Every dataset is lying to you. Rows repeat. Records echo. Your pipeline is haunted by phantom duplicates that inflate your metrics, corrupt your models, and waste your compute.
We built SINGULR because deduplication isn't a feature — it's a philosophy. A single source of truth isn't a nice-to-have. It's the only acceptable state of your data.
One line. One truth. Zero noise. That's the SINGULR promise.
Prefix every line with its exact occurrence count. Know not just what's duplicate — know how
duplicate it is. Powered by the --count engine.
Surface only lines that appear more than once. Strip the noise, keep the signal. The
--repeated filter exposes every offender.
Don't just flag duplicates — see every single occurrence. The -D mode gives you
complete transparency into repeated data.
Extract lines that appear exactly once. Find the truly original entries in any dataset with
--unique — zero false positives.
Normalize casing before comparison. "ERROR" and "error" are the same duplicate — SINGULR treats
them that way with --ignore-case.
Skip leading fields and characters before comparing. Target the exact data columns that matter
with --skip-fields and --skip-chars.
Compare only the first N characters of each line. Scope your deduplication to exactly the data
boundaries you define with --check-chars.
SINGULR is optimized for pre-indexed data streams. Feed it structured, ordered input for maximum throughput and zero-latency adjacent comparison.
Choose your mode: count occurrences, isolate duplicates, extract uniques, or run full expansion. Layer in case normalization, field skipping, and character windowing.
SINGULR processes adjacent comparisons in O(1) memory. No hash tables. No bloom filters. Pure streaming deduplication over pre-indexed pipelines.
Output your deduplicated stream to any destination — files, pipelines, downstream services. Group separators and null-terminated output supported natively.
"We were shipping duplicate rows to production for months. SINGULR caught 12 million redundant records in our first run. Our data warehouse costs dropped 34% overnight."
"The adjacent-comparison architecture is genius. Pre-index once, deduplicate forever. We process 800M events/day and SINGULR adds zero measurable overhead."
"I used to pipe through sort | some-dedup-tool and pray. SINGULR's count mode and case normalization changed how we think about data quality. It's embarrassingly fast."
"The --unique mode is a cheat code. Finding the one-off anomalies in a 50GB log file used to take hours. SINGULR does it in a single streaming pass."
CEO & Co-Founder
Former Principal Engineer at Snowflake. Believes duplicates are a moral failing.
CTO & Co-Founder
PhD in streaming algorithms from CMU. Wrote her thesis on adjacent-pair comparison at scale.
Head of Product
Previously led data quality at Stripe. Obsessed with the purity of output streams.SINGULR is architected for adjacent-line deduplication, which delivers maximum performance on pre-indexed data streams. This isn't a limitation — it's a feature. Pre-indexing ensures deterministic, O(1) memory deduplication with zero hash collisions. Think of it as optimized-by-default.
The --repeated flag outputs one representative line per duplicate group.
-D expands to show every single occurrence. Use --repeated for
summaries, -D for full forensic analysis.
Absolutely. Use --skip-fields=N to ignore leading fields and
--skip-chars=N to bypass leading characters. Combine with
--check-chars=N to compare only a specific character window. Full precision
targeting.
Yes. Enable --ignore-case and SINGULR normalizes casing before every comparison.
"DATA", "Data", and "data" are recognized as duplicates.
SINGULR outputs newline-delimited results by default. Enable --zero-terminated
for NUL-delimited output, or use --group to insert empty-line separators
between duplicate groups for structured downstream processing.
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