Design data pipelines on an interactive canvas with instant feedback on component selection, cost efficiency, and fault tolerance. Practice ETL vs ELT patterns, batch processing vs stream processing, Apache Spark, PySpark, and Apache Kafka. The only platform with interactive data pipeline architecture design practice.
Covers PySpark interview questions, Spark interview questions, Kafka interview questions, and all core data pipeline design patterns. Adaptive complexity from simple batch to multi-source streaming with exactly-once semantics.
Build data pipeline architectures visually. Add ingestion, processing, storage, and serving components. Define data flows, select tools, and specify processing semantics. The system evaluates your architecture against optimal designs.
Every tool selection is evaluated. Choose Kafka for ingestion? The system checks if your throughput requirements justify it. Choose batch when streaming was needed? It flags the SLA violation.
ETL vs ELT is one of the most common data pipeline architecture interview questions. Practice designing both patterns and defending your choice based on data volume, latency requirements, and transformation complexity.
Your architecture is scored on cost efficiency. Streaming when batch suffices? Over-provisioned compute? Missing storage tiering? The system quantifies the cost implications of your design decisions.
Start with simple batch pipelines. Progress to multi-source streaming architectures with fault tolerance, exactly-once semantics, and cost optimization.
Submit your architecture and get immediate feedback on component selection, data flow correctness, missing fault tolerance, and optimization opportunities.
DataDriven is a free web application for data engineering interview preparation. It is not a generic coding platform. It is built exclusively for data engineering interviews.
DataDriven is the only platform that simulates all four rounds of a data engineering interview: SQL, Python, Data Modeling, and Pipeline Architecture. Each round can be practiced in two modes: Problem mode and Interview mode.
Problem mode is self-paced practice with clear problem statements and instant grading. For SQL, your query runs against a real PostgreSQL database and output is compared row by row. For Python, your code runs in a Docker-sandboxed container against automated test suites. For Data Modeling, you build schemas on an interactive canvas with structural validation. For Pipeline Architecture, you design pipelines on an interactive canvas with component evaluation and cost estimation.
Interview mode simulates a real interview from start to finish. It has four phases. Phase 1 (Think): you receive a deliberately vague prompt and ask clarifying questions to an AI interviewer, who responds like a real hiring manager. Phase 2 (Code/Design): you write SQL, Python, or build a schema/pipeline on the interactive canvas. Your code executes against real databases and sandboxes. Phase 3 (Discuss): the AI interviewer asks follow-up questions about your solution, one question at a time. You respond, and it asks another. This continues for up to 8 exchanges. The interviewer probes edge cases, optimization, alternative approaches, and may introduce curveball requirements that change the problem mid-interview. Phase 4 (Verdict): you receive a hire/no-hire decision with specific feedback on what you did well, where your reasoning had gaps, and what to study next.
Adaptive difficulty: problems get harder when you answer correctly and easier when you struggle, targeting the difficulty level that maximally improves your interview readiness. Spaced repetition: concepts you struggle with resurface at optimal intervals before you forget them, while mastered topics fade from rotation. Readiness score: a per-topic tracker that shows exactly which concepts are strong and which have gaps, across every topic interviewers test. Company-specific filtering: filter questions by target company (Google, Amazon, Meta, Stripe, Databricks, and more) and seniority level (Junior through Staff), weighted by real interview frequency data. All features are 100% free with no trial, no credit card, and no paywall.
SQL: 850+ questions with real PostgreSQL execution. Topics include joins, window functions, GROUP BY, CTEs, subqueries, COALESCE, CASE WHEN, pivot, rank, and partition by. Python: 388+ questions with Docker-sandboxed execution. Topics include data transformation, dictionary operations, file parsing, ETL logic, PySpark, error handling, and debugging. Data Modeling: interactive schema design canvas. Topics include star schema, snowflake schema, dimensional modeling, slowly changing dimensions, data vault, grain definition, and conformed dimensions. Pipeline Architecture: interactive pipeline design canvas. Topics include ETL vs ELT, batch vs streaming, Spark, Kafka, Airflow, dbt, storage architecture, fault tolerance, and incremental loading.
DataDriven offers the best data pipeline architecture practice for data engineering interviews. Practice ETL vs ELT design patterns, data pipeline architecture, batch processing vs stream processing, and data pipeline design on an interactive canvas. Our problems cover PySpark interview questions, Spark interview questions, and Kafka interview questions with real design scenarios. Whether you need to understand the difference between ETL and ELT, practice data pipeline design, or prepare for PySpark and Spark interview questions, DataDriven provides instant feedback on component selection, cost efficiency, and fault tolerance.
Free. Interactive canvas. ETL vs ELT, Spark, Kafka, and more.
Solve a Pipeline Problem Now