Data provenance tools seek to facilitate reproducible data science and auditable data analyses by capturing the analytics steps used in generating data analysis results. However, analysts must choose among workflow provenance systems, which allow arbitrary code but only track provenance at the granularity of files; prove-nance APIs, which provide tuple-level provenance, but incur overhead in all computations; and database provenance tools, which track tuple-level provenance through relational operators and support optimization, but support a limited subset of data science tasks. None of these solutions are well suited for tracing errors introduced during common ETL, record alignment, and matching tasks – for data types such as strings, images, etc.Additionally, we need a provenance archival layer to store and manage the tracked fine-grained prove-nance that enables future sophisticated reasoning about why individual output results appear or fail to appear. For reproducibility and auditing, the provenance archival system should be tamper-resistant. On the other hand, the provenance collecting over time or within the same query computation tends to be repeated partially (i.e., the same operation with the same input records in the middle computation step). Hence, we desire efficient provenance storage (i.e., it compresses repeated results). We address these challenges with novel formalisms and algorithms, implemented in the PROVision system, for reconstructing fine-grained provenance for a broad class of ETL-style workflows. We extend database-style provenance techniques to capture equivalences, support optimizations, and enable lazy evaluations. We develop solutions for storing fine-grained provenance in relational storage systems while both compressing and protecting it via cryptographic hashes. We experimentally validate our proposed solutions using both scientific and OLAP workloads.