An analytical sandbox is a testing environment that is used by data analysts and data scientists to experiment with data and explore various analytical approaches without affecting the production environment. It is a separate, isolated environment that contains a copy of the production data, as well as the necessary tools and resources for data analysis and visualization.
Analytical sandboxes are typically used for a variety of purposes, including testing and validating new analytical approaches and algorithms, trying out different data sets, collaborating and sharing work with colleagues, and testing new data visualization techniques and dashboards.
Analytical Sandbox’s Essential Components Include:
- Business Analytics (Enterprise Analytics) – The self-service BI tools for situational analysis and discovery are part of business analytics.
- Analytical Sandbox Platform – The capabilities for processing, storing, and networking are provided by the analytical sandbox platform.
- Data Access and Delivery – Data collection and integration are made possible by data access and delivery from a number of data sources and data kinds.
- Data Sources – Big data (unstructured) and transactional data (structured) are two types of data sources that can come from both inside and outside of the company. Examples of these sources include extracts, feeds, messages, spreadsheets, and documents.
Graphical view of Analytical Sandbox Components
Importance of an Analytical Sandbox
- Data from various sources, both internal and external, both unstructured and structured, can be combined and filtered using analytical sandboxes.
- Data scientists can carry out complex analytics with the help of analytical sandboxes.
- Analytical sandboxes enable working with data initially.
- Analytical sandboxes make it possible to use high-performance computing while processing databases because the analytics takes place inside the database itself.