Description:
AI isn’t just about better models — it’s about better data infrastructure. In this video, we break down why data lakes have quietly become one of the most important foundations for modern AI systems.
Traditional analytics relied on clean tables and predefined schemas. AI doesn’t. Modern AI consumes logs, text, images, embeddings, model outputs, and feedback loops — data that changes constantly and doesn’t fit neatly into rows and columns.
You’ll learn:
What a data lake really is (and what it’s not)
Data lakes vs data warehouses — and why AI needs both
Why schema-on-read is critical for AI workflows
How data lakes support experimentation, reuse, and model iteration
The rise of lakehouse architectures
Open table formats like Delta Lake, Apache Iceberg, and Hudi
How tools like Spark, DuckDB, Trino, and BigQuery query lake data
Why data history, versioning, and governance matter for trustworthy AI
This video is for data scientists, ML engineers, data engineers, and tech leaders who want AI systems that scale over time — without rebuilding pipelines every year.
AI thrives when data is treated as a long-term asset, not a one-off input. Data lakes make that possible.
???? Comment below: Are you using a data lake, a warehouse, or both?
#DataLakes #ArtificialIntelligence #DataEngineering
#DataScience #MachineLearning #BigData
#Lakehouse #DeltaLake #ApacheIceberg #ApacheHudi
#AIInfrastructure #MLEngineering #Analytics
#CloudData #ModernDataStack
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