Data Warehouse vs Data Lake vs Data Lakehouse – Key Comparisons

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.

Unstructured data, such as data sourced from the internet, needs to be processed and formatted through an ETL step before being loaded into a data warehouse.

Pros and Cons

Pros	Cons
Centralized Data: All data in one place for easy access.	High Cost: Expensive to set up and maintain.
Better Data Quality: Clean and consistent data.	Complex Maintenance: Needs regular upkeep.
High Security: Strong data protection.	Scalability Limits (for on-premises): Costly to grow with more data.
Historical Data Storage: Stores old data for long-term analysis.	Slow Real-Time Updates: Not ideal for real-time data.
Optimized for Big Queries: Fast analysis of large data sets.	Requires Special Skills: Needs experts for setup and management.
Supports BI Tools: Works well with business analytics tools.	Duplicate Data: Can store extra, unused copies of data.
Data Governance: Helps meet compliance standards.	Rigid Structure: Hard to change once set up.
Improves Decisions: Reliable data for better decisions.	Complex ETL: Moving data in is complicated and needs monitoring.

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 data quality, which brings to attention the need to think about better data organization to avoid data loss and stagnation.

Pros and Cons

Pros	Cons
Flexible Data Storage: Stores all types of data, structured or unstructured.	Data Quality Issues: Raw data may be messy and inconsistent.
Cost-Effective: Generally cheaper storage, especially for large data volumes.	Complex Data Management: Hard to manage and organize large volumes of diverse data.
Supports Advanced Analytics: Great for big data and machine learning.	Slow Query Performance: Raw data is slower to analyze than structured data.
Scalable: Easily scales with growing data.	Limited Security: Typically offers less security than traditional warehouses.

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.

Pros and Cons

Pros	Cons
Combines Data Lake and Warehouse: Merges flexible storage with structured data analysis.	Newer Technology: Still evolving, so it may lack full features.
Better Data Quality: Supports structured data organization while keeping raw data.	Higher Costs: May be more expensive than traditional data lakes.
Fast Query Performance: Optimized for both structured and unstructured data analysis.	Complex Setup: Requires setup to support both data lake and warehouse features.
Scalable and Flexible: Handles both large-scale storage and analytics.	Requires Expertise: Needs skills in both data lake and warehouse management.

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

Want to understand the differences between Data Lake and Delta Lake? Explore our guide to see how these data storage solutions compare and find the best fit for your data needs.

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 also 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.

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