, Load) process | ELT (Extract, Load, Transform) process |
Cost Efficiency | Generally, more expensive due to the need for more processing power and storage optimization. | Generally, more cost-effective for large volumes of raw data storage |
Key Difference – Data Warehouse vs Data Lakehouse
The Data Lakehouse has a processing layer that allows advanced data analysis and preparation. This is not a feature of the Data Warehouse. The latter is mainly for storage, whereas Data Lakehouse offers more advanced capabilities related to analysis, which makes it suitable for more advanced data users.
Feature | Data Warehouse | Data Lakehouse |
Storage | Reliable and effective for storing large amounts of structured data | Stores structured, semi-structured, and unstructured data efficiently |
Querying Process | Fast querying process | Advanced querying capabilities |
Schema Definition | Schema must be defined before writing data | Combines schema-on-read and schema-on-write |
Data Structure | Requires structured data | Can handle all types of data structures |
Use Case | Ideal for business intelligence, reporting, and data analysis | Suitable for advanced data analysis and preparation |
Data Ingestion | ETL (Extract, Transform, Load) process | Supports both ETL and ELT processes |
Cost Efficiency | Low flexibility requires a pre-defined schema | Balanced cost efficiency with advanced capabilities |
Processing Layer | Not available | Available, supports advanced data analysis and preparation |
Advanced Analysis Capabilities | Limited | Extensive, supports advanced data users |
Flexibility | Low flexibility, requires a pre-defined schema | High flexibility, supports a wide range of data formats and use cases |
Data Warehouse vs Data Lake vs Data Lakehouse – Key Difference Overview
Let’s summarize our learnings so far:
A data warehouse is reliable for structured data and fast querying, and it is ideal for BI and reporting using ETL. A Data Lake stores raw and semi-structured data, suitable for big data analytics with ELT, and is cost-effective. A Data Lake House combines both, handling all data types, offering advanced querying, and balancing cost efficiency.
Below, you can see their key differences:
Feature | Data Warehouse | Data Lake | Data Lakehouse |
Storage | Reliable and effective for storing large amounts of data | Capable of storing semi-structured and raw data | Stores structured, semi-structured, and unstructured data efficiently |
Querying Process | Fast querying process | Can query but might require additional processing | Advanced querying capabilities |
Schema Definition | Schema must be defined before writing data | Schema can be defined after writing data | Combines schema-on-read and schema-on-write |
Data Structure | Requires structured data | Can handle structured, semi-structured, and unstructured data | Can handle all types of data structures |
Use Case | Ideal for business intelligence, reporting, and data analysis | Ideal for big data analytics, machine learning, and data discovery | Suitable for advanced data analysis and preparation |
Data Ingestion | ETL (Extract, Transform, Load) process | ELT (Extract, Load, Transform) process | Supports both ETL and ELT processes |
Cost Efficiency | Generally, more expensive due to the need for more processing power and storage optimization. | Generally, more cost-effective for large volumes of raw data storage | Balanced cost efficiency with advanced capabilities |
Which data storage option is best for you?
To conclude, the comparison between the three. Here are some brief points to consider in your future choice of data storage platform:
- Use a Data Lake if you need to store unstructured data such as audio and video files or images. This will save you time and effort since no schema definition is required, allowing you to already consider the next step in your analysis journey. Apart from supporting various data formats, the Data Lake solution supports large-scale analytics.
- Use Data Warehouse to store structured, historical data, including customer information, transaction records, and inventory data. Take into consideration that significant planning and understanding of the data is required to structure a well-defined schema which is a main requirement for storing data in a Warehouse. Another important note is that the solution is typically more expensive due to the optimized storage and performance.
- Use Data Lakehouse to collect and analyze various data types and formats. This solution combines the best features of both Data Lakes and Data Warehouses. Not only does it support storing data in any format, but it is possible to run any analysis including SQL-based querying, and performance is consistent for both analytical and storage workloads.
- And finally, Check the current possibilities offered by your service provider. There might be already existing integrations with your tools and workflows.
To connect to any database, data warehouse, or data lake of your choice, Try Hevo’s 14-day free trial and enhance your data migration process seamlessly.
Gabriela Aleksandrova has 3+ years of experience in Data Science, specializing in business process automation. She is proficient in data analysis and visualization using RStudio and Power BI and has basic knowledge of Machine Learning for creating Data Science models. Her current interest lies in Data Engineering, focusing on cloud technologies from Microsoft and Amazon.