What are the basic concepts of data warehousing?
A data warehouse is a system with its own database. It draws data from diverse sources and is designed to support query and analysis. To facilitate data retrieval for analytical processing, we use a special database design technique called a star schema.
What is an integrated data warehouse?
What is Data Warehouse Integration? Data warehouse integration combines data from several sources into a single, unified warehouse. The data warehouse can be accessed by any department within an organization, and the data can be easily structured into spreadsheets or tables for research and analysis purposes.
What is non volatile in data warehouse?
Non-volatile − Non-volatile means the previous data is not erased when new data is added to it. A data warehouse is kept separate from the operational database and therefore frequent changes in operational database is not reflected in the data warehouse.
What is Agile Data Warehousing?
Agile data warehousing is designed to increase speed to value. Starting small with a specific subject area or data source, then building from data ingestion to data visualization allows the business to see measurable results quickly and confirm their vision.
What are the four major components of the data warehousing process?
A typical data warehouse has four main components: a central database, ETL (extract, transform, load) tools, metadata, and access tools.
What are the stages of Datawarehousing?
4 Stages of Data Warehouses
- Stage 1: Offline Database. In their most early stages, many companies have Data Bases.
- Stage 2: Offline Data Warehouse.
- Stage 3: Real-time Data Warehouse.
- Stage 4: Integrated Data Warehouse.
What is OLAP and OLTP?
OLTP and OLAP: The two terms look similar but refer to different kinds of systems. Online transaction processing (OLTP) captures, stores, and processes data from transactions in real time. Online analytical processing (OLAP) uses complex queries to analyze aggregated historical data from OLTP systems.
Is data warehouse volatile or nonvolatile -> explain with example?
Using Data Warehousing, we can create DWH tables. We can get the data from Operational data store (ODS). Data is not volatile and DWH maintains history data. Data changes in particular interval of time.
What is Agile Modelling?
Agile data modeling describes a more simplified provisioning of data models, allowing business users to create their own models. This reduces or eliminates the need for human data engineers to provision data, considerably expediting the data modeling process.
Why data warehouse is less agile with fixed configuration?
Likewise, databases are less agile to configure because of their structured nature. Conversely, a data lake lacks structure. This agility makes it easy for data developers and data scientists to easily configure and reconfigure data models, queries, and applications. The lack of structure keeps non-experts away.)
What are the three main components of data warehouse architecture?
Three-Tier Data Warehouse Architecture It consists of the Top, Middle and Bottom Tier. Bottom Tier: The database of the Datawarehouse servers as the bottom tier. It is usually a relational database system. Data is cleansed, transformed, and loaded into this layer using back-end tools.