DataLemur vs DataDriven for Data Engineering Interviews

DataLemur's pricing page sells lifetime access to 250+ interview questions. Its monthly and yearly plans list 100+ SQL questions among them, and its questions page holds about 40 Python questions. Those are the questions a data engineer would practise there; its remaining categories serve data science rounds.

DataDriven's practice catalogue holds 1,569 problems: 928 SQL, 415 Python, 144 pipeline architecture, 67 data modeling, and a set of Spark-native problems. The count leaves out AI coding problems. Every SQL problem also runs in PySpark or Scala Spark, which puts 943 problems in reach of a Spark answer without counting any problem twice in the total.

Every problem in that count is one you solve by writing code that runs or by drawing a design on a canvas, and each is a question asked in real data engineering interviews.

SQL is DataLemur's strongest area, and it covers a data engineer's SQL round within those limits. Each question carries a company label and comes with hints and a written solution, and it runs in PostgreSQL 14 and MySQL. Its questions page tags 38 questions with window functions and 40 with CTEs or subqueries, the core every SQL round tests. The questions themselves are analytics problems: a histogram of tweets per user, a click-through rate, a retention rate.

DataDriven lets you pin a SQL answer to any of 6 dialects: Presto / Trino, PostgreSQL, MySQL, SQL Server (T-SQL), Snowflake and BigQuery. By default its editor accepts any mix of dialects, as most interviewers do. Pin one and the query is held to what that vendor accepts: SELECT TOP 10 runs under SQL Server and fails under PostgreSQL, and a `QUALIFY` clause, which filters the results of window functions, runs under Snowflake and BigQuery and fails everywhere else, with the error that engine would raise.

A data engineering SQL round tests the same core and then adds problems about the data itself: deduplicating a table that was loaded twice, finding gaps in an event stream, building a type 2 history from daily snapshots, and writing an upsert that can run again without doubling rows. DataDriven's SQL interview questions for data engineers carry those patterns, and its SQL practice problems put them in front of you to solve against live tables, in the dialect your target company runs.

DataLemur sorts its questions into SQL and Python plus 2 categories for machine learning and for statistics and probability. Spark is not among them, and its SQL editor runs no Spark engine. Spark is where many data engineering loops go after SQL: the interviewer asks you to write a transformation as a DataFrame job, then asks where it shuffles and how you would partition the output.

DataDriven runs every SQL problem in PySpark and Spark (Scala), which puts 943 problems in reach of a Spark answer. It also has problems written for Spark alone, and its lessons include a Spark track where you can practise the partitioning and shuffle questions before the round.

The second design round is pipeline design, and DataLemur has no question category for it either. The prompt describes a system to build: land clickstream events in the warehouse within 15 minutes, or fix a daily report that double counts after a rerun. A strong answer names the sources, the queue, the transforms, where data lands, where a quality check stops bad data, and how a failed run is retried without duplicating rows.

DataDriven's 144 pipeline architecture problems run on a canvas with the 6 node types an interviewer expects you to draw: source, queue, transform, quality gate, storage and consumer. You join the nodes with the edges data moves along, set freshness on each one, and then defend the design in a mock interview that asks what happens when a node fails or a record arrives after the load has run.

Every DataLemur question carries the name of a company, and those names are a real help when you are preparing for one employer. Counted across its question listings on 2026-10-01, they come to about 50. Amazon and Google are among them, alongside Stripe and Snowflake and Databricks, with some questions labelled only FAANG or Fintech. Its blog adds articles of SQL questions named after individual companies, which you read rather than run in its editor.

DataDriven's problems are tagged with 293 companies. Its company interview pages lay out the stages of a company's data engineering loop and how long each one takes. They list the problems tagged with that company, sorted by area and difficulty, and you can open any of them to solve it. A salary page by level sits beside them.

DataLemur's site offers no mock interview of its own. Its $300 lifetime plan includes a 1-hour video call with its founder, Nick Singh, alongside a signed copy of his book.

DataDriven has an AI mock interview for every problem it holds, 1,569 in all, in SQL, Python, Spark, data modeling and pipeline architecture. That includes the 2 design rounds: you build the schema or the pipeline on the canvas while the interviewer asks follow-ups the way a person would, such as what changes when the table is 100 times larger. Each session closes with a verdict and the reasons behind it.

DataLemur's SQL tutorial has 3 sections, from basic through intermediate to advanced. It starts at SELECT and WHERE and works through joins and CTEs to window functions, LEAD and LAG. A case study closes it. It is open without paying. Its site lists no tutorial for Python or Spark, and none for data modeling or pipelines.

DataDriven's lessons number more than 150, in SQL, Python, Spark, data modeling and pipeline architecture. Each lesson is interactive, with practice drills placed in the reading at the point where the idea they test is introduced.

DataLemur's home page describes no daily problem or streak, and no recurring challenge either. DataDriven's daily challenge serves a new SQL problem and a new Python problem every day, rolling over at your local midnight, and a solve counts toward your streak.

DataDriven's community also runs 1 data engineering challenge a week, in seasons, scored blind against hidden data: a right value earns 1 point, a wrong value costs 1, and a row that should not exist costs 2. Submissions close when the week freezes, results post at the freeze, and each week has its own discussion thread.

Every DataLemur question has a Discussion tab next to its solution and your submissions. DataDriven's problems carry the same, as a Discussions tab and a Solutions tab next to your submissions and a Deep Dive.

Beyond the problem page, DataDriven's members' forum holds interview debriefs and compensation threads, along with preparation topics and the weekly challenge's threads. A debrief records an interview a member sat at a named company and what it asked, which is what a candidate weighs when deciding where to spend the weeks before an onsite.

DataLemur opens some of its questions and its SQL tutorial without payment; the rest of its questions need Premium. Its pricing page lists $15 a month, $60 a year, or $300 once for lifetime access with the founder call and a signed book.

DataDriven is open to every member: every problem and lesson on this page, along with the mock interviews and the daily and weekly challenges.