Databench by Alkera

Alkera is an agentic data platform that brings data engineering, analysis, and science into collaborative multiplayer workspaces for humans and agents. Its open-source offering, Databench by Alkera, is described as the multiplayer workspace for data science, analytics, and engineering, where teammates and agents collaborate live inside notebooks and chats. The platform is built so that users can run any cell or agent on their laptop, another computer, or a GPU node, and can launch many agents in parallel to explore ideas. Every result traces back to the data and code behind it, so the people working in a workspace can follow a number straight to its source. Alkera is aimed at data teams that want one agentic platform to cover their entire data stack, rather than moving between disconnected tools. The context Alkera addresses is a data stack where engineering, analysis, and science are the daily work of the same team, and where agents are increasingly part of that work. Alkera's positioning is to bring those disciplines together in one place: the site promises "One agentic platform. Your entire data stack." and describes data engineering, analysis, and science happening in collaborative multiplayer workspaces for humans and agents. The promotional copy for the platform frames the outcome as "bringing confidence and speed to your agentic data stack." That combination — confidence and speed — is reflected in two of the platform's stated properties: results that trace back to the data and code behind them, and the ability to run many agents in parallel rather than one at a time. Alkera also emphasizes that it works with the applications and tools teams already use, so the platform is intended to sit alongside an existing stack instead of replacing it. Collaboration is the core of the workspace. Alkera supports multiplayer notebooks and chats in which humans and agents work side by side in the same session. The company's demo shows this directly: a user named Priya asks a signals agent to chart monthly revenue by segment for the year; the agent uses notebook tools, runs the notebook file q3-revenue.alknb.py across three cells, reports that enterprise is growing fastest at roughly 5% a month and drives most of the year's growth, and the run is marked finished. Marcus then joins the same conversation and asks to split the chart by region as well. The dbt agent responds by adding a region facet to the trend chart and editing a single cell in the same q3-revenue.alknb.py notebook. Because everyone is in the same workspace, these exchanges happen live: questions, agent actions, notebook edits, and results all appear in the same thread. Notebooks and dashboards are documented as first-class features, with a feature page dedicated to them. Agents in Alkera are not limited to a single machine or a single thread of work. The Product Hunt description states that users can run any cell or agent on their laptop, on another computer, or on a GPU node, and can launch many agents in parallel to explore ideas. That flexibility matters because different pieces of data work need very different compute: a quick chart can run locally, while a large model training run needs accelerators. The website illustrates this with a pretraining example that shows an FSDP-wrapped Llama model on 8x NVIDIA B200 hardware, with a loss curve charting training progress against tokens. In the interface, agents are presented with a model and behavior configuration: the demo shows Claude Opus selected, alongside settings labeled "High" and "Ask first," with the agent's activity counted as it uses tools (for example, "Used 2 notebook tools"). Agents also work with a charting API: the demo code calls alkera.chart(revenue).line with parameters for the x axis, the summed y value, a color split by segment, a title, a tooltip, and a facet, producing a monthly revenue by segment chart with Enterprise, Mid-market, and SMB series. Traceability is a stated property of the platform: every result traces back to the data and code behind it. Alkera extends this idea in several documented ways. Column-level lineage is shown across warehouse, transformation, and analysis layers, so a field can be followed from where it is stored, through the transformation that shapes it, to the analysis that consumes it. The platform also includes a knowledge base in which each knowledge entry shows its sources and whether it is human-verified — a visible signal of provenance for the information agents and people rely on. For changes, Alkera provides sandbox environments so modifications can be tested safely before they touch production, illustrated by a self-healing pipeline demo with a page for reviewing occurrences. Together these features give the workspace a record of where numbers come from, what depends on what, and what has been checked by a person. Alkera is organized as one platform that connects to the rest of a data stack through plugins and connections. The site states that Alkera works with the applications and tools you already use, and lists connectors spanning orchestration (Airflow), transformation (dbt), analytics databases (ClickHouse, DuckDB), lakehouse (Databricks), data warehouses (Snowflake, BigQuery, Redshift), query engines (Trino), databases (PostgreSQL, MySQL, SQLite, and generic SQL), object storage (AWS S3), data ingestion (Fivetran), business intelligence (Tableau, Looker, Sigma), data analysis (Hex), observability (Datadog), code and CI/CD (GitHub), communication (Slack), issue tracking (Linear), and knowledge sources (Google Docs, Confluence, Notion). A dedicated documentation page covers available plugins. On top of those connections, the workspace supplies notebooks, dashboards, chats, agents, knowledge entries, lineage, and sandboxes. Databench, the open-source workspace, can also be hosted by the user rather than used as a hosted service. The benefits Alkera claims are confidence and speed in an agentic data stack. Confidence comes from traceability and verification: results link back to the data and code that produced them, lineage runs down to the column level, and knowledge entries indicate their sources and whether a human has verified them. Speed comes from working with agents inside the same workspace where people already collaborate: an agent can run notebook cells, produce a chart, or edit a single cell in response to a teammate's follow-up question, and a user can fan out many agents in parallel instead of waiting on one. Running cells and agents on a laptop, another computer, or a GPU node lets teams match compute to the job, and sandbox environments let them test changes safely before they go live. All of this happens in a single workspace shared by humans and agents, so the work itself stays in one place. Several concrete scenarios appear in the material. In an analytics workflow, a user asks an agent to chart monthly revenue by segment for the year; the agent runs a .alknb.py notebook, returns the chart and a short read on growth, and a second teammate asks for a regional breakdown, which the agent adds as a facet to the same chart. In a data engineering workflow, changes are tested safely in sandbox environments before being applied, and column-level lineage shows how a field moves through warehouse, transformation, and analysis, which supports understanding impact. In an engineering and research workflow, a pretraining run is executed on 8x NVIDIA B200 GPUs with a loss curve tracking progress against tokens, using code and a run display that appear alongside the rest of the workspace. In a knowledge workflow, entries capture information with their sources and human verification status. Across all of them, the same thread of notebooks, chats, and agent actions carries the work forward. Alkera is built for data teams: data scientists, analysts, and data engineers, plus the agents that work alongside them. Its connector list indicates the surrounding stack such teams already use, from Airflow and dbt to Snowflake, BigQuery, Databricks, Tableau, Looker, and Slack. The public materials mention a generous free tier and a "Start for free" call to action, along with the option to book a demo with the founders. Databench, the open-source workspace, is available on GitHub for self-hosting. Alkera also publishes documentation for its foundations and plugins, provides security, privacy policy, and terms of service pages, and can be contacted at contact@alkera.ai. Because the open-source workspace and the hosted platform are described together, teams can choose to adopt the hosted experience or run the workspace themselves. Alkera's primary value proposition is a single agentic platform for the whole data stack: data engineering, analysis, and science performed in collaborative multiplayer workspaces where humans and agents work together. It combines parallel agent execution, flexible compute from laptop to GPU node, full traceability from result back to data and code, column-level lineage, verified knowledge entries, and safe sandbox testing, while connecting to the tools teams already use. For data teams that want to move quickly with agents without losing confidence in what those agents produce, Alkera is designed to keep the work — and the evidence behind it — in one shared place.