Challenges and Strategies for Integrating AI into Data Pipelines

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)