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?
Session Window Implementation for User Activity Analytics
Harnessing user activity data is pivotal for informed decision-making, providing organizations actionable insights into customer behavior, product effectiveness, and strategic optimization opportunities. However, extracting meaningful analysis from continuous,...
Partial Processing Recovery: Resuming Failed Pipeline Steps
In the age of big data, analytics pipelines form the cornerstone of informed and agile strategies for companies aiming to innovate faster and optimize every facet of their operations. However, complicated pipelines running vast amounts of data inevitably encounter...
Feature Flag Implementation for Progressive Data Pipeline Rollout
In today's rapidly evolving data landscape, deploying data pipelines with agility, control, and reduced risk is critical. Feature flags—also known as feature toggles—offer data engineering teams the powerful ability to progressively roll out new features, experiment...
Data Pipeline Canary Deployments: Testing in Production
Imagine rolling out your latest data pipeline update directly into production without breaking a sweat. Sounds risky? Not if you're embracing canary deployments—the strategic practice tech giants like Netflix and Google trust to safely test in real-world conditions....
Tumbling Window vs. Sliding Window Implementation in Stream Processing
In the evolving landscape of real-time data processing, the way organizations utilize data streams can profoundly impact their success. As real-time analytics and data-driven decision-making become the norm, understanding the key differences between tumbling windows...
Snowflake Stored Procedure Optimization for Data Transformation
In an era dominated by data-driven decision-making and rapid data analytics growth, enterprises strategically seek frameworks and platforms enabling robust data transformations with minimal latency and cost. The Snowflake ecosystem stands firmly as one of the leading...
Handling Sensitive Data in ETL Processes: Masking and Tokenization
In an age where data has become the critical backbone fueling innovation, companies grapple daily with the significant responsibility of protecting sensitive information. Particularly within extract-transform-load (ETL) processes, where data is frequently moved,...
Impact Analysis Automation for Data Pipeline Changes
In today's fast-paced data-driven world, decisions are only as good as the data upon which they are based—and that data is only as reliable as the pipelines building and curating its foundations. Business leaders already recognize the immense value of timely, accurate...
Backfill Strategies for Historical Data Processing
Historical data processing can feel like digging into an archaeological expedition. Buried beneath layers of data spanning months—or even years—lies valuable information critical for enhancing strategic decisions, forecasting future trends, and delivering exceptional...