Informatica

by Salesforce • • ETL Tools

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Informatica is a cloud data integration and data quality platform owned by Salesforce, for enterprises with hybrid data estates, priced by consumption on quote.

Informatica moves, checks and governs the data a large organisation holds across cloud services and its own servers. It does that work in one service, the Intelligent Data Management Cloud, rather than in a separate tool for each job. Salesforce completed its acquisition of Informatica on 18 November 2025, and the platform now carries the label Informatica from Salesforce. The company is based in Redwood City, California, and keeps its own name and site at informatica.com. Data Integration and Engineering designs pipelines from prebuilt cloud connectors, with reusable parameterised mappings and a serverless runtime that scales compute on its own. Its pushdown option turns a mapping into SQL that runs inside a target such as Snowflake. Cloud Data Ingestion and Replication runs batch, real-time and change data capture flows, including streaming and IoT sources, and tracks schema changes. Data Quality and Observability profiles columns, flags anomalies with root-cause analysis, scores datasets and applies cleansing and standardisation rules. Data Catalog, MDM and 360, Data Governance, Access and Privacy, Data Marketplace and API and App Integration complete the platform. The platform is built for large enterprises that run data integration, quality, cataloguing, governance and master data as one programme. Data engineers build and run the pipelines, data stewards own the quality rules and scorecards, and governance teams set policy and access. It suits estates that mix cloud warehouses with databases behind a firewall, which a Secure Agent installed inside the network reaches. Teams with older PowerCenter assets can migrate them onto these cloud services. A common job is replicating an operational database into Snowflake or Databricks with change data capture, so the copy stays current. Another is landing data in Azure Data Lake Storage as Iceberg tables for analysis in an open format. When a source has no prebuilt connector, the INFAConnect SDK gives the team a wizard-driven way to build one. Quality teams profile what has landed, publish scorecards to the catalog and trace anomalies to their cause. Data engineers draft routine pipelines from natural-language prompts, and governance teams use the catalog to prepare trusted data for AI projects.

Features

  • Included: Managed source connector library
  • Included: Custom connector builder
  • Not included: Incremental loads after the first sync
  • Included: CDC mode for database sources
  • Included: Automatic schema drift handling
  • Not included: Sync frequency down to minutes
  • Not included: Column selection and exclusion
  • Not included: In-flight column hashing
  • Not included: Post-load transformations in the warehouse
  • Included: Data lake destinations in open formats
  • Not included: Per-sync run logs and row counts
  • Included: Sync failure notifications
  • Not included: Single-table resync
  • Not included: Pipelines managed by API or Terraform
  • Included: Self-hosted data plane option
  • Not included: Open source ETL engine

Additional Features

  • Intelligent Data Management Cloud services
  • CLAIRE AI engine
  • Natural-language pipeline generation
  • Prebuilt cloud connectors
  • INFAConnect SDK for custom connectors
  • Batch, real-time and CDC workflows
  • Change data capture replication
  • Schema drift tracking in ingestion
  • Streaming, messaging and IoT ingestion
  • Advanced pushdown optimization for Snowflake
  • Reusable parameterised mappings
  • Serverless runtime with automatic scaling
  • Secure Agent for on-premises sources
  • Iceberg tables on Azure Data Lake Storage Gen2
  • Job failure email notifications
  • Continuous data profiling
  • Anomaly detection with root-cause analysis
  • Data quality scorecards
  • Cleansing and standardisation rules
  • Data Catalog asset classifications
  • Quality scores in Data Governance and Catalog
  • MDM and 360 applications
  • API and App Integration
  • Data Governance, Access and Privacy
  • Data Marketplace
  • Usage dashboard with IPU threshold alerts

Best for

  • Large enterprises running integration, quality, catalog and MDM on one platform
  • Hybrid estates with databases behind a firewall
  • Data engineering teams loading Snowflake, Databricks or cloud data lakes
  • Data stewards who own quality rules and scorecards
  • Governance teams preparing trusted data for AI projects
  • Organisations moving PowerCenter assets to cloud services
  • Buyers comfortable with quoted, consumption-based contracts

Use cases

  • Replicating operational databases to a cloud warehouse with change data capture
  • Ingesting streaming, messaging and IoT data into the cloud
  • Landing data in Azure Data Lake Storage as Iceberg tables
  • Pushing transformation logic down to Snowflake as SQL
  • Building a connector for an unsupported source with INFAConnect
  • Drafting routine pipelines from natural-language prompts
  • Profiling new datasets for patterns and anomalies
  • Scoring dataset quality for the people who use the data
  • Cleansing and standardising enterprise records
  • Building customer 360 master data
  • Governing and cataloguing data for AI use
  • Integrating APIs and business applications

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