Cloud Data Platform Cost Optimization Guide

Cloud Data Platform Cost Optimization

/tldr: Strategies to minimize operational costs for Spark/Lakehouse compute and storage without sacrificing performance.

Compute Efficiency Storage Management Serverless & Spot Performance Engines

1. Compute Optimization: Minimizing Runtime

Compute resources (VMs running Spark workers) typically represent the highest cost in a Lakehouse architecture. The goal is to maximize utilization and minimize idle time.

A. Leveraging Spot Instances

Spot instances (AWS Spot, Azure Low-Priority VMs, GCP Preemptible VMs) offer up to **90% discount** compared to On-Demand instances.

  • **Use Case:** Ideal for fault-tolerant workloads (e.g., ETL jobs) where sudden termination is acceptable.
  • **Strategy:** Use Spot for the majority (e.g., 80-90%) of worker nodes and keep a few On-Demand nodes for stability (the driver should always be On-Demand).

B. Auto-Scaling and Auto-Termination

These two features eliminate paying for idle clusters:

  • **Auto-Scaling (Vertical/Horizontal):** Automatically adds or removes worker nodes based on queue depth and resource utilization, ensuring you only pay for what's actively processing.
  • **Auto-Termination:** Crucial for interactive clusters. It shuts down the entire cluster after a defined period of inactivity (e.g., 60 minutes), preventing perpetual standby charges.

C. Right-Sizing and Performance Tuning

Selecting the correct VM size (right-sizing) and configuration for the job:

  • **I/O Bound:** Use instances with optimized network/disk bandwidth (e.g., AWS C-series).
  • **Memory Bound:** Use memory-optimized instances (e.g., AWS R-series) for large shuffles or caching.
  • **Avoid Oversizing:** Running a small job on a massive cluster wastes resources. Match cluster resources to the data volume and processing complexity.

D. Leveraging Specialized Engines (Photon)

Engines like **Databricks Photon** or highly optimized Spark runtimes are essential for cost savings, as they reduce the **total runtime** of a job.

  • **Mechanism:** Photon is a vectorized, high-performance query engine written in C++ that executes Spark API calls much faster than the standard JVM-based Spark engine.
  • **Result:** A job that takes 1 hour on standard Spark might take 15 minutes on Photon, reducing compute costs by **75%** for that specific job duration.

2. Storage Optimization: Reducing Data Read & Storage

While typically cheaper than compute, object storage costs (and the associated I/O fees) add up, especially for large datasets.

A. Compaction and File Size

Running regular compaction (covered in the previous topic) is a direct cost-saver:

  • **I/O Cost Reduction:** Fewer large files mean fewer API calls (GET requests) to cloud storage, which directly lowers I/O transaction costs.
  • **Efficiency:** Optimal file size (e.g., 512MB to 1GB) maximizes read throughput by aligning with cloud storage architecture and minimizing the overhead of opening files.
OPTIMIZE table_name

B. Data Life Cycle Management and Tiering

Move older, less frequently accessed data to cheaper storage tiers:

  • **Hot Storage:** Used for frequently queried data (e.g., last 30 days).
  • **Infrequent Access (IA) / Cold Storage:** Used for archival or low-access historical data (e.g., AWS Glacier Instant Retrieval, Azure Archive).
  • **Strategy:** Implement retention policies (via `VACUUM`) and life cycle rules to automatically transition partitions or tables to cheaper tiers after they age out of the active window.

3. Data Skipping and Indexing

A core principle of cost efficiency is **only reading the data you absolutely need**. Effective indexing and file layout maximize data skipping, which saves both I/O costs and compute time.

A. Z-Ordering / Data Clustering

This technique physically organizes related data within the same set of files based on multiple dimensions.

  • **Impact:** When a query filters on the Z-Ordered columns, the query engine can skip reading 90%+ of the files, drastically reducing compute processing and I/O.
  • **Requirement:** Requires running `OPTIMIZE ... ZORDER BY` regularly on heavily filtered tables.

B. Dynamic Partition Pruning (DPP)

DPP is a query optimization technique (especially in Spark) that dynamically determines which partitions need to be scanned during join operations.

  • **Mechanism:** If you join a small table (dimension) to a large table (fact), DPP uses the filtered rows from the small table to narrow down the partitions to read in the large table.
  • **Benefit:** This avoids full scans on the large fact table, saving substantial read and compute time during ETL.

Effective cost optimization relies on balancing compute (speed) with storage (size). Faster runtime (Photon, Z-Ordering) often results in the largest cost savings.

Data Engineering Fundamentals: Cost Management