ATX AnalyticsTable Handbook | Every Module, Chapter by Chapter

ATX AnalyticsTable Handbook27 chapters covering every module of ATX AnalyticsTable — what each one does, how it works, the practices that keep it reliable, and the governance implications for a regulated environment. Each chapter has a page of its own, with screenshots taken from the product.

The chapters

01 — The workspace & project model. A familiar grid on top of a real query engine. How the ribbon, sheets, query tabs and folder-based projects fit together — and the three kinds of data a cell can hold.
02 — Connections & the credential vault. Define a source once. Never put a password in a project file. DuckDB, SQLite, PostgreSQL, SQL Server, Oracle, BigQuery, Snowflake, Databricks, Amazon Redshift, Microsoft Fabric / Azure Synapse, MySQL / MariaDB and cloud object storage — with credentials held in an encrypted vault behind the OS keychain, or no stored secret at all where the platform offers an identity.
03 — Get Data — ingestion & bindings. Pin a table, import a file, or pull a multi-gigabyte Parquet straight from a bucket. Pinned tables and queries, workbook/CSV/JSON imports that remember their source, and direct cloud ingestion that bypasses the application window.
04 — SQL workbench & the semantic layer. A query editor, a table browser, a saved-query library and Git — in the same window as the grid. Write SQL beside the sheet it feeds, save queries the whole project can point at, and layer friendly business definitions over raw tables.
05 — Transform & data quality. Sort, filter, recode, dedupe — then find the problems you cannot see. Durable sorting and filtering on pinned data, value mapping and dedupe, plus anomaly and outlier detection built for real extracts.
06 — Pipelines — repeatable preparation. The difference between cleaning it every morning and the cleaning being part of the refresh. An ordered, versioned recipe bound to a dataset, re-applied automatically whenever that dataset refreshes.
07 — Scheduler, parameters & unattended operation. Sequence numbers, timezone-correct triggers, and formulas that always follow data. Refresh, import, export and back up on a schedule — in a guaranteed order, with parameters resolved at run time.
08 — Dashboards. KPIs, slicers, tables, pivots and every chart type — forecasts and histograms included — on a free canvas, bound directly to the source. As many dashboards per project as needed, with widgets that query their source directly rather than depending on what is pinned — from a single KPI to a backtested forecast.
09 — Reports & PDF distribution. Every chart type, pivots, timelines and annotations on the sheet — composed into a branded, traceable PDF that runs on a schedule. Build the report on the sheet where the numbers are — any chart from column to forecast — annotate it so it survives editing, and schedule the PDF.
10 — Export & distribution. One dialog, every destination — and the whole configuration is schedulable. Workbooks, flat files, cloud storage or a database table, with multi-sheet composition and parameters in the path.
11 — PII masking & privacy. Four methods, a detector that shows its evidence, and honest limits. Redact, partial, hash and synthetic masking for grid data, static pins and pipelines — one-way, repeatable and keyed per project.
12 — Security, deployment & licensing. Enterprise-ready by construction: keychain-backed secrets, role-based access, a tamper-evident audit trail, and nothing automatic until a project is trusted. Encryption at rest and in transit, project trust, roles and single sign-on (Entra ID, OIDC or SAML), a hash-chained audit trail with SIEM forwarding, Group Policy and Intune control, a hardened desktop shell, signed releases and offline licensing.
13 — Ask Greg — the AI designer and analyst. Describe what you want. It proposes objects the project already knows how to build — and every one has been run before you see it. An AI analyst and designer that works across the whole project: it answers with queries it actually ran, proposes charts, pivots, KPIs and dashboards — and now automation too: one-time transforms, saved pipelines, a new step in an existing pipeline, PDF reports, e-mail alerts and schedules. Every proposal is dry-run against your data before you see it, and nothing lands until you place it.
14 — Table Queries — the library, the visual builder & the SQL editor. Every query is a file in the project folder. Build it by clicking or by typing — it is the same file either way. The saved-query library beside the grid, the visual builder that writes SQL as you click, the SQL editor bound to a named connection, parameters, pinning, and the monitor that shows what is running.
15 — Git integration & version control. The project is already a folder of readable files. Git turns that into a history. Initialise a repository on the project folder, commit from the sidebar, push and pull against GitHub, GitLab or Bitbucket, and restore a single file from an earlier revision.
16 — Settings — the complete reference. Twenty-one sections, and the one rule that explains all of them: a setting takes effect where it is read, and is stored where it belongs. Every settings section in order — appearance and locale, connections, email, semantic objects, sync, query and grid, export, logging, Git, identity, licence, network, advanced, backup, access, data access, catalog and usage, MCP servers, audit log and about — with what each decides and where it is kept.
17 — Mailbox — reading and sending email. Six providers, two pipeline steps, and an alert that knows it already told you. Mail profiles for SMTP, IMAP, Outlook, Microsoft Graph, MailerSend and Azure Communication Services; a mailbox as a data source; templated sends with attachments; and alerts with cooldown, suppression and recovery.
18 — AI formulas — AI_PROMPT, a model's answer in a cell. Any text, any question, an ordinary cell value back. Written like a formula, filled down like a formula — and it calls the model only when the answer would differ. The second, separate way the product puts a model to work: a function that asks a configured model a question about a cell or a range and writes the answer into the cell — with answer reuse, a call ceiling per recalculation, and the same egress policy, masking and audit as the chat panel.
19 — Forecasting — where the numbers are heading. A value over time, projected the next N periods with honest bands — fitted live from the database, the method chosen by backtest, scenarios drawn beside it. The Forecast widget and chart type: eleven methods with an automatic, backtested choice, seasonality, prediction intervals, one forecast per region or product, scenarios and a management overlay, accuracy printed under the chart — every option shown on the Showcase project.
20 — Histograms — the shape of a column. One numeric column, cut into bins: the typical value, the spread, the skew and the stragglers, visible before anyone computes a statistic. The Histogram widget and chart type: bin rules and custom edges, a range of interest with overflow bars, tail trimming, normal and density curves, a cumulative line, percentile markers, specification limits with Cp and Cpk, splits by category, weighted bars, and counting in the database for tables of any size — every option shown on the Showcase project.
21 — The semantic model — one definition per business number. Datasets, dimensions, measures, calculated members, joins, filters and parameters — defined once, addressed by a name that never changes, and turned into SQL by the engine rather than retyped by every analyst. The semantic model in depth: the seven object types and every field they carry, stable URNs, how a widget's measures and dimensions become one SELECT with joins, typed parameters and fan-out warnings, validation of dangling references and cycles, lineage and impact before a delete, unused-object detection, JSON bundles for moving a model between machines, and the roles, licence and audit rules around it.
22 — Alerts — conditions, suppression and alert rules. Six conditions, four things to watch, and the rules that stop an alert from mailing the same news ninety-six times a day. Data alerts in depth: the six conditions (every run, row count, an aggregate threshold, any single row breaching, a new period, the data changing), standalone alert rules that watch a SQL query, a saved query, a pipeline's data or a sheet range, rising-edge suppression, cooldown, daily caps and recovery messages, message templates and attachments, Check now, scheduling, machine-local alert memory, Ask Greg proposals and licensing.
23 — Row- and column-level security, roles and groups. Who may open what, who sees which rows, and how each column is shown to them — decided once from your Entra ID groups and enforced on the server for every query, sheet and export. Row- and column-level security in ATX AnalyticsTable: roles from Entra ID groups, per-user row filters, column masking and pseudonyms, persona preview and audit.
24 — Single sign-on, SCIM provisioning and revoking access. Sign in with the identity provider you already run, let your directory decide who exists and what they may do, and stop a person in seconds rather than when their token runs out. Single sign-on for ATX AnalyticsTable server installations with Microsoft Entra ID, any OpenID Connect provider (Okta, Auth0, Keycloak, Ping, Google) or any SAML 2.0 identity provider (ADFS, Shibboleth), SCIM 2.0 user and group provisioning, and immediate revocation of a person's access.
25 — REST API, headless metrics and MCP servers. Open the platform to scripts, notebooks, other BI tools and AI agents — and connect BigQuery, PostgreSQL, Jira and Confluence through the Model Context Protocol — without opening a second, weaker door. The public REST API (/api/v1, OpenAPI 3.1, RFC 9457 errors, rate limits, idempotency and cursor paging), headless metrics that serve the governed semantic layer over HTTP, and MCP servers for BigQuery, PostgreSQL, Jira, Confluence and any other — read-only by default, with secrets in the vault, circuit breakers, and Ask Greg able to use them when policy allows.
26 — Clusters, key drivers and insight questions. Which kinds of customers do we have, what drives the number, and what changed — answered by statistics that run on your own machine, never by a guess. Cluster charts that find k-means segments in several numeric columns at once, key-driver charts that rank what explains a number or a yes/no outcome by its Shapley share of R², and Ask Greg's tools for anomalies, forecasts and period-over-period change — all reproducible, cross-filtering and privacy-aware.
27 — Governed analytics — certified numbers everywhere. Define revenue once, certify it once, and get the same number in a chart, a cell, an alert e-mail, an AI agent's answer and a dashboard embedded in your customer portal. Governed metrics as the source of charts, pivots and widgets; the METRIC() formula; a publish gate that serves only certified definitions, with one-click restore; insight watches; data stories whose every figure is checked; ATX AnalyticsTable as an MCP server; embedded dashboards scoped per customer; dbt and OSI import; and a content catalog with lineage, four-eyes certification and a usage report.

Glossary terms on this page

ATX Institute on LinkedIn

This site needs JavaScript for the full experience. Every page above is reachable without it.