Hal9 vs Tecton

Start by importing data from Google Sheets, Excel or any other service that supports exporting your data. Simply drag the type of data you have: CSV, Excel, or JSON and open the URL or file with your data; After importing your data, drag the chart and fields to visualize, is that simple. You can choose from bar, line, scatter, bubbles, wordclouds and many more charts; Forecast future values by dragging a prediction, select the field to predict, the type of prediction that best fits your needs and how many values to predict into the future, is that easy; If you want to go the extra mile, you can export your analysis to HTML and embed the results in any site that supports static HTML: Wix, WordPress, you name it. In this example, we use Hal9 and Google Analytics to visualize and embed our own worldwide daily product use

Feature Pipelines - automatically compute and orchestrate the feature transformation process with unified batch and real-time abstractions. Tecton includes efficient pre-engineered pipelines that compute windowed aggregations on batch and real-time data with a single line of code; Feature Store - store features in an offline store to optimize for large-scale retrieval during training and an online store for low-latency retrieval during online serving. Easily generate accurate training data through a Python SDK and backfill feature data. Serve data at very high scale (over 100,000 QPS) and low latency (under 100ms) through a REST endpoint. Tecton eliminates train-serve skew by ensuring consistency across training and serving environments, and also eliminates data leakage through correct time-travel; Feature Repository - Manage features as files in a git repository using a declarative framework. Deploy features with confidence by integrating CI/CD processes and unit testing your features before deploying to production. Manage dependencies of features across models and version-control features; Monitoring - Monitor the health of feature pipelines and automatically resolve issues that could produce stale feature data. Control costs by tracking the computation and storage costs for each feature; Sharing - Discover features through an intuitive Web UI and produce new production-grade models with existing features with a single line of code. Break down silos, increase collaboration between data scientists, data engineers, and application engineers. Eliminate duplication across the ML data development cycle