Data engineering is a sought-after service across the globe in the 21st century. Most companies use data to make critical business decisions, improve offerings, and enhance customer experience these days. Therefore, the amount of data organizations capture is higher than ever at the moment. In fact, a report by ABI Research suggests that industrial enterprises around the world will generate 4.4 Zettabytes of data by the end of 2030. But the question is: what’s the point of having such a huge amount of data if you can’t utilize it quickly? Well, that’s where the discussion of data lakes vs data lakehouses comes into the picture. So, let’s begin:

What is Data Lake Architecture?

A data lake is a centralized yet scalable storage repository that holds a massive amount of unstructured and structured data in its raw form. Since these lakes are durable, flexible, and cost-effective for businesses, they help firms gain advanced insights using unstructured data.  

Just to let you know, the data market size was worth USD 5.80 billion in 2022 and is expected to touch the USD 34.07 billion mark by 2030. This indicates how important it is to pick the right data architecture among data lake vs data warehouse vs data lakehouse if your business deals with a lot of data on a daily basis.  

If you are unversed, we must let you know that a data warehouse is a unified data repository to keep a vast amount of information from various sources in an establishment. In other words, a data warehouse refers to a single source of “data truth” in a company, which acts as a key reporting and business analytics component.    

Benefits of Data Lake Architecture

Given that data lakes can have both unstructured and structured data, they provide multiple benefits, such as: 

• Data Flexibility: You can store information in any format without the need to have a predefined plan. Allowing the data to be in its original format provides access to more data for analysis purposes and catering to future data applications.     
• Data Consolidation: In cloud data lakes and lakehouses comparison, the former can hold both unstructured and structured information, removing the need to keep both data formats in different environments. They offer a central storage to keep all kinds of organizational information.    
• Cost Savings: In the modern data architecture for business, data lakes are more affordable than traditional data warehouses. They are designed to be kept on less expensive commodity hardware, such as object storage.  

Drawbacks of Data Lake Architecture

Even though data lakes offer multiple benefits, they also pose certain challenges: 

• Lack of Data Reliability & Security: The lack of data consistency in data lakes makes it hard to enforce data reliability and security. Since data lakes can include several data formats, it might be difficult to implement robust data security and governance policies to serve confidential data types.   
• Poor Performance for Business Intelligence & Data Analytics Applications: In the data lakehouse vs traditional data lake comparison, the latter can become disorganized if not managed properly. This makes it challenging to link them with business intelligence and analytics tools. Apart from that, the unavailability of a consistent data structure and ACID (atomicity, consistency, isolation, and durability) transactional support can lead to inferior query performance when needed for reporting and analytics applications.   

What is Data Lakehouse Architecture?

A data lakehouse architecture is the latest big-data storage architecture that allows a single storage for your entire information, whether structured, unstructured, or semi-structured. This powers top-notch business intelligence, machine learning, and streaming capabilities. Data lakehouses often start as data lakes having all kinds of data, which is then transformed into the Delta Lake format to introduce reliability into data lakes.    

Benefits of Data Lakehouse Architecture

Choosing the right data architecture is a hard nut to crack. Because data lakehouse architecture comes with its own set of benefits, it combines data structure and management features of a data warehouse with inexpensive storage and flexibility of a data lake. The advantages of this implementation are many, including:

• Minimal Data Redundancy: When availing data engineering services, choosing a data lakehouse minimizes data duplication scope by offering a single comprehensive repository to fulfill all business data demands. Due to the pros of data warehouses and data lakes, most organizations invest in a hybrid solution, which could emerge as a costly decision because of data duplication issues.     
• Ease of Data Versioning, Governance, & Security: Considering the future of enterprise data architecture, the data lakehouse architecture implements schema and data integrity, facilitating the journey to adopt powerful data security and governance mechanisms.   
• Cost-effectiveness: Data lakehouse architecture adopts cost-effective storage features of data lakes by taking advantage of economical object storage options. In addition, this architecture removes the need to invest cost and time in maintaining various data storage systems by offering a single solution. 

So, if you want to build and maintain the pipeline and infrastructure that brings data into the lakehouse and ensure it is ready for business intelligence, machine learning, and analytics purposes, hire data engineers without a second thought.  

Drawbacks of Data Lakehouse Architecture  

A few shortcomings of a data lakehouse are:

• New Technology: It is still a new and immature technology compared to other data storage solutions out there. 
• Unpredictable Nature: It is still not clear whether this data storage solution will live up to its promises. It may take years before data lakehouse can compete with big data storage and analytics solutions in the market.  Thus, with the current pace of innovation, it is hard to predict whether data lakehouses will become dominant in the future.     

Data Lakes vs Data Lakehouses: An In-depth Comparison  

Data lakes excel in managing raw data in multiple formats on affordable storage for machine learning and data science workloads. Although data lakes function well with unstructured data, they lack the ACID transactional features of data warehouses. This makes it quite difficult to ensure data consistency and reliability. 

The data lakehouse is the cutting-edge data storage architecture that brings together the cost-efficiency and scalability of data lakes and the reliability and consistency of data warehouses.  

Here’s a table summarizing the data lakes vs data lakehouses comparison that you just learned above: 

Parameters	Data Lake	Data Lakehouse
Objective	Best for AI and ML workloads	Best for ML and data analytics workloads
Storage Data Type	Functions well with unstructured and semi-structured data	Can manage structured, unstructured, and semi-structured data
ACID Compliance	Non-ACID Compliant which makes it difficult to perform update and delete operations.	ACID Compliant to ensure consistency since more than one parties read or write data simultaneously
Cost	Repository is quick, cost-effective, and flexible	Repository is quick, cost-effective, and scalable

Which one is better, data lakes vs data lakehouses, is still a topic of debate. Your choice will eventually depend on the data type you deal with, the data source, and how the concerned stakeholders will leverage that data.  

Business Use Cases of Data Lakehouse vs Data Lake

Here’s how the difference between data lake and data lakehouse architecture appears to be in real life:

1. Healthcare:

• A hospital network utilizes a data lake to hold raw IoT data from patient monitors and wearables. This allows state-of-the-art machine learning models to predict any abnormality in the medical conditions of patients.  
• The same network shifts to a data lakehouse architecture to incorporate patient records with real-time IoT data, enabling uninterrupted analytics for customized treatment. 

2. Finance:

• A financial institution makes the most of a data lake to collect unstructured consumer feedback for sentiment analysis.   
• The same organization leverages a data lakehouse to integrate this feedback with transaction data. This leads to improved fraud detection and risk management systems for banking institutions. 

3. Retail:

• A retail chain employs a data lake for sentiment analysis on social media mentions, recognizing consumer preferences and complaints. 
• The same chain embraces a data lakehouse to integrate inventory data with business intelligence dashboards. It enhances supply chains and allows decision-making through real-time analytics with data lakehouse. 

This multi-industry perspective gives an idea of how companies can strategically select between data lakes and data lakehouses to achieve specific goals and scale their data operations effortlessly. 

Final Thoughts: How to Choose Data Architecture for Business Needs

Now that you have gone through the data management architecture comparison, you must know that it could be difficult to develop a data lakehouse from scratch. And while doing this, you will probably use a reliable platform designed to support open data lakehouse architecture. Therefore, it is advisable to research every platform’s capabilities and implementations before investing.    

However, you must learn that data lakes are suitable for establishments seeking a scalable, affordable, and big-data solution to support machine learning and data science workloads on unstructured data. In case the data lake method isn’t living up to your organizational data expectations or you are finding a way to implement both advanced analytics and machine learning workloads on your data, a data lakehouse is an ideal choice.     

But whatever solution you choose for your firm, InnovationM has got your back. Schedule a call with our experts today and learn how our data engineering services can take your operational efficiency to the next level. 

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