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My learningMatalin Interview QuestionsLesson 01
What is Matillion, and what does it mean that it is a _cloud-native, push-down ELT_ tool rather than a traditional ETL tool_

Lesson 01

What is Matillion, and what does it mean that it is a _cloud-native, push-down ELT_ tool rather than a traditional ETL tool_

Matillion is a cloud-native data integration (ELT) platform used to extract, load, and transform data in modern cloud data warehouses like:Snowflake Amazon Redshift Google BigQuery Azure Synapse Analytics. It is primarily used for building analytics data pipelines without heavy infrastructure management.

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Curriculum

20 lessons · 40m

0/20 lessons done40m left
  1. 02:09

    What is Matillion, and what does it mean that it is a _cloud-native, push-down ELT_ tool rather than a traditional ETL tool_

    02:09

  2. 0202:00

    Explain the difference between ETL and ELT, and why Matillion pushes transformation logic down into the data warehouse.

    02:00

  3. 0301:56

    What is the difference between an orchestration pipeline (job) and a transformation pipeline (job) in Matillion_

    01:56

  4. 0401:56

    Which cloud data warehouses does Matillion work with, and why does the choice of warehouse matter for how transformations execute_

    01:56

  5. 0502:03

    You need to extract data from a REST API, land it in Snowflake, then clean and join it. Which pipeline types and components would you use, and in what order_

    02:03

  6. 0601:56

    A transformation pipeline has grown to 40+ components and is hard to read and maintain. How would you restructure it_

    01:56

  7. 0701:56

    You need to load data from an API that has no native Matillion connector. What are your options_

    01:56

  8. 0801:58

    Your source data is semi-structured JSON nested several levels deep. How would you make it queryable as flat relational columns in Matillion_

    01:58

  9. 0902:10

    A single orchestration pipeline needs to run several transformation pipelines, some in parallel and some in sequence. How would you wire this up_

    02:10

  10. 1001:56

    You want to reuse the same load-and-transform logic for 50 different source tables. How would you avoid building 50 near-identical pipelines_

    01:56

  11. 1102:01

    The same pipeline must run against dev, test, and prod warehouses with different schemas and credentials. How would you structure this without duplicating pipelines_

    02:01

  12. 1201:41

    You need a pipeline to behave differently based on a value computed at runtime (e.g., a row count). How would you use variables to control the flow_

    01:41

  13. 1301:47

    Explain the difference between job variables and environment variables, and when you'd use a grid variable.

    01:47

  14. 1402:05

    You need to loop over a list of table names and run the same extract for each one. How would you implement iteration in Matillion_

    02:05

  15. 1502:05

    A variable's value set inside an iterator isn't persisting the way you expect across loop runs. What variable behavior (scope_branch) is likely causing this_

    02:05

  16. 1601:59

    A source table has 500 million rows and you only want to load records changed since the last run. How would you design an incremental load_

    01:59

  17. 1702:06

    You need to implement slowly changing dimension (SCD) Type 2 logic in a Matillion transformation pipeline. How would you approach it_

    02:06

  18. 1801:59

    Your full reload of a large table is slow and expensive every night. What strategies would you use to reduce load time and warehouse cost_

    01:59

  19. 1901:59

    A nightly load partially failed and left the target table in an inconsistent state. How would you make the load idempotent and safely re-runnable_

    01:59

  20. 2001:58

    You need to capture and process only new files arriving in S3_Blob storage. How would you orchestrate this_

    01:58