Data Lake vs. Data Warehouse: 10 Key Differences

Today, we are living in a time where we need to manage vast amounts of data. In today's data management world, the growing concepts of data warehouse and data lake have often been a major part of the discussions. In this article, we discuss the pros and cons of each concept. Undeniably, both serve as the repository for storing data, but there are fundamental differences in capabilities, purposes, and architecture. 

We will mainly discuss the 10 major differences between data lakes and data warehouses to make the best choice. This will help identify which one is best for your business.

Data Variety

In terms of data variety, a data lake can easily accommodate the diverse data types, which include semi-structured, structured, and unstructured data in the native format without any predefined schema. It can include data like videos, documents, media streams, data, and a lot more. On the contrary, a data warehouse can store structured data that has been properly modeled and organized for specific use cases. Structured data can be referred to as the data that confirms the predefined schema and makes it suitable for traditional relational databases. The ability to accommodate diversified data types makes data lakes much more accessible and easier.   

Processing Approach

When it comes to data processing, data lakes follow a schema-on-read approach. Hence, it can ingest raw data on its lake without the need for structuring or modeling. It allows users to apply specific structures to the data while analyzing and, therefore, offers better agility and flexibility. However, for data warehouses, in terms of processing approach, data modeling is performed prior to ingestion, followed by a schema-on-write approach. Hence, it requires data to be formatted and structured as per the predefined schemes before being loaded into the warehouse.  

Storage Cost

When it comes to data cost, data lakes offer a cost-effective storage solution as they generally leverage open-source technology. The distributed nature and the use of unexpected storage infrastructure can reduce the overall storage cost even when organizations are required to deal with large data volumes. Compared to it, data warehouses include higher storage costs because of their proprietary technologies and structured nature. The rigid indexing and schema mechanism employed in the warehouse results in increased storage requirements along with other expenses.  

Agility

Data lakes provide improved agility and flexibility because they do not have a rigid data warehouse structure. Data scientists and developers can seamlessly configure and configure queries, applications, and models, which enables rapid experimentation. On the contrary, data warehouses are known for their rigid structure, which is why adaptation and modification are time-consuming. Any changes in the data model or schema would require significant coordination, time, and effort in different business processes. 

Security

When talking about data lakes, security is continuously evolving as big data technologies are developing. However, you can remain assured that the enhanced data lake security can mitigate the risk of unauthorized access. Some enhanced security technology includes access control, compliance frameworks, and encryption. On the other hand, the technologies used in data warehouses have been used for decades, which means that they have mature security features along with robust access control. However, the continuously evolving security protocols in data lakes make it even more robust in terms of security. 

User Accessibility

Data lakes can cater to advanced analytical professionals and data scientists because of the unstructured and raw nature of data. While data lakes provide greater exploration capabilities and flexibility, it has specialized tools and skills for effective utilization. However, when it is about data warehouses, they have been primarily targeted for analytic users and business intelligence with different levels of adoption throughout the organization.  

Maturity

Data lakes can be said to be a relatively new data warehouse that is continuously undergoing refinement and evolution. As organizations have started embracing big data technologies and exploring use cases, it can be expected that the maturity level has increased over time. In the coming years, it will be a prominent technology among organizations. However, even when data warehouses can be represented as a mature technology, the technology faces major issues with raw data processing.  

Use Cases

The data lake can be a good choice for processing different sorts of data from different sources, as well as for machine learning and analysis. It can help organizations analyze, store, and ingest a huge volume of raw data from different sources. It also facilitates predictive models, real-time analytics, and data discovery. Data warehouses, on the other hand, can be considered ideal for organizations with structured data analytics, predefined queries, and reporting. It's a great choice for companies as it provides a centralized representative for historical data.   

Integration

When it comes to a data lake, they require robust interoperability capability for processing, analyzing, and ingesting data from different sources. Data pipelines and integration frameworks are commonly used for streamlining data, transformation, consumption, and ingestion in the data lake environment. A data warehouse can be seamlessly integrated with traditional reporting platforms, business intelligence, tools, and data integration frameworks. These are being designed to support external applications and systems that enable data collaboration and sharing across the organization.  

Complementarity

Data lakes complement data warehouses by properly and seamlessly accommodating different data sources in their raw formats. It includes unstructured, semi-structured, and structured data. It provides a cost-effective and scalable solution to analyze and store a huge volume of data with advanced capabilities like real-time analytics, predictive modeling, and machine learning. The data warehouse, on the other hand, is generally a complementary transactional system as it provides a centralized representative for reporting and structured data analytics.  

So, these are the basic differences between data warehouses and data lakes. Even when data warehouses and data lakes share a common goal, there are certain differences in terms of processing approach, security, agility, cost, architecture, integration, and so on. Organizations need to recognize the strengths and limitations before choosing the right repository to store their data assets. Organizations who are looking for a versatile centralized data repository that can be managed effectively without being heavy on your pocket can choose data lakes. The versatile nature of this technology makes it a great decision for organizations.