Extracting data from multiple sources involves gathering and consolidating information from different systems, databases, and files to create a unified dataset.
Transforming data from multiple sources involves the process of extracting, cleaning, and integrating data from various heterogeneous sources to create a unified, consistent, and usable format.
Loading data from multiple sources can be a complex and time-consuming process, but it is necessary to provide users with access to comprehensive and accurate information.
Understand the Structure
The proper way to extract data from multiple sources starts with identifying the sources and understanding their formats and structures.
Document the Process
Proper documentation should also be maintained to keep track of the source and purpose of each data set.
Protect Sensitive Data
Additionally, proper security measures should be put in place to protect sensitive data and ensure compliance with relevant regulations.
Monitor Data Drift
Regular updates and monitoring should be performed to ensure the data remains accurate and up-to-date.
Identify the variables.
It is important to start by identifying the common variables and data types across the sources and ensure that they are aligned.
Resolve the Inconstincies
Once this is done, any discrepancies or inconsistencies must be resolved, such as missing values, duplicate records, or data outliers. Transformation can involve cleaning and structuring the data to create a consistent and organized format that is easy to understand and analyze.
It is essential to maintain data integrity and quality throughout the transformation process and keep documentation of any changes made.
Test and Validate
Additionally, the transformed data must be tested and validated to ensure its accuracy and completeness before it can be used by others. Proper transformation of data from multiple sources is essential to enable informed decision-making and drive business outcomes.
Loading data and making it available for consumption using data visualization tools such as Microsoft BI or Tableau requires a systematic approach to ensure that the data is accurate, complete, and in the proper format.
To make the data available to share across multiple platforms, it is important to select a data visualization tool that allows for easy sharing and collaboration.
Many data visualization tools, such as Tableau, allow users to publish their dashboards and reports to a server, where they can be accessed by others with appropriate permissions.
Alternatively, the data can be exported to a common format, such as CSV or Excel, and shared via email or a file sharing service.
Enforce Security and Governance
It is important to ensure the security and privacy of the data. This can be achieved through the use of secure data connections, user authentication and access controls, and data masking or anonymization techniques where appropriate