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?
The Memory Wall: Working Sets Larger Than RAM
In today's data-driven landscape, performance bottlenecks become painfully obvious, especially when handling datasets larger than system memory. As your analytics workload grows, the gap between the sheer volume of data and the speed at which your hardware can access...
Irregular Intervals: Wrangling Messy Time-Series
Imagine navigating unfamiliar terrain armed with nothing more than a vague map and inconsistent landmarks placed unpredictably along your path. That's precisely how working with messy, irregularly spaced time-series data can feel. Yet—as daunting as irregular...
Circuit Breakers: Designing Fail-Safe Stream Drivers
The rapid evolution of real-time data analytics has ushered in an era where milliseconds matter more than ever. Imagine overseeing streaming analytics for your organization's critical operations, only to watch helplessly as streams falter under unexpected workloads or...
High-Cardinality Categories: Encoding Strategies That Scale
When diving deep into analytical and machine learning projects, organizations inevitably encounter the challenging realm of high-cardinality categorical variables. Whether you're trying to analyze customer data across thousands of regions or categorize products from...
Long-Running Jobs vs JVM GC: A Love-Hate Story
If you work in data-intensive environments, the phrases "long-running job" and "JVM garbage collection" probably stir both admiration and frustration. They're like those pairs of coworkers who, despite occasional tension, can deliver remarkable results when...
Choreography vs Orchestration: Coordinating Complex Workflows
Imagine watching a symphony perform without a conductor—each musician intuitively knowing precisely when to begin playing and seamlessly harmonizing their contribution with the group. Now, picture the same orchestra, this time guided meticulously by a conductor who...
Network Effects: Bandwidth Pitfalls in Distributed Engines
In the hyper-connected landscape of today's data-driven business ecosystem, distributed engines promise scalability, agility, and the power of real-time analytics. Yet, hidden beneath these compelling advantages lies a subtle and often underestimated challenge:...
Sparse Datasets: Techniques When Most Values Are Null
Picture a grand library filled with books—but as you open them, you realize most pages are blank. Welcome to the complex yet exciting world of sparse datasets. In today's data-driven world, datasets are enormous, expansive, and, quite frequently, sparse—filled with...
Cold-Start Optimization: Bootstrapping New Pipelines Fast
In the hyper-competitive digital landscape, being first isn't always about having the biggest budget or dedicated research departments; it's about velocity—how quickly your organization can define needs, develop solutions, and deploy into production. Decision-makers...