The main difference between data lake and data warehouse is that data lake obtains non-relational and relational data from IoT (Internet of Things) devices, websites, mobile apps, social media, and corporate applications, while data warehouse obtains data from transactional systems, operational databases, and line of business applications.
A data lake is a centralized repository that allows storing structured and unstructured data at any scale. A data warehouse, in contrast, is a system that helps to analyze data, report and visualize them to make better decisions.
Key Areas Covered
1. What is Data Lake
– Definition, Functionality
2. What is Data Warehouse
– Definition, Functionality
3. What is the Difference Between Data Lake and Data Warehouse
– Comparison of Key Differences
Big Data, Data Lake, Data Mart, Data Warehouse, ETL
What is Data Lake
A data lake is a storage repository that holds a vast amount of raw data in its native format until it is required. Data lake stores relational data from various business applications and non-relational data obtained from IoT devices, social media, and mobile apps. It is possible to use various techniques such as SQL queries, big data analytics, real-time analysis, machine learning to take business insights.
Moreover, data lake provides multiple advantages. It can collect data from various sources and store in their original formats. Therefore, it prevents the additional time taken to define structures, schemas and perform data transformations. Data scientists and business analysts can also analyze data without moving data to a separate analytics system. Additionally, it is possible to apply machine learning techniques to achieve optimal results and to take business decisions.
Furthermore, it improves innovation, customer interactions, and operational efficiency. On the other hand, there can be data without any oversight of the contents. Therefore, there should be mechanisms to catalog and secure data.
What is Data Warehouse
A data warehouse is a system that improves the business intelligence process. It converts data into valuable information in order to analyze the business. Thus, this helps to monitor the current status and to make future decisions. Furthermore, data warehouses are subject oriented, integrated, time variant and nonvolatile. There are data marts in a data warehouse. These data marts contain data for specific users. For example, HR and sales departments have separate data marts. It increases data integrity and security.
There are various data sources in an organization. Data from these sources are extracted, transformed and loaded into the data warehouse. And, this process is also called an ETL process. Then, the data is integrated and processed to take useful business insights. Before storing data, it is necessary to define the structure and schema of the data warehouse. The results of a data warehouse allow operational reporting and analysis.
Difference Between Data Lake and Data Warehouse
A data lake is a massive, easily accessible, centralized repository of large volumes of structured and unstructured data. A data warehouse, in contrast, is a central location which stores consolidated data from multiple data sources. Thus, this is the main difference between data lake and data warehouse.
Moreover, data lake obtains non-relational and relational data from IoT devices, websites, mobile apps, social media, and corporate applications. In contrast, data warehouse obtains data from transactional systems, operational databases, and line of business applications.
Query results are another difference between data lake and data warehouse. Data lakes obtain fast query results using low-cost storage while data warehouses obtain fast query results using higher cost storage.
Furthermore, data lakes use machine learning, predictive analytics, data discovery, and profiling whereas data warehouses use batch reporting, business intelligence, and visualization. Hence, this is another difference between data lake and data warehouse.
Besides, data scientists, data developers, and business analysts use data lakes while business analysts mainly use data warehouses.
The main difference between data lake and data warehouse is that the data lake obtains non-relational and relational from IoT devices, websites, mobile apps, social media, and corporate applications while the data warehouse obtains data from transactional systems, operational databases, and line of business applications.