Data engineering tools

Format messy warehouse SQL in your browser with a formatter tuned for analytics queries — CTEs, window functions, nested subqueries and dialect-specific keywords stay readable without sending SQL to a web beautifier. DataEngUtils supports nine dialects (Postgres, BigQuery, Snowflake, Redshift and more), keyword casing, indentation and dbt mode, and it works offline once installed. Unlike ad-supported online formatters, nothing you paste is logged on a remote server, which matters when queries reference production schemas, PII columns or internal project names you would rather not leak.

Format & validate

JSON Format / Validate is built for data engineers who need to validate and format pipeline configs, warehouse DDL, and serialized records before they hit production systems on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, JSON Format / Validate keeps your data on your machine.

Format & validate

Validate dbt schema.yml and model YAML before it breaks parse-time in CI. DataEngUtils checks structure, indentation and common schema-file mistakes in your browser, so you catch errors while editing instead of after a long dbt run. Analytics engineers use it when refactoring sources, when reviewers paste YAML fragments in chat, or when you need a quick sanity check on tests and documentation blocks without running a full parse command. Keeping validation client-side means project names, database identifiers and column descriptions from private repos never touch a web validator.

Format & validate

JSON Schema Validator is built for data engineers who need to validate and format pipeline configs, warehouse DDL, and serialized records before they hit production systems on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is part of the Pro Parquet and Avro workbench — one of 35 data-engineering tools that read files locally and never upload them. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, JSON Schema Validator keeps your data on your machine.

Format & validate

YAML ↔ JSON is built for data engineers who need to convert between file formats and schema representations used in analytics engineering workflows on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, YAML ↔ JSON keeps your data on your machine.

Convert

Unix Timestamp / Timezone is built for data engineers who need to convert between file formats and schema representations used in analytics engineering workflows on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Unix Timestamp / Timezone keeps your data on your machine.

Convert

Parquet → JSON is built for data engineers who need to convert between file formats and schema representations used in analytics engineering workflows on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Parquet → JSON keeps your data on your machine.

Convert

Generate CREATE TABLE statements from JSON payloads without pasting sample records into a web form. DataEngUtils infers column names and types from representative objects and outputs dialect-specific DDL you can paste into migrations or a scratch worksheet. Analytics engineers reach for it when product teams ship event samples, when APIs change fields mid-sprint, or when you need a quick staging table before writing a dbt source block. Because the tool runs in your browser, customer JSON is never exposed to a third-party server.

Convert

JSON → SQL INSERT is built for data engineers who need to convert between file formats and schema representations used in analytics engineering workflows on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is part of the Pro Parquet and Avro workbench — one of 35 data-engineering tools that read files locally and never upload them. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, JSON → SQL INSERT keeps your data on your machine.

Convert

Turn CSV exports into columnar Parquet without shipping them to a random web converter. DataEngUtils parses your CSV locally with an Apache Arrow engine compiled to WebAssembly, auto-detects the delimiter, lets you skip preamble rows, and writes a compressed Parquet file you can download. It is built for recurring pipeline tasks: drop a multi-hundred-megabyte export, confirm inferred types, and produce Parquet that downstream systems accept. Because everything runs in your browser, you can convert customer files, finance extracts and ad-hoc dumps that should never transit a third-party server.

Convert

Convert Parquet to CSV in your browser without uploading the file to a cloud converter. DataEngUtils reads the Parquet bytes locally with an Apache Arrow engine compiled to WebAssembly, then streams the output back to you — select columns, set the delimiter, and download the CSV. Nothing is sent to a server, so you can export customer data, finance extracts, and production drops that would never pass a security review if uploaded elsewhere. The engine handles Snappy, Zstd, Gzip, Brotli and LZ4 compression, and the page stays responsive because parsing happens inside a Web Worker.

Convert

Combine multiple CSV extracts into one Parquet dataset in your browser — ideal when logs, exports or shard files land separately but need to load as a single table. DataEngUtils merges locally with an Apache Arrow engine compiled to WebAssembly, so you never concatenate sensitive rows in an external uploader. Align schemas, drop duplicate headers and emit one compact Parquet file ready for DuckDB, Spark or warehouse COPY commands. It is tuned for data engineers who receive weekly vendor dumps and want a repeatable merge step before validation, all without leaving the browser.

Convert

CSV → CREATE TABLE is built for data engineers who need to convert between file formats and schema representations used in analytics engineering workflows on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is part of the Pro Parquet and Avro workbench — one of 35 data-engineering tools that read files locally and never upload them. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, CSV → CREATE TABLE keeps your data on your machine.

Convert

Inspect Parquet metadata, row groups, column statistics and encodings without uploading the file to a cloud viewer. DataEngUtils opens Parquet locally with an Apache Arrow engine compiled to WebAssembly so you can debug schema drift, missing statistics and surprisingly large row groups before a pipeline incident becomes a pager alert. Data engineers use it when a job suddenly doubles storage, when a partner changes a nested field, or when you need to confirm compression before approving a release. Everything stays on your device, which is what you want for regulated datasets and production extracts.

Inspect & debug

Parquet Row Group Pruner is built for data engineers who need to inspect, debug, and explain data artifacts when something breaks in staging or a support ticket lands on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Parquet Row Group Pruner keeps your data on your machine.

Inspect & debug

Parse and explain cron expressions in plain language when schedules from Airflow, Kubernetes or legacy cron files need a second opinion. DataEngUtils shows the human-readable description, the next ten run times in local time and UTC, and a seven-day heatmap of when the job fires — all in your browser, so internal job names never hit a public cron calculator. Platform and data engineers keep it open next to the terminal when onboarding new pipelines or debugging why a DAG did not trigger, especially for expressions copied from a chat that omit timezone context.

Inspect & debug

Decode and inspect JSON Web Tokens in your browser without pasting bearer tokens into a public JWT debugger. DataEngUtils splits the header and payload, validates base64url encoding and highlights expirations locally — the workflow you want when debugging service-to-service auth, OAuth callbacks or short-lived warehouse tokens. Security-conscious teams prefer an offline decoder because tokens embed internal audience claims, tenant identifiers and scopes that should never be logged by a third-party website. It is free, needs no account, and works offline once installed as an app.

Inspect & debug

Break apart database connection strings for Postgres, MySQL, Snowflake, Redis and Kafka without pasting credentials into a public parser. DataEngUtils extracts host, port, database, user and query parameters locally — critical when rotating secrets, debugging VPN issues or documenting which environment a stale .env entry points to. Data platform teams use it because connection URIs show up in logs, Helm values and incident threads where exfiltrating even redacted strings to a website is unacceptable. The parser keeps passwords masked while still showing the parameters you need to compare staging against production endpoints.

Inspect & debug

Regex Tester is built for data engineers who need to inspect, debug, and explain data artifacts when something breaks in staging or a support ticket lands on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Regex Tester keeps your data on your machine.

Inspect & debug

Text Diff Checker is built for data engineers who need to inspect, debug, and explain data artifacts when something breaks in staging or a support ticket lands on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Text Diff Checker keeps your data on your machine.

Inspect & debug

Inspect JSONL (NDJSON) files line by line without uploading them to a converter. DataEngUtils validates each line, flags the ones that fail to parse, and summarises the file so you can find a single bad record in a log stream or event export. Data engineers use it when a JSONL dump fails to load, when you need a quick count and shape of events, or when a producer emits one malformed line among millions. It is free, needs no account, runs entirely in your browser, and works offline once you install the app.

Inspect & debug

Data Type Inspector is built for data engineers who need to inspect, debug, and explain data artifacts when something breaks in staging or a support ticket lands on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Data Type Inspector keeps your data on your machine.

Inspect & debug

Query a Parquet file in your browser — pick columns, filter rows and limit the result, then export to CSV, Parquet or JSON. DataEngUtils runs the query locally on an Apache Arrow engine compiled to WebAssembly, so the file never leaves your machine and no warehouse or Spark cluster is needed. It is the fast path for the questions you ask a dataset a dozen times a day: what is in this extract, which rows match this predicate, and what does the slice look like. Because results are computed in a Web Worker with predicate pushdown over the file's own statistics, the page stays responsive even on large files.

Inspect & debug

Compare two Parquet schemas and get a compatibility verdict before you ship a pipeline change. DataEngUtils diffs the schemas locally in your browser, listing added, removed and retyped columns and flagging whether the change is backward or forward compatible for readers. It is the check you want during code review when a model is about to change shape, when a producer ships a new field, or when an old consumer might break. Because the comparison runs client-side on an Apache Arrow engine compiled to WebAssembly, the schemas and the underlying files never leave your machine.

Inspect & debug

Profile a Parquet file in your browser and get per-column statistics — null percentage, distinct counts, min and max, mean and standard deviation, histograms and top values. DataEngUtils computes the profile locally with an Apache Arrow engine compiled to WebAssembly, so production datasets never leave your machine. Reach for it when you inherit an extract, when you want a data-quality read before modelling, or when a dashboard number looks wrong and you need to check distributions fast. There is nothing to install and no query to write; drop the file and read the profile.

Inspect & debug

Parquet File Repair is built for data engineers who need to inspect, debug, and explain data artifacts when something breaks in staging or a support ticket lands on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is part of the Pro Parquet and Avro workbench — one of 35 data-engineering tools that read files locally and never upload them. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Parquet File Repair keeps your data on your machine.

Inspect & debug

Avro File Inspector is built for data engineers who need to inspect, debug, and explain data artifacts when something breaks in staging or a support ticket lands on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is part of the Pro Parquet and Avro workbench — one of 35 data-engineering tools that read files locally and never upload them. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Avro File Inspector keeps your data on your machine.

Inspect & debug

Avro Schema Validator is built for data engineers who need to inspect, debug, and explain data artifacts when something breaks in staging or a support ticket lands on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is part of the Pro Parquet and Avro workbench — one of 35 data-engineering tools that read files locally and never upload them. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Avro Schema Validator keeps your data on your machine.

Inspect & debug

SQL Query Explainer is built for data engineers who need to inspect, debug, and explain data artifacts when something breaks in staging or a support ticket lands on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is part of the Pro Parquet and Avro workbench — one of 35 data-engineering tools that read files locally and never upload them. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, SQL Query Explainer keeps your data on your machine.

Inspect & debug

UUID Generator is built for data engineers who need to generate identifiers, test rows, and mock payloads when building fixtures or load tests on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, UUID Generator keeps your data on your machine.

Generate

Hash Generator is built for data engineers who need to generate identifiers, test rows, and mock payloads when building fixtures or load tests on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Hash Generator keeps your data on your machine.

Generate

Test Data Generator is built for data engineers who need to generate identifiers, test rows, and mock payloads when building fixtures or load tests on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is part of the Pro Parquet and Avro workbench — one of 35 data-engineering tools that read files locally and never upload them. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Test Data Generator keeps your data on your machine.

Generate

Mock SQL Data is built for data engineers who need to generate identifiers, test rows, and mock payloads when building fixtures or load tests on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is part of the Pro Parquet and Avro workbench — one of 35 data-engineering tools that read files locally and never upload them. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Mock SQL Data keeps your data on your machine.

Generate

Base64 Encode / Decode is built for data engineers who need to encode and decode payloads for APIs, logs, and storage without pasting secrets into ad-hoc websites on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, Base64 Encode / Decode keeps your data on your machine.

Encode & decode

URL Encode / Decode is built for data engineers who need to encode and decode payloads for APIs, logs, and storage without pasting secrets into ad-hoc websites on a daily basis. Unlike online utilities that upload your production samples to a third-party server, DataEngUtils runs entirely in your browser: the file is read locally and never leaves your device. It is free to use — no install, no account — and sits alongside 35 data-engineering tools, from the SQL Formatter and JSON tools to a full Parquet and Avro workbench. Whether you are debugging a failed dbt run, preparing a handoff for analytics, or validating a file before it lands in Snowflake or BigQuery, URL Encode / Decode keeps your data on your machine.

Encode & decode