XenonStack Recommends

Data Lake vs Warehouse vs Data Lake House | The Difference

Acknowledging Data Management
          Best Practices with DataOps


XenonStack White Arrow

Thanks for submitting the form.


In the ever-shifting era of technologies where each day a new term emerges and evolves, data being generated is also increasing, and businesses are investing in technologies to capture data and capitalize on it as fast as possible. But a question arises what benefits does real-time data bring if it takes an eternity to use it. The quandary the stack faces is at roots on what to use data warehouse or data lake.

While data warehouse is inefficient to store your streaming data, using a data lake is also less compelling as you can’t query the model and data while it is fresh enough.

What cloud architecture do we opt for? Shall we settle with the limitations of the warehouse, or we accept the lake, or should we ponder over newer concepts data lakehouse?

What is Data Warehouse?

Structured data is integrated into the traditional enterprise data warehouse from external data sources using ETLs. Enterprise data warehouses were built for BI and reporting purposes. But with the increase in demand to ingest more data, of different types, from various sources, with different velocities, the traditional data warehouses have fallen short.

Transition your Data into Modern Data Warehouse with Xenonstack

Remember the time when changing the operating system required formatting hard drives. If you ever wanted to use a different operating system, you would need a separate hard drive explicitly formatted for the operating system, as with data warehouses. The data warehouse link you with a single vendor to process your data either because your storage and analytics are lumped together, or processing requires data in a specific format only, on the contrary, it makes the data availability rapid, valuable, organized, and pretty straightforward, thus empowering business intelligence and reporting.

What are the Pros and Cons of Data Warehouse?

Pros Cons
Easy data discovery and query Cannot leverage other vendor capabilities
Straight forward data preparation with clean data Not a very cost-effective way to store and analyze unstructured or streaming data.
Create effective Data Warehouse Modernization and Automation Strategy with Xenonstack. Click here to Talk to our Data Warehouse Expert

What is Data Lake?

Data lakes promise and enable all kinds of data. It helps to store data at one location in an open format that is ready to be read. For example, you could integrate semistructured click stream data on the fly and provide real-time data without incorporating that data into a relational database structure. The data lake offers great potential, but on the other, we need to be wary about the amount of data we put in and avoid situations like data swamps.

Data Lake also brings us to one major issue of the data lake: the ingested open formatted data still needs to be queried and prepared. The analytics team often waits before the complex data pipeline has been set to drive value out of the data. In addition, any issue would require the engineers to tweak the code to get the desired result, which makes the process cumbersome.

What are the Pros and Cons of Data lake?

Pros Cons
Can handle both structured and semi-structured data.
Take time for data to be queryable.
Optimum for streaming and complex data processing.
Requires building complex pipeline.
Cost-effective solutions for any data type.
Takes time to ensure data quality and reliability.

What is the difference between Data Warehouse and Data Lake?

Data in your Data Warehouse is rigid and normalized. It is well structured, making it easily readable, whereas data in the Data Lake is raw, loosely bounded, and decoupled. Hence, while moving from data warehouse to data lake, we lose rigidity and atomicity (no partial success), Consistency, Isolation, Durability.

  • Data warehouse tends towards schema-on-write whereas data lake tends towards on schema-on-read
  • Data lakes can store both structured and unstructured data, whereas structure is required for a data warehouse.
  • The data warehouse is tightly coupled, whereas data lakes have decoupled compute and storage.
  • Data lakes are easy to change and scale in comparison with a data warehouse.
  • Data retention in the data warehouse is less due to storage expense.

What is Data Lakehouse?

As the name suggests, a data lakehouse provides an attempt to satisfy the desire to bring in the best of both the worlds data warehouse and data lakehouse, alluding to give reliability and structure present in data warehouses with scalability and agility of data lake. A data lakehouse is a trend that provides a one-size-fits-all approach. It is not merely an integration data warehouse with a data lake but a combination of data lake, data warehouse, and purpose-built store enabling easy, unified data governance and movement.

What are the Pros and Cons of Data Lakehouse?

Pros Cons
Atomicity, Consistency, Isolation, Durability remain intact Relatively new and is far away to stand as a mature storage system
BI tools can be empowered hence critical decision making is possible Need out of a box approach or else is costly to maintain
All data resides in one platform also implying fewer hostname to maintain It May take time to setup
Data duplicity gets reduced No one for all tool is yet present to utilize full potential
Doesn’t binds to a single platform and can leverage different tech  
Easy to maintain and problem fixing takes less time  
Make it easier to build a pipeline  

How Data Lakehouse works?

The lakehouse has dual layered architecture in which a warehouse layer resides over a data lake enforcing schema on write and providing quality and control, thus empowering the BI and reporting. It is a hybrid approach and proved an amalgamation between structured and unstructured data.

What are the usecases of Data Lakehouse?

  • Analysis of Clickstream Data - as the data collected from the web can be integrated into a data lake, some of the data could be stored in the warehouse for daily reported while others for analysis.
  • Creating a Larger Dataset - by copying data from sales of product from warehouses to data lakes to provide the best product recommendation
  • Other Situations - for moving data from purpose-built store to another for more effortless movement taking into account the data gravity

What is the difference between Data Lake vs Warehouse vs Data Lakehouse?

The data lakehouse is an upgraded version of the data lake that taps its advantages, such as openness and cost-effectiveness, while mitigating its weaknesses. It increases the reliability and structure of the data lake by infusing the best warehouse.

Parameters Data Lake  Data Warehouse  Data Lakehouse
Purpose of Data For ML and AI workloads ( Purpose of the data is not yet determined) For Data Analytics or Business Intelligence ( The data is currently in use)  Can be used for ML/AI workload and Data Analytics/BI needs
Type of Data  Unstructured Data  Structured Data  Unstructured and Structured Data 
Users  Data scientists, data engineers, data  Business professionals Business professionals and data teams
Data Quality  Raw Data, Low Quality and Not Reliable Highly curated data, reliable Raw and curated data, high quality with in-built data governance
ACID Compliance Non-ACID compliance: updates and deletes are complex operations ACID-compliant :  guarantee the highest levels of integrity ACID-compliant to ensure consistency as many sources concurrently read/write data
Storage Cost-effective, rapid and flexible Costly and time-consuming Cost-effective, rapid and flexible
Schema  Schema on read Schema on write  Schema enforcement


To conclude select the right solution of the stack will always depend on how you want to access your data while taking into consideration the velocity of the data and the gravity of data and other factors like scalability and flexibility of your solution, The amount of effort you want to commit the future scope of your data and the actual value you want to drive through.

Thanks for submitting the form.

Thanks for submitting the form.