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Building AI and Data Systems | Working where unstructured data and AI intersect | Using data engineering in AI and AI in data engineering Aisbach CEO | Host of How AI Is Built
When should you use Spark to process your data for your AI Systems? In today’s episode of How AI Is Built 🛠, Abhishek Choudhary, principal data engineer at Bayer, breaks down data processing with Spark: - 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 Key takeaways: 1. Data Volume: Spark shines when dealing with terabytes+ of data. For datasets under 100GB, simpler tools like Pandas or DuckDB often suffice. 2. Uncertainty in Data Growth: If you expect rapid, unpredictable growth in your data volume, Spark's scalability becomes a major advantage. 3. Complex Data Pipelines: Spark excels when you need to perform multiple operations (e.g., loading, transforming, aggregating) on large datasets. 4. Existing Infrastructure: If you already have a Spark cluster set up (e.g., in Databricks), leveraging it for AI data processing can be efficient. 5. Team Expertise: Consider your team's familiarity with Spark. If you're starting from scratch, simpler Python-based tools might be easier to adopt. 6. Performance Requirements: For compute-intensive operations on large datasets, Spark's distributed computing model can significantly boost performance. Link to the episode is in the comments below! Let me know what tools you use for data processing and why. #dataengineering #llms #ai