ETL vs ELT in 2026: Data Pipeline Architecture Explained
ETL vs ELT comparison for modern data pipelines. Understand the architectural differences, performance trade-offs, and when to use each approach with Snowflake, BigQuery, and dbt in 2026.
Data Engineering
DATA
Comprehensive Data Engineering curriculum covering the entire data production chain. From environment setup with Docker and GCP to pipeline orchestration with Airflow and dbt, through Data Warehouse creation with BigQuery and PostgreSQL. Learn to handle data streaming with PySpark, Pub/Sub and Apache Beam, and deploy to production with Kubernetes and Terraform. Master CI/CD best practices, monitoring and modern data architectures.
What you'll learn
Development environments: Linux, Git, GitHub, VS Code, advanced Python
CI/CD and code quality: Ruff, Pylint, Poetry, GitHub Actions
Containerization with Docker and Docker Compose
APIs with FastAPI: design, deployment, documentation
Data Lake: ingestion, storage, raw data organization
Data Warehouse with BigQuery: schemas, partitioning, optimization
PostgreSQL: setup, administration, comparison with managed solutions
Data ingestion with Fivetran and Airbyte
Transformation with dbt: models, tests, documentation, modularity
Orchestration with Apache Airflow: DAGs, scheduling, monitoring
Big Data with PySpark: large-scale transformations
Data streaming: Google Pub/Sub, Apache Beam, Dataflow
Kubernetes: container deployment, scaling, production clusters
Infrastructure as Code with Terraform
Advanced databases: GraphDB, Document DBs, Wide Column DBs
Logging, monitoring and pipeline observability
Key topics to master
The most important concepts to understand this technology and ace your interviews
1
Linux & Shell: essential commands, bash scripting, permissions, cron jobs
2
Git & GitHub: branching, merge, rebase, pull requests, CI/CD workflows
3
Advanced Python: OOP, decorators, generators, context managers, typing, async/await
4
CI/CD: linting (Ruff, Pylint), packaging (Poetry), tests, GitHub Actions, pipelines
5
Docker: Dockerfile, images, containers, volumes, networks, multi-stage builds
6
Docker Compose: multi-container services, dependencies, healthchecks, local orchestration
7
FastAPI: routes, Pydantic models, dependencies, middleware, deployment
8
Advanced SQL: window functions, CTEs, analytical queries, optimization, indexing
9
BigQuery: serverless architecture, partitioning, clustering, costs, UDFs, federated queries
10
PostgreSQL: configuration, replication, indexing (B-tree, GIN, GiST), VACUUM, EXPLAIN ANALYZE
11
Data Modeling: star schema, fact/dimension tables, normalization, SCD, data vault
12
ELT vs ETL vs ETLT: patterns, trade-offs, architecture choices
13
Fivetran & Airbyte: connectors, sync modes, CDC, schema evolution
14
dbt: models, sources, refs, tests, snapshots, incremental models, Jinja macros
15
Apache Airflow: DAGs, operators, sensors, XCom, connections, pools, task dependencies
16
PySpark: RDD vs DataFrame, transformations, actions, partitioning, broadcast variables
17
Streaming: Pub/Sub (topics, subscriptions), Apache Beam (PCollections, transforms, windowing), Dataflow
18
Kubernetes: pods, deployments, services, ingress, ConfigMaps, Secrets, Helm, scaling
19
Terraform: providers, resources, state, modules, plan/apply, infrastructure as code
20
IAM & security: least privilege principles, service accounts, GCP roles
21
NoSQL databases: GraphDB (Neo4j), Document DBs (MongoDB, Firestore), Wide Column (Cassandra, Bigtable)
22
Data Architecture: Data Lake vs Data Warehouse vs Data Lakehouse, Data Mesh, Data Contracts
23
Monitoring & observability: logging, metrics, alerting, SLA/SLO/SLI, data quality checks
Recent Data Engineering articles
Discover our latest articles and guides on Data Engineering
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Top 25 Data Engineering Interview Questions in 2026
The 25 most asked data engineering interview questions in 2026, covering SQL, data pipelines, ETL/ELT, Spark, Kafka, data modeling, and system design with detailed answers.