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Teradata Dba is the administrator who designs, develops and maintain the databases. Databases are based on Teradata, SQL Server, DB2 and Oracle. Teradata can contain huge amounts of data in it. So managing such data is herculean task and dba should keep checking the status of the database. SQL is the key to work with database. One should have good knowledge on working on SQL. Teradata Dba offers many other certifications in various applications. Good number of positions is available for the candidates. Good knowledge on Teradata Dba will boost your confidence. Follow our Wisdomjobs page for Teradata Dba interview questions and answers page to get through your job interview successfully in first attempt. Interview questions are exclusively designed for job seekers to assist them in interviews.
Just like channel driver, Teradata Gateway acts as a medium of communication between the Parse Engine and applications that are attached to network clients. Only one Gateway is assigned per node.
If the teradata is going to be only a data base means It won’t change the System development life cycle (SDLC).
If you are going to use the teradata utilities then it will change the Architecture or SDLC.
If your schema is going to be in 3NF then there won’t be huge in change.
There are 12 codd’s rules applied to the teradata database.
OLTP stands for Online Transaction Processing. OLTP uses normalized tables to quickly record large amounts of transactions while making sure that these updates of data occur in as few places as possible. Consequently OLTP database are designed for recording the daily operations and transactions of a business. E.g. a timecard system that supports a large production environment must record successfully a large number of updates during critical periods like lunch hour, breaks, startup and close of work.
Data in a Database is Detailed or Transactional, Both Readable and Write able and current.
Data in data warehouse is detailed and summarized, storage place for historical data.
The three-tier differs from the two-tier architecture by strictly enforcing a logical separation of the graphical user interface, business logic, and data. The three-tier is widely used for data warehousing today. Organizations that require greater performance and scalability, the three-tier architecture may be more appropriate. In this architecture, data extracted from legacy systems is cleansed, transformed, and stored in high –speed database servers, which are used as the target database for front-end data access.
Analyzing of large volumes of relatively simple data to extract important trends and new, higher level information. For example, a data-mining program might analyze millions of product orders to determine trends among top-spending customers, such as their likelihood to purchase again, or their likelihood to switch to a different vendor.
The difference between real time and near real time can be summed up in one word: latency. Latency is the time lag that is between an activity completion and the completed activity data being available in the data warehouse. In real time, the latency is negligible whereas in near real time the latency is a tangible time frame such as two hours.
An operational data store (ODS) is primarily a “dump” of relevant information from a very small number of systems (often just one) usually with little or no transformation. The benefits are an ad hoc query database, which does not affect the operation of systems required to run the business. ODS’s usually deal with data “raw” and “current” and can answer a limited set of queries as a result.
A data mart is a special purpose subset of enterprise data used by a particular department, function or application. Data marts may have both summary and details data, however, usually the data has been pre aggregated or transformed in some way to better handle the particular type of requests of a specific user community. Data marts are categorized as independent, logical and dependant data marts.
CASE value_expression_1 WHEN value_expression_n THEN scalar_expression_n
Group by those fields and select id, count(*) from table group by id having count (*) > 1.
FALLBACK requests that a second copy of each row inserted into a table be stored on another AMP in the same cluster. This is done when AMP goes down or disk fails.
Sub string is used with char fields. So it cannot be used. To extract month from a date column, ex select extract (month from ). Same thing for year or day. Or hour or minutes if it’s a time stamp (select extract (minute from column name).
No, because they become a single amp operation and my company didn’t encourage that.
Logon tdipid/username, password.
Create table Customerdummy as Customer with data / with no data
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Teradata Parallel Architecture
Fundamental Sql Using Select
On-line Help And Show Commands
Date And Time Processing
Character String Processing
Data Definition Language
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