Data Warehousing Fundamentals Quiz

ETL Process Overview

• ETL stands for Extract, Transform, Load. It's a process that moves data from source systems to a target system.
• Data is extracted from various sources (databases, files, SaaS Applications).
• Data is transformed to match the schema and requirements of the target system. This includes data mapping, linking data from different sources, and cleaning the data.
• Data is loaded into the target system, often a cloud data warehouse.

Data Cleaning

• Incomplete data: Data missing at the time of collection, or a difference between collection and analysis time. Issues with human interaction, software, and/or hardware can lead to incomplete data.
• Noisy or incorrect data: Errors in data collection instruments, human errors, transmission errors, and buffer overflows.
• Inconsistent data: Different sources of data, transgression of functional dependency rule.

Logical Extraction

• Full extraction: Extracts all data currently available from the source system. Used when the system can't identify updated data, requiring a complete copy.
• Incremental extraction: Keeps track of updated/changed data since the last successful extraction. Only new or updated data is extracted and loaded, making it more efficient than a full extraction.

Physical Extraction

• Online extraction: Data directly extracted from source systems; no external files are needed.
• Offline extraction: Data copied to an external file first, then the extraction process connects to the file for processing.

Initial Load

• All data from source system is loaded into the target system (the datawarehouse) at one time.

Incremental Load

• Only updated/new records are loaded into the data warehouse. The system periodically updates data.

Full Refresh

• All data in the target system is deleted. Then, the full data set is reloaded from the source system.

Transform (1/3)

• The transformation stage of the ETL process is responsible for converting data structures and formats to match the target system schema.
• Data manipulation (converting structure/format, mapping, linking data, data cleansing) occurs here.

Transform (2/3)

• Data conversion often requires multiple steps.
• Staging areas are often used to temporarily hold data during processing.
• Data unification (for example, changing date formats) can occur here.

Transform (3/3)

• Basic transformations: Removing duplicates, mapping null values, Format conversion (e.g., converting an integer ID to a string), and establishing key relationships between tables
• Advanced transformations: Splitting columns, combining data from multiple sources, creating new columns, aggregating data from multiple sources, data validation.

Load (1/2)

• Loading data into the target system; typical target is a cloud data warehouse.
• Performance is critical, especially if large amounts of data are loaded in short periods.
• The load process may need recovery mechanisms if it fails.

Load (2/2)

• Three types of load:
  • Initial load: All data loaded from source system into data warehouse.
  • Incremental load: Only new or updated records from source systems are loaded periodically.
  • Full refresh: Existing data in target system is deleted, then fully refreshed from the source.

Semantic Modeling (1/4)

• A conceptual model that describes the meaning of data elements often in a business setting.
• Organizations often have unique terms with different meanings and synonyms across systems.
• This model facilitates data relationships and analysis.

Semantic Modeling (2/4)

• Abstract representation of database schema.
• Simplifies data access for end-users.
• Renaming columns to be more user-friendly enables clarity and understanding.

Semantic Modeling (3/4)

• Hide non-relevant columns, tables, and relationships.
• Standardize data naming conventions across the data warehouse to improve clarity and efficiency.

Semantic Modeling (4/4)

• Two primary types of semantic models.
  • Tabular: Uses relational modeling constructs.
  • Multidimensional: Uses traditional OLAP constructs (cubes, dimensions, measures)

Pros of Semantic Modeling

• Allows reporting tools to properly display calculated results
• Logical structure for business logic and calculations
• Includes time-oriented calculations
• Data often from multiple sources that are integrated into the structure.
• Provides data abstraction so users do not have to know the technical complexities of the data warehouse.

OLTP vs. OLAP

• OLTP (Online Transaction Processing): Systems for recording transactions, one record at a time. Optimized for write operations.
• OLAP (Online Analytical Processing): Systems for business intelligence, analytics & analysis queries. Optimized for read operations.

ROLAP (Relational OLAP)

• Data stored in a relational database.
• OLAP engine allows to simulate the behavior of multidimensional DBMS.
• Easier and cheaper to implement than other OLAP models. Less efficient during calculation phases.

MOLAP (Multidimensional OLAP)

• Uses native multidimensional structures (cubes).
• Direct data access in the cube.
• Harder to implement and often proprietary formats.

HOLAP (Hybrid OLAP)

• A hybrid solution combining features of MOLAP and ROLAP.
• Fact and dimension tables are stored in a relational DBMS, but aggregate data is stored in cubes.
• A good compromise in terms of cost and performance.

Cube

• Multidimensional data model for analysis, including dimensions such as time and geographical location.
• Calculations are performed during cube loading and updates.

Manipulation of Multidimensional Data

• Rotation: Showing a different side of the cube.
• Slicing: Reducing one dimension to a single value.
• Dicing: Extracting a portion, or sub-cube.

Roll-up vs Drill-down

• Roll-up (zoom out) for summary data aggregation at higher levels of granularity
• Drill-down (zoom in) for detailed data at lower levels of granularity; used with aggregation functions.

MDX (Multidimensional Expressions)

• Query language for online analytical processing (OLAP) query language for databases.

ETL (Extract, Load, Transform) vs ELT (Extract, Load, Transform)

• ETL: Data is transformed in a staging area before being loaded into the target system.
• ELT: Data is loaded directly into the target system and then transformed.

Pros and Cons of ELT

• Pros:
  • Faster processing, particularly with massive datasets
  • Less time and resources required.
• Cons:
  • Fewer tools readily available to perform transformation tasks, limiting expertise in this area.

Data Warehousing Support

• ETL typically works with data warehouses.
• Data warehousing can support cloud-based as well as traditional data warehouses.
• OLAP and structured data types are compatible.

Data Size

• ETL typically works with smaller datasets.
• ELT is typically used with massive datasets.

Business Intelligence Tools

• Market share information is available for popular business intelligence tools.

Open Source Solutions

• Lists specific open-source solutions/tools for various roles/steps in the ETL/BI process.

Example of an ETL

• Describes a pathway for data processing, starting with multiple source systems and ending with business users or other reporting tools.
• Shows different systems and tools that are in play at the start to end stages.

Data Warehousing Environment

• Shows the data sources, the stages, and the reporting tools.

Test your knowledge on the essential aspects of data warehousing, including the ETL process, data quality issues, and the architecture of data warehouses. This quiz covers key concepts vital for effective data management and decision-making.