Data Engineering Integration
A fast, flexible way to build and manage data pipelines at scale.
Overview
Intelligently manage big data engineering pipelines in the cloud and on premises for faster insights.
Accelerate data pipeline processing
Execute data processes for highest performance, scalability, and utilization without any knowledge of the underlying technologies such as Spark and Hadoop.
Rich data integration
Simplify and accelerate data engineering integration with mass ingestion and connectors to hundreds of data sources such as databases, applications, or the cloud.
Serverless cloud data pipelines
Automate the deployment and management of distributed data processing resources with a serverless architecture on AWS, Microsoft Azure, and Google Cloud Platform.
Key Features
See how the industry’s most comprehensive data engineering product helps you access, integrate, clean, catalog, and govern big data at scale.
Data integration on Spark and Hadoop
Access an extensive library of advanced prebuilt data transformations, including PythonTx to operationalize data science projects and analytics.
Advanced Spark support
Leverage innovations in the latest Spark engine for advanced data management, including data ingestion, data quality, data masking, and data processing at scale.
Visual design interface
Employ easy-to-use visual interface to rapidly build data transformation logic for your data engineering pipelines.
AI-powered pipeline recommendations
Enhance data engineer productivity with data pipeline recommendations and cross data pipeline categorization, both powered by the Informatica CLAIRE™ engine.
High-speed mass ingestion
Ingest data from source systems and applications into cloud and big data using high-performance connectivity, mass ingestion, and dynamic mappings.
Serverless deployment
Automatically scale and deploy big data engineering workloads in cloud environments, such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
Intelligent data parsing
Parse complex multi-structured, hierarchical, and unstructured data automatically with Informatica Intelligent Structure Discovery. Easily handle schema drifts.
Flexible deployment
Automate deployment and management of data processing compute clusters with Spark Serverless services, such as Databricks, Qubole, and Google Dataproc.
Big data profiling
Profile big data to better understand the data, identify data quality issues, and collaborate on data pipelines.
Advanced DataOps
Improve collaboration with DevOps by deploying data engineering jobs in a CI/CD pipeline, and using Informatica Operational Insights to monitor big data resources.
Data Engineering in the Cloud
Informatica Data Lake Management on AWS
Take advantage of the security and scalability of the managed Hadoop framework in AWS EMR to easily find, prepare, and govern big data to quickly drive business value.
Informatica Data Lake Management on Microsoft Azure
Leverage the flexibility of the managed Hadoop framework in Microsoft Azure HDInsight to easily find, prepare, and govern big data to quickly drive business value.
Intelligent Data Pipelines with Databricks
Accelerate data pipelines for AI and analytics with intelligent data integration and ingestion from Informatica and Databricks.
