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Top 5 reasons why your data needs Data Lakehouse

Data Lakehouse

Data is the quintessential ingredient of a successful business recipe. How you treat your data determines your sustainable business growth. Data needs a place to live, organize, analyze, and convert into insights.

Organizations across industries have been relying on Data warehouses for several years to handle their data. With the proliferation of a massive amount of operational data, organizations envisioned a single system to host a large amount of data for different analytics workloads. That single system that every data architecture is relying on is the Data Lakes.

While the data lakes succeeded in the legacy architectures with an edge over the critical competencies, they failed to match the changing demands of the businesses in the areas of integrations, consistency, data quality, and machine learning algorithms. But here goes the saying, every problem comes with a solution. These limitations laid a foundation for a flexible and cost-effective data management architecture called Data Lakehouse.

So this pops a question in a different dimension. Are the previous data models obsolete or a failure? To answer this question, we first need to understand data Lakehouse, its emergence, and the purpose it serves.

Data Lakehouse: It is an open data architecture that includes the best data lake and data warehouse components. It addresses the problems and limitations of the previous data architecture models – Data lakes and Data warehouses.

“A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data.”


Data Lakehouse is the combination of these two data models  – Data Lakes and Data Warehouses. The previous models are not dead, but they are combined into one architecture while addressing these two models’ limitations and offering higher efficiency at a low cost.

Here are some of the challenges with the current data models

Lack of open formats – It takes enormous efforts, time, and cost to migrate data to other systems from the data warehouse owing to the complexity of storing data into exclusive trademarked formats only.

Lack of machine learning support –  Several research and studies were done to get ML and data management work smoothly, but none of the pioneered ML systems delivered exceptional results.

Higher Cost – Storing data in Data Lakes and Data Warehouses seemed to be costly for the organizations.

The Emergence Of Data Lakehouse – Inventions are the outcome of the necessity. The current data architectures have limitations that cause problems for the team members. To eliminate these limitations, Data Lakehouse is emerged. The past models demanded too much effort, cost, and, most importantly, time. These limitations prevent the leaders to get prompt real-time insights.

Can you imagine how much data we generate every day? 

Over 2.5 quintillion bytes of data are created every single day – (Domo) 

Yes, with this enormous volume of data comes the challenge to extract, transfer and load the data promptly. 

5 Reasons To Move Your Data to Data Lakehouse

There are many reasons to move your current data architecture to data Lakehouse as every organization has its own challenges in creating and managing a sustainable data architecture. We have curated the top 5 reasons why you should consider storing data in data lake house:


Data Lakehouse gives you features of these two architectures under one roof. Data Lakehouse is pretty much easy to install, and use compared to previous data architecture. Organizations having less volume of data can also get advantages by integrating this architecture in their business. Organizations found a solution for the limitations they have been facing with the previous architectures. With the increasing data volume, we can expect the scope of improvements. We need faster insights more than ever. Would Data Lakehouse be able to survive the enormous volume of data?

Is Data Lakehouse the latest paradigm shift, or is something more innovative coming? We have heard about Data Mesh, a more decentralized data architecture platform. We will discuss data mesh in detail in our next blog. 

Here is the podcast episode link of our podcast series, The Data Story. This episode was about Data Lakehouse: Debunking the hype. Listen to the conversation between industry veterans James Serra and Khalil Sheikh.

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