Name: ATX AnalyticsTable
Brand: ATX Institute

ATX AnalyticsTable — AI business intelligence and AI-driven analyticsA familiar grid on a real analytical engine. Connect PostgreSQL, SQL Server, Oracle, MySQL, BigQuery, Snowflake, Databricks, Amazon Redshift, Microsoft Fabric, DuckDB and cloud storage; handle data preparation and data manipulation with repeatable pipelines; and let AI automation refresh, recalculate, export and back up the morning report unattended — auditable, version-controlled and on your own infrastructure. The interface runs in seven languages: English, German, Spanish, French, Polish, Portuguese and Simplified Chinese.

ATX AnalyticsTable in 73 seconds — what the film shows

Live pinned queries: Write SQL once against live DuckDB tables and pin the answer to the grid — refreshed on schedule.
Dashboards: KPIs, filters and charts bound to the database — not to a copy.
PII masking: Personal columns found automatically, then hashed, redacted or partly masked before the data leaves.
Pipelines: Turn routine work into a recipe — rehearse every step, then apply it in one go.
Scheduling: Jobs run in sequence, unattended — weekdays at 07:30, with an e-mail if a run fails.
Parameters: Define ${asOf} once and use it in SQL and in the PDF file name, resolved at run time.
Query monitor: See every job as it runs — its SQL, rows and time — and stop it if you need to.
Ask Greg: And Ask Greg, the AI analyst that shows the SQL behind every answer.

What it is for

An AI analyst that shows its work — Ask Greg answers with queries it actually ran against your own database, and proposes charts, dashboards, pipelines, PDF reports, e-mail alerts and schedules as objects you place — every one dry-run against your data and certified before you see it.
Unattended by design — Sequenced schedules refresh, recalculate, export and back up before the business day starts — with formulas guaranteed to settle before the next step reads them.
Cleaning that survives the refresh — Pipelines turn a morning routine into a bound, versioned recipe that re-applies automatically every time its source reloads.
Privacy built into the extract — Four masking methods — redact, partial, hash and synthetic — applied one-way to columns or ranges, and repeatable inside a scheduled pipeline.
Git-native governance — Pipelines, schedules, filters and export definitions are small JSON files in the project folder. Every change to a production report is a reviewable diff.
Credentials that never travel — An AES-256-GCM vault behind the OS keychain, or your own AWS, Azure, GCP or HashiCorp secret manager. Project files carry references, never values.
Data quality that finds the invisible — Nine outlier detectors plus an anomaly scan for zero-width characters, smart quotes, control codes and markup left behind by a paste.
Dashboards bound to the source — KPI, filter, pivot and chart widgets query their connection directly, so a dashboard never depends on what happens to be pinned in a sheet.
Seven interface languages — English, German, Spanish, French, Polish, Portuguese and Simplified Chinese — the ribbon, the dialogs, the settings and the in-app manual, switched without a restart. An eighth is a JSON file you drop in, not a release you wait for.
An audit trail that proves itself — Every credential reveal, sign-in, query, export, e-mail and AI action lands in an append-only, hash-chained log. A changed byte or a missing record is found by the app, the API or an auditor's own script — and it streams to your SIEM.
Nothing runs until it is trusted — A project from another computer or person opens paused: no startup schedules, refreshes, exports or e-mails until someone chooses to trust it. Administrators pre-trust the folders approved reports live on.
Deploys the way you already deploy — A per-machine MSI with Group Policy and Intune templates, silent Linux packages, Entra ID, OIDC and SAML sign-in with SCIM provisioning for server mode, a Docker image and a Helm chart with cloud overlays — every feature switch enforced by the backend.

In numbers

22 Manual chapters
10 Ribbon tabs
6 Schedulable target types
4 PII masking methods

Who it is for

Controllers & financial analysts: The morning pack builds itself — refreshed, recalculated, formatted and exported before anyone opens a laptop.
Data & platform teams: Reporting logic leaves personal drives and enters version control, with connections standardised across the fleet from one JSON bundle.
Risk, audit & compliance: Every configuration change is a diff, every pipeline run is a log line, and every extract can be masked before it leaves the machine.
Business intelligence developers: SQL, a semantic layer, parameters and a scriptable pipeline engine — without giving up the grid the business already reads.

What it connects to

Databases & warehouses: PostgreSQL, Microsoft SQL Server, Oracle, MySQL / MariaDB, Google BigQuery, Snowflake, Databricks, Amazon Redshift, Microsoft Fabric / Azure Synapse, DuckDB, SQLite
Cloud storage: Azure Blob Storage, Google Cloud Storage, S3-compatible object storage
Files: Workbooks (.xlsx), CSV / TSV, JSON / NDJSON, Parquet, .duckdb databases
Secret managers: OS keychain, AWS Secrets Manager, Azure Key Vault, Google Secret Manager, HashiCorp Vault

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