Skeyenote brings the interactive notebook experience to Apache Spark — letting data teams run exploratory analysis, iterate fast, and scale effortlessly across massive parallel workloads.
| product_line | total_rev | avg_margin | Δ yoy |
|---|---|---|---|
| Enterprise SaaS | € 4.2M | 38.4% | +12.1% |
| Analytics Suite | € 2.8M | 31.7% | +6.3% |
| Data Connectors | € 1.1M | 18.2% | -2.4% |
Point Skeyenote at any JDBC source, Hive metastore, S3-compatible lake, or local files. No data movement required.
Cells execute on your Spark cluster in real time. Inspect DataFrames, chart distributions, and refine logic cell by cell.
Export annotated notebooks as live reports. Markdown cells let you embed context alongside your code and outputs.
Skeyenote keeps the full power of Spark within reach — without infrastructure headaches or context-switching between tools.
Every cell dispatches jobs to your Spark cluster. Shuffle hundreds of millions of rows while you read the partial results streaming into the output pane.
Query Postgres, MySQL, Oracle, Hive, Trino, or any JDBC-compliant store without leaving the notebook. Schema introspection included.
Narrate your analysis inline. Toggle between code and markdown to build self-documenting notebooks that communicate as well as they compute.
Run sh cells for data downloads, file management, or CLI tools — all from the same interface, without switching terminals.
Inspect DataFrame schemas, row counts, and column statistics without extra code. The variable panel updates live as each cell completes.
Share notebooks with your data team, leave comments, and revisit checkpointed cell states — exploratory work that doesn't disappear after the session ends.
Switch interpreter per cell. Query Spark, relational databases, NoSQL stores, search engines, statistical tools, and more — all within a single notebook session, without leaving the interface.
Skeyenote talks directly to your Spark cluster. As your data grows, add executors — your notebooks stay the same. No rewrites, no migration.