
Uncomplicate Data
Create end-to-end data analytics solutions in one app that is simple to use, and self-explanatory.
About ET1
ET1 is a visual data workbench that lets you explore, clean, and explain data solutions.


- Access ET1
- Unlock 24 nodes
- Free ETL software
- Desktop friendly
- Code on GitHub iCore utilities, UI pieces, and node definitions live in the public repository so you can review how processing works.
- No storage
- ET1.1 + GitHub OAuth
- Unlock 29 nodes
- Workflows + Neon LakeiNeon Lake is the persistent store for workflow data and results. The free tier has no storage. This tier includes limited storage and 1 branch.
- 100/CU-monthiCU = Compute Unit. An internal measure we use to track processing cost across transforms.
- 500 MB storageiStorage in Neon Lake for tables and results. Retention policies may apply while we tune usage.
- 1 BranchiThis tier includes a single branch only.
- ET1.1 + GitHub OAuth
- Unlock 29 nodes
- Workflows + Neon Lake
- $0.28/CU-houriMetered compute beyond included quota. Priced per compute-hour equivalent derived from CU usage.
- $0.46/GB-monthiNeon Lake storage billed by logical GB-month. We may introduce archival tiers.
- 3 BranchesiEach branch has isolated CPU and storage. You only pay for the delta (the difference) between branches.
- SOC 2 • HIPAA • GDPR
- Regional locations
- User-level pricing
- $0.52/CU-hour
- $0.49/GB-month
- 10 Branches
- Unlimited databases & tables
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.
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The Core Paradox: Why More CPUs Don’t Always Mean Faster Jobs
In today's fast-paced IT landscape, the prevailing wisdom is clear: if a process is running slowly, simply throwing more processing power at it—meaning more CPUs or cores—is the immediate go-to solution. After all, more cores should mean more simultaneous threads,...
Seasonality Effects: Adapting Algorithms to Cyclical Data
In the dynamic landscape of data analytics, seasonality is an undeniable force shaping your strategic decisions. Businesses confronting cyclical data variations—whether daily, monthly, or annual trends—must adapt algorithms intelligently to uncover impactful insights...
Hot, Warm, Cold: Choosing the Right Temperature Tier for Your Bits
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Trees, Graphs, and Other Recursive Nightmares in Hierarchical Workloads
If you’ve ever ventured into the realm of hierarchical data, you've surely encountered the bittersweet reality of recursive relationships—those intricate, repeating patterns embedded within trees, graphs, and nested structures that both fascinate and frustrate data...
The Metadata Maze: Extracting Schemas from Unstructured Blobs
In today's data-driven landscape, the volume and variety of unstructured information flowing daily into organizations can quickly become overwhelming. With business leaders and technologists recognizing the immense potential hidden in unstructured data—such as images,...
Data on a Shoestring: Open Source vs Enterprise Pipeline Costs
Every organization aims to become data-driven, but not every organization enjoys unlimited resources to achieve that vision. Leaders tasked with managing data-rich environments find themselves confronting a perennial question: Should we embrace cost-effective...
Sampling Isn’t Dead: Modern Stats Techniques for Big-Data Workloads
When the term “big data” emerged, many tech leaders believed that traditional statistical strategies such as sampling would quickly become extinct. However, rather than fading away, sampling has evolved, keeping pace with rapid innovation and the massive data influxes...
Graceful Degradation: Surviving When Everything Goes Wrong in Batch Jobs
Picture this: your data-driven enterprise relies heavily on nightly batch processing to power critical business decisions, but one evening, disaster strikes—pipelines break, dependencies fail, and your morning analytics dashboard starts resembling an empty canvas....
Parquet vs ORC vs Avro: The File-Format Performance Showdown
In today's data-driven landscape, selecting the right file format isn't merely a technical detail; it's a strategic business decision. It affects query performance, storage efficiency, ease of data transformation, and, ultimately, your organization's competitive edge....
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