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.
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Optimistic vs. Pessimistic Locking in Data Integration Processes
In today's interconnected business landscape, data drives decisions, powers innovation, and inspires new opportunities. Effective data integration is crucial to ensuring processes run smoothly and insights stay relevant. Yet, even with robust frameworks and advanced...
Pipeline Orchestration: Airflow vs. Prefect vs. Dagster Comparison
In the data-driven world we operate in today, robust and efficient pipeline orchestration is not just a technical luxury—it’s a vital cornerstone of operational excellence. Organizations accumulating massive datasets require intelligent workflows to capture, process,...
Implementing Dead Letter Queues for Failed Data Processing
In today's rapidly evolving data landscape, even the most robust data processing pipelines occasionally encounter failures. Missing or lost data can pose a significant threat to operational efficiency, strategic analytics, and ultimately, competitive advantage....
Converting Batch Pipelines to Stream Processing: Migration Path
Data has become the cornerstone of modern organizations, illuminating crucial insights and accelerating decision-making. As data ecosystems evolve rapidly, businesses reliant on batch processing pipelines are now turning their gaze towards real-time processing...
Payload Compression Strategies in Data Movement Pipelines
In today's rapidly evolving digital landscape, businesses frequently face the challenge of efficiently moving vast volumes of data through their analytics pipelines. As organizations increasingly leverage cloud-based solutions, real-time processing, and integrate...
Idempotent Processing Implementation for Pipeline Reliability
Imagine orchestrating your data pipelines with the confidence of a seasoned conductor leading a symphony—each instrument perfectly synchronized, harmonious, and resilient even under unexpected interruptions. In data engineering, idempotency empowers this confidence by...
Continuous Integration for Data Transformation Logic
In the dynamic landscape of data-driven businesses, speed and accuracy are paramount. Organizations increasingly rely on complex data transformation processes to distill their raw data into actionable insights. But how can teams deliver consistent, reliable data...
Watermark Management in Event-Time Data Processing
In the dynamic landscape of real-time data analytics, precision and timeliness reign supreme. Enterprises consuming vast streams of event-time data face unique challenges: delays, disordered events, and the inevitable reality of continuously arriving information. When...
Building a Data Catalog: Tools and Best Practices
In an age where data is not just abundant, but overwhelming, organizations are increasingly recognizing the value of implementing a reliable data catalog. Much like a digital library, a data catalog streamlines your data landscape, making it coherent and accessible....