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14 Datastage Developer Interview Questions (With Example Answers)

It's important to prepare for an interview in order to improve your chances of getting the job. Researching questions beforehand can help you give better answers during the interview. Most interviews will include questions about your personality, qualifications, experience and how well you would fit the job. In this article, we review examples of various datastage developer interview questions and sample answers to some of the most common questions.

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Common Datastage Developer Interview Questions

What is Datastage?

Datastage is a software product used for data integration, extraction, transformation, and loading (ETL). It is important for a Datastage Developer to know this product in order to effectively perform their job duties.

Example: “Datastage is a data warehouse ETL tool that enables users to extract, transform, and load data from a variety of sources into a data warehouse. The tool includes a graphical interface that allows users to easily drag and drop components to create ETL jobs. Datastage also includes a number of built-in operators and functions that can be used to perform various tasks such as data cleansing, data transformation, and data loading.”

What are the different components of Datastage?

There are several reasons an interviewer might ask this question:

1. To gauge the Datastage Developer's level of knowledge and expertise.

2. To get a better understanding of the Datastage Developer's work process and how they approach problem solving.

3. To see if the Datastage Developer is familiar with the different components of Datastage and how they work together.

4. To determine if the Datastage Developer is able to effectively communicate their thoughts and ideas.

It is important for the interviewer to ask this question in order to get a better sense of the Datastage Developer's skillset and abilities. This question also allows the interviewer to see how the Datastage Developer thinks about and approaches problems.

Example: “Datastage has four main components:

1. The Datastage Designer: This is used to design and develop ETL jobs.

2. The Datastage Director: This is used to deploy and schedule ETL jobs.

3. The Datastage Server: This is used to execute ETL jobs.

4. The Datastage Repository: This is used to store information about ETL jobs, such as job definitions and execution logs.”

What are the different stages in a Datastage job?

There are different stages in a Datastage job in order to process and manipulate data in different ways. The stages can be used to extract data from sources, transform data, and load data into targets. The different stages in a Datastage job are important in order to process data efficiently and effectively.

Example: “There are typically four stages in a Datastage job:

1. Extract: This stage is responsible for extracting data from the source system.

2. Transform: This stage is responsible for transforming the data according to the business requirements.

3. Load: This stage is responsible for loading the data into the target system.

4. Validate: This stage is responsible for validating the data to ensure that it meets the business requirements.”

What is a Datastage sequence job?

A datastage sequence job is a type of job that processes data in a sequential order. This is important because it allows the developer to control the order in which the data is processed, and ensure that the data is processed correctly.

Example: “A Datastage sequence job is a type of ETL job that processes data in a sequential order. This means that each stage in the job is processed one after the other, and the output of each stage is used as the input for the next stage. This makes sequence jobs ideal for processing data that needs to be processed in a specific order, such as when you need to perform data cleansing or transformation tasks.”

What is a Datastage parallel job?

There are a few reasons why an interviewer might ask this question:

1. To gauge the candidate's technical knowledge - Datastage parallel jobs are an important part of the product, and so it is important for a developer to understand how they work.

2. To see if the candidate is familiar with the terminology - if the candidate does not know what a parallel job is, they may not be able to effectively communicate with other members of the team.

3. To assess the candidate's problem-solving skills - Datastage parallel jobs can be complex, and so being able to explain how they work shows that the candidate has good analytical and problem-solving skills.

Example: “A Datastage parallel job is a type of data processing job that is designed to run on a parallel processing system. This type of job is typically used for large data sets that need to be processed quickly.”

What is a Datastage server job?

The interviewer is trying to determine if the Datastage Developer is familiar with the product they will be working on. It is important to know the basics of the product you will be working on so that you can be more effective in your role.

Example: “A Datastage server job is a type of Datastage job that runs on a Datastage server. It is used to process data from one or more data sources, and can be either a standalone job or part of a larger Datastage project.”

What are the different types of data sources that can be used in Datastage?

There are a few reasons why an interviewer might ask this question:

1. To gauge the candidate's technical knowledge. Datastage is a complex tool, and being able to identify different types of data sources is a good indication that the candidate knows what they're doing.

2. To see if the candidate is familiar with the company's data sources. If the company uses a lot of different types of data sources, they'll want to make sure that the candidate is familiar with all of them.

3. To determine if the candidate is resourceful. Being able to find and use different types of data sources shows that the candidate is resourceful and can find creative solutions to problems.

Example: “There are four different types of data sources that can be used in Datastage:

1. Relational databases
2. Flat files
3. XML files
4. Web services”

What are the different types of data targets that can be used in Datastage?

There are many different types of data targets that can be used in Datastage, and it is important for the interviewer to know which ones the developer is familiar with. This question will help to gauge the developer's knowledge of the various types of data targets and their capabilities.

Example: “The different types of data targets that can be used in Datastage are:

-Relational databases: These are the most common type of data target, and include databases such as Oracle, SQL Server, DB2, and so on.

-Flat files: These are simple text files that can be used to store data.

-XML files: These are files that store data in an XML format.

-Web services: These are web-based services that can be used to store or retrieve data.”

What are the different types of transformations that can be used in Datastage?

There are many reasons an interviewer might ask this question, but one possibility is to gauge the candidate's knowledge of the tool. Datastage is a powerful data transformation tool, and understanding the different types of transformations available is critical to using it effectively. Additionally, this question can help the interviewer understand how the candidate approaches problem solving with Datastage.

Example: “There are three types of transformations in Datastage:
1. Active Transformation
2. Passive Transformation
3. Hybrid Transformation

Active Transformation: An active transformation can change the number of rows that pass through it. For example, the Filter transformation can remove rows from the data set that do not meet specified criteria.

Passive Transformation: A passive transformation does not change the number of rows that pass through it. All passive transformations are idempotent, which means that they can be applied more than once without changing the data set. For example, the Copy Column transformation creates a new column in the data set that is a copy of an existing column.

Hybrid Transformation: A hybrid transformation is a combination of an active and a passive transformation. For example, the Join transformation combines two data sets into one, and as a result, can change the number of rows in the output data set.”

What is a Lookup transformation?

A Lookup transformation is used to look up data in a table, view, or synonym. The transformation can return all rows from the lookup source, or only rows that meet a specified condition.

The transformation uses an ODBC, DB2, or Oracle data source.

Example: “A Lookup transformation is used to look up data in a table, view, or file and retrieve matching values. The Lookup transformation can look up data in a relational table, view, or flat file. It can also look up data in a cube or dimension table in a SQL Server Analysis Services data source.”

What is a Join transformation?

The interviewer is testing the Datastage Developer's knowledge of the Join transformation. The Join transformation is important because it allows two or more datasets to be combined into a single dataset. This can be useful when you want to combine data from multiple sources, or when you want to perform a join operation on a dataset.

Example: “A Join transformation is used to combine two or more input datasets into a single output dataset. The transformation can be used to perform an inner join, outer join, or left join operation.”

What is a Sort transformation?

A Sort transformation is used to sort data in a specific order. This is important because it allows the developer to control the order in which data is processed. It also allows the developer to ensure that data is processed in a consistent manner.

Example: “A Sort transformation is used to sort data in a data pipeline. The Sort transformation can be used to sort data in ascending or descending order, or to sort data by a specific column or columns.”

What is an Aggregate transformation?

An interviewer might ask "What is an Aggregate transformation?" to a Datastage Developer to gauge their understanding of the tool. It is important to know how to use Aggregate transformations in Datastage because they are commonly used to summarize data.

Example: “The Aggregate transformation is used to perform aggregate operations, such as calculating sums, averages, and counts, on a data set. The transformation can also be used to create new columns based on existing columns in the data set.”

What is a Filter transformation?

A Filter transformation is an important part of data processing in Datastage. It allows the developer to select a subset of data from a larger dataset, based on certain criteria. This can be useful in many situations, such as when you only want to process data for a specific time period, or when you only want to include data that meets certain conditions.

Example: “A Filter transformation is an active transformation that allows you to select rows from a dataset based on specified conditions. The Filter transformation can be used to select rows based on one or more conditions, and can be connected to multiple upstream and downstream transformations.”

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