 |
| Msbi Training Hyderabad |
SQL Server Analysis Services unified and integrated the view of all business data. The foundation for all traditional reporting, online analytical scorecards, and data mining.
SSAS 2008 Analysis Services
The basic idea of OLAP is simple. The catch is that it might take a long time for SQL Server ton through that many rows of data.
The data was not all on a single SQL Server table. But around in various databases throughout your organization. The customer an Oracle database and supplier information in a legacy xBase database. SQL Server can handle distributed queries, but they're slower.
If after seeing the monthly numbers wanted to drill down to weekly or daily numbers. That would be even more time-consuming and must write queries.
This is where OLAP comes in. The basic idea is to trade off increased storage space now for the speed of querying later. OLAP does this by precalculating and storing aggregates. When identify the data want to store in an OLAP database. Analysis Services analyzes in advance daily, weekly, and monthly numbers and stores. This takes up plenty of disk space, but it means that when you want to explore the data.
Analysis Services to extract summary information from data. First, though, you need to familiarize yourself with a new vocabulary.
1. Cube
2. Dimension table
3. Dimension
4.Hierarchy
5.Level
6.Fact table
7.Measure
8.Schema
1. Cube
The basic unit of storage and analysis in Analysis Services is the cube. A cube is a collection of data to allow queries to return data. A cube of order data might add by time. The cube facts on orders by week or orders by title.
Cubes ordered into dimensions and measures. The data for a cube comes from a set of tables, sometimes called a star-schema database. Dimensions in the cube come from dimension tables in the database. While measures come from fact tables in the database.
2. Dimension table
A dimension table lives in the staging database and contains data. That you'd like to use to group the values you are summarizing. Dimension tables contain a primary key and any other attributes. Those describe the entities stored in the table. Customers table that contains city, state and postal code information to analyze sales. A Products table contains categories and product lines to break down sales figures.
3. Dimension
Each cube has one or more dimensions, each based on one or more dimension tables. A dimension represents a category for analyzing business data. Time or category in the examples above. A dimension has a natural hierarchy so that lower results can be "rolled up" into higher results. In a geographical level city totals aggregated into state totals. State totals into country totals.
4. Hierarchy
A hierarchy can be best visualized as a node tree. A company's organizational chart is an example of a hierarchy. Each dimension can contain many hierarchies. Some of them are natural hierarchies others are navigational hierarchies.
5. Level
Each layer in a hierarchy called a level. Speak of a week level or a month level in a fiscal time hierarchy. A city level or a country level in a geography hierarchy.
6. Fact table
A fact table lives in the staging database and contains the basic information. This order detail information, payroll records, drug effectiveness information. Anything else that's amenable to summing and averaging. Any table used with a Sum of Avg function in the query a fact table. The fact tables contain fields for the individual facts. Foreign key fields relating the facts to the dimension tables.
7. Measure
Every cube will contain one or more measures, each based on a column in a fact table that likes to analyze. The cube of book order information measures such as unit sales and profit.
8. Schema
Fact tables and dimension tables related. This is hardly surprising. Those use the dimension tables to group information from the fact table. The relations cubes form a schema. There are two basic OLAP schemas star and snowflake. In a star schema, every dimension table related to the fact table. In a snowflake schema, some dimension tables related to the fact table. If cube includes Order Details as a fact table. Customers and Orders as dimension tables and Customers related to Orders. Turn related to Order Details, and then you're dealing with a snowflake schema.
SSAS 2008 Analysis Services
The basic idea of OLAP is simple. The catch is that it might take a long time for SQL Server ton through that many rows of data.
The data was not all on a single SQL Server table. But around in various databases throughout your organization. The customer an Oracle database and supplier information in a legacy xBase database. SQL Server can handle distributed queries, but they're slower.
If after seeing the monthly numbers wanted to drill down to weekly or daily numbers. That would be even more time-consuming and must write queries.
This is where OLAP comes in. The basic idea is to trade off increased storage space now for the speed of querying later. OLAP does this by precalculating and storing aggregates. When identify the data want to store in an OLAP database. Analysis Services analyzes in advance daily, weekly, and monthly numbers and stores. This takes up plenty of disk space, but it means that when you want to explore the data.
Analysis Services to extract summary information from data. First, though, you need to familiarize yourself with a new vocabulary.
1. Cube
2. Dimension table
3. Dimension
4.Hierarchy
5.Level
6.Fact table
7.Measure
8.Schema
1. Cube
The basic unit of storage and analysis in Analysis Services is the cube. A cube is a collection of data to allow queries to return data. A cube of order data might add by time. The cube facts on orders by week or orders by title.
Cubes ordered into dimensions and measures. The data for a cube comes from a set of tables, sometimes called a star-schema database. Dimensions in the cube come from dimension tables in the database. While measures come from fact tables in the database.
2. Dimension table
A dimension table lives in the staging database and contains data. That you'd like to use to group the values you are summarizing. Dimension tables contain a primary key and any other attributes. Those describe the entities stored in the table. Customers table that contains city, state and postal code information to analyze sales. A Products table contains categories and product lines to break down sales figures.
3. Dimension
Each cube has one or more dimensions, each based on one or more dimension tables. A dimension represents a category for analyzing business data. Time or category in the examples above. A dimension has a natural hierarchy so that lower results can be "rolled up" into higher results. In a geographical level city totals aggregated into state totals. State totals into country totals.
4. Hierarchy
A hierarchy can be best visualized as a node tree. A company's organizational chart is an example of a hierarchy. Each dimension can contain many hierarchies. Some of them are natural hierarchies others are navigational hierarchies.
5. Level
Each layer in a hierarchy called a level. Speak of a week level or a month level in a fiscal time hierarchy. A city level or a country level in a geography hierarchy.
6. Fact table
A fact table lives in the staging database and contains the basic information. This order detail information, payroll records, drug effectiveness information. Anything else that's amenable to summing and averaging. Any table used with a Sum of Avg function in the query a fact table. The fact tables contain fields for the individual facts. Foreign key fields relating the facts to the dimension tables.
7. Measure
Every cube will contain one or more measures, each based on a column in a fact table that likes to analyze. The cube of book order information measures such as unit sales and profit.
8. Schema
Fact tables and dimension tables related. This is hardly surprising. Those use the dimension tables to group information from the fact table. The relations cubes form a schema. There are two basic OLAP schemas star and snowflake. In a star schema, every dimension table related to the fact table. In a snowflake schema, some dimension tables related to the fact table. If cube includes Order Details as a fact table. Customers and Orders as dimension tables and Customers related to Orders. Turn related to Order Details, and then you're dealing with a snowflake schema.
Sign up here with your email
2 comments
Write commentswhat is microsoft azure
Replyazure free trial
azure adf
azure data factory interview questions
azure certification path
azure traffic manager
ConversionConversion EmoticonEmoticon