What are the functions of distributed database management system?
The basic function of DDBMS is to keep track of the data distribution, fragmentation and replication by expanding the DDBMS catalog. The basic function of DDBMS is basically its ability to access remote sites and to transmits queries and data among the various sites via a communication network.
What are the features of distributed database?
In general, distributed databases include the following features:
- Location independent.
- Distributed query processing.
- Distributed transaction management.
- Hardware independent.
- Operating system independent.
- Network independent.
- Transaction transparency.
- DBMS independent.
What are advantages and disadvantages of distributed DBMS?
Advantages and Disadvantages of DDBMS
- Data are located near the greatest demand site.
- Faster data access.
- Faster data processing.
- Growth facilitation.
- Improved communications.
- Reduced operating costs.
- User-friendly interface.
- Less danger of a single-point failure.
What are the disadvantages of distributed database?
Disadvantages of distributed database:
- Since the data is accessed from a remote system, performance is reduced.
- Static SQL cannot be used.
- Network traffic is increased in a distributed database.
- Database optimization is difficult in a distributed database.
What is are the main advantages of distributed systems?
All the nodes in the distributed system are connected to each other. More nodes can easily be added to the distributed system i.e. it can be scaled as required. Failure of one node does not lead to the failure of the entire distributed system.
What is the advantage of denormalization?
Denormalization can improve performance by: Minimizing the need for joins. Precomputing aggregate values, that is, computing them at data modification time, rather than at select time. Reducing the number of tables, in some cases.
What are the advantages and disadvantages of denormalization?
Repeating a single detail with its master
| Advantages | Disadvantages |
|---|---|
| No need to create joins for queries that need a single record | Data inconsistencies are possible as a record value must be repeated |
What is denormalization with example?
Denormalization is a database optimization technique in which we add redundant data to one or more tables. For example, in a normalized database, we might have a Courses table and a Teachers table. Each entry in Courses would store the teacherID for a Course but not the teacherName.
Are fact tables normalized or denormalized?
Fact tables are completely normalized To get the textual information about a transaction (each record in the fact table), you have to join the fact table with the dimension table. Some say that fact table is in denormalized structure as it might contain the duplicate foreign keys.
What are the advantages of snowflake schema?
Benefits of the Snowflake Schema
- Uses less disk space because data is normalized and there is minimal data redundancy.
- Offers protection from data integrity issues.
- Maintenance is simple due to a smaller risk of data integrity violations and low level of data redundancy.
What is a snowflake schema in data warehousing?
In computing, a snowflake schema is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake shape. The snowflake schema is represented by centralized fact tables which are connected to multiple dimensions..
Can a star schema have multiple fact tables?
Although the diagram in this chapter shows a single fact table, a star schema can have multiple fact tables. A more complex schema with multiple fact tables is useful when you need to keep separate sets of measurements that share a common set of dimension tables.
Can a snowflake schema have more than one fact table?
The snowflake schema is represented by centralized fact tables which are connected to multiple dimensions. However, in the snowflake schema, dimensions are normalized into multiple related tables, whereas the star schema’s dimensions are normalized with each dimension represented by a single table.
Can you join two fact tables?
The answer for both is “Yes, you can”, but then also “No, you shouldn’t”. Joining fact tables is a big no-no for four main reasons: 1. Fact tables tend to have several keys (FK), and each join scenario will require the use of different keys.
Why do we use star schema?
In computing, the star schema is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. The star schema consists of one or more fact tables referencing any number of dimension tables.
What is the best alternative to star schema?
This makes the snowflake schema a better choice than the star schema if you want your data warehouse schema to be normalized . However, complex joins mean that the performance of the snowflake schema is generally worse than the star schema.
How do you implement star schema?
Steps in designing Star Schema:
- Identify a business process for analysis(like sales).
- Identify measures or facts (sales dollar).
- Identify dimensions for facts(product dimension, location dimension, time dimension, organization dimension).
- List the columns that describe each dimension.
How many dimensions can a star schema have?
four dimensions