DataLemur vs DataDriven

DataLemur's SQL questions are short and well specified, and each carries a company name. That makes them good repetition for the analytical screen many data engineering loops open with. Each question runs against real tables in a PostgreSQL 14 editor with hints available, and once you're done it shows a written solution, so you see whether your query returns the right rows rather than only reading an answer.

Its free SQL tutorial is 32 lessons in 3 sections. It starts at SELECT and WHERE and works through GROUP BY and CASE to joins; later lessons cover window functions like LEAD and LAG, and self joins and pivoting come last. For an engineer coming back to SQL after years of ORM code, that is a fast refresher on the clauses an interviewer expects you to write without looking anything up.

The site also carries a long run of company articles that list SQL questions by employer. They are useful for seeing the shape of an analytics SQL question at a given company, as long as you remember they describe the analyst and data scientist loops DataLemur was built for.

DataLemur runs your query on PostgreSQL 14, with MySQL as the alternative. That covers the core of most SQL screens, but data engineers rarely write production SQL in either. Some of the jobs they interview for run Snowflake or BigQuery and others run Trino or SQL Server. Those dialects disagree on date truncation and string functions, and QUALIFY and semi-structured data split them too. DataDriven's editor can be pinned to Trino, PostgreSQL, MySQL, SQL Server, Snowflake, BigQuery, and it rejects a function your chosen dialect does not have, so the habits you build are the ones the interviewer's warehouse accepts.

The version matters too. MERGE, the statement most upserts and slowly changing dimension loads are written with, arrived in PostgreSQL 15, according to the PostgreSQL 15 release notes, so an editor on version 14 cannot run it. PostgreSQL supports each major version for 5 years, and version 14 reaches its final release on November 12, 2026.

A data engineering SQL round also has a second half that analytics questions skip: deduplicating a feed that delivered the same event twice, loading only the rows that changed, and explaining what the query does to a table with billions of rows. If you want the write side in your own words, the guide to SQL upsert with INSERT ON CONFLICT and MERGE covers each dialect's form. For the read side at interview difficulty, the SQL practice problems for data engineers hold 928 problems that run live, and the SQL interview questions group them by the patterns interviewers ask about most.

DataLemur has no pipeline design or Spark practice. Beyond SQL and Python, its categories are probability and statistics plus machine learning, which match what data science loops ask and leave out 2 rounds that many data engineering loops include.

In a pipeline design round you draw a pipeline from its sources to storage, with queues in between and transforms and checks placed along the way. Then you defend each choice: why a queue sits between the producer and the warehouse, where duplicates are removed, what happens when an event arrives 3 hours late, and how a failed day is rerun without double counting. DataDriven's 144 pipeline architecture problems are drawn on a canvas with those node types, and the follow-up questions probe the failure cases a design leaves open.

Spark rounds test whether you know what a transformation costs: which joins shuffle, when a broadcast helps, why 1 skewed key stalls a stage. DataDriven runs PySpark and Scala Spark problems in the browser, 943 in all, since every SQL problem can also be solved in Spark SQL.

A correct query is half of an interview answer. The other half is the conversation that follows it: what breaks at scale and which index or partition would be used, and after that, what you would change if the data arrived late. DataLemur's pricing page lists 1 live option for rehearsing that part, the $300 plan's 1 hour call with Nick Singh, which can go to a mock interview or to a resume review and job search advice.

On DataDriven, every problem in the catalogue has a mock interview version, 1,569 in all across SQL and Python plus Spark, along with data modeling and pipeline design. The interviewer asks the follow-up questions a real panel would, reads your answers along with your code, and closes with a hire or no hire verdict and the reasons for it, so you can repeat the conversation as often as the query.

DataLemur is partly free. Many SQL questions, the 32 lesson SQL tutorial and a 9 day interview crash course are open to everyone. Premium opens the rest of the questions and their full solutions for $15 a month or $60 a year, which works out to $5 a month, and the yearly plan adds a video course on the job search.

The $300 coaching plan adds the 1 hour call, a signed copy of the book and lifetime access to 250+ questions. If your interview is a month away and analytics SQL is the only gap, 1 month of Premium is a cheap way to cover it. DataDriven is open to every member, so the 2 can sit side by side without forcing a choice.

Use DataLemur when the next interview is an analytics SQL screen at a company whose data engineers mostly write reporting SQL, or when you are rebuilding SQL fluency from a low base. Its questions are quick, and the company tags help you guess the flavour of a screen.

Use DataDriven when the loop includes any round besides SQL, as many data engineering loops do, or when your target company runs Snowflake or BigQuery and you want that dialect's rules enforced while you rehearse. The same goes for Trino and SQL Server. The guide to data engineering interview prep lays out every round of the loop and how to prepare for each.

If you have already solved most of LeetCode's SQL set as well, the LeetCode SQL guide for data engineers explains which of those habits carry over to a data engineering round and which do not.