Talend has grown very fast in the ETL tools segment because of its robustness and unique solutions. Below mentioned are some of the advantages of Talend:

• Faster: Talend not only automates various tasks but also manages them for you.
• Cost-Effective: Talend is an open-source tool and downloading it is absolutely free.
• Future-Oriented: Talend is easily scalable and designed in such a way to meet the present as well as future requirements.
• Unified Platform: Talend comes with multiple features to meet the diversified needs of organizations.

In Talend Studio a ‘Project’ is the highest physical structure where you can store multiple types of data integration jobs, routines, metadata, context variables, and many other technical resources.

A Job in Talend is defined as a basic executable unit of anything that is created using Talend.

To express it in technical terms it is a single Java class that defines the execution and scope of data available with a graphical representation. It translates the business needs into code, programs, and routines.

In Talend, a component is defined as a functional piece that is being used to execute a single operation in Talend. All the operations in Talend are performed by components and connectors. Components in Talend are presented in a palette. Components are easily usable with simple drag and drop features. At the backend, a component is considered as a snippet of java code generated as a part of a job.

Connections play a vital role in Talend. They define whether the data has to be processed, logical sequence of a job, or data output. Different types of connections available in Talend are:

• Iterate: This connection is used to perform a loop on rows contained in a file or on files contained in a directory or on the database entries.
• Link: This connection is responsible for transferring the table schema data to the ELT mapper component.
• Row: It deals with the data flow process in Talend. Below mentioned are the different Row connects supported by Talend:

1. Main
2. Filter
3. ErrorRejects
4. Uniques/Duplicates
5. Lookup
6. Rejects
7. Output
8. Multiple Input/Output

• Trigger: this connection creates a dependency between jobs or sub-jobs.  These jobs and sub-jobs are triggered according to the nature of triggers. Triggers connections categorized into two types:

1. Subjob Triggers:

• OnSubjobError
• OnSubjobOK
• Run if

2.  Component Triggers

• OnComponentError
• OnComponentOK
• Run if

Talend has a user-friendly GUI in which you can easily design a job using drag and drop features. Once the job is executed, this information is automatically translated to java class by Talend Studio. The components presented in a job are categorized into three parts in java such as begin, main and end. This is how Talend got the name as a code generator.

Following are some of the major types of schemas supported by Talend:

• Repository Schema: this schema can be used by multiple numbers of jobs and any changes made to the scheme will automatically reflect the jobs using it.
• Generic Schema: Generic schema particularly not belong to any source and can be used as shared resources across different data sources.
• Fixed Schema:  Fixed schemas are predefined schemas and they come predefined with few components

No, it is not possible to define during run time. Because schemas define data movement so it must be defined when configuring the components.

A subjob is defined as a solo component or combination of multiple components joined together by data-flow. Using context variables you can transfer data from parent job to child job.

The Outline View in Talend Open Studio mainly used for tracking the return values present in a component. These values include user-defined values that are configured in a tSetGlobal.

tMap is one of the major components of ‘Processing’ family in Talend. The major task performed by tMap is to map input data to output data.  Using tMap we can perform below functions.

• Apply transformational rules
• Add or remove columns
• Reject data
• Using contains for filtering input and output data
• Interchange and concatenate data
• Multiplex and demultiplex data.

A scheduler is software that selects processes from the queue and loads them into the memory. Talend does not come with the in-built scheduler.

ETL is an acronym for Extract, Transform and Load. It is associated with three levels and that coordinates to transfer raw data from its source to a business intelligence system, a data warehouse, or a big data platform.

• Extract: in this step, the data can be accessed from the systems like Excel files, RDBMS, flat files, XML files etc.
• Transform: In this step, the data is analyzed and multiple tasks are performed to transform the data into the required format.
• Load: in this area, the data will be loaded into the targeted data storage system with the help of required resources.

Insert or update: In this action, Talend inserts a record, but when a record is found matching with the primary key value then it will update that record.

Update or insert: in this action, Talend updates a record with a matching primary key, if in case it does not found any matching records then the record will be inserted.

Expression Editor allows you to view & edit the various expressions like Var or Output, Input, and constraint statements. Expression Editor allows you to write any transformation or function. You can directly write the expressions required for data transformation in the Expression editor or you can also open an Expression Builder dialogue box in which you can write data transformation expressions.

The Heap Space issue arises when JVM tries to inject excess data into the heap space than the available. This issue can be resolved by changing the memory allocated to the Talend Studio. Here you have to make changes to the Studio .ini configuration file to match your system requirements.

This tXMLMap’ component is used to transform or use data from single or multiple sources to multiple or single destinations. It is an advanced technique used for routing and transforming data flow. It helps in a great way especially when we are required to process numerous XML data sources.

Big data is a very large family and the following are the Big data technologies supported by Talend:

• Cassandra
• Google Storage
• HDFS
• MapRDB
• Pig
• CouchDB
• HBase
• Hive
• MongoDB
• Sqoop etc.

Yes, it is possible to run multiple jobs in parallel. In Talend, multiple jobs and Subjobs in various threads can be executed to minimize the runtime of a Job. Talend offers three-way for parallel processing, which are:

1. tparallelize component
2. Multithreading
3. Automatic parallelization

Once the data validation over the tPigLoad component helps in loading original data to an output stream in a single transaction. It routes a connection to a data source for the present transaction.

Pig Latin is the language used for Pig scripting

In order to connect with HDFS you need to provide the following details:

• NameNode URI
• Distribution
• User name

ETL:  In this process, the data is extracted, transformed and loaded into the targeted database or data warehouse. This process is suitable when multiple data sources are associated with the data warehouse landscape. This process involves transferring data from one place to another place so the best way to accomplish this task is using a separate specialized engine.

ELT: Extract, Load, Transform (ELT) in this the data is extracted and loaded into a targeted system and later it gets transformed.  It provides unstructured data and data lake support as well.

The word MDM abbreviated as a Master Data Management.  Organizations use MDM as a single, accurate, consistent enterprise data manager. MDM gives clear insights into business information and helps in finding the areas where improvement is required. It also helps organizations in strategic planning, achieving operational efficiency, and marketing effectiveness.

tJoin adds two tables by conducting exact matches on various columns. It makes a comparison between the columns from the main flow and columns from the lookup flows and provides the output as the main flow data and/or the data which is rejected.

It is possible to access global and context variables by simply clicking  Ctrl-Space.

The XML allows the users to add multiple input and output flows whenever needed into a visual map editor.

The major difference between the XMX and XMS is that the XMS is used to define the initial heap size in java and the XMS parameter is used to define the maximum heap size in Java.