Uncomplicate Data
ET1, for humans who hate complex
No ET phone home.
Data Inputs
CSV, JSON, and Github CSVs. Extract the data.
Filters
Filter by measure, use regex, wild cards, across your data.
Merge
Inner join, left join, stack with union, and concat columns.
Aggregate
Minimum, maximum, mean, sum, and yes, group by.
Streaming
Flow data downstream. Never click run.
Consulting
Expert help.
Keep it Simple.
About ET1
ET1 is a visual data workbench that lets you explore, clean, and explain data in-memory. Built for non-technical humans, (but developer friendly) who want to wrangle data, without complexity.
Create
Hands-on ETL
Training Documentation
Use the training material to help you understand more about ET1 and how it helps solve data wrangling problems.
ET1 Basic Training
If you need help getting started, begin here.
ET1 Video Training
Learn the basics, the features, and more.
Future Insight
We see the future being focused on adoption, training, and creating Easy Tools for anyone. We are building an emerging technology while also maintaining a creative user experience that is inviting and friendly for all ages.
Inspiration
We are inspired by software, video games, and Sci-Fi movies like The Matrix, Minority Report and Ironman. ET1 is created to be “some-what” similar to other legendary software like Alteryx Desktop, and KNIME Analytics Platform.
Join beta.
Why do you want to access beta?
Edge Computing Visualization: Rendering Analytics at the Data Source
The exponential surge in data volume is transforming how businesses strategize, compete, and innovate. Today, capturing insights in real-time has shifted from being a nice-to-have capability to a critical necessity. The conventional model of centralized analytics,...
Processing Temporal Data: Dealing with Late-Arriving Events
Imagine your analytics system as a tightly choreographed dance performance. Every performer (data event) needs to enter the stage precisely on cue. But real-world data seldom obeys our neatly timed schedules. Late-arriving data, events that report well beyond their...
The Economics of Data Deduplication: Storage vs Compute Trade-offs
In the age of big data, modern businesses rely heavily on collecting, storing, and analyzing massive amounts of information. Data deduplication has emerged as a vital technology in managing this growing demand, achieving cost reductions and performance efficiency....
Handling Time Zones in Global Data Processing Without Losing Your Mind
Imagine you're an analytics manager reviewing dashboards in London, your engineering team is debugging SQL statements in Austin, and a client stakeholder is analyzing reports from a Sydney office. Everything looks great until you suddenly realize numbers aren't lining...
The Great Debate: Push vs Pull Data Processing Architectures
Picture this: your business is thriving, your user base is growing, and the data flowing into your enterprise systems is swelling exponentially every single day. Success, however, can quickly turn into chaos when poorly-planned data architecture choices begin to...
Data Processing Anti-Patterns That Destroy Performance
In the fast-paced landscape of data-driven organizations, the efficiency and speed of data processing directly influences strategic decisions and performance outcomes. Unfortunately, many companies unknowingly implement certain data processing anti-patterns that...
Backpressure Mechanisms in High-Throughput Data Streams
In a world increasingly driven by data, organizations face the growing necessity to process vast streams of information swiftly and reliably. High-throughput data streams, such as those encountered in real-time analytics, IoT, and complex event processing, push...
The Psychology of Data Types: Why Integer Overflow Kills Analytics
Data may appear dispassionate, but there's a psychology behind how it impacts our decision-making and business insights. Imagine confidently building forecasts, dashboards, and analytics, only to have them subtly fail due to a seemingly invisible technical...
Processing Dirty CSVs: Handling Malformed Headers and Encoding Issues
In today's data-driven landscape, organizations rely on structured data files such as CSVs (Comma Separated Values) to unlock crucial insights and foster strategic decisions. Despite their simplicity and widespread use, CSV files frequently present challenges such as...