SAS management console application provides a single user interface for performing SAS administrative tasks.
Physical Data Integration is all about creating new system that replicates data from the source systems. This process is done to manage the data independent of the original system. Data Warehouse is the example of Physical Data Integration. The benefits of PDI include data version management, combination of data from various sources, like mainframes, flat files, databases.
Staging area of the data warehouse is both the storage area and set of process commonly referred as extract transformation load. The data staging area is everything between the operational source systems and the data presentation area.
NO_CACHE – It is used for not caching values.
PRE_LOAD_CACHE – Result column preloads and compares the column into the memory, prior to executing the lookup.
PRE_LOAD_CACHE is used when the table can exactly fit in the memory space.
DEMAND_LOAD_CACHE – Result column loads and compares the column into the memory when a function performs the execution.
DEMAND_LOAD_CACHE is suitable while looking up the highly repetitive values with small subset of data.
Change analysis is the process of comparing one set of metadata to another set of metadata and identifying the differences between the two sets of metadata.
Browser-based analysis and reporting capabilities are provided by Metadata reports.
The DI Metadata Reports are generated on metadata that associates with Data Integration jobs.
Other BO applications those are associated with Data Integration.
Three modules are provided by Metadata Reports. They are:
Table that describes the relationships between two or more tables. For example, an intersection table could describe the many-to-many relationships between a table of users and a table of groups.
SAS application server provides SAS services to a client. On the other hand database server provides relational database service to a client. Oracle, DB2, and Teradata are examples of relational databases. SAS OLAP server provides access to multidimensional data. SAS metadata server provides metadata management services to one or more client application.
It is the access by selected business users to raw (untransformed) data loads.
Companies are realizing that in order to succeed they need an integrated view of their data and SAS Data Integration Studio is the single tool that provides the flexibility, reliability and agility needed to respond to new data integration challenges. Regardless of the project, SAS Data Integration Studio users can respond with speed and efficiency, reducing the overall cost of data integration.
Star schema is defined as database in which single fact table is connected to multiple dimension tables. This is represented in a star schema.
Following are the benefits of data integration:
It is the robust, reliable, repeatable and controlled process both at point of input and through subsequent downstream control checks. This process exists to manage updates of business rules to maintain a level of consistency.
Operational data is used as source data for a data warehouse. While operational system is one or more programs that provide source data for a data warehouse.
SAS Data Integration Studio empowers data integration managers and designers to work more efficiently, manage change effectively and deliver high-quality results faster.
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It is a metadata object which determines how to extract data, transform data and load data into data stores.
It is a set of attributes that describe a table, a server, a user and another resource on a network.
This is the technique for tracking changes to dimensional table values in order to analyze trends. For example, a dimension table named customers might have columns for customer id, home address and income. Each time the address or income changes for a customer, a new row could be created for that customer in the dimensional table and old row could be retained.
The scheduler used for scheduling job is control m while CONTROL-m also user to view process flow and dependencies so that they can optimize business processes easily and efficiently, even in a data center that includes multiple platform types (for example, Unix, Microsoft Windows, and MVS)
Snowflake schema is defined in which a single fact table is connected to multiple dimension tables. The dimension are structured to minimize update anomalies and to address single themes.
Types of the data transformation are append, apply lookup standardization, create match code transformation, data transfer, data validation, extract, fact table lookup, key effective data transformation, lookup, SAS rank, SAS sort, SAS splitter, SCD type 2 loader, SQL join, standardize transformation, Surrogate key generator , Transpose transformation, User written code transformation.
Following are the ways to perform this:
Unique key is one or more columns that can be used to uniquely identify a row in a table. A table can have one or more unique keys. Unique keys can contain null values. While on the other hand table can have only one primary key. One or more columns in a primary key cannot contain null values.
Change analysis is the process of comparing one set of metadata to another set of metadata and identifying the differences between the two sets of metadata.