Framework ( usually followed ) in ETL Testing

To discuss how testing relates to the different phases of data mart design,  this frame-work includes eight phases:

Requirement analysis:

Requirements are elicited from users and represented either informally by means of proper glossaries or formally.

Analysis and reconciliation:

Data sources are inspected, normalized, and integrated to obtain a reconciled schema,

Oracle Interview Questions - Part-2

26. What is hash cluster?
A row is stored in a hash cluster based on the result of applying a hash function to the row’s cluster key value. All rows with the same hash key value are stores together on disk.

27. When can hash cluster used?
Hash clusters are better choice when a table is often queried with equality queries. For such queries the specified cluster key value is hashed. The resulting hash key value points directly to the area on disk that stores the specified rows.

Oracle Interview Questions - Part-1

1. What are the components of physical database structure of Oracle database?
Oracle database is comprised of three types of files. One or more datafiles, two or more redo log files, and one or more control files.

2. What are the components of logical database structure of Oracle database?
There are tablespaces and database’s schema objects.

3. What is a tablespace?
A database is divided into Logical Storage Unit called tablespaces. A tablespace is used to grouped related logical structures together.

Physical Data Model

              Physical data model represents how the model will be built in the database. A physical database model shows all table structures, including column name, column data type, column constraints, primary key, foreign key, and relationships between tables. Features of a physical data model include:

DataStage Configuration file : Explained - 3

Below is the sample diagram for 1 node and 4 node resource allocation:

 

DataStage Configuration file : Explained - 2

1.    When configuring an MPP, you specify the physical nodes in your system on which the parallel engine will run your parallel jobs. This is called Conductor Node. For other nodes, you do not need to specify the physical node.  Also, You need to copy the (.apt) configuration file only to the nodes from which you start parallel engine applications. It is possible that conductor node is not connected with the high-speed network switches. However, the other nodes are connected to each other using a very high-speed network switches. How do you configure your system so that you will be able to achieve optimized parallelism ??

1.    Make sure that none of the stages are specified to be run on the conductor node.

2.    Use conductor node just to start the execution of parallel job.

3.    Make sure that conductor node is not the part of the default pool.

DataStage Configuration file : Explained - 1

The Datastage configuration file is a master control file (a textfile which sits on the server side) for jobs which describes the parallel system resources and architecture. The configuration file provides hardware configuration for supporting such architectures as SMP (Single machine with multiple CPU , shared memory and disk), Grid , Cluster or MPP (multiple CPU, mulitple nodes and dedicated memory per node). DataStage understands the architecture of the system through this file.

This is one of the biggest strengths of Datastage. For cases in which you have changed your processing configurations, or changed servers or platform, you will never have to worry about it affecting your jobs since  all the jobs depend on this configuration file for execution. Datastage jobs determine which node to run the process on, where to store the temporary data, where to store the dataset data, based on the entries provide in the configuration file. There is a default configuration file available whenever the server is installed.

Data Modeling - Conceptual, Logical, And Physical Data Models

The three level of data modeling, conceptual data model, logical data model, and physical data model, were discussed in prior sections. Here we compare these three types of data models. The table below compares the different features: