Reporting Automation Services

Connectors to every source system your report draws from: direct database queries (PostgreSQL, MySQL, SQL Server, BigQuery) with connection pooling and read-replica routing to avoid adding load to production write replicas, ERP API calls (SAP RFC/BAPI, NetSuite SuiteQL, Dynamics OData), CRM data pulls (Salesforce SOQL with bulk query for large datasets, HubSpot API), SaaS platform exports (Google Analytics 4 Data API, Stripe Sigma/API, Xero Reports API), and file-based ingestion for legacy systems that export CSV or XML on a schedule (SFTP pickup with SHA-256 file integrity verification). Source data is validated for completeness and consistency before entering the pipeline: expected row counts checked against prior period ranges (a daily revenue feed that typically has 500-2,000 rows and returns 12 rows triggers an alert), required fields verified for null presence, and date range coverage confirmed to catch gaps in time-series data. Alerts fire immediately when a data source returns an error, is unavailable beyond its expected response time, or produces data outside expected volume bounds, so you know about the problem before the report is distributed, not after recipients find the discrepancy.

Your calculation logic encoded in the pipeline as version-controlled code rather than embedded in a spreadsheet where it can be accidentally overwritten, silently broken by a formula change, or lost when the person who built it leaves. Aggregations (sum by region, count by category), period comparisons (month-over-month, year-over-year variance), rolling averages (28-day rolling churn rate), multi-step formulas (gross margin → contribution margin → EBITDA), and conditional logic (different calculation rules for different product lines or business units) all implemented in SQL transforms or Python with full test coverage. Data joins across source systems handle the relational logic that requires knowing which customer ID in the CRM maps to which account ID in the ERP. Edge cases that your current report handles with a note or a manual adjustment are handled by explicit logic. Validated against 3+ months of historical output from your existing reports before go-live.

Report output in the format your recipients already use and trust, not a new format they need to adopt. PDF generation (using WeasyPrint or ReportLab) with your existing template: company logo, color scheme, fonts, table layouts, chart styles, and page break logic that keeps related sections together. Excel generation (using OpenPyXL or Apache POI) with the exact sheet structure, column widths, frozen headers, conditional formatting, and chart objects your recipients have built their own downstream analysis on top of. Email summaries with key metrics embedded inline (not attached) for stakeholders who need the headline numbers without opening a file. PowerPoint generation for boards and leadership teams who consume data in presentation format. The output your recipients already know how to navigate, generated automatically from the same source data.

Scheduled pipeline execution on the cadence your reports require: daily close reports running at 6am before the business day starts, weekly summaries generated Monday morning with the prior week's data, monthly management packs generated on the first working day of each month, quarterly board reports generated 5 business days before the board meeting date. Cron expressions (e.g. 0 6 * * MON-FRI for weekday 6am) with business-day awareness, a report configured for "first working day of the month" does not run on a Saturday or bank holiday, it advances to the next working day. Timezone-aware scheduling handles multinational businesses where the data close time is in one timezone and the primary recipients are in another.

Event-triggered pipelines for reports that need to run on demand or in response to a business event (a large transaction closing, an inventory threshold being hit, a data feed completing earlier or later than expected). Delivery to specific recipient lists via email with personalised subject lines and body text (each recipient's email addresses them by name and includes their relevant metrics if the report is segmented by team or region), SharePoint or Google Drive with automatic version management and folder structure maintained per reporting period, or direct system push to a BI tool or data warehouse. Delivery confirmation logged for audit purposes. Retry logic handles transient source system failures, three retries at 5-minute intervals before escalating to the pipeline owner, automatically, so a brief API timeout does not prevent the report from reaching recipients.

Automated data pipeline that populates your existing BI dashboards with current data, so the Power BI report your operations team uses every morning always shows yesterday's data when they open it, not last week's. Data pushes to Power BI datasets via the Power BI REST API or Fabric pipelines, Tableau data extracts refreshed on schedule, Looker PDT (persistent derived table) refreshes, Metabase data sync, or direct writes to the database or data warehouse that your dashboard queries. For teams that want a live operational dashboard rather than a scheduled report, equipment status, order pipeline, support queue depth, we build the streaming data pipeline (via Kafka or WebSocket) that updates the dashboard in real time. The data integration layer that makes your existing BI investment reflect current reality rather than requiring a manual refresh to be useful.

Pipeline observability that gives you confidence every report that was scheduled to run actually ran, with the right data, and was delivered to all intended recipients. Data source health monitoring checks each source system connection before the pipeline begins processing, a failed check triggers an alert immediately rather than producing a silent empty report. Execution success tracking logs every pipeline run with start time, completion time, records processed, and any warnings encountered. Delivery confirmation logs when each report was delivered and to whom, providing the audit trail your compliance team needs for regulatory reports. Business rule monitoring raises alerts when extracted data falls outside defined bounds, revenue that's 40% below last month's, a zero count in a column that's never zero, flagging data quality issues before they produce a wrong report that gets distributed and acted on.