Matillion Masterclass

Matillion is a cloud-native ETL tool that extracts, transforms, and loads data into a cloud data warehouse, enabling efficient data integration for business intelligence and analysis.

Compare on-premise etl with cloud elt, as Matillion demonstrates, highlighting elastic storage and scalable compute, and transforming data directly in the warehouse via sql for faster, flexible analytics.

Explore Matillion's product offerings: deploy via marketplace on your cloud account with a secure ETL environment, or use the data productivity cloud for Snowflake ETL with a 14-day free trial.

Learn how pushdown optimization makes Matillion leverage cloud data warehouses to run transformed sql directly in Snowflake, BigQuery, or Redshift, boosting performance, reducing cost, and enabling scalable workloads.

Set up a free trial cloud account for the Matillion Masterclass, using Google Cloud or other providers, and configure payment methods to start using cloud services.

Learn how to deploy Matillion from the Google Cloud marketplace, create service account and keys, configure BigQuery access, and launch Matillion ETL for BigQuery large with a 14 day trial.

Learn to set up Matillion from the data productivity cloud, access a 14-day free trial with MIA AI assistance, and connect Snowflake to run your first project.

Configure Matillion with Snowflake by setting the default role, warehouse, database, and schema, then open the canvas to create and name orchestration or transformation pipelines.

Discover the Matillion user interface with Maya AI, learn orchestration and transformation pipelines, and manage runs, validation, scheduling, and credits across Snowflake connections.

Explore building your first etl process in the Matillion masterclass by using an sql script to create and transform a sales_raw table, then produce a transformed sales table.

Master how transformation jobs change data formats, clean and standardize values, perform aggregations and feature engineering, while orchestration jobs coordinate data loads and workflow sequencing inside Matillion's engine.

Learn how to run a transformation after an orchestration in a Matillion Masterclass data pipeline, using run orchestration and run transformation to sequence steps.

Connect your cloud data sources and databases to Matillion to enable ETL workflows. Upload data to a cloud database first, since Matillion cannot ingest local CSV, JSON, or Excel files.

Connect Google Cloud with Matillion by creating cloud credentials and pasting the service account JSON key; then associate the credentials to enable loading Google Cloud data into Snowflake.

connect snowflake with google cloud and matillion by creating a storage integration, configuring gcs access, and testing the connection to ensure snowflake, google cloud, and matillion are linked.

Create a Google Sheet API profile in Matillion to securely connect to Google Sheets using zero auth 2.0, configure the spreadsheet, and load sales data into Salesforce.

Explore the database query component in Matillion, a data staging orchestration tool that runs SQL against a source database (cloud or on-premise) and writes results to a destination table.

Explore data extraction, loading, and transformation fundamentals, handling diverse formats from regional customer, product, and sales data, and learn to load and join data across Google Cloud storage.

Create a Matillion orchestration to load a tab-delimited text file into Snowflake, building a customer data central table with defined columns and a Google Cloud Storage load.

Load excel files in matillion with the excel query component, select sheet1 data, map key columns, and create the CustomerData East table loaded with 220 rows.

Learn to load JSON files into Matillion, handling nested structures, by using the JSON load or Google Cloud load, then convert a single variant column into multiple columns.

Learn to use the file iterator to load multiple csv files from a Google Cloud Storage folder named CustomerDataWest into a single CustomerDataWest table, with a current file variable.

Explore how Matillion implements SQL logic through a visual drag-and-drop ETL workflow that translates components into an optimized SQL query and runs in Snowflake.

Explore how Matillion's filter and distinct components remove unwanted rows and duplicates, using conditions and/or logic, with examples on ID, order line, and product ID.

Learn how the Matillion join component creates unified views by combining sales and products data with an inner join on product ID, mapping columns to the relevant fields.

Combine identical regional tables with union in Matillion to create a single combined customer dataset. Rename mismatched columns and manage duplicates to ensure a clean, aligned schema.

Explore data aggregation and group by operations to combine raw data into a consolidated dataset, using sum to compute total sales by ship mode and identify top performers.

Learn practical data cleaning and validation techniques by casting data types, handling missing values, mapping to reference values, detecting data changes, fixing flow components, and using the rename component.

Learn how data type conversion in Matillion supports extract, transform and load by changing a column from integer to varchar, preventing improper aggregation and ensuring compatibility with the data warehouse.

Learn to identify and treat missing values in Matillion with filter components, handling nulls and null or blank, and replacing or dropping data, plus using the calculator for SQL.

Map country variants with the value mapper in matillion to unify USA, United States, and United States of America, then rename the column to country updated.

Learn to clean the city column in matillion by applying a regex replace in a calculator step to remove hash values and non-letter characters, then rename city to city updated.

Identify changes by comparing data to master table, enabling incremental loading and updating only new, updated, or deleted records, with unchanged rows preserved for data warehousing and slowly changing dimensions.

Explore variables in Matillion data productivity cloud, including project and pipeline variables, to avoid hard coded values, enable dynamic default values that vary by environment, and support pipeline overrides.

Learn how to implement flow control with the if component to conditionally run a transformation in an orchestration pipeline, based on a row count check and true or false outcomes.

Explore the loop iterator in flow control by looping a SQL script to load data and run transformation when the row count exceeds 10, using a batch number variable.

Matillion masterclass teaches how retry logic automatically retries failed components until they succeed, improving pipeline reliability with immediate, short, long delays and back off strategies to handle transient issues.

Configure error handling and alerting in matillion orchestration by choosing on success, on failure, or unconditional flows, and set up email alerts with SMTP credentials and Gmail app passwords.

Create an error log table and log failures by inserting a row with the timestamp, pipeline name, and error message on each component failure.

Explore webhook integration for real-time, event-driven alerts from Matillion to Slack or Teams via a unique URL and JSON payloads, with no polling and instant notifications.

Schedule Matillion pipelines to run automatically at predefined times, ensuring timely data for dashboards while enabling off-peak processing and reducing manual effort.

Master user management in Matillion by creating and controlling access to projects and environments to protect data and pipelines through admin, developer, operator, and viewer roles.