Data warehouse
27 bites tagged Data warehouse: interview questions with model answers, and 60-second explainers.
Outline a data pipeline for web events to a data warehouse
Tests end-to-end streaming design from web clients to warehouse. Strong answers list five stages: client capture, broker buffering, stream processing, object storage, and warehouse load. Red flag: proposing only batch cron jobs or ignoring schema registries.
Design a data model for feature adoption tracking
Tests dimensional modeling for high-volume events so PMs can query Feature A not B without complex SQL. A strong answer uses an event fact table plus a materialized user-feature summary. Red flag: a wide user table with boolean columns per feature.
Slow dashboard querying a large fact table: first three checks?
This tests systematic diagnosis of fact-table query latency. A strong answer checks the execution plan and indexing, evaluates partitioning and data model fit, and inspects caching or pre-aggregation.
Explain data warehouse purpose and how it differs from OLTP
This tests whether you know the OLTP versus analytics split. A great answer contrasts OLTP row-level writes and normalized schemas with warehouse denormalized schemas and BI reads. A red flag is calling a warehouse just a bigger OLTP database.
Trade-offs: Bundled Analytics vs. a Warehouse-Native Stack?
This tests your grasp of modern data stack trade-offs: cost, data governance, and flexibility. Discuss the pros of warehouse-native (unified data, lower cost, security) vs. the cons (loss of specialized UI, implementation complexity).
Design a data model for tracking feature adoption
This tests your grasp of data warehouse star schemas for analytics. Outline a fact table for events and dimension tables for users and features, explaining how this structure enables fast, ad-hoc cohort analysis for a product manager.
Design a Data Model for a Feature Adoption Dashboard
This tests your ability to translate a business need into a scalable star schema. A great answer defines a central fact table (e.g., fct_feature_usage) and its dimensions (dim_users, dim_features, dim_date).
What is data partitioning in a cloud data warehouse?
Tests your grasp of physical data layout optimization. A good answer defines partitioning as dividing a table by a column (e.g., date), then explains how this enables partition pruning to improve query speed and reduce cost by scanning less data.
Star vs. Snowflake Schemas: Trade-offs
Tests your grasp of data warehouse design trade-offs. Define star (denormalized, fast queries) and snowflake (normalized, storage efficient) schemas. Explain the core trade-off: star's query speed vs. snowflake's storage/integrity.
Design a data warehouse model for tracking feature adoption
This tests your grasp of data warehousing star schemas for efficient behavioral analysis. A strong answer proposes a central events fact table linked to users, features, and time dimension tables.
How would you design a data model for a feature adoption dashboard?
Tests applying dimensional modeling to a business need. A good answer defines a central fact table (e.g., feature_usage) and related dimensions (user, feature, date). A red flag is designing a transactional model or being too vague about the schema.
How would you diagnose a slow dashboard query?
This tests your systematic approach to performance tuning. A great answer investigates query optimization, strategic indexing/partitioning, and the data model (like star schema).
Compare a data warehouse, data lake, and lakehouse
Tests your grasp of data architectures for BI vs. ML workloads. Contrast warehouses (structured) and lakes (raw), then explain how a lakehouse adds ACID/schema features to a lake's storage. A red flag is confusing schema-on-write vs. schema-on-read.
Describe star and snowflake schemas and their trade-offs.
Tests your grasp of data warehouse design trade-offs. Define star (denormalized dimensions) and snowflake (normalized dimensions) schemas. Contrast them: star is faster for queries but uses more space; snowflake is space-efficient but requires more joins.
What is a data warehouse vs. a transactional database?
Tests your grasp of read-optimized (OLAP) vs. write-optimized (OLTP) systems. A great answer defines warehouses for analysis, contrasts them with transactional DBs for operations, and explains the resulting differences in workload, schema, and data structure.
ETL: The Assembly Line for Your Data
ETL (Extract, Transform, Load) is an assembly line for data, moving it from various sources into a single destination for analysis. It's used to populate data warehouses by combining data from databases, logs, and APIs into a unified view.
ELT: Load Raw Data, Transform in Place
ELT flips the data pipeline: load raw data first, then use the data warehouse's own power to transform it. It's used in ML feature pipelines. The footgun is assuming it's ETL; with ELT, the transformation logic is coupled to the warehouse's SQL engine.
Apache Hive: SQL on Hadoop
Apache Hive translates SQL-like queries into Hadoop MapReduce jobs, letting you analyze huge datasets with familiar syntax. Don't mistake it for a real-time database; its power is in high-throughput batch processing, not low-latency queries.
Data Warehouse: The Central Repository for Analytics
A data warehouse is a central repository for historical analysis, integrating data from many systems. It's used for reporting and complex queries to find business insights, not for day-to-day transactions.
Snowflake: Decoupled Storage and Compute
Snowflake decouples storage from compute, acting like a shared-disk system for data management but a shared-nothing system for query performance. This lets you scale compute and storage independently, ideal for variable analytic workloads.
Google BigQuery: A Serverless Data Warehouse
Think of BigQuery as a massive SQL database you don't have to manage. It's a serverless data warehouse for analyzing huge datasets, like terabytes of application logs. The footgun is treating it like a regular database for real-time transactions.
Reverse ETL: From Warehouse Insights to Operational Action
Reverse ETL pushes clean data from your central warehouse back into the operational tools business teams use daily. This powers sales with customer scores in their CRM or marketing with personalized segments, all from a single source of truth.
Data Vault Modeling: An Audit-First Data Warehouse
Data Vault modeling builds a warehouse like a financial ledger, where every entry is permanent and traceable. It excels at storing historical data from multiple systems for auditing.
Snowflake Schema: Trading Query Speed for Storage
A snowflake schema saves storage by normalizing a star schema's dimensions into smaller, related tables. It's used in data warehouses to reduce redundancy, but the extra joins required can slow down queries, making it a trade-off against a simpler star schema.
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