69 free cheat sheets
Data engineering cheat sheets
One-page references you can print or save as PDF (every sheet has a print button). Bookmark them for interviews and daily work.
SQL & Databases
Query syntax, warehouse SQL dialects, and database patterns.
SQL Window Functions
Ranking, offsets, frames, and the five production patterns — one printable page.
Intermediate5 sections
Snowflake SQL
Time Travel, streams, tasks, warehouses, and VARIANT — one printable page.
Intermediate4 sections
Postgres for Data Engineers
COPY, upserts, partitioning, EXPLAIN, and the catalog queries you need to run Postgres as a serious pipeline source or sink.
Intermediate6 sections
BigQuery SQL
Partitioning, clustering, arrays and structs, cost control, and the BigQuery-specific SQL that saves real money.
Intermediate6 sections
DuckDB SQL
Querying Parquet and CSV directly, friendly SQL shorthand, extensions, and using DuckDB as a local transformation engine.
Beginner5 sections
JSON in SQL (Snowflake, BigQuery, Postgres, DuckDB)
Extracting, flattening, and typing semi-structured JSON across the four engines you actually use, side by side.
Intermediate5 sections
Regex in SQL
Match, extract, and replace with regular expressions across Snowflake, BigQuery, Postgres, and DuckDB — with the dialect quirks.
Intermediate6 sections
SQL Date & Time Functions Across Warehouses
Truncation, intervals, time zones, and date spines in Snowflake, BigQuery, Postgres, and DuckDB — side by side.
Intermediate5 sections
MERGE & Upsert Patterns
MERGE syntax across warehouses, dedup before merge, SCD2 upserts, and the idempotency rules that make reruns safe.
Advanced5 sections
Indexes & Constraints
B-tree, partial, and covering indexes in Postgres, plus how warehouses replace indexes with pruning and clustering.
Intermediate5 sections
Orchestration & Tools
Airflow, dbt, Dagster, and the CLI tools around your pipelines.
dbt CLI
Selectors, state-based CI, testing, and debugging commands — one printable page.
Intermediate5 sections
Airflow CLI & Concepts
TaskFlow patterns, testing commands, backfills, and scheduling gotchas — one printable page.
Intermediate5 sections
Dagster
Software-defined assets, resources, partitions, and sensors — the Dagster model that differs most from Airflow.
Intermediate6 sections
Prefect
Flows, tasks, deployments, work pools, and blocks — Prefect 3 patterns for teams who want plain Python pipelines.
Intermediate6 sections
dbt Tests & Contracts
Generic tests, unit tests, contracts, severity, and store_failures — the dbt quality surface in one page.
Intermediate6 sections
Great Expectations & Soda
Expectation suites, checkpoints, SodaCL checks, and where standalone DQ tools beat dbt tests.
Intermediate6 sections
Kafka CLI
Topic management, console producers and consumers, consumer group lag, offset resets, and config inspection.
Intermediate5 sections
Git for Data Engineers
Branching for dbt and pipeline repos, rebase versus merge, recovering mistakes, and hygiene that keeps CI green.
Beginner6 sections
Docker for Data Engineers
Dockerfiles for Python pipelines, layer caching, compose stacks for local Postgres and Kafka, and image slimming.
Intermediate5 sections
Cloud Platforms
AWS, Azure, GCP, Snowflake, and Databricks platform references.
AWS for data engineers
S3, Glue, Athena, Kinesis, and what to skip on day one.
Intermediate3 sections
Azure & Fabric
ADLS, Data Factory, Databricks, and where Fabric changes the default path.
Intermediate3 sections
GCP for Data Engineers
BigQuery, Dataflow, Pub/Sub, Composer, and Dataplex — services, gcloud commands, and cost levers.
Intermediate6 sections
Snowflake Cortex and Gen AI
COMPLETE, Analyst, Search, embeddings, and token cost for SnowPro Specialty Gen AI.
Intermediate5 sections
Databricks CLI & Unity Catalog
Bundles, jobs, clusters, and Unity Catalog grants — the commands and SQL for running Databricks from code.
Advanced6 sections
S3 Patterns for Data Lakes
Prefix layout, partitioning, file sizing, lifecycle rules, and the CLI commands for operating a lake on S3.
Intermediate6 sections
IAM for Data Teams
Roles versus users, assume-role patterns, least privilege for pipelines, and the policy conditions that pass audits.
Advanced6 sections
Terraform for Data Infrastructure
Managing warehouses, buckets, and Snowflake objects as code — state, modules, and the workflow that avoids drift.
Advanced5 sections
Microsoft Fabric
Capacities, OneLake shortcuts, lakehouse versus warehouse items, and the Fabric concepts that differ from Synapse.
Intermediate6 sections
Snowflake Admin Commands
Warehouses, roles, resource monitors, ACCOUNT_USAGE queries, and the DDL every Snowflake admin runs weekly.
Advanced6 sections
Programming
Python, PySpark, pandas, polars, and testing patterns.
PySpark
DataFrame API, windows, and the knobs that keep jobs from shuffling the world.
Intermediate3 sections
pandas
Reading, selecting, grouping, joining, and reshaping DataFrames, plus the Copy-on-Write and dtype changes that break old code.
Intermediate7 sections
polars
Expressions, lazy scans, group by, joins, and the streaming engine that let one machine handle what used to need a cluster.
Intermediate7 sections
Python Stdlib for Data Engineers
pathlib, itertools, collections, datetime, serialization, and concurrency patterns that replace most one-off pipeline utilities.
Intermediate7 sections
Python Regex
Matching, capture groups, lookaround, flags, and substitution patterns for parsing logs, filenames, and messy source columns.
Intermediate7 sections
pytest for Data Pipelines
Fixtures, parametrization, dataframe assertions, fakes for warehouses and object stores, and the CI settings that keep suites fast.
Advanced7 sections
PySpark Tuning Flags
Adaptive execution, shuffle sizing, join strategy, executor memory, and skew handling — the flags that decide whether a job finishes.
Advanced7 sections
Architecture
Modeling, medallion layers, CDC, streaming, and table formats.
Data modeling
Grain, star schemas, SCD types, and medallion layers — one printable page.
Intermediate4 sections
Streaming Architectures
Lambda versus Kappa, delivery semantics, windowing, watermarks, and the sink patterns that keep streams correct.
Advanced6 sections
Medallion Architecture
Bronze, silver, and gold layer contracts — what belongs in each, naming, testing, and where teams get it wrong.
Intermediate6 sections
Open Table Formats (Iceberg, Delta, Hudi)
Metadata layouts, time travel, schema evolution, catalogs, and maintenance across the three table formats.
Advanced6 sections
CDC Patterns
Log-based versus query-based CDC, Debezium, warehouse-native change tracking, and applying changes idempotently.
Advanced6 sections
Interview Prep
Question-and-answer sheets for interviews and certifications.
Top Snowflake Interview Questions
The most-asked Snowflake questions on architecture, warehouses, Time Travel, cloning, and performance — with expert answers.
Intermediate5 sections
Top SQL Interview Questions for Data Engineers
Joins, window functions, aggregation traps, NULL semantics, and query patterns that come up in nearly every data engineering interview.
Intermediate5 sections
Python Interview Questions
Data structures, generators, the GIL, decorators, typing, and the Python patterns data engineering interviews actually test.
Intermediate6 sections
Spark Interview Questions
Shuffles, skew, partitioning, memory, and Adaptive Query Execution — the Spark answers senior data engineering interviews expect.
Advanced6 sections
Airflow Interview Questions
Scheduling semantics, executors, XComs, backfills, and Airflow 3 changes that orchestration interviews focus on.
Intermediate5 sections
dbt Interview Questions
Materializations, incremental models, tests, snapshots, and project design questions that come up in analytics engineering interviews.
Intermediate6 sections
Kafka Interview Questions
Partitions, consumer groups, delivery semantics, ISR, and the tuning details senior streaming interviews dig into.
Advanced5 sections
Data Modeling Interview Questions
Star schemas, slowly changing dimensions, grain, normalization, and the modeling trade-offs interviewers use to separate levels.
Intermediate5 sections
Databricks Interview Questions
Delta Lake internals, Unity Catalog, cluster and warehouse choices, and lakehouse design questions asked at senior level.
Advanced5 sections
AWS Data Engineer Interview Questions
S3, Glue, Redshift, Kinesis, EMR, and Lambda questions with the service trade-offs AWS data interviews expect you to name.
Intermediate5 sections
Azure Data Engineer Interview Questions
ADLS, Data Factory, Synapse, Fabric, and Event Hubs questions with the service trade-offs Azure data interviews probe.
Intermediate5 sections
GCP Data Engineer Interview Questions
BigQuery, Dataflow, Pub/Sub, Dataproc, and Composer questions with the design trade-offs Google Cloud data interviews expect.
Intermediate5 sections
BigQuery Interview Questions
Storage internals, partitioning, slots, nested data, and cost control answers that BigQuery-focused interviews look for.
Intermediate5 sections
ETL & ELT Interview Questions
Batch versus streaming, incremental loads, idempotency, data quality, and pipeline design fundamentals every data interview covers.
Beginner5 sections
Data Warehouse Interview Questions
Dimensional modeling, slowly changing dimensions, fact grain, loading patterns, and the warehouse design questions interviewers actually ask.
Intermediate6 sections
Data System Design Interview
How to frame, size, and defend a data platform design — ingestion, storage, processing, serving, and the failure modes interviewers push on.
Advanced7 sections
PySpark Coding Questions
Live-coding and whiteboard PySpark questions on transformations, joins, shuffles, skew, windows, UDFs, and Structured Streaming.
Advanced6 sections
SQL Query Optimization Questions
Plans, pruning, join order, window costs, and the rewrites interviewers use to test whether you can actually make a query cheaper.
Advanced6 sections
Behavioral Interview for Data Engineers
STAR answers for pipeline failures, cost incidents, stakeholder conflict, and the ownership stories data interviews actually probe.
Beginner5 sections
Iceberg & Lakehouse Interview Questions
Snapshots, catalogs, compaction, hidden partitioning, and the Iceberg versus Delta questions lakehouse interviews now ask.
Advanced6 sections
Streaming Interview Questions
Delivery semantics, watermarks, state, lag, and the Kappa versus Lambda questions streaming interviews actually ask.
Advanced6 sections
Data Quality Interview Questions
Contracts, tests versus expectations, freshness, reconciliation, and the quality questions interviews use to test production judgment.
Intermediate6 sections
pandas Interview Questions
Indexing, copies, groupby, merges, dtypes, and the pandas performance questions that still show up in data interviews.
Intermediate6 sections
DevOps for Data Interview Questions
CI for dbt, infrastructure as code, secrets, containers, and the platform questions that separate pipeline authors from people who can ship them.
Intermediate6 sections
Senior Data Engineer Interview Questions
Judgment, ownership, and system design for senior loops — the questions that test whether you can be the person other engineers ask.
Advanced6 sections
Best Practices
Production-hardened checklists for the tools you run daily.
Snowflake Best Practices
Warehouse, cost, security, and modeling practices that survive production — plus the defaults you should always change.
Advanced6 sections
dbt Best Practices
Project structure, model design, testing, CI, and performance practices for dbt projects that stay maintainable.
Intermediate6 sections
Airflow Best Practices
DAG design, idempotency, scheduling, resource control, and monitoring practices for Airflow you can be on call for.
Intermediate6 sections
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Cheat sheets give you the quick reference. The articles give you working SQL, diagrams, and the production trade-offs behind each command.
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