Data is an invaluable asset for any business. When organized, well-structured, and processed, it provides valuable insights that empower businesses to make highly effective decisions. The Big Data Industry has witnessed rapid growth in the last couple of years. Both Data Lake and Data Warehouse refer to data storage, but there are some differences between them. Experienced data analysts of data engineering companies know well about them.
Before understanding the differences, you must know what is a data lake and a data warehouse. A data lake is a centralized repository of structured, semi-structured and unstructured data with no specific limit of account size or file. A data warehouse on the other hand is a database or storage house of structured data that is used to gain valuable business insights.
Here is a checklist of some differences between Data Lake and Data Warehouse.
1. Data Structure
One of the major differences between Data Lake and Data Warehouse lies in data structure and organization. A Data Lake mainly stores raw data, on the other hand, a Data Warehouse stores refined, well-processed data. Raw data is ideal for machine learning and can be rapidly examined for a definite purpose. Data Lake in comparison to data warehouses needs a bigger storage volume. Sometimes Data Lakes become data swamps due to a lack of relevant governance measures and poor data quality. A Data warehouse only stores processed data to save storage space and is easily understandable by a vast community.
2. Purpose
The motive of data pieces in the Data Lake is not determined. It remains floating in the data lake, either for its specific use in the time ahead or without any plan. Whereas, the purpose of data pieces within the data warehouse is predefined inside the organization. It means that the data warehouse has more planning and filtration than its complement.
3. Users
Sometimes, it gets tough for non-familiar people to navigate the data within the Data Lake. It requires qualified data scientists and special tools to recognize and analyze the unprocessed data for effective utilization. On the other hand, structured data is well expressed in tables, charts, and spreadsheets that are easily understandable by anyone.
4. Accessibility
Due to the lack of structure and organization within the Data Lake, it’s effortless to access and modify. You can use data in various ways or make changes in the data quickly as Data Lake has no restrictions. Whereas the Data Warehouse, by being structured and well organized is easy to decrypt and acknowledge. But managing and manipulating data warehouses is a bit expensive.
5. Cloud Storage
A Data Lake performs way better with cloud data services as opposed to a Data Warehouse. Cloud computing facilitates features and properties that are reliable, scalable, secured, less redundant, and easy to manage. Cloud data warehouses merge with traditional ones to reduce the managerial burden and facilitate enhanced performance.
A data lake and data warehouse both are important for an organization. With the increasing importance of big data and analytics, experienced data engineering companies play a pivotal role in empowering businesses to generate maximum value from data for enhanced growth and profits.
