big-data-analyticslisted

# Big Data Analytics ## Objective Scale only when measured data and workload justify the operational complexity. ## Workflow 1. Measure data size, file count, partitions, skew, growth, query pattern, latency target, memory, compute, network, and cost constraints. 2. Prototype the logic on sampled data with a local dataframe or analytical database and establish correctness. 3. Choose Polars, Dask, Spark, or another approved engine based on scale, team skills, deployment, streaming, connectors, and operations. 4. Design partitioning, file formats, schemas, joins, shuffles, caching, checkpoints, and incremental processing. 5. Test skew, small files, serialization, retries, worker loss, partial output, duplicate processing, and backfills. 6. Monitor job stages, spill, memory, shuffle, I/O, executor failures, cost, and output quality. 7. Reconcile distributed output against the validated sample and document capacity, recovery, and simpler fallback paths. ## Safety and authorization - Do not introduce a cluster when DuckDB, Polars, or bounded SQL can meet the requirement. - Keep secrets out of job configuration and protect temporary storage, logs, and checkpoints. - Limit test scale and cloud spend with quotas, timeouts, and explicit teardown. ## Evidence and completion - Record source, owner, scope, environment, versions, grain, row counts, assumptions, exclusions, and authorization. - Preserve reproducible queries or transformations without credentials or unnecessary conf