Tips for leaders on data collection to successfully drive data initiatives.
Every organization wants to derive insights from data. Most midsize organizations with smaller or no data teams struggle with data quality. “Garbage in, garbage out.” Business leaders of these organizations have a tough time deploying a data strategy that can get their data cleaned and processed for meaningful insights. One of the top questions the business leaders have when it comes to storage is whether to invest in Data Lake, Data Warehouse, or both to successfully get the right amount of data in a state that can be consumed for modern applications, including Data Analytics, BI (Business Intelligence), ML (Machine Learning), AI (Artificial Intelligence) and Data Dashboards. A high-level understanding of the difference between the two can help devise a data strategy aligned with the business strategy for the desired outcome.
A Data Lake is a massive repository of structured and unstructured data, and the purpose of this data is not defined at the time of storage. A large volume of company data can be stored cheaply and utilized later by various applications.
A Data Warehouse is a repository of highly structured historical data processed for a defined purpose. The data stored in the warehouse is cleaned and processed and serves as a consistent “single source of truth,” which is invaluable to business data analysis, collaboration, and better insights.
Depending on the size and cost, most organizations use a Data Lake and Data Warehouse to cover their data storage needs.
The following are the significant differences between Data Lake and Data Warehouse:
Data Lake: Unstructured data, the entire organization’s data for immediate or future use.
Data Warehouse: Structured data cleaned and processed for specific business needs.
Data Lake: Used by data scientists and engineers to get unique business insights.
Data Warehouse: Used by business users to get specific business insights.
Data lake: Predictive analytics, Machine Learning, Data Visualization, BI, Big data Analytics.
Data Warehouse: Data Visualization, BI, data analytics.
Data Lake: Schema is defined after the data is stored.
Data Warehouse: Defined before the data is stored.
Data Lake: ETL (Extract, Load, Transform). Extracted and stored.
Data Warehouse: ETL. Data is extracted and transformed, ready to be used for specific purposes.
Data Lake: Storage cost is inexpensive.
Data Warehouse: Expensive because of the operational cost.
Understanding the difference between Data Lake and Data Warehouse is vital for achieving the desired results for a successful data strategy. A more profound discussion and understanding of the company and the industry are needed to create and refine a successful data strategy. However, if you would like a more detailed overview, don't hesitate to contact me at email@example.com.
As a CTO, I have years of experience defining strategies for Data Warehouses and Data Lakes in different organizations, including non-profits, and I am available for fruitful discussions.