Matillion ETL: Cloud-Native Data Integration & Pipelines

Create a transformation job with the fixed flow component to insert static data into the target table. Map application and purpose columns, run the job, and verify six rows loaded.

Use the multi table input component to extract data from tables with a common pattern, map columns, cast types, and load into the public target table underscore extract.

Learn to build a transformation job in Matillion ETL to delete 67 rows from a target table where city equals St Petersburg, using key column mapping and table literals.

Use the table update component in Matillion ETL to delete and insert records, update existing records, and map input to the details target table for a large transformation job.

demonstrates an update/insert workflow on a users and score detail table, updating null state values for ten records and inserting remaining records based on a key, and verifying the results.

Explore the rewrite table component to create a new table on job execution, map four columns in a public schema, and verify city sorting in alphabetical order after each run.

Create a standard view in the target database using the public schema and venue table, mapping venue name, city, and state. Verify the view appears with these columns.

Use the calculator component to perform string and mathematical transformations, concatenating first and last names into name, increasing salary by 10%, and appending email domains to the employees data target.

Use the aggregate component in a transformation job to sum salaries and count departments, extracting job id, salary, and department id from employees and loading results into a new table.

Use the direct changes component in Matillion ETL to compare two tables, identify changes, and create a changes indicator column in the output, loading results into a new table.

Create a transformation job using the distinct component to retrieve unique records from the departments table by department id, then load them into a new table named distinct departments.

Create a transformation job using filter component to filter employee's table by salary greater than or equal to 10,000, load 19 records into target table, and verify results.

Build a first/last transformation in a first class component to return the first and last rows per group, ordered by department id, selecting first name, last name, and salary.

Explore lead and lag functions by building a transformation job that computes salary offsets, partitions by department id, and orders by department ascending to output a new table.

Use map values component in a transformation job to map salaries >=10000 to 20000 and department 60 to 40, with default 0, loading results into a new table.

Apply the rank component to partition data by department and order ascending by department ID column, creating output columns rank and row number, then load results into a table.

Use the rename component to align columns by renaming first name to name, dropping last name, and mapping email to email domain during data load from employees to employees data.

Configure a replicate component to extract data from a source and emit to multiple targets, creating an employees replicate table and a calculator view, then run the job.

Learn to use the scale component to write a custom sql query, extract data, join employees with departments, alias names, and load results into a new employees table.

Transpose rows to columns in Matillion ETL, grouping by job and aggregating salary with a delimiter, replacing nulls, and loading the result into a new table.

Explore the except component in Matillion ETL to compare input flows and extract records from the primary departments table not present in the departments detail table, with multi-column checks.

Use the intersect transformation to derive the common, distinct rows from two input flows, creating a departments intersect table based on department id with duplicates removed.

Create a transformation job using a join component to combine employees and departments on department_id, producing a new table with department name and location alongside employee fields.

Create a transformation job that joins the employees, departments, and locations tables using inner joins, defines join conditions, selects and renames columns, and loads the result into a table join_multiple.

Create a self join transformation in Matillion ETL to join the employees table to itself with a left join, derive the manager name, and rename and select output columns.

Learn self join in Matillion ETL by using a single input component, mapping two join components, linking m1.manager to m2.employee_id, removing duplicates, and loading the join table into the schema.

Create a Matillion ETL transformation job to perform a cartesian join between employees and departments, map inputs, and load the results into a new table.

Explore building an orchestration job in Matillion, enabling change data capture, incremental loads, and cluster management across Redshift, Snowflake, and Azure data warehouse.

Learn to build orchestrations in matillion etl by chaining a calculator and a transform job to a view, handle success and failure flows, and use scalar variables.