Informatica IICS for Data Engineers: Cloud Integration & ETL

Create an Oracle database connection in IICS for the HR schema, with host, service name xc, and code page Windows latin. Test the connection and save with advanced security options.

Create a PostgreSQL connection in Informatica Cloud Data Integration using an ODBC data source on a Windows machine with the secured agent, test, and save.

Create a new flat file connection by naming it flat file, selecting flat file as type, configuring the Windows directory, date format, and code page, then test the connection.

Learn how to connect to an AWS S3 bucket called Employees Data using Informatica Cloud Data Integration, create an S3 connection named AWS_S3, and configure access key and secret key.

Learn how to load data from Oracle to Postgres by creating a mapping, configuring source and target, mapping fields, and running the mapping to load 107 rows.

Join multiple objects from an Oracle HR source to combine employees, departments, and locations, define join criteria and relationships, map fields to a PostgreSQL target, and load 106 rows.

Configure source filters in Informatica Cloud Data Integration by choosing non-parameterized or parameterized conditions and using advanced expressions with system variables to filter salary greater than or equal to 20,000.

Sort the dataset at the source level by salary using the source query options, add a sort column for employees, and confirm the target loads in ascending salary order.

Apply a source level pre sql to update commissions_pct to zero where null in the Oracle table employees_iix before loading into the target.

Update commission_pct from null to zero during data consumption, then revert to null after consumption; ensure commissions_pkt becomes zero in the target Postgres while Oracle reflects null.

Apply sql override to increment salaries by 10 percent during data load from source to target. Verify results by checking employee 100's salary shows the target value increased.

Design and run a target update flow in Informatica Cloud Data Integration (CDI) to update employee 100's salary from 24,000 to 30,000 in Oracle and Postgres.

Perform upsert to update existing records or insert new ones in a Postgres target; update employee 100 salary from 24,000 to 30,000 and insert new employee 207.

Learn how to configure and execute a target delete operation in a data integration flow using Informatica Cloud Data Integration, deleting records by primary key in a Postgres target.

Validate job execution properties by examining task name, instance ID, start/end times, duration, runtime environment, and row counts. Use the session log to trace the process flow and errors.

Apply expression transformation to perform calculations, string operations, and type conversions while loading a flat file into Postgres, concatenating names, building emails, converting hire date, and increasing salaries by 10%.

Use the filter transformation to apply inclusion and exclusion rules, passing records where salary is greater than or equal to 20,000 with simple, advanced, or parameterized conditions, validating results.

Route Oracle employees data through a router transformation to three PostgreSQL targets: higher salary, average salary, and lower salary, using salary ranges and a default group.

Learn how the sorter in Informatica Cloud Data Integration performs sorted transformations, sorting records by salary in descending order, with options for null handling, distinct values, and transformation scope.

Create an aggregator transformation to group by department_id and compute sum, count, max, min, and average salary, mapping Oracle HR to PostgreSQL and producing 12 records from 107 source records.

Explore the joiner transformation by joining employees and departments from Oracle HR to load department name into PostgreSQL, using master-detail mapping and inner and outer join options.

Learn how to join more than two sources in Informatica cloud data integration by chaining multiple joiner transformations. Prefix the master source and map the state to the target.

Explore join types in Informatica cloud data integration, including master, detail, and full outer join, using join conditions to see how records match and where nulls appear.

Learn connected lookup transformations in Informatica Cloud Data Integration to join Oracle HR with PostgreSQL, manage caches, resolve field conflicts with prefixes, and return the first row for multiple matches.

Use an unconnected lookup to join employees with departments by department id and return the department name via an expression transformation.

Practice union transformation by uniting three PostgreSQL sources: higher salary, lower salary, and average salary, into a single employees_underscore_union target with auto-mapped fields and a successful 107-record run.

Explore sequence transformation to generate per-record values, share a sequence across the flow, and configure increment, end value, initial value, cycle behavior, and generated next and current values.

Learn to apply the rank transformation in Informatica Cloud Data Integration, ranking records by salary to yield top ten, bottom ten, and top three per job ID using group by.

Discover how SCD Type 1 in Informatica Cloud (IICS) upserts employee data from Oracle to Postgres, overwriting titles and salaries without keeping history, and concatenating names for the target.

demonstrate type 1 SCD with a lookup transformation by building a mapping from Oracle employees to a Postgres table, showing insert and update paths and record added and updated dates.

Understand SCD type 2 and how it preserves information using start and end dates with active flags. Implement with versioning and lookup logic to insert records and deactivate old ones.

Build and manage SCD type 2 versioning logic in iics by using lookups and queries to track employee id, job title, and salary, updating record versions to maintain history.

Explore SCD type three, maintaining current and previous job titles and salaries, with column and row representations, using a mapping to insert and update records.

learn how to create and schedule a mapping task in informatica cloud iics, configure runtime options, parameters, and pushdown optimization, and monitor data loaded from source to target.

Create and configure a synchronization task to move data from Oracle HR to PostgreSQL for the employees table, with salary filters and mapping options, including insert, update, upsert, or delete.

Orchestrate a linear task flow that runs 1-to-1 mappings loading Oracle data to Postgres for employees, departments, and jobs in predefined order, with stop on error and email notifications.

Build a data integration task in a task flow from scratch or using templates, then sequence employees, departments, and jobs with publish, run, and error handling.

Configure a file watch task by creating a file listener, setting the folder path and file pattern, and enabling arrival or update notifications. Test with flow_trigger and monitor failure notifications.

Design parallel task flows in Informatica Cloud - IICS by building task flow tf underscore parallel that runs employees and employees info in parallel, then departments and jobs in sequence.

Create a decision task to govern a task flow across employees info, departments, and jobs, triggering employees then departments and jobs when zero records exist, otherwise departments and jobs.

Discover how the jump task iterates the flow via a decision on employees info, proceeding to departments and jobs when success source rho is not equal to zero.

Create a wait task in a data integration workflow named TF_weight. Set a wait period in seconds, minutes, hours, or days, and add a 10-second delay between employees and departments.

Create a data integration task flow with a through task that throws an exception based on a defined condition; if employees info is nonzero, execute departments and jobs.

Configure a notification task within a data integration flow, choose plain text or HTML emails, publish the task, and trigger email notifications with row counts and timing details.

Define and manage runtime parameters in Informatica Cloud, distinguishing input and inout parameters, and assign their types in the parameters panel or per transformation, including connection types.

Define input parameters and create a mapping from Oracle employees to Postgres employees. Apply an expression that adds 10% to salary with runtime overrides for the salary percentage.

Access and manage parameters dynamically from a file in Informatica Cloud by configuring parameter file location, directory, and file name, with global parameters usable across tasks and mappings.

Explore in-out parameters and variables in Informatica Cloud. Learn to use set variable, set max variable, and set min variable with count, max, and min aggregations.

Demonstrates using set count variable to increment an employee order value across runs, persisting the last counter in an integer out parameter, and applying it in a mapping task.

Use set max variable to capture the maximum value of a field during each execution. Update the higher year parameter (integer, default 2011) to the latest max during incremental load.

Learn how to pass in-out parameters between tasks in Informatica Cloud by creating two mappings that separate employees by salary and move data from Oracle to Postgres.

Parse unstructured data with the hierarchy parser transformation by creating a hierarchical schema from a JSON file, mapping all fields to the employees unstructured target, and processing 107 rows.

Parse an xml file using a hierarchy parser transformation, create a hierarchical schema, map source xml fields to a Postgres employees table, and run the mapping to process 107 rows.

Master JSON parsing with structured parser in Informatica Cloud, building an intelligent structure model, discovering JSON structure, mapping fields to a relational table, and running the parser to output rows.

Use the structure parser transformation to parse XML, JSON, PDF, Excel, COBOL, Avro, and parquet files by creating an intelligent structure model, then map XML data to a relational output.