Top Talend Interview Questions And Answers
Top Answers to Talend Interview Questions
|Distinguishing feature||First Data integration software as a service|
|Deployment||Business modeling, graphical development|
|ETL functionality||Makes ETL mapping faster and simpler for diverse data sources|
Talend stands for Talend Open Studio.
Talend open studio is the open source data integration product produced by Talend and it is designed to convert, combine and update data in various areas across a business.
Talend launched in October 2006
It is written in Java language.
The latest version is 5.6.0
ETL stands for Extract, Transform and Load which is a process that involves gaining data from exterior source, converting it to get fit into operational requirement, then load it into the end target database.
ELT stands for Extract, Load and Transform which is the process in which data is get, then loaded into the staging table in the database and then data is converted according to the need.Read this incisive blog to clearly understand the process of ETL now.
It is a component for mailing correct address belongs to the respective customer data to make sure a single customer view and good delivery for their respective customer mailing.
Yes, we can change the background color of job designer.
We can change the background of job designer by clicking on the preferences of the window menus, after the talend, appearance,designer then click on color menu.
Yes, we can declare a static variable in the rountine and add the setter method for the respective variable in the routine. Then this variable can be accessed from various jobs.
No, we can not save our personal setting in the DQ Portal.
No, this is not possible we cannot generate code directly for Talend.
We can use tJava, tJavaFlex component, tJavaRow, etc to include our own Java code in a Job.
No, in SFTP we cannot use the binary transfer mode because SFTP is not like the FTP. Hence, we cannot apply the concept like ‘current mode directory’ and ‘transfer mode’.
We can use tExternalSortRow and tSortRow.synthesizing sorted input
By default the date pattern is dd-MM-yyyy.
Component is simply a functional piece which is used for a single operation. It is a bundle of file kept within a folder named followed by component name.
Insert or update means first we insert a record, but if a record is matching with the primary key then the record is updated.
Update or insert means first we update the record with same primary and if the record doesn’t exists then we insert the record.
In Built-In we can manually edit the data as data is kept locally in the job whereas in repository all the data is stored there only. We can extract only Read-only-information into the job from repository.
It simply depends on the way we use it. We should use Built-in for the data which we use rarely and use the Repository for the data which we use repeatedly.
They both are trigger links which can connect to another subject job. The major difference between both of them is that they both lies in the execution order of the connected subjobs.
We can normalized the delimited data by clicking on the tNormalized component.
tMap is the latest component which simply converts and routes data from one or many sources to one or many destinations.
TMap supports inner,unique,outer, and all joins.
tDenormalizeSortedRow is bundled in a group of all input sorted rows. It helps in saving the memory by synthesizing sorted input flow.
For transforming the data by utilizing custom we can use tDotNETRow component.
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By exact matching the several columns of tables the tJoin joins the two tables.
It is a management by which an organization makes and manage a single, consistent and correct view of key enterprise data.
The new feature in talend 5.6 is that it have more techical notes. It also have enterprise and open studio solution.
It is highly versatile, cost effective, user friendly and readily adaptable.
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It is the bundle of technical resources and their respective metadata. All the jobs and business items which we designe is known as project only.
It is kind of repository where we can store our folders. It is mandatory to have one workspace repository per connection.
An item is a fundamental technical part in a project. They are bundled according to their types as code,metadata,contex, etc.
It is done to ensure the worth fullness of a project which we have developed with the previous version of Talend.
It allow us to launch the studio more fastly because by using this only the current component is loaded in the project.
It is a function which allow us to create group of set data. They are based on the entry of first name, address,town, etc.
We can replace one element with another in a string by using Change routine along with tJava components.
We can store a string in an alphabetical order by using ALPHA routine with tJava component.
It allow us to take out many operations and test on alphanumeric expressions relay on Java methods.
It allow us to revisit whole or decimal numbers in order to use them as setting in one or more job mechanism.
It shows many information belongs to the open job on the design workspace.
This view is used to arrange a task in a sequence that will launch one by one the job which we select through the crontab program.
It is situated in the bottom half of the design workspace. Every tab open a view which shows the properties of the selected elements in the design workspace.
They are the somewhat complicated Java functions, mostly used to factorize code. It recover Job capacities and optimized data procedure.
With using this we are able to add various input and output flow as needed into the visual map editor to execute.
By clicking Cntrl+ Space key we can access global and contex variable.
This join is a specific type of join which differentiate itself by the way refusal is performed.
data transformation on any type of fields
data multiplexing and demultiplexing,
fields concatenation and interchange,
field filtering using constraints
Talend Open Studio for Data Integration is an open source data integration product developed by Talend and designed to combine, convert and update data in various locations across a business.
Find out more in the Talend Training Videos!
Extract, Transform, and load(ETL) is a process that involves extracting data from outside source, transforming it to fit operational needs (sometimes using staging tables), then loading it into the end target database or data warehouse. This approach is reasonable as long as many different databases are involved in your data warehouse landscape. In this scenario you have to transport data from one place to another anyway, so it’s a legitimate way to do the transformation work in a separate specialized engine.
Extract, Load, Transform(ELT) is a process where data is extracted, then loaded into staging table in the database, transforming it. Where it sits in the database and then loading it into the target database or data warehouse.
This Component is used to correct mailing addresses associated with customer data to ensure a single customer view and better delivery for their customer mailings.
Master Data Management, through which an organization builds and manages a single, consistent, accurate view of key enterprise data, has demonstrated substantial business value including improvements to operational efficiency, marketing effectiveness, strategic planning and regulatory compliance. To data, however, MDM has been the privilege of a relatively small number of large, resource-rich organizations. Thwarted by the prohibitive costs of proprietary MDM software and the great difficulty of building and maintaining an in-house MDM solution, most organization have had to forego MDM despite its clear value.
This technical note highlights the important new features and capabilities of version 5.6 of Talend’s comprehensive suite of Platform, Enterprise and Open Studio solutions.
With version 5.6 Talend:
- Extends it big data leadership position enabling firms to move beyond batch processing and into real-time big data by providing technical previews of the Apache Spark, Apache Spark Streaming and Apache Storm frameworks.
- Enhances its support for the Internet of Things (loT) by introducing support for key loT protocols (MQTT, AMQP) to gather and collect information from machines, sensors, or other devices.
- Improves Big Dta performance: Map Reduce executes on average 24% faster in v5.6 and 53% faster than in v5.4, while Big Data profiling performance is typically 20 times faster in v5.6 compared to v5.5.
- Enables faster updates to MDM data models and provides deeper control of data lineage, more visibility and control.
- Offers further enterprise application connectivity and support by continuing to add to its extensive list of over 800 connectors and components with enhanced support for enterprise applications such as SAP BAPI and Tables, Oracle 12 GoldenGate CDC, Microsoft HDInsight, Marketo and Salesforce.com
Master the latest version of Talend in this Talend Certification training!
Talend is cost-effective, easy to use, readily adaptable and extremely versatile. With the help of the graphical user interface we can easily and quickly link up a large number of source systems using the standard connectors.
Extraction, Transformation and Loading (ETL) processes are critical components for feeding a data warehouse, a business intelligence system, or a big data platform. While mostly invisible to users of a business intelligence platform, an ETL process retrieves data from operational systems and pre-processes it for further analysis by reporting and analytics tools. The accuracy and timeliness of the entire business intelligence platform rely on ETL processes, specifically:
- Extraction of the data from production applications and databases (ERP, CRM, RDBMS, files, etc.)
- Transformation of this data to reconcile it across source systems, perform calculations or string parsing, enrich it with external lookup information, and also match the format required by the target system (third normal form, star schema, slowly changing dimensions, etc.)
- Loading of the resulting data into The business intelligence (BI) applications: Data Warehouse or Enterprise Data Warehouse, Data Marts, Online Analytical Processing (OLAP) applications or “cubes”, etc.
tJoin joins two tables by doing an exact match on several columns. It compares columns from the main flow with reference columns from the lookup flows and outputs the main flow data and/or the rejected data.