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Introduction:
Data analysts and security engineers often juggle massive datasets in formats like Parquet, JSON, or Avro, but traditional GUI tools can be slow or inaccessible on remote servers. Datui is a high-performance terminal interface for macOS and GNU/Linux that lets you explore, query, and visualize tabular data directly from your command line, whether stored locally, on S3, or via HTTP. This article unpacks Datui’s capabilities, provides step-by-step tutorials for installation and advanced usage, and integrates cybersecurity-relevant commands for log analysis and threat hunting.
Learning Objectives:
- Install and configure Datui on Linux/macOS (and Windows via WSL) to explore diverse data formats without leaving the terminal.
- Execute fuzzy keyword searches, SQL queries, and pivot operations to analyze security logs, CSV exports, or JSON threat feeds.
- Generate terminal-based charts and export visualizations for rapid incident reporting and data correlation.
You Should Know:
- Installing Datui on Linux, macOS, and Windows (WSL)
Datui is written in Rust and distributed via Cargo or pre-built binaries. For Linux (Ubuntu/Debian) and macOS, use Homebrew or cargo install. Windows users can leverage WSL2 with Ubuntu.
Step-by-step guide:
- Linux (Debian/Ubuntu): Install Rust and Cargo first if missing:
sudo apt update && sudo apt install -y curl git build-essential curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh source ~/.cargo/env
Then install Datui:
cargo install datui
– macOS: Using Homebrew:
brew install datui
Or from source:
git clone https://github.com/derekwisong/datui.git hypothetical, use actual repo from lnkd.in/eH4UdTf6 cd datui && cargo build --release sudo cp target/release/datui /usr/local/bin/
– Windows (WSL2): Install WSL2 with Ubuntu, then follow Linux steps. For native Windows, use the binary from releases page (if available) or run via WSL.
– Verify installation:
datui --version
2. Loading and Browsing Local and Remote Datasets
Datui supports local files (CSV, JSON, Parquet, Avro, ORC, Excel) and remote sources via S3 or HTTP/HTTPS. This is invaluable for security analysts who need to inspect cloud-stored logs or downloaded threat intel.
Step-by-step guide:
- Local CSV example: Download a sample security log (e.g., Suricata alerts in CSV):
wget https://raw.githubusercontent.com/johndoe/sample-logs/master/alert.csv -O alerts.csv datui alerts.csv
- Remote HTTP(S) dataset: Directly analyze a JSON feed from an API (e.g., CISA known exploited vulnerabilities):
datui https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json
- S3 bucket object: Use S3 URI format (requires AWS credentials configured via
aws configure):datui s3://my-security-bucket/cloudtrail-logs/parquet/2025/01/01.parquet
- Navigation within Datui: Use arrow keys or Vim-style `h/j/k/l` to move between rows and columns. Press `?` for help.
3. Performing Fuzzy Keyword Searches and SQL Queries
Datui combines interactive fuzzy search with full SQL support (SQLite dialect), enabling rapid filtering of large datasets—perfect for finding specific IP addresses, usernames, or error codes.
Step-by-step guide:
- Fuzzy search: While viewing a table, type `/` followed by a keyword (e.g.,
192.168.1.100) to highlight matching rows. Press `n` for next match. - SQL query mode: Press `:` and type `sql` to open a query editor. Example: Find all failed SSH login attempts from a CSV with columns
timestamp,src_ip,user,status.SELECT src_ip, COUNT() as attempts FROM mytable WHERE status = 'FAILED' AND user = 'root' GROUP BY src_ip ORDER BY attempts DESC;
- Saving SQL results: After executing, press `Ctrl+S` to export the result set as a new CSV or Parquet.
- Real-world security use case: Analyze Windows Event Logs converted to CSV (using `wevtutil` or
Get-WinEvent):On Windows, export Security log to CSV wevtutil epl Security C:\temp\security_events.csv /lf
Then transfer to Linux/WSL and run:
datui security_events.csv --sql "SELECT TimeCreated, EventID, Task FROM mytable WHERE EventID=4625"
4. Generating Terminal-Based Charts and Exporting as Images
Datui’s built-in analytical tools create ASCII charts (histograms, scatter plots, correlation matrices) directly in the terminal, and you can export them as PNG images for reports.
Step-by-step guide:
- Create a histogram: Load a dataset with numeric columns (e.g., packet sizes in a pcap converted to CSV via
tshark). Press `Ctrl+C` to open the analytics panel, select “Distribution”, then choose the column. - Export chart as image: In the chart view, press `e` to export. You’ll be prompted for format (PNG/ASCII) and filename. Requires `gnuplot` or `term-image` for PNG generation:
sudo apt install gnuplot Linux brew install gnuplot macOS
- Correlation analysis: For two numeric columns (e.g., `duration` and
bytes_transferred), select “Correlation” to see Pearson coefficient and a scatter plot. - Command-line chart export: Automate chart generation using Datui’s headless mode (if supported) or script with `–chart` argument:
datui network_flows.csv --chart histogram:bytes_out --output bytes_hist.png
(Check official docs for exact flags; use
datui --help.)
- Transforming Data: Sort, Filter, Pivot, Melt, and Group
Advanced data wrangling is crucial when normalizing messy security logs or preparing data for SIEM ingestion. Datui offers in-memory transformations similar to pandas or dplyr.
Step-by-step guide:
- Sorting: Press `s` and select a column to sort ascending/descending. For multi-column sort, use SQL:
ORDER BY severity DESC, timestamp ASC. - Filtering: Press `f` to open filter builder. Example: Show rows where
bytes_out > 1000000. Or use SQLWHERE. - Pivot table: Create a pivot to count events per user per day. Press `Ctrl+P` and set rows (user), columns (date), values (count of event_id).
- Melt (unpivot): Convert wide format to long. Useful for log data that has multiple similar columns (e.g.,
failed_attempts_jan,failed_attempts_feb). Command::melt --id-vars timestamp --value-vars failed_attempts_jan,failed_attempts_feb --var-name month --value-name attempts. - Group by and aggregate: Use SQL for complex aggregations:
SELECT user, DATE(timestamp) as day, AVG(latency_ms) as avg_latency FROM mytable GROUP BY user, day HAVING avg_latency > 500;
- Integrating Datui into a Cybersecurity Workflow (Linux Hardening Example)
Combine Datui with standard security tools to analyze system logs, monitor failed authentication attempts, and visualize attack patterns.
Step-by-step guide:
- Collect auth logs: On Linux, convert `/var/log/auth.log` to a structured format using `awk` or `lnav` export:
sudo awk '{print $1, $2, $3, $5, $9, $10}' /var/log/auth.log | sed 's/ /,/g' > auth.csv - Analyze with Datui: Load and query for brute-force patterns:
datui auth.csv --sql "SELECT $5 as ip, COUNT() as failures FROM mytable WHERE $9='Failed' GROUP BY ip ORDER BY failures DESC LIMIT 10"
- Visualize over time: Extract date and hour, then create a histogram:
datui auth.csv --sql "SELECT strftime('%H', timestamp) as hour, COUNT() as attempts FROM mytable GROUP BY hour" --chart bar:hour:attempts - Export report: Generate an image of the chart and embed it into an incident ticket.
- Windows counterpart: Use PowerShell to export Security log to CSV, then copy to WSL:
Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4625} | Select-Object TimeCreated, @{n='TargetUser';e={$<em>.Properties[bash].Value}}, @{n='SourceIP';e={$</em>.Properties[bash].Value}} | Export-Csv C:\temp\failed_logons.csv -NoTypeInformation
Then in WSL:
datui /mnt/c/temp/failed_logons.csv --sql "SELECT SourceIP, COUNT() FROM mytable GROUP BY SourceIP ORDER BY COUNT() DESC"
What Undercode Say:
- Key Takeaway 1: Datui eliminates the need for heavyweight GUI data tools, enabling security professionals to query multi-gigabyte log files directly on a headless server or container with minimal overhead.
- Key Takeaway 2: Its support for remote data sources (S3, HTTP) and multiple formats (Parquet, Avro, ORC) makes it a swiss-army knife for incident responders who need to correlate cloud audit logs, web server JSON outputs, and legacy CSV exports without data migration.
- Analysis: By integrating SQL and fuzzy search with terminal-based visualizations, Datui lowers the barrier to interactive data exploration in CLI-only environments. This is particularly relevant for DFIR teams working within restricted jump hosts or air-gapped systems. While not a replacement for full SIEM platforms, it empowers ad-hoc analysis with zero cost and high speed. The Vim-style navigation appeals to seasoned sysadmins, and the ability to export charts as images bridges the gap between terminal efficiency and report readability. As data volumes grow, Datui’s Rust-based performance ensures sub-second responses even on million-row datasets.
Prediction:
In the next 12–18 months, terminal-based data exploration tools like Datui will become standard additions to security analyst toolkits, especially as more security telemetry shifts to columnar formats (Parquet, Arrow) stored in object storage. We predict that SIEM vendors will either embed similar CLI interfaces or Datui will be integrated into open-source SOAR frameworks, allowing analysts to pivot from alerts to raw data without leaving the terminal. Additionally, expect community-driven plugins for direct integration with Zeek logs, Elasticsearch queries, and VirusTotal API feeds, turning Datui into a lightweight, extensible threat-hunting console.
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