# bitly/data_hacks

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1,979 stars · 190 forks · Python

## Links

- GitHub: https://github.com/bitly/data_hacks
- Homepage: http://github.com/bitly/data_hacks
- awesome-repositories: https://awesome-repositories.com/repository/bitly-data-hacks.md

## Description

Data Hacks is a collection of command-line utilities designed for statistical computation, real-time stream processing, and text-based data visualization. The toolkit enables users to perform rapid analysis on large datasets directly within the terminal by processing information through standard input and output streams.

The project distinguishes itself through its focus on memory-efficient, stream-oriented operations that allow for the analysis of large-scale data without requiring heavy infrastructure. It utilizes stateless functional transformations and reservoir sampling to handle data streams, ensuring that computational tasks remain performant and representative even when dealing with unknown or infinite input sizes.

The suite provides a range of capabilities for statistical analysis, including the calculation of percentiles and the generation of frequency distributions. Users can visualize these results through ASCII-based histograms and bar charts, providing immediate feedback on data patterns and outliers. Additionally, the tools support operational controls such as time-boxed execution and periodic command scheduling to facilitate automated monitoring and resource-efficient workflows.

## Tags

### Data & Databases

- [Command Line Analytics Tools](https://awesome-repositories.com/f/data-databases/command-line-analytics-tools.md) — Provides a collection of terminal utilities for statistical computation, stream processing, and data visualization.
- [Data Stream Filters](https://awesome-repositories.com/f/data-databases/data-stream-filters.md) — Filters and samples large volumes of incoming data in real-time without requiring heavy database infrastructure.
- [Reservoir Samplers](https://awesome-repositories.com/f/data-databases/data-management/sample-data-loaders/statistical-sampling/reservoir-samplers.md) — Implements reservoir sampling to select representative random subsets from infinite data streams.
- [Single-Sample Stream Iterators](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/batch-processing-systems/data-iterators/async-iterable-streams/single-sample-stream-iterators.md) — Samples data streams by selecting random subsets of records to facilitate analysis of large datasets. ([source](https://github.com/bitly/data_hacks#readme))
- [Frequency Distribution Calculators](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/data-processing/distributed-processing-frameworks/distributed-computing/frequency-distribution-calculators.md) — Generates ASCII-based histograms and bar charts to visualize frequency distributions and data density. ([source](https://github.com/bitly/data_hacks#readme))
- [Percentile Calculation](https://awesome-repositories.com/f/data-databases/percentile-calculation.md) — Calculates statistical percentiles from input data streams to identify performance patterns and outliers. ([source](https://github.com/bitly/data_hacks#readme))

### Development Tools & Productivity

- [Command Line Data Explorers](https://awesome-repositories.com/f/development-tools-productivity/command-line-data-explorers.md) — Provides lightweight terminal utilities for rapid statistical analysis and exploration of large datasets.
- [CSV Command Line Toolkits](https://awesome-repositories.com/f/development-tools-productivity/csv-command-line-toolkits.md) — Offers a suite of command-line utilities for real-time analysis of large datasets using streaming algorithms.
- [Stream Piping Utilities](https://awesome-repositories.com/f/development-tools-productivity/stream-piping-utilities.md) — Chains small, single-purpose utilities together using standard input and output streams for modular data analysis.

### Operating Systems & Systems Programming

- [Line-Based Input Processing](https://awesome-repositories.com/f/operating-systems-systems-programming/line-based-input-processing.md) — Processes data streams line-by-line to maintain a constant memory footprint regardless of input size.

### Part of an Awesome List

- [Distribution Charts](https://awesome-repositories.com/f/awesome-lists/data/charts-and-visualization/distribution-charts.md) — Visualizes frequency distributions and outliers through text-based charts and histograms.

### DevOps & Infrastructure

- [Execution Time Limits](https://awesome-repositories.com/f/devops-infrastructure/execution-rate-limiters/execution-time-limits.md) — Enforces strict duration limits on external processes to prevent resource exhaustion during long-running tasks.

### System Administration & Monitoring

- [Interval Command Monitoring](https://awesome-repositories.com/f/system-administration-monitoring/command-output-streamers/interval-command-monitoring.md) — Executes commands repeatedly at defined intervals to monitor system behavior and collect time-series data. ([source](https://github.com/bitly/data_hacks/tree/master/data_hacks))

### User Interface & Experience

- [ASCII Data Visualizations](https://awesome-repositories.com/f/user-interface-experience/ascii-data-visualizations.md) — Renders data as ASCII charts and histograms directly in the terminal for immediate visual feedback.
- [Terminal Data Visualizers](https://awesome-repositories.com/f/user-interface-experience/data-visualization-tools/data-visualization/terminal-data-visualizers.md) — Generates ASCII bar charts and histograms directly in the shell for immediate feedback on data patterns.