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?
Jitter Implementation for Overlapping Data Point Visualization
Have you ever faced difficulty visualizing your data clearly because multiple data points overlap, obscuring important insights? When datasets become dense, traditional graphical representations often conceal the full story, leaving business leaders and analysts...
Building a Data Engineering Career Path: Skills and Progression
Data engineering is no longer just a support function—today, it's a strategic cornerstone that powers innovative insights and drives business growth. However, constructing a successful data engineering career path takes more than just coding skills or academic...
Extract-Load-Transform vs. Extract-Transform-Load Architecture
In an era increasingly driven by data, organizations across every industry stand at a critical crossroads of choosing the right data integration approach. As the volume, variety, and velocity of data continue to grow exponentially, the strategic decision between ETL...
Data Pipeline Branching Patterns for Multiple Consumers
In today's increasingly data-driven market, companies that leverage their information assets effectively achieve a distinct competitive edge. However, as organizations scale and add more analytics and applications to serve various departments and stakeholders,...
Custom UDF Development for Specialized Data Processing
In today's world, data holds the power to transform decision-making—but standard analytics alone are no longer enough. Enterprises require precise, customized analytics capabilities tailored exactly to their operational contexts. Developing custom User Defined...
Configuration-Driven Pipeline Design vs. Hard-Coded Logic
In today's dynamic technology landscape, organizations must evolve swiftly to leverage data effectively. The decisions we make now regarding data pipeline architecture shape not only immediate performance, but also the agility and adaptability of our organizations for...
Schema Evolution Handling in Data Pipeline Development
In today's dynamic data ecosystem, businesses and innovators are being driven towards rapid, iterative growth in their data pipelines. With more robust analytics platforms, continuous integration, and near real-time data processing, schema evolution emerges as a...
Data Transformation Debugging Techniques and Tools
In our increasingly data-driven landscape, transforming raw data into meaningful insights sits at the core of every successful business strategy. Yet, for decision-makers and technology strategists alike, the journey of data transformation is rarely a smooth ride....
Time-Partitioned Processing for Large-Scale Historical Data
Handling massive datasets collected over extended periods can quickly become overwhelming without a clear and strategic approach. In today's rapidly evolving landscape, data-driven businesses are collecting historical data at an unprecedented rate, yet many struggle...