84 free articles for analytics engineers.
No paywall, no email gate. Each article is a self-contained, opinionated deep-dive on a topic that comes up on the job.
Topic hubs.
Curated paths through the library — articles, practice topics, and projects organized by skill area.
- Analytics EngineeringTopic hub
- SQL for Analytics EngineersTopic hub
- dbt for Analytics EngineersTopic hub
- Data ModelingTopic hub
- BigQuery for Analytics EngineersTopic hub
- Snowflake for Analytics EngineersTopic hub
- Looker & Looker StudioTopic hub
- Python for Analytics EngineersTopic hub
- Analytics Engineering CareerTopic hub
- Analytics Engineering Interview PrepTopic hub
- Interviewsintermediate
Analytics Engineering Interview Mistakes and How to Avoid Them: A Complete Guide
Discover common analytics engineering interview mistakes and learn strategies to avoid them. Enhance your preparation and communication skills for success.
Interview Prep - AIintermediate
How Generative AI is Changing the Role of Analytics Engineers: New Skills, Workflows, and Impact
Generative AI transforms analytics engineering by automating tasks, shifting focus from coding to strategic oversight, and requiring new AI-related skills.
AI ToolsCareer - Fundamentalsintermediate
Automated Data Catalogs: DataHub vs Amundsen vs Atlan Compared
Compare DataHub, Amundsen, and Atlan for automated data cataloging. Understand their governance, setup, and enterprise capabilities to choose the best fit.
Data QualityAnalytics Engineering - dbtadvanced
dbt Macros & Jinja Tips Every Analytics Engineer Should Know: Expert Guide
Learn how dbt macros and Jinja can transform repetitive SQL tasks into dynamic, reusable code, enhancing scalability and efficiency in data projects.
dbt - dbtintermediate
dbt Cloud vs Core: Feature Comparison 2025—Comprehensive Guide
Compare dbt Cloud and Core to understand their features, costs, and operational differences. This guide helps data teams make informed decisions.
dbt - Architectureintermediate
Apache Airflow vs Prefect: Which Scheduler for Analytics Engineering?
Explore the differences between Apache Airflow and Prefect, focusing on workflow design, ease of use, and integration to choose the right scheduler for your team.
ETL / ELTAnalytics Engineering - Architectureintermediate
Implementing Data Products in a Data Mesh: Essential Strategies and Practices
Explore strategies for implementing data products within a data mesh. Learn how to enhance team agility and maintain quality through decentralized data ownership.
Data ModelingAnalytics Engineering - Architectureadvanced
What Is a Lakehouse? Architecture & Use-Cases Explained in Depth
Explore how lakehouses combine data lake storage and data warehouse analytics into one system, supporting diverse workloads from reports to AI projects.
Data ModelingAnalytics Engineering - Fundamentalsintermediate
Change Data Capture Patterns for Analytics Pipelines: The Complete Guide
Change Data Capture (CDC) enables real-time analytics by tracking data modifications instantly, optimizing data pipelines, and reducing processing overhead.
ETL / ELTData Modeling - Fundamentalsintermediate
Surrogate vs Natural Keys: Choosing the Right Primary Key for Databases
Learn the differences between surrogate and natural keys in databases. This guide covers their benefits, drawbacks, and when to use each type effectively.
Data Modeling - dbtintermediate
Building Streaming Data Models with dbt & Kafka: A Modern Guide
Explore how to integrate dbt and Kafka for real-time data modeling. This guide covers architecture setup, pipeline automation, and maintaining data quality.
dbtETL / ELT - Data Modelingintermediate
Slowly Changing Dimensions Type 2 Explained: Complete Guide
Explore Type 2 Slowly Changing Dimensions to maintain historical data records. Learn how to structure tables and implement tracking methods effectively.
Data Modeling - Fundamentalsintermediate
Storing & Querying Parquet Data in BigQuery: Best Practices and Techniques
Explore how to efficiently store and query Parquet data in BigQuery. Learn best practices to optimize performance and reduce storage costs significantly.
BigQuery - SQLintermediate
Working with XML in SQL Server for Analytics: Complete Guide
Explore how SQL Server's XML features enable efficient storage, querying, and transformation of XML data for enhanced analytics and reporting.
SQL - Architectureintermediate
Partitioning Strategies in Snowflake & BigQuery: Cost Control Guide
Discover how partitioning strategies in Snowflake and BigQuery can cut query costs by up to 40%. Learn to choose effective partition columns and optimize queries.
BigQuerySnowflake - SQLintermediate
BigQuery SQL Best Practices for Analysts: Optimize Performance, Reduce Cost
Learn essential BigQuery SQL optimization techniques to enhance performance and reduce costs. Master strategies like partitioning, clustering, and efficient queries.
BigQuerySQL - Fundamentalsintermediate
DuckDB Tutorial: Analytics on Your Laptop for Fast Local Data Analysis
Learn how DuckDB enables fast local data analysis on your laptop. This tutorial covers installation, SQL queries, and integration with Python workflows.
Analytics EngineeringSQL - SQLintermediate
Clustered vs Non-Clustered Indexes: Performance Benchmarks Explained
Discover the performance differences between clustered and non-clustered indexes. Learn when each index type excels with real-world benchmarks and scenarios.
SQL - SQLintermediate
Window Functions Explained: Ranking, Aggregation & Gap-Filling in Analytics
Explore SQL window functions to perform advanced calculations like rankings, running totals, and gap-filling without reducing rows into groups.
SQL - SQLintermediate
SQL Anti-Patterns That Hurt Dashboard Performance: Key Issues & Solutions
Learn how SQL anti-patterns slow down dashboards and discover solutions to optimize query performance for faster, more responsive data visualization.
SQLLooker - SQLbeginner
Common Table Expressions (CTE) Tutorial for Beginners: Essential SQL Skills
Discover how Common Table Expressions (CTEs) simplify complex SQL queries by creating temporary result sets, improving readability and maintainability.
SQL - SQLintermediate
SQL Joins Explained with Visual Diagrams: A Comprehensive Guide
Learn how SQL joins bridge tables in databases using visual diagrams. Understand join types and syntax to optimize data retrieval and analysis.
SQL - Qualityintermediate
Data Observability Explained: Why Analytics Engineers Should Care
Explore how data observability empowers analytics engineers with visibility into data quality and pipeline health, preventing costly data issues.
Data Quality - SQLintermediate
SQL Window Functions Explained With Examples: A Complete Guide
Discover SQL window functions with practical examples. Learn to rank, calculate totals, and perform advanced analytics while preserving row structure.
SQL - Interviewsintermediate
Interview Prep: 50 Questions and Answers for Analytics Engineer Roles
Prepare for analytics engineer interviews with 50 essential questions. This guide covers technical skills, data modeling, and problem-solving scenarios.
Interview PrepCareer - dbtintermediate
dbt Cloud vs. Airflow: Comparing Popular Data Engineering Tools in 2025
Compare dbt Cloud and Apache Airflow for data engineering. Understand their roles, use cases, and how they complement each other in data workflows.
dbtETL / ELT - Analytics Engineeringintermediate
Mastering Analytics Engineering: The Definitive Guide (2025 Edition)
Explore the evolving role of analytics engineers, bridging data engineering and analysis. Learn key skills for building data pipelines and transformation workflows.
Analytics Engineering - Data Modelingbeginner
Data Modeling Basics: Star Schema vs. Snowflake Schema Explained
Learn the differences between star and snowflake schemas in data modeling. Discover how each affects query performance, storage, and maintenance.
Data ModelingSnowflake - SQLintermediate
Mastering SQL: A Comprehensive Tutorial for Data Success
Explore SQL from basic concepts to advanced techniques. This tutorial provides a structured approach to mastering SQL for effective data management.
SQL - Gitintermediate
The Complete Guide to GitHub for Data Professionals: Master Version Control and Collaboration
Explore GitHub's version control and collaboration features for data professionals. Learn setup, Git commands, and best practices for project management.
Git - Architecturebeginner
A Beginner’s Guide to Snowflake for Analytics Engineers: Essential Concepts and Best Practices
Explore Snowflake's key concepts and best practices for analytics engineers. Learn about its architecture, data loading strategies, and performance optimization.
Snowflake - BIintermediate
Top 5 BI Tools Every Analytics Engineer Should Know in 2025
Explore the top BI tools for analytics engineers in 2025, including Looker, Power BI, and Tableau. Understand their strengths in visualization and integration.
Analytics EngineeringLooker - SQLbeginner
Beginner’s Guide to JSON in SQL: Understanding and Using JSON Data
Explore how SQL databases like SQL Server manage JSON data. Learn to store, query, and convert JSON, enhancing your data handling capabilities.
SQL - Communityintermediate
Best Analytics Engineering Communities & Forums to Join: Top Picks for 2025
Discover the best analytics engineering communities for 2025. Engage with peers, access resources, and enhance your skills in data transformation and analytics.
CareerAnalytics Engineering - Interviewsbeginner
Common SQL Mistakes Beginners Make During Interviews: Essential Pitfalls to Avoid
Discover common SQL interview mistakes like JOIN errors and WHERE clause neglect. Learn how to improve your problem-solving approach for better outcomes.
Interview PrepSQL - Fundamentalsintermediate
Effective Communication Skills for Analytics Engineers: Presenting Data Insights for Impact
Learn how analytics engineers can bridge the gap between technical data and business impact with effective communication and presentation skills.
Career - Analytics Engineeringintermediate
The Future of Analytics Engineering: 2025 and Beyond Explained
Discover how analytics engineering is transforming into a specialized field by 2025, integrating AI, modern architectures, and governance for scalable analytics.
Analytics EngineeringAI Tools - Fundamentalsbeginner
Data Lineage for Analytics Engineers: An Introduction Guide
Learn how data lineage maps the journey of data through systems, helping analytics engineers debug, ensure quality, and maintain visibility in complex ecosystems.
Data QualityAnalytics Engineering - Fundamentalsadvanced
How to Implement CI/CD for Your Analytics Projects: Step-by-Step Guide
Learn to implement CI/CD in analytics projects, automating workflows to reduce errors and save time. This guide covers version control, testing, and tool selection.
CI/CDdbt - AIintermediate
AI-Powered Data Cleansing Tools You Should Try: Transform Your Data Workflow
Explore AI data cleansing tools that automate error detection and correction, saving time and improving data accuracy. Learn how to integrate them into your workflow.
AI ToolsData Quality - AIintermediate
Prompt Engineering for Analytics Engineers: Maximize Productivity with AI
Explore how prompt engineering helps analytics engineers automate SQL generation, data analysis, and reporting, boosting productivity and maintaining accuracy.
AI Tools - AIintermediate
Best AI Data Assistants for Analytics Professionals: 2025 Guide
Explore top AI data assistants for 2025 that automate data analysis, from cleaning to visualization, enabling faster insights without coding expertise.
AI Tools - Analytics Engineeringintermediate
What Companies Look for When Hiring Analytics Engineers: Skills, Trends & Employer Expectations
Explore what companies seek in analytics engineers, focusing on technical skills like SQL, Python, and data modeling, alongside business acumen and adaptability.
CareerInterview Prep - Careerintermediate
How to Negotiate Your Salary as an Analytics Engineer: Expert Steps
Learn how analytics engineers can negotiate salaries effectively by understanding market rates and showcasing their technical skills for better compensation.
Career - AIintermediate
How to Integrate AI Tools into Your Analytics Workflow: A Step-by-Step Guide
Learn how to integrate AI tools into analytics workflows for automation and better decision-making. This guide covers strategies for effective implementation.
AI Tools - SQLintermediate
SQL Date and Time Functions Every Analytics Engineer Should Know: A Practical Guide
Discover key SQL date and time functions that empower analytics engineers to perform precise data analysis and reporting across various database systems.
SQL - AIbeginner
AI and Machine Learning Fundamentals for Analytics Engineers: Essential Skills and Concepts
Explore AI and machine learning fundamentals essential for analytics engineers. Learn to integrate modern AI techniques with traditional data skills.
AI ToolsPython - Data Modelingbeginner
Explaining Fact and Dimension Tables for Beginners: Essential Concepts in Data Warehousing
Explore the roles of fact and dimension tables in data warehousing. Learn how they work together to enhance data analysis and business insights.
Data Modeling - dbtintermediate
Open-Source Alternatives to dbt: Are They Worth It?
This article examines open-source alternatives to dbt, comparing their features and challenges, and offers guidance on when they might be preferable.
dbt - Analytics Engineeringadvanced
Introductory Guide to Data Mesh for Analytics Engineer: Principles, Architecture, and Implementation
Discover how data mesh decentralizes data ownership, empowering analytics engineers with autonomy and improving data access and quality across domains.
Data ModelingAnalytics Engineering - Architecturebeginner
Data Warehouse vs Data Lake: Simplified for Beginners – Key Differences, Uses, and Decision Guide
Explore the distinctions between data warehouses and data lakes, their uses, and how to choose the right solution for your business needs.
Data ModelingAnalytics Engineering - Qualityintermediate
Data Testing Essentials: Ensure Accuracy in Your Models for Reliable Results
Learn how data testing ensures model accuracy by validating datasets for accuracy, completeness, and consistency, preventing costly business errors.
Data QualityTesting - Fundamentalsintermediate
Essential Cloud Computing Skills for Analytics Engineers: AWS, Azure, GCP Guide
Explore essential cloud computing skills for analytics engineers. Learn to leverage AWS, Azure, and GCP for scalable data solutions and career growth.
Analytics Engineering - Careerintermediate
From BI Analyst to Analytics Engineer: Successful Career Transition Guide
Learn how to transition from a BI analyst to an analytics engineer by developing technical skills in data modeling and infrastructure while leveraging your business acumen.
Analytics EngineeringCareer - Fundamentalsbeginner
The Basics of Data Version Control (DVC): How to Manage Data for ML
Learn how Data Version Control (DVC) manages large datasets and models in machine learning projects, enabling efficient versioning and collaboration.
GitData Quality - Careerintermediate
Analytics Engineer Resume Guide: Templates and Tips for Success
Learn how to craft an effective analytics engineer resume that showcases technical achievements and business impact, using tools like SQL and Python.
CareerInterview Prep - Pythonintermediate
Python Pandas Tutorial for Analytics Engineers: Mastering Data Analysis and Engineering Techniques
Explore a comprehensive guide on using Python's Pandas library for data manipulation and analysis. Learn essential techniques for analytics engineering.
Python - Data Modelingintermediate
Understanding Database Normalization and Denormalization: Concepts, Forms, and Applications
Discover how database normalization and denormalization impact data storage and retrieval. Learn when to use each approach for optimal system performance.
Data ModelingSQL - SQLadvanced
Query Optimization Basics: Improve Your SQL Performance Quickly
Learn how to optimize SQL queries to improve database performance. Discover techniques like indexing and query rewriting for faster execution times.
SQL - Careerintermediate
Navigating Analytics Engineering Internships: Complete Guide for Success
Analytics engineering internships blend technical skills and business insights, offering hands-on experience in data transformation and career advancement.
Career - Gitintermediate
Optimizing Your GitHub Profile for Analytics Roles: Proven Strategies
Learn to optimize your GitHub profile for analytics roles. Discover strategies for impactful projects, effective documentation, and community engagement.
GitCareer - Qualitybeginner
Best Practices for Data Documentation: Fundamentals & Strategies
Explore essential data documentation practices to improve data management, ensure quality, and support compliance. Learn strategies for sustainable workflows.
Data QualityAnalytics Engineering - Architectureintermediate
ETL vs ELT Explained Simply: Key Differences, Pros, and Use Cases
ETL and ELT are data integration processes differing in transformation timing. ETL transforms data before loading, while ELT does so after loading.
ETL / ELT - Interviewsintermediate
How to Handle Technical Interviews Without Panicking: Proven Strategies for Success
Learn strategies to handle technical interviews calmly. Focus on problem-solving, clear communication, and structured preparation to impress interviewers.
Interview Prep - Interviewsintermediate
How to Explain Analytics Engineering Projects During an Interview: A Step-By-Step Guide
Discover how to effectively explain analytics engineering projects in interviews, highlighting technical skills and business impact with structured responses.
Interview PrepCareer - Analytics Engineeringintermediate
Creating a Powerful Analytics Engineering Case Study: Essential Steps and Best Practices
Learn to create analytics engineering case studies that highlight technical skills and business impact. Follow essential steps for compelling storytelling.
CareerAnalytics Engineering - Pythonbeginner
Python Basics for Analytics Engineering: The Essential Starting Guide
Explore why Python is essential for analytics engineering. Learn to set up environments, work with databases, and automate workflows efficiently.
Python - Careerintermediate
Analytics Engineering Job Titles Explained: Roles, Skills & Career Paths
Discover the diverse job titles in analytics engineering, their roles, and skills required. Learn how these positions fit into career paths and organizational structures.
Analytics EngineeringCareer - Careerintermediate
Top Certifications for Aspiring Analytics Engineers: Comprehensive Guide
Discover the top certifications for analytics engineers, including Google, Microsoft, and AWS programs. Learn about entry-level to advanced credentials.
Career - SQLintermediate
SQL Joins Explained in Simple Terms: Your Essential Guide
Learn how SQL joins combine data from multiple tables using shared columns. This guide explains join types and offers tips for effective query writing.
SQL - Analytics Engineeringintermediate
What Does an Analytics Engineer Do? Daily Tasks and Key Responsibilities
Explore the role of analytics engineers who transform raw data into reliable datasets using SQL and dbt, bridging data engineering and analysis.
Analytics EngineeringCareer - Gitintermediate
How to Set Up Your First GitHub Repository for Data Projects: Step-by-Step Guide
This guide provides a step-by-step process to create a GitHub repository for data projects, covering account setup, Git installation, and configuration.
Git - AIbeginner
Intro to AI Tools for Analytics Engineers: Essential Guide
Learn how AI tools transform analytics engineering by automating tasks, improving data quality, and providing predictive insights for efficient data pipelines.
AI Tools - Analytics Engineeringintermediate
How to Become an Analytics Engineer Without a CS Degree: The Essential Guide
Learn how to enter analytics engineering without a CS degree. Focus on practical skills, certifications, and building a strong project portfolio.
CareerAnalytics Engineering - Careerintermediate
Analytics Engineering Career Path: How to Advance Quickly in Data
Explore how mastering technical skills and communication can accelerate your career in analytics engineering, offering diverse progression routes and growth potential.
Analytics EngineeringCareer - dbtbeginner
What is dbt (Data Build Tool)? A Simple Explanation for Data Teams
Explore dbt, the Data Build Tool that transforms raw data into structured insights using SQL. Understand its role in modern data workflows and ELT processes.
dbt - Gitintermediate
Top 10 Git Commands for Data Professionals: Key Concepts & Best Practices
Discover 10 essential Git commands tailored for data professionals. Learn how to manage code, track changes, and collaborate effectively on data projects.
Git - SQLintermediate
5 Essential SQL Commands Every Analytics Engineer Should Know: Core Queries for Data Impact
Discover the five essential SQL commands every analytics engineer should master, including SELECT, WHERE, and JOIN, to effectively analyze and manipulate data.
SQL - Analytics Engineeringintermediate
Analytics Engineer vs. Data Analyst: Key Differences Explained
Explore the distinct roles of analytics engineers and data analysts, including their responsibilities, required skills, and career growth opportunities.
Analytics EngineeringCareer - Analytics Engineeringbeginner
What is Analytics Engineering? Key Concepts, Roles & Skills Explained
Analytics engineering bridges the gap between data engineering and analysis, creating reliable datasets for business insights with SQL and software practices.
Analytics Engineering - Analytics Engineeringintermediate
Analytics Engineer Role & Responsibilities: Skills, Tools, and Impact
Learn about the analytics engineer role, which combines technical skills and business insights to transform raw data into actionable business information.
Analytics EngineeringCareer - BIintermediate
Optimizing Looker Performance: Best Practices for Faster Dashboards
Learn best practices to enhance Looker dashboard performance, from query optimization to backend improvements, ensuring faster and efficient insights.
Looker - Fundamentalsadvanced
How Analytics Engineers Can Implement Incremental Models
Learn how to implement incremental models in dbt, optimizing data processing by handling only new or modified data. Explore types, benefits, and examples.
dbtData Modeling - Data Modelingintermediate
Top Data Modeling Best Practices for Efficient Analytics Engineering
Explore essential data modeling best practices to build scalable and efficient models, ensuring data accuracy and performance in analytics engineering.
Analytics EngineeringData Modeling
The Analytics Engineering Interview Kit
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