TapData Live Data Platform Documentation

TapData Community is an open-source data integration platform designed for basic data synchronization and transformation. It serves as an entry point for exploring and implementing data integration projects, with a seamless upgrade path to TapData Cloud or TapData Enterprise for users requiring advanced features and professional service support.

TapData integrates database Change Data Capture (CDC) with streaming computing technology to simplify data processing workflows and enable real-time data synchronization beyond traditional methods.

Tapdata supports Lark Docs as a data source, enabling the real-time extraction of document content for synchronization to target databases, search engines, or data lakes. This integration is designed to support intelligent knowledge systems, BI analytics, and question-answering systems.

The Tapdata MCP (Model Context Protocol) Server is a protocol designed to provide structured business data in real time to Large Language Models (LLMs) and AI Agents. It acts as a unified AI Context Service Layer, allowing AI models to access real-time business context without requiring direct connections to raw databases. This solves common enterprise challenges such as data silos (CRM, ERP, core banking), AI hallucinations due to lack of real-time data, and security/compliance restrictions regarding raw database access.

Tapdata's data validation feature verifies that replicated data remains consistent between source systems and the Platform Cache (FDM Layer). It allows users to verify record counts and field-level accuracy, detect discrepancies in real-time operations, and configure alerts for data integrity failures.

The Aggregate node groups, summarizes, and derives values from detail or model data, continuously writing a reusable result set (such as an ADM table) for downstream reports or APIs. This reduces repeated calculations, ensures consistent metric definitions, and improves query performance.

Incremental Materialized Views (IMVs) in TapData allow users to automatically join and transform data across multiple tables in real time. This eliminates the need for manual ETL pipelines and provides an up-to-date view for analytics, dashboards, and APIs without overloading operational databases.

Incremental Materialized Views (IMV) in TapData are real-time, high-performance analytics tables that automatically combine data from multiple sources. They eliminate the need for complex, resource-intensive multi-table JOINs on production systems by syncing a denormalized, always-up-to-date view to a target system (such as MongoDB). This allows BI tools and APIs to query a single table for instant insights without impacting operational database performance.

TapData is a real-time operational data platform designed to unify, standardize, and deliver business data from legacy CRMs, ERPs, databases, and SaaS applications into a single governed Operational Data Hub (or Data-as-a-Service). It eliminates batch latency using Change Data Capture (CDC) to provide always-fresh, consistent information for analytics, APIs, and digital applications.

Incremental Data Validation is a real-time mechanism in TapData designed to ensure data accuracy and consistency between heterogeneous systems. Unlike traditional full data comparisons, it uses low-frequency, high-efficiency sampling to monitor newly inserted or updated records at second-level intervals. It automatically detects and corrects mismatches, minimizing performance impact on production systems by only comparing changed data.

Tap Flow is a programmable API framework for the TapData Live Data Platform. It provides a Python SDK and an interactive CLI to manage replication pipelines, build wide tables or materialized views, and perform general data integration tasks.