This episode of "How AI Is Built" is all about data processing for AI. Abhishek Choudhary and Nicolay discuss Spark and alternatives to process data so it is AI-ready.

Spark is a distributed system that allows for fast data processing by utilizing memory. It uses a dataframe representation "RDD" to simplify data processing.

When should you use Spark to process your data for your AI Systems?

→ Use Spark when:

• Your data exceeds terabytes in volume
• You expect unpredictable data growth
• Your pipeline involves multiple complex operations
• You already have a Spark cluster (e.g., Databricks)
• Your team has strong Spark expertise
• You need distributed computing for performance
• Budget allows for Spark infrastructure costs

→ Consider alternatives when:

• Dealing with datasets under 1TB
• In early stages of AI development
• Budget constraints limit infrastructure spending
• Simpler tools like Pandas or DuckDB suffice

Spark isn't always necessary. Evaluate your specific needs and resources before committing to a Spark-based solution for AI data processing.

In today’s episode of How AI Is Built, Abhishek and I discuss data processing:

• When to use Spark vs. alternatives for data processing
• Key components of Spark: RDDs, DataFrames, and SQL
• Integrating AI into data pipelines
• Challenges with LLM latency and consistency
• Data storage strategies for AI workloads
• Orchestration tools for data pipelines
• Tips for making LLMs more reliable in production

Abhishek Choudhary:

• LinkedIn
• GitHub
• X (Twitter)

Nicolay Gerold:

• ⁠LinkedIn⁠
• ⁠X (Twitter)