Sitemap - 2025 - Pipeline To Insights
Getting Started with OpenMetadata: An Open-Source Data Catalogue Solution
Storage Fundamentals for Data Engineers
Pandas vs. Polars vs. DuckDB vs. PySpark: Benchmarking Libraries with Real Experiments
How to Gather Requirements Effectively as a Data Engineer
How to Succeed in Data Engineering Interviews
Week 28/31: Behavioural Interview Questions for Data Engineers
Infrastructure as Code for Data Engineers
Week 27/31: Data Visualisation for Data Engineers
Survival Tips for Data Engineers in the Age of Generative AI
Week 26/31: Machine Learning for Data Engineers
Centralised Orchestration in Dagster Using Code Locations
Week 26/31: Data Governance for Data Engineers
Week 25/34: System Design for Data Engineering Interviews
Week 24/31: DevOps and DataOps Practices for Data Engineering Interviews
7 key factors every Data Engineer Should Consider When Choosing Tools
Week 23/31: Data Contracts for Data Engineering Interviews
Stop Being the Invisible Data Engineer: 8 Strategies for Career Success
Week 23/31: Real-Time Processing for Data Engineering Interviews
How to Choose Between Batch and Stream Processing?
Semantic Models: Data Modelling for the Modern Data Stack
Week 22/34: Batch Processing for Data Engineering Interviews
Proactive Mindset for Data Engineers
Week 21/34: Open Table Formats for Data Engineering Interviews
Week 20/34: Data Storage Paradigms for Data Engineering Interviews
dbt in Action #4: Snapshots and Slowly Changing Dimensions
Week 19/31: Cloud Computing for Data Engineering Interviews
What is Data Observability and How Does It Support Data Quality
Week 18/34: Data Pipelines and Workflow Orchestration for Data Engineering Interviews (Part #3)
Pipeline Design and Implementation for Small-Scale Data Pipelines
Week 17/31: Data Pipelines and Workflow Orchestration for Data Engineering Interviews (Part #2)
dbt in Action #3: Analyses, Materialisations and Incremental Models
What is Data Architecture and why Data Engineers should consider it
Week 16/34: Data Pipelines and Workflow Orchestration for Data Engineering Interviews (Part #1)
Common Data Engineering mistakes and how to avoid them
Week 15/34: Data Transformation with dbt for Data Engineering Interviews
Pandas vs. Polars: Benchmarking Dataframe Libraries with Real Experiments
Week 14/34: Data Engineering with Databricks for Data Engineering Interviews
Week 13/34: Spark Fundamentals for Data Engineers
dbt in Action #2: Seeds, Tests and Macros
Week 12/34: Data Warehousing with Snowflake for Data Engineering Interviews
Metadata: What it is and why do we need it?
Week 11/34: ETL and ELT Processes for Data Engineering Interviews #2
How to Transition from Data Analytics to Data Engineering
Week 10/33: ETL and ELT Processes for Data Engineering Interviews #1
Data Serialisation: Choosing the Best Format for Performance and Efficiency
dbt in Action #1: Fundamentals
Why Data Quality Is the Key to AI Success
Week 9/33: Data Structures and Algorithms for Data Engineering Interviews
Implementing Data Quality Framework with dbt
Week 8/31: Programming for Data Engineering Interviews
Why I Moved from Data Science to Data Engineering
Zero-ETL: What It Is and What It Isn't
Week 7/31: NoSQL and Vector Databases for Data Engineering Interviews
Building Trust in Data: The Fundamentals of Data Quality
Week 6/31: Data Modelling for Data Engineering Interviews (Part #3)
Week #9: 100 Days of SQL Optimisation
Week 5/31: Data Modelling for Data Engineering Interviews (Part #2)
Data Modelling Fundamentals: Normalisation, 3NF and Dimensional Modelling
Week #8: 100 Days of SQL Optimisation
Week 4/31: Data Modelling for Data Engineering Interviews (Part #1)