Data analysis tools that run in your browser

Free data analysis tools

These 18 tools are free, need no account, and run entirely client-side. They are the ones most analysts reach for first: format and validate configs, convert between formats, inspect a file, decode a token.

• SQL Formatter
• JSON Format / Validate
• YAML ↔ JSON
• Unix Timestamp / Timezone
• Parquet → JSON
• Parquet Inspector
• Parquet Row Group Pruner
• Cron Expression Parser
• JWT Debugger
• Connection String Parser
• Regex Tester
• Text Diff Checker
• JSONL Inspector
• Data Type Inspector
• UUID Generator
• Hash Generator
• Base64 Encode / Decode
• URL Encode / Decode

Pro data analysis tools

The other 17 tools are part of the one-time Pro licence. They cover the heavier Parquet and Avro work — querying, profiling, schema evolution, file repair — plus the validators, SQL explainer and test-data generators that turn a one-off check into a repeatable workflow.

• dbt YAML Validator
• JSON Schema Validator
• JSON → CREATE TABLE
• JSON → SQL INSERT
• CSV → Parquet
• Parquet → CSV
• Merge CSVs → Parquet
• CSV → CREATE TABLE
• Parquet Query
• Parquet Schema Evolution
• Parquet Data Profiler
• Parquet File Repair
• Avro File Inspector
• Avro Schema Validator
• SQL Query Explainer
• Test Data Generator
• Mock SQL Data

Data analysis tools by task

Data analytics tools and techniques

Most analysis work is not modelling. It is the unglamorous middle: understanding what is in the file, making it consistent, and getting it into the tool that answers the question. A practical technique stack looks like this.

• Profile before you trust it. Row counts, nulls, min/max and distinct values catch the upstream bug before it reaches a chart. Use the Parquet Data Profiler or the Data Type Inspector.
• Validate the contract. A schema that drifts silently is the most expensive kind of bug. The JSON Schema Validator, dbt YAML Validator and Avro Schema Validator check the shape before it lands in a warehouse.
• Clean and convert. Normalise formats at the edge:Parquet to CSV, CSV to Parquet, YAML and JSON.
• Query a slice, not the warehouse. When you only need one question answered, Parquet Query filters and exports locally.
• Document the shape. Turn JSON into DDL or INSERTs with JSON to CREATE TABLE and JSON to SQL INSERT.

Data analysis tools vs Excel, Python and BI

These tools are not a replacement for a spreadsheet, a notebook or a BI platform — they replace the ad-funded converter you would otherwise use for a one-off job.

• Excel is still the fastest place for a small, clean table. Use DataEngUtils to get the file into a shape Excel can open, and to check it before you do.
• pandas and Polars are the right tools for repeatable analysis. DataEngUtils is the ten-second check in front of the notebook: what is in this Parquet file, does the schema match, why does this CSV fail to load.
• BI tools answer the business question. They are slow and expensive to use as a file inspector, which is where a local Parquet profiler earns its place.
• Other online converters are convenient but upload your data. If the file is a sample or is already public, that is a fair trade. If it belongs to a customer, it is not.