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Data Engineering

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Data Quality Responsibility Is a Product Problem, Not an Engineering Failure

data-engineering

Featured

Data Quality Responsibility Is a Product Problem, Not an Engineering Failure

Why engineering teams are tired of cleaning up upstream messes that product managers should own from day one.

#data-engineering#data-governance#data-quality...

data-architecture

The Medallion Architecture Debate: Why Nobody Agrees on Silver vs. Gold

Exploring the hidden inconsistencies in how teams implement raw-silver-gold layering models, from transformation logic disagreements to the platinum layer controversy.

#data-architecture#data-engineering#databricks...

data-architecture

The Postgres Betrayal: When Your Database Becomes a Data Lakehouse

Snowflake just open-sourced pg_lake, letting Postgres talk directly to Iceberg tables. The SQL you know now touches petabytes.

#data-architecture#data-lake#postgres...

cloud-storage

Enterprise Cloud Storage Showdown: Why Your ‘Strategic’ S3 Decision Is Just the Beginning of Your Problems

Analyzing enterprise cloud storage decisions through practitioner experiences, including trade-offs between performance, cost, and integration within data ecosystems.

#cloud-storage#cost-optimization#data-lakes...

artificial-intelligence

The Data Scientist’s Job Isn’t Disappearing, But 80% of Your Work Is

AI agents are transforming data workflows, but human expertise remains crucial for strategy, interpretation, and ethical leadership.

#artificial-intelligence#automation#career-development...

data-engineering

JSON in PySpark: The Performance Trap You’re Probably Falling For

Why writing large PySpark DataFrames as JSON to S3 is fundamentally flawed – and what you should do instead

#data-engineering#json#pyspark...

apache-iceberg

Parquet Was Never Enough: Why Your Raw Data Lake Is a Data Swamp Waiting to Happen

While Parquet excels at storage efficiency, it lacks transactional integrity. Open table formats like Iceberg, Delta Lake, and Hudi are increasingly seen as essential for reliable data pipelines by adding ACID compliance and schema evolution.

#apache-iceberg#apache-parquet#data-architecture...

data-engineering

The Fivetran-dbt “Marriage of Convenience” That’s Quietly Declaring War on Snowflake

The merger between Fivetran and dbt Labs isn’t just consolidation, it’s a strategic power play to flip the data stack hierarchy and make warehouses the commodities, not the kings.

#data-engineering#databricks#dbt...

data-architecture

The Great Data Tooling Debate: SQL vs. Spark Ecosystem

Enterprise data teams are increasingly questioning the need for complex tooling stacks when SQL in platforms like Snowflake can handle most data transformations.

#data-architecture#data-engineering#snowflake...

cdc

CDC vs. Microbatching: The Data Pipeline Cold War You’re Fighting Daily

Change Data Capture and microbatching aren’t just technical choices, they’re architectural philosophies that dictate how responsive your data systems can be.

#cdc#data-pipelines#microbatching...

best-practices

ETL Testing: The Silent Killer of Data Engineering Projects

Why testing ETL pipelines remains the most overlooked and underestimated challenge in modern data architectures

#best-practices#data-engineering#data-pipelines...

data-warehouse

The Great Data Dream vs. Architectural Reality: Why Your Low-Code Warehouse Choice Matters

Snowflake and Microsoft Fabric face off as organizations without mature data teams demand centralized platforms. Here’s what the battle reveals about accessibility versus performance.

#data-warehouse#enterprise-analytics#low-code...