Podcast
Questions and Answers
What is the general guideline for the number of records to be updated when considering a full refresh of the data warehouse?
What is the general guideline for the number of records to be updated when considering a full refresh of the data warehouse?
When should a full refresh of the data warehouse be seriously considered?
When should a full refresh of the data warehouse be seriously considered?
Who is responsible for ensuring that the data in the source systems conforms to the business rules?
Who is responsible for ensuring that the data in the source systems conforms to the business rules?
Who establishes the acceptable levels of data quality?
Who establishes the acceptable levels of data quality?
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What is the primary role of the Data Expert?
What is the primary role of the Data Expert?
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Who is ultimately responsible for resolving data corruption as data is transformed and moved into the data warehouse?
Who is ultimately responsible for resolving data corruption as data is transformed and moved into the data warehouse?
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What is the primary role of the Data Correction Authority?
What is the primary role of the Data Correction Authority?
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Who is responsible for the quality of data input into the source systems?
Who is responsible for the quality of data input into the source systems?
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What is the role of the Data Consistency Expert?
What is the role of the Data Consistency Expert?
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When is a full refresh of the data warehouse usually done?
When is a full refresh of the data warehouse usually done?
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Study Notes
Data Update vs. Full Refresh
- When updates range between 15% and 25% of total records, cost per record is consistent whether using a full refresh or selective updates.
- If over 25% of records change daily, consider full refresh for cost-effectiveness.
- Update processes are typically favored, but major changes may warrant a full refresh.
Importance of Data Quality
- Data quality ensures reliability and usefulness of data in reports and analysis.
- Data Consumers establish acceptable quality levels for warehouse data.
- Data Producers ensure accurate input into source systems.
- Data Experts identify and rectify issues in source data.
- Data Policy Administrators oversee data integrity during transformations.
- Data Integrity Specialists maintain conformity with business rules.
- Data Correction Authorities implement cleansing techniques.
ETL Process Overview
- ETL (Extract, Transform, Load) is performed by enterprise-grade applications like SQL Server Integration Services (SSIS).
- Extraction is the most time-consuming and human-intensive part of ETL due to varied source systems.
Types of Data Extraction
- Immediate (Full) Extraction: Real-time data extraction as transactions occur.
- Deferred (Incremental) Extraction: Data pulled based on timestamps and updates.
Data Staging
- Acts as an interim phase between extraction and further ETL processes.
- Gathers data from various asynchronous sources and loads it into the warehouse at cutoff times.
- User access to staging files is typically restricted.
Transformation Types in ETL
- Includes format revisions, field decoding, derived values, field merging/splitting, and unit conversions.
- Parsing identifies and organizes data elements; standardizing ensures consistent formatting.
- Searching matches records to eliminate duplicates, and consolidating merges related records.
Reasons for “Dirty” Data
- Causes include dummy values, incomplete data, cryptic entries, and violations of business rules.
- Issues span across misused address lines, duplicate identifiers, and integration challenges.
Loading Process
- Involves transferring data into the warehouse and includes methods such as incremental updates and scheduled refreshes.
- Updates apply changes in real-time, while refreshes involve complete reloads at set intervals.
- Refresh costs remain consistent, while update costs depend on the volume of records modified.
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Description
Test your understanding of the ETL process, from data extraction to data loading, and its challenges. Learn about the robust enterprise-grade ETL applications and the nature of source systems that make ETL functions difficult. Evaluate your knowledge of data warehouse and transaction processing concepts.
