FAQ

A database is an organized collection of structured data, or information, that is stored electronically in a computer system. Databases are designed to store, retrieve, and manage data efficiently. 

A data warehouse is a central repository for structured and unstructured data that is extracted from operational systems, transformed, and loaded into a database for analytical purposes. Data warehouses are designed to support business intelligence (BI) and analytics applications. They provide a consolidated view of data from multiple sources, making it easier for analysts to identify trends, patterns, and anomalies.

A data lakehouse is a unified data architecture that combines the flexibility and cost-effectiveness of a data lake with the data management and ACID transactions of a data warehouse. Data lakehouses are designed to store and process all types of data, including structured, semi-structured, and unstructured data. They provide a single platform for data scientists, analysts, and engineers to work with data in a variety of ways.

A data lake is a repository that stores all types of data in its raw format. Data lakes are designed to store large amounts of data in a cost-effective way. They are often used to store data that is not yet well understood or that may not be needed for immediate analysis.

A data hub is a central repository that consolidates data from multiple sources and makes it available for a variety of purposes. Data hubs are designed to provide a single source of truth for data and to make it easier for organizations to access, analyze, and share data.

A data mart is a subset of a data warehouse that is designed to support a specific department or business unit. Data marts are typically smaller and more focused than data warehouses, and they often contain data that is tailored to the specific needs of the user group.

Data fabric: The data fabric unifies data integration, preparation, cataloging, security, and discovery into a cohesive and automated process. It uses metadata, machine learning (ML), and automation to combine data across formats and locations.

Data mesh is a distributed data architecture that breaks down the data warehouse into smaller, domain-specific data products. Data mesh is designed to be more scalable, flexible, and agile than traditional data warehouse architectures.

A data pipeline is a set of processes that move data from one system to another. Data pipelines are typically used to extract data from source systems, transform it into a usable format, and load it into a target system.

Data governance is the process of managing and controlling the availability, usability, integrity, and security of data. Data governance is important for ensuring that data is accurate, reliable, and compliant with regulations.

Data management is the process of collecting, storing, organizing, and using data. Data management is important for ensuring that data is used effectively and efficiently.

Data warehouse automation (DWA) is the use of software and tools to automate the process of building and maintaining a data warehouse. Data warehouse automation can help to improve the efficiency, accuracy, and reliability of data warehouses.

Data modeling is the process of creating a visual representation of data. Data models are used to communicate the structure and relationships of data to stakeholders. They can also be used to design and implement data warehouses, data marts, and other data storage systems.

Business intelligence (BI) is the process of collecting, analyzing, and transforming data into actionable insights. BI can be used to improve decision-making, identify new opportunities, and optimize business processes.

Structured data is data that is organized in a predefined format and can be easily stored and queried. Examples of structured data include data in a database table or spreadsheet.

Unstructured data is data that does not have a predefined format and cannot be easily stored or queried. Examples of unstructured data include text documents, images, and audio files.

Slowly changing dimensions are dimensions in a data warehouse that change over time. There are four main types of slowly changing dimensions:

• Type 1: Overwrite the existing value with the new value.
• Type 2: Add a new row to the dimension table with the new value and a new effective date.
• Type 3: Create a new historical record with the old value and an effective date range.
• Type 4: Merge the old value with the new value into a new record.

A fact table is a table in a data warehouse that stores quantitative data. Fact tables are typically used to store facts about business transactions or events.

A dimension table is a table in a data warehouse that stores descriptive data. Dimension tables are typically used to store information about the entities or categories that are represented in the fact tables.

A data catalog serves as a comprehensive inventory of an organization’s data assets. It provides a centralized repository where data professionals can discover, understand, and access relevant data sources. Key features of a data catalog include:

• Metadata Enrichment: Data catalogs capture metadata about data tables, columns, relationships, and lineage. This contextual information enhances data discovery and promotes collaboration among data users.
• Search and Exploration: Users can search for specific datasets, explore data lineage, and understand data dependencies. A well-organized data catalog simplifies the process of finding relevant data for analysis.
• Business Glossary: A data catalog often includes business-friendly descriptions, data definitions, and terms. This bridges the gap between technical metadata and business context.

A metadata repository is a structured storage mechanism that houses metadata related to data assets. It serves as the backbone of a data warehouse metadata framework. Key functions of a metadata repository include:

• Centralized Storage: A metadata repository consolidates technical, process, and business metadata. It ensures consistency and provides a single source of truth for data-related information.
• Version Control: Metadata repositories maintain historical versions of metadata artifacts. Changes are tracked, allowing teams to understand how metadata evolves over time.
• Data Lineage: By defining relationships between data sources, transformations, and downstream tables, a metadata repository establishes data lineage. This lineage information is critical for impact analysis and understanding data flow.

A Metadata Framework is a set of rules, standards, and guidelines for describing and organizing data within an organization. It defines how data elements are identified, classified, and documented.