Today we live in a time where we have to manage huge amounts of data. In today’s world of data management, the growing concepts of data warehouse and data lake are often a major part of discussions. In this article, we discuss the pros and cons of each concept. Undoubtedly, both serve as a repository for data storage, but there are fundamental differences in capabilities, purpose, and architecture.
We will mainly discuss the 10 main differences between data lakes and data warehouses make the best choice. This will help you determine which one is best for your business.
Diversity of data
In terms of data diversity, a data lake can easily accommodate different types of data, which include semi-structured, structured and unstructured data in native format without any predefined schema. It can include data such as videos, documents, media streams, data and more. On the contrary, a data warehouse can store structured data that is properly modeled and organized for specific use cases. Structured data can be referred to as data that validates a predefined schema and makes it suitable for traditional relational databases. The ability to accommodate diverse data types makes data lakes much more accessible and easy to use.
Access to processing
When it comes to data processing, data lakes follow a read-only schema approach. Therefore, he can input raw data about his lake without the need for structuring or modeling. It allows users to apply specific structures to data during analysis and therefore offers better agility and flexibility. However, for data warehouses, in terms of processing access, data modeling is performed before injection, followed by schema access on write. Therefore, it requires data to be formatted and structured according to predefined schemas before being loaded into storage.
Cost of storage
When it comes to the cost of data, data lakes offer a cost-effective option storage solution because they generally use open source technology. The distributed nature and use of an unexpected storage infrastructure can reduce the total cost of storage even when organizations have to work with large amounts of data. In comparison, data warehouses involve higher storage costs due to their proprietary technologies and structured nature. The rigid indexing and schema mechanism used in the warehouse results in increased storage requirements along with other costs.
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, enabling rapid experimentation. On the contrary, data warehouses are known for their rigid structure, which makes adjustments and modifications time-consuming. Any changes in the data model or schema require significant coordination, time and effort across different business processes.
Security
When it comes to data lakes, security is continuously evolving as big data technologies evolve. However, you can rest assured that improved data lake security can mitigate the risk of unauthorized access. Some enhanced security technologies include access control, compliance frameworks, and encryption. On the other hand, the technologies used in data warehouses have been in use for decades, which means they have mature security features along with robust access control. However, the continuous development of security protocols in data lakes makes it even more robust in terms of security.
User accessibility
Data lakes can appeal to advanced analysts and data scientists due to the unstructured and raw nature of the data. While data lakes provide greater research capabilities and flexibility, they require specialized tools and skills to be used effectively. However, when it comes to data warehouses, they are primarily targeted at analytical and business intelligence users with varying levels of adoption across 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 begun to embrace big data technologies and explore use cases, the level of maturity can be expected to increase over time. In the coming years, it will be a prominent technology among organizations. However, even when data warehouses can be presented as a mature technology, the technology faces major problems with raw data processing.
Use cases
A data lake can be a good choice for processing different types of data from different sources, as well as for machine learning and analysis. It can help organizations analyze, store and ingest vast amounts of raw data from various sources. It also facilitates predictive models, real-time analytics and data discovery. On the other hand, data warehouses can be considered ideal for organizations with structured data analytics, predefined queries and reporting. It’s a great choice for businesses because it provides a centralized representative for historical data.
Integration
When it comes to the data lake, they require a robust interoperability capability to process, analyze and ingest data from disparate sources. Data pipelines and integration frameworks are commonly used for data simplification, transformation, consumption, and ingestion in a data lake environment. The data warehouse can be seamlessly integrated with traditional reporting platforms, business intelligence, tools and data integration frameworks. They are designed to support external applications and systems that enable collaboration and data 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 for analyzing and storing massive amounts of data with advanced capabilities such as real-time analytics, predictive modeling and machine learning. On the other hand, a data warehouse is generally a complementary transactional system since it provides a centralized representative for reporting and analytics of structured data.
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 access to processing, security, agility, cost, architecture, integration, and so on. Organizations must recognize the advantages and limitations before choosing the right repository to store their data. Organizations looking for a versatile centralized data store that can be efficiently managed without putting a strain on your pocket can choose data lakes. The versatile nature of this technology makes it a great choice for organizations.