Data Lake vs Data Warehouse

Data Lake and Data warehouse are both strong contenders when it comes to storing Big data. Many users cannot decide which one is the better option due to lack of proper understanding about the two. Let us now explore each one closely, so that an informed decision can be made to suit every business need.

Data Lake 

Data lakes can simply be defined as a highly scalable storage repository that holds large amounts of raw data in its natural format until it is required for use. Data lakes contain data that comes from varied sources and includes data in all kinds of formats. In a way it resembles the actual lakes and the data comes in from various sources and the structured, unstructured and semi-structured data is stored together.

Data-Warehouses

Data warehousing is combining different technologies and components to enable strategic organizing and usage of the data. The data in the data warehouses are cleaned and transformed before storing. 

Let us now compare Data lakes with Data warehouses to study their effectuality:

• Data Retention
  Data Lakes retain all data irrespective of their source, usage, and format. Whereas, in Data warehousing a substantial period is spent analyzing data sources, understanding business processes, and reporting the data.

• Data type Support
  Data Lakes support all kinds of data types, both traditional and non-traditional. All the data is kept in its raw form until it is ready to be processed. This approach is also called ‘Schema on Read’. However, data warehousing uses the ‘schema on write’ approach as they mostly consist of data which is extracted from transactional systems and has quantifiable metrics along with their attributes.

• User Support
  Data Lakes are suitable for users such as Data scientists who require advanced analytical tools to analyze the data, while data warehouses are more appropriate for operational users as they are easy to use and understand and are well structured.

• Storage Cost
  Data Lakes are comparatively cheaper than data warehouses as data warehouses need more effort and are time-consuming.

• Data Processing
  The Data Lakes use the Extract Load Transform (ELT) process for data processing, while the Data warehouse uses a traditional Extract Transform Load (ETL) process.

Conclusion

Therefore to summarize, we can say that it is better to consider both the data storage approaches closely and depending upon the business needs a choice can be made. For instance, if an organization has a fully functioning data warehouse, then it is better to implement a data lake together with the data warehouse to get the best of both worlds. 

Here, Datametica can help you in establishing a data lake and data warehouse on the cloud. Datametica is a global leader in data warehouse migration and modernization to the cloud. Through our innovative technologies, we automate and streamline your cloud journey and help leverage data lake and data warehouse capabilities of the cloud environment.