Data Warehousing Concepts Overview

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Questions and Answers

What is a system that aggregates data from one or more sources into a single, consistent datastore, to support data analytics?

A data warehouse

Which of these is NOT a type of analytics supported by data warehouse systems?

  • Data mining
  • Machine learning
  • Process automation (correct)
  • Artificial intelligence

Where were traditional data warehouses initially hosted?

  • On-premises within enterprise datacenters (correct)
  • Appliances with specialized hardware
  • Mainframes (correct)
  • Cloud data warehouses

Cloud data warehouses eliminate the need to purchase hardware.

<p>True (A)</p> Signup and view all the answers

What is a data mart specifically designed for?

<p>Tactical decision-making</p> Signup and view all the answers

What are the two common schemas used in data marts?

<p>Star and Snowflake (A)</p> Signup and view all the answers

What is a large repository that stores all types of data, both structured and unstructured, in its raw format?

<p>A data lake</p> Signup and view all the answers

Data lakes require predefined schemas and structures for data loading.

<p>False (B)</p> Signup and view all the answers

Which of these is a benefit of using data lakes?

<p>Scalable storage capacity (B)</p> Signup and view all the answers

What is the general process by which data is extracted, transformed, and loaded into a data warehouse?

<p>ETL (Extract, Transform, Load)</p> Signup and view all the answers

Data marts can be either dependent or independent of an enterprise data warehouse.

<p>True (A)</p> Signup and view all the answers

Which of these is a typical characteristic of dependent data marts?

<p>They inherit security from the EDW (B)</p> Signup and view all the answers

What is a multidimensional data structure used for online analytical processing?

<p>Data cube</p> Signup and view all the answers

Which of these is NOT a valid cube operation?

<p>Filtering (D)</p> Signup and view all the answers

What is a materialized view in a data warehouse?

<p>A snapshot of query results</p> Signup and view all the answers

Materialized views cannot be used to replicate data in a staging database.

<p>False (B)</p> Signup and view all the answers

Which of these is NOT a valid refresh option for materialized views?

<p>Continuously (D)</p> Signup and view all the answers

What is the primary function of fact tables in a data warehouse?

<p>Store facts about a business process</p> Signup and view all the answers

Which of these is a characteristic of dimension tables?

<p>They hold attributes that provide context to facts (B)</p> Signup and view all the answers

Facts and dimensions are always linked using foreign keys.

<p>True (A)</p> Signup and view all the answers

Which of these is a key design consideration for modeling with a star schema?

<p>Identifying the dimensions and facts (A)</p> Signup and view all the answers

Star schemas are optimized for writes, while Snowflake schemas are optimized for reads.

<p>False (B)</p> Signup and view all the answers

What is the primary difference between a star schema and a Snowflake schema?

<p>A Snowflake schema is a normalized version of a star schema, where dimensions are further broken down into child tables.</p> Signup and view all the answers

Flashcards

Star Schema

A data warehouse schema designed for optimized analytical queries (reads) and data exploration. It uses a central fact table with surrounding dimension tables.

Snowflake Schema

A data schema that utilizes normalization techniques to create a hierarchical structure for dimensional data, with multiple related tables for each dimension.

Normalization

The process of reducing data redundancy within a database by breaking down tables into smaller, related tables.

Denormalization

The process of combining data from multiple tables into a single table, potentially introducing redundancy for faster data access.

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Fact Table

A central table in a star schema that contains the primary metrics or measures of interest. It's often connected to multiple dimension tables.

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Dimension Tables

Tables in a star or snowflake schema that contain descriptive attributes or characteristics related to the fact table, such as customer information, product details, or time periods.

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Data Mart

A data warehouse designed specifically for analytical queries, often using star or snowflake schemas to support efficient data exploration.

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Transactional Data Warehouse

A data warehouse designed to support transactional systems, often using normalized schemas for efficient data updates and consistency.

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Read Speed

The speed at which data can be read or retrieved from the database.

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Write Speed

The speed at which data can be written or inserted into the database.

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Storage Space

The amount of storage space required to store the data in the database.

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Data Integrity Risk

The potential for errors or inconsistencies in the data within the database.

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Query Complexity

The complexity of the SQL queries required to retrieve information from the database.

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Schema Complexity

The complexity of the schema design in terms of the number of tables, relationships, and data types.

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Dimension Normalization

The level of data normalization applied to dimensions in a snowflake schema.

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Joins Per Dimension Hierarchy

The number of joins required to retrieve data from the fact table to a specific dimension in a schema.

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OLAP

Online Analytical Processing, focuses on analyzing and exploring existing data to gain insights and patterns

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OLTP

Online Transaction Processing, focused on efficiently handling high volumes of transactions (updates) in real-time.

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Study Notes

Data Warehouse Overview

  • A data warehouse is a system that collects data from various sources, aggregates it into a consistent store, and supports data analytics.
  • Objectives for a data warehouse include defining it, identifying its use cases, and listing its benefits.
  • Data warehouse systems support data mining, artificial intelligence, machine learning, front-end reporting, and OLAP (Online Analytical Processing).

Data Mart Overview

  • A data mart is a smaller subset of a data warehouse, focused on a specific business function or area.
  • It is designed for tactical decision-making, providing timely, relevant data, and supports faster query responses.
  • Data marts typically use star or snowflake schemas.
  • Data marts offer cost efficiency, secure access, and help end-users focus on relevant data.

Data Lake Overview

  • A data lake is a storage repository for raw data, including structured, semi-structured, and unstructured data.
  • Data lakes do not require pre-defined schemas.
  • Data lakes are scalable and handle various data types.
  • Data lakes serve as self-service staging areas for machine learning development and advanced analytics.

Data Warehouse Architecture Overview

  • Data warehouse architecture depends on specific use cases, including report generation, exploratory data analysis, automation, and self-service analytics.
  • A general data warehouse architecture includes data sources, a staging area/sandbox, an enterprise data warehouse repository, data marts, and analytics/BI tools.
  • This structure facilitates data extraction, transformation, and loading (ETL) processes and allows linking to fact and dimension tables.

Cubes, Rollups, and Materialized Views and Tables

  • Data cubes are multidimensional data arrangements where coordinates are dimensions, and cells represent facts.
  • Important cube operations include slicing, dicing, drilling up or down, pivoting, and rolling up.
  • Materialized views are pre-computed query results stored in a staging area to provide fast access.
  • Different materialized view refresh options exist, including never, upon request, and immediately.

Facts and Dimensions

  • Facts represent measurable quantities in a business process; examples include sales amounts, rainfall, or temperature.
  • Dimensions are categorical variables that describe and categorize the facts.
  • Dimensions provide context to facts, enabling analysis based on different characteristics. For example, "24°C" temperature alone is unhelpful, but with additional dimensions (e.g., location, time), it becomes more meaningful.

Star and Snowflake Schema

  • Star schemas organize data with a central fact table connected to multiple dimension tables using keys.
  • Snowflake schemas are a normalized version of star schemas, splitting dimension tables into separate child tables for greater flexibility in data management.
  • Modeling process considerations when constructing these schemas include business processes, granularity, facts, and dimensions.

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Data Warehouse and Data Lakes - Overview PDF

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