ETL Process in Data Integration

Questions and Answers

Insert the transformed data into a target system.

Retrieve raw data from diverse sources. (correct)

Merge data from different sources for a unified view.

Convert raw data into a clean, usable format.

Ensuring data is standardized.

Handling different data formats. (correct)

Enriching data with additional information.

Loading data into target systems.

Enhancing data through calculations.

Converting data into a common format. (correct)

Integrating data from different sources.

Removing duplicates from the data.

Bulk loading. Signup and view all the answers

Apache NiFi. Signup and view all the answers

Maintaining data security. Signup and view all the answers

Study Notes

Objective: Retrieve raw data from various sources.
Sources of Data: Common sources include:
- Databases: SQL and NoSQL.
- APIs: Interface for data retrieval.
- Flat files: CSV and Excel formats.
- External sources: Other data suppliers or repositories.
Techniques: Involves using tools or scripts for data connection:
- Query databases to retrieve data.
- Call APIs for direct data access.
- Read data from files for processing.
Challenges: Key issues faced during extraction:
- Handling diverse formats and structures.
- Managing large data volumes efficiently.
- Ensuring timely and accurate data extraction.

Objective: Prepare raw data for analysis by converting it into a clean, usable format.
Cleaning: Essential steps include:
- Removing duplicates and resolving inconsistencies.
- Handling missing values accurately.
- Correcting data errors where necessary.
Standardization: Aim to create uniformity in data representation:
- Convert date formats for consistency.
- Standardize currencies used in datasets.
Enrichment: Add value to the data through:
- Aggregating sales data for analysis.
- Calculating important metrics to enhance insights.
Integration: Combine data from various sources for a comprehensive view:
- Merging customer data across multiple databases for unified analysis.
Tools Used: Common ETL tools include:
- Apache Nifi, Talend, Informatica.
- Custom scripting in Python or SQL.

Objective: Move the transformed data into designated target systems.
Targets: Data can be loaded into:
- Data warehouses like Snowflake or Amazon Redshift.
- Traditional databases or data lakes for storage and analysis.
Methods of Loading:
- Bulk loading: Suitable for inserting large datasets at once.
- Incremental loading: Used for updating existing datasets with new data.
Considerations: During the loading process, focus on:
- Ensuring data integrity for accuracy.
- Managing performance effectively, particularly with large datasets.
- Maintaining data security throughout the loading process.