The ETL process, standing for Extract, Transform, Load, is an important concept in data warehousing and business intelligence.
This process is essential for businesses and organizations as it enables them to consolidate data from multiple sources into a single, coherent framework.
This consolidated data is then used for reporting, analytics, business intelligence, and decision-making purposes.
What does ETL stand for?
This is the first phase where data is collected or extracted from various sources. These sources could be databases, CRM systems, flat files, web services, or other varied data repositories
The main challenge in this stage is to ensure that data is extracted efficiently and consistently.
What is the purpose of the Extract step in the ETL process?
Once the data is extracted, it undergoes the transformation process. This step involves cleaning the data to ensure quality, converting it to a desired format, and applying business rules to make it suitable for analysis.
Transformation can include a range of tasks such as filtering, sorting, aggregating, joining, deduplication, and more. The goal here is to convert raw data into a format that is more appropriate for reporting and analysis.
Which step in the ETL process involves mapping and reformatting data?
In the final stage, the transformed data is loaded into a target data store, typically a data warehouse, data mart, or a large database
This step must be optimized to ensure that the loading process minimally impacts system performance and that the data is stored securely and in a way that supports efficient querying and reporting.
What is the purpose of the Load step in the ETL process?
Data warehouses are specialized systems designed for storing and analyzing large volumes of historical data. Unlike traditional OLTP databases, which prioritize data integrity and normalization to minimize redundancy, data warehouses focus on performance and ease of analysis. This leads to the widespread use of unnormalized databases, where data redundancy is intentionally introduced to improve query efficiency.
Which of the following is a characteristic of data warehouses?
Data warehouses are designed for Online Analytical Processing (OLAP), where queries involve large aggregations, summaries, and complex joins.
Denormalized data reduces the need for frequent joins, improving query performance.
In a normalized structure, retrieving data often requires multiple joins across tables.
Denormalized tables allow analysts to run simpler and faster queries without complex relationships.
Analytical queries often require grouping, counting, and summing over large datasets.
Storing pre-aggregated and redundant data in unnormalized form
Why is denormalization used in databases designed for OLAP?