SQL Window Functions: ROW_NUMBER, RANK and DENSE_RANK Explained
Learn how window functions keep row-level detail while calculating rankings and other values across related rows.
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Selected from the ARTECH libraryFeatured Articles
Snowflake Architecture Explained for Data Engineers
Understand Snowflake's storage, compute, and cloud services layers and how they work together in warehouse workloads.
Data Engineer Roadmap: From SQL to Cloud Data Engineering
A practical sequence for learning the foundations, platforms, operations, and communication skills used in data engineering.
THE LATEST
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Compare where transformation happens, what each pattern optimizes for, and how cloud warehouses influence the design.
Azure Data Factory Explained for Beginners
Learn the building blocks of an ADF pipeline, from connections and datasets to triggers, parameters, and monitoring.
How to Explain a Data Engineering Project in an Interview
Tell a clear, evidence-based project story that connects the business problem to the architecture and your contribution.
Python for Data Engineers: What Should You Learn?
Focus Python study on readable functions, reliable file and API handling, validation, logging, and small automation tasks.
Databricks and PySpark: A Practical Introduction
Connect Databricks workspace concepts to Spark DataFrames, lazy transformations, partitions, joins, and Delta tables.
SCD Type 1 vs SCD Type 2 Explained
Choose whether a dimension overwrites old attributes or preserves versions with effective dates and a current-row indicator.
How to Prepare for a Data Engineer Interview
Build a focused preparation plan across SQL, Python, platform concepts, project stories, scenarios, and practice conversations.
CDC in Data Engineering: Concepts and Architecture
Design incremental processing around inserts, updates, deletes, ordering, checkpoints, data quality, and replay safety.
Data Quality Checks Every Data Engineer Should Know
Turn quality expectations into checks for completeness, uniqueness, schema, reconciliation, freshness, and recoverable failures.
PySpark DataFrames: Transformations, Actions, and Partitions
A focused PySpark guide to DataFrame operations, lazy execution, joins, partition behavior, and practical performance checks.
Cloud Data Platforms: Storage, Compute, and Workload Design
Separate durable data from processing capacity and make cloud platform choices around workload, security, and operating needs.