<b>Delta Lake Optimization Techniques for Scalable Lakehouse Architectures </b>

This paper provides a validated, tested playbook to tune Delta Lake on cloud object store to a terabyte-level scale. It is posed as a multi-objective control problem that improves the read latency, write cost, maintenance cost, and reliability against bursty ingestion and mixed workloads. These methods include metadata (checkpoint cadence, log compaction, conservative VACUUM), file hygiene (bin-packing to 512-1024 MB, small file prevention), partitioning and skew (time+entity, salting), clustering to achieve selectivity, and read/write performance (AQE, dynamic file pruning, broadcast caps, shuffle parallelism, selective caching). Guardrails, re-writable budgets, cooldowns, canaries, small-file ratio, fragmentation, pruning hit rates, conflicts, coldstart timings, and visibility into these allow closed-loop reactions. TPC-DS-like and telemetry datasets (0.510 TB) have been applied on evaluation using back ETL, streaming CDC upserts, ad-hoc selective queries, and BI scans by repeatedly running the tests and building interval confidence. Compaction decreases the overhead of planning; combined with workload-driven clustering, it provides the most significant performance boost on narrow predicates: for median and P95 tail latency, it increases 44-58 percent and 37-49 percent, respectively, at low to medium selectivity (0.1-5 percent). Staged upserts purges candidate files batch and file-level statistics are cut in bytes, shuffled by 2741 percent, and P95 times are sunk on MERGE by 2436 percent. This is because daily compaction maintains a small-file ratio at ~8% compared to hourly schedules with minimal to no viable read advantage (write amplification increases ~1.6 x). AQE truncates long tails; past two nesting columns, the returns vanish, and effectiveness has a broad 512 MB to 1 GB file-size peak.