Range: a data mart is limited to a single focus for one line of business; a data warehouse is typically enterprise-wide and ranges across multiple areas.
Sources: a data mart includes data from just a few sources; a data warehouse stores data from multiple sources.
What is datamart in Snowflake
A data mart is a curated subset of data often generated for analytics and business intelligence users.
Data marts are often created as a repository of pertinent information for a subgroup of workers or a particular use case.
What is the difference between database and Datamart
A database is a transactional data repository (OLTP). A data mart is an analytical data repository (OLAP).
A database captures all the aspects and activities of one subject in particular. A data mart will house data from multiple subjects.
Is data mart normalized or denormalized
A data mart holds highly denormalized data in a summarized form. A data warehouse has large dimensions and integrates data from many sources, which may cause a risk of failure.
A data mart has smaller dimensions to integrate data sets from a smaller number of sources, so there’s less risk of failure.
What is an example of a data lake
There is a gradual academic interest in the concept of data lakes. For example, Personal DataLake at Cardiff University is a new type of data lake which aims at managing big data of individual users by providing a single point of collecting, organizing, and sharing personal data.
What is the difference between data lake and data warehouse
Data lakes and data warehouses are both widely used for storing big data, but they are not interchangeable terms.
A data lake is a vast pool of raw data, the purpose for which is not yet defined.
A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.
What is top down approach in data warehouse
In the “Top-Down” design approach, a data warehouse is described as a subject-oriented, time-variant, non-volatile and integrated data repository for the entire enterprise data from different sources are validated, reformatted and saved in a normalized (up to 3NF) database as the data warehouse.
Which is the most common source of change data in refreshing a data warehouse
Identify the most common source of change data in refreshing a data warehouse. Answer – C) Queryable change data is the most common source of change data in accessing a data warehouse.
What is multidimensional database example
Conceptually, a multidimensional database uses the idea of a data cube to represent the dimensions of data available to a user.
MDBs have three or more dimensions to them, labeled as X, Y and Z dimensions.
This is opposed to databases with two dimensions, which have rows and columns and only use X and Y labels.
What is meant by data vault
Data Vault is a method and architecture for delivering a Data Analytics Service to an enterprise supporting its Business Intelligence, Data Warehousing, Analytics and Data Science requirements.
At the core it is a modern, agile way of designing and building efficient, effective Data Warehouses.
Is a data warehouse normalized or denormalized
Data warehouses often use denormalized or partially denormalized schemas (such as a star schema) to optimize query performance.
OLTP systems often use fully normalized schemas to optimize update/insert/delete performance, and to guarantee data consistency.
What is a subject area Mart
Subject areas within a data warehouse or data mart are physical tables that are grouped together in a dimensional model or star schema that reflect general data or functional categories.
What is top down and bottom up approach in data warehousing
Bottom-up approach: First, the data is extracted from external sources (same as happens in top-down approach).
Then, the data go through the staging area (as explained above) and loaded into data marts instead of datawarehouse.
The data marts are created first and provide reporting capability.
What is data mesh concept
Data mesh is a data platform architecture that allows end-users to easily access important data without transporting it to a data lake or data warehouse and without needing expert data teams to intervene.
What is a metadata example
A simple example of metadata for a document might include a collection of information like the author, file size, the date the document was created, and keywords to describe the document.
Metadata for a music file might include the artist’s name, the album, and the year it was released.
Why is it called a data lake
Data Lake. Pentaho CTO James Dixon has generally been credited with coining the term “data lake”.
He describes a data mart (a subset of a data warehouse) as akin to a bottle of water…”cleansed, packaged and structured for easy consumption” while a data lake is more like a body of water in its natural state.
What are data silos and why are they bad
When data is siloed, the same information is often stored in different databases, leading to inconsistencies between departmental data.
As data ages, it can become less accurate, and therefore, less useful.
How does a data lake work
A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data.
It can store data in its native format and process any variety of it, ignoring size limits.
Is Data Lake OLTP or OLAP
Both data warehouses and data lakes are meant to support Online Analytical Processing (OLAP).
What is an example of OLTP
An OLTP system is a common data processing system in today’s enterprises. Classic examples of OLTP systems are order entry, retail sales, and financial transaction systems.
Who owns data lake
Most data practices are developed around organizational structures: IT owns the data and the data lake itself, while the various line of business data or analytics teams use it.
Is Data Marting more cost effective justify
Cost of Data Marting If detailed data and the data mart exist within the data warehouse, then we would face additional cost to store and manage replicated data.
Note − Data marting is more expensive than aggregations, therefore it should be used as an additional strategy and not as an alternative strategy.
What is the difference between ODS and data lake
An ODS doesn’t require the same kind of transformations. Instead, data remains in its existing schema.
In this sense, ODS is more like a data lake, which uses the schema-on-write approach, although an ODS is much smaller than a data lake (and can only store structured data.)
What’s the meaning of meta data
Definition of metadata : data that provides information about other data.
What is ETL example
As The ETL definition suggests that ETL is nothing but Extract,Transform and loading of the data;This process needs to be used in data warehousing widely.
The simple example of this is managing sales data in shopping mall.
What is an example of OLAP
For example, a user can request that data be analyzed to display a spreadsheet showing all of a company’s beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September and then see a comparison of other product sales in Florida in the same time period.
What is Starnet query model
3.4.1 Starnet Query Model. OLAP is a multidimensional database, and queries in OLAP can be based on the starnet query model.
In a starnet model, radial lines emanate from a central point. Each radial line represents a dimension of the data, and the hierarchy of that dimension is represented along the line.
What is ROLAP model
ROLAP (Relational Online Analytical Processing) uses multidimensional data models to analyze data, and does not require the pre-computation and storage of information.
ROLAP tools access the data in a relational database and generate SQL queries to calculate information.
Why is Snowflake DB so popular
The platform provides fast, flexible, and easy-to-use options for data storage, processing, and analysis.
Initially built on top of the Amazon Web Services (AWS), Snowflake is also available on Google Cloud and Microsoft Azure.
As such, it is considered cloud-agnostic.
What is fact & dimension table
A fact table holds the data to be analyzed, and a dimension table stores data about the ways in which the data in the fact table can be analyzed.
Thus, the fact table consists of two types of columns.
References
https://www.geeksforgeeks.org/difference-between-rolap-and-molap/
https://www.techtarget.com/searchdatamanagement/definition/fact-table
https://www.interviewbit.com/data-warehousing-mcq/
