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Become a data engineer

The catalogue prepares you for the data engineer role with a path of 18 courses (91 lessons, 304 exercises, about 43 hours), from “Terminal and Command Line” to “Deploying an Application to the Cloud”. 17 catalogue courses list this job among their outcomes.

  1. Terminal and Command Line
  2. Git and GitHub
  3. SQL Fundamentals (SELECT, Filters, Aggregates)
  4. Intermediate SQL (joins, subqueries)
  5. Advanced SQL (CTEs, window functions)
  6. Database Modeling
  7. Python Fundamentals
  8. Intermediate Python (Functions, Modules, OOP)
  9. Data Analysis with pandas and NumPy
  10. Data Warehouses and Analytical Modeling
  11. Data Quality and Governance
  12. Transforming Data with dbt
  13. Orchestrating Pipelines with Airflow
  14. Distributed Processing with Spark
  15. PostgreSQL in Practice (indexes, performance)
  16. Docker and Deployment
  17. Cloud Fundamentals
  18. Deploying an Application to the Cloud
Build my pathSee the 18 courses in order

The steps of the path

  1. Foundations

  2. SQL

  3. Python

  4. Data engineering

  5. SQL

  6. Back-end

  7. Cloud and DevOps

Skills practised

Foundations15

NavigationFile managementSearching with grepPipesBash scriptsPermissionsEnvironment variablesCommitStaging areaHistory (log, diff)BranchesMergeConflictsRemote repository

1 more skills

Pull request

SQL43

SELECT and FROMWHERE clauseORDER BYDISTINCTCOUNT, SUM, AVG, MIN, MAXGROUP BYHAVINGTesting NULL with IS NULLINNER JOINLEFT JOINAnti-joinSelf-joinSous requetesExists

29 more skills

CASE WHENUNION and UNION ALLCTE (WITH)WITH RECURSIVEOVER clausePARTITION BYROW_NUMBER, RANK, DENSE_RANKRunning totalROWS BETWEEN frameLAG and LEADNTILECREATE TABLENOT NULL, UNIQUE, CHECK, DEFAULTFOREIGN KEY / REFERENCESCardinalities1NF, 2NF, 3NFStar schemaViewsSchema migrationsPostgreSQL typesJSONBINSERT ... ON CONFLICTEXPLAIN ANALYZE and BUFFERSB-tree indexComposite indexGIN indexBRIN indexKeyset paginationMVCC, VACUUM and autovacuum

Python48

VariablesBasic typesf-stringsConditionsBooleans and logical operatorsListsfor and while loopsList comprehensionsDictionariesTuplesSetsFunctionstry/except exceptionsAdvanced parameters

34 more skills

Type annotationsLambda functionsHigher-order functionsGeneratorsModules and importsStandard libraryVirtual environments and pipClasses and instancesMethodsSpecial methodsInheritanceDataclassesCustom exceptionsndarray arraysVectorized computationBoolean masksSeriesDataFrameReading CSV filesInitial explorationloc and ilocBoolean filteringSortingComputed columnsRecoding valuesDates with .dtText with .strgroupbyMultiple aggregationsPivot tablesCountsConcatenationJoinsJoin validation

Data engineering71

Data warehouseLakehouseELT and transformation layersDimensional modelingSlowly changing dimensionsPartitioningWarehouse costsFonctions fenetreQuality dimensionsData contractIncident managementData catalogLineageGDPR

57 more skills

Pseudonymization and anonymizationModelSourceThe ref functionJinjaGeneric testsIncremental modelSnapshotsLineage and selectionDAG (directed acyclic graph)TaskFlow APIOperatorsTask dependenciesXComScheduling (cron, presets)Logical dateData intervalCatchup and backfillJinja templatesAutomatic retriesIdempotenceTrigger rulesFailure callbacksExecution timeoutsSensorsPoke and reschedule modesDeferrable operatorsAssets and data-driven schedulingDAG testsMonitoring and alertsArchitecture and executorsConnections, variables, and secretsDeploymentDriver, executors, and clusterSparkSessionSpark DataFrameSchema and file readingColumn functionsLazy evaluationTransformations and actionsNarrow and wide transformationsJobs, stages, and tasksCachingPartitionsHash partitioningrepartition and coalesceDistributed joinsBroadcast joinSpark SQLTemporary views and tablesWindow functionsPartitioned writesJob diagnosisAdaptive query execution (AQE)Skew and saltingPruning and columnar formatsUDFs and pandas UDFs

Back-end9

ContainersImagesDockerfileMulti-stage buildImage securityDocker ComposeImage registryCI/CDReverse proxy and HTTPS

Cloud and DevOps28

Definition of the cloudIaaS, PaaS, SaaSPublic, private, hybrid cloudShared responsibilityRegionsAvailability zonesHigh availabilitySLA and composite availabilityIdentity and access management (IAM)Least privilegeMulti-factor authenticationService identitiesBilling modelsCommitments and interruptible capacity

14 more skills

Outbound data transferCost management (FinOps)Service equivalencesResource organizationChoosing a providerConsole, CLI and APIManaged containersRevisions and trafficManaged databasesBackups and restoreObject storageSecret managerStructured logsAlerts and budgets

Courses that mention this job

Frequently asked questions

How long does the Data engineer path take?

The base path is about 43 hours of lessons and exercises, across 18 courses and 91 lessons. You go at your own pace, lesson by lesson.

Do I need any prior knowledge to start?

No. The path starts with “Terminal and Command Line”, a course with no prerequisites.

Which domains does the path cover?

In order: Foundations, SQL, Python, Data engineering, Back-end, Cloud and DevOps. The last course is “Deploying an Application to the Cloud”.

Does the path lead to a certification?

Not at this stage. The platform does not issue certifications yet, and claims no RNCP registration, no Qualiopi certification and no CPF eligibility.

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Say what you want to be able to do, we build your path

Your path is assembled from the catalogue courses. With the Pro plan, every lesson is told with examples from your job.

The preview is free, and you can take the courses one at a time without paying. The path only contains lessons from the catalogue.