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Informatica Cloud Integration for Amazon Redshift

Kickstart and scale an AWS Redshift data warehouse, integrating data from both cloud and on-premises sources.

Deliver Actionable Data With Amazon Redshift

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Data connectivity eliminates silos

Optimize cloud data warehousing with Informatica’s native Amazon Web Service Redshift connector that integrates data from both cloud and on-premises sources. Benefits include:

  • Out-of-the box connectivity with data from hundreds of sources, including Amazon S3, RDS, Aurora, and DynamoDB; Salesforce, NetSuite, Workday, Marketo, big data, and social media
  • Reuse of integration code across sources and targets, effectively future-proofing your investment
  • Connectivity as service, which relieves you from management of data sources’ versioning and fast-changing SaaS releases
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Development agility accelerates time to market

Role-based SaaS development tools, optimized for both developers and business users are accessible anywhere, any time. Features include:

  • A visual, easy-to-use user interface that automates tedious hand coding such as data ‘upsert’
  • Out-of-the-box templates and 300+ transformations, including aggregators, joiners, lookups and masking, which accelerate development
  • Dynamic mappings, automatic target table creation, pre- and post-SQL processing, which simplifies the development lifecycle
  • Hourly consumption models in the AWS Marketplace, so you can rapidly kickstart new Amazon Redshift project in minutes, not months
  • No lengthy infrastructure and software procurement and installation
  • A six-step Data Wizard that guides development by citizen integrators
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Deployment scalability and operational confidence

Pushdown optimization takes full advantage of Amazon Redshift’s native processing, maximum scalability, and performance, including:

  • Automatic partitioning for maximum throughput, with no developer pre-configuration required
  • Automatic parallel loading of compressed files from Amazon S3 to Amazon RedShift cluster, which auto-scales to ingest massive data volumes and eliminates tedious programming
  • Clustering of secure agents on a grid for maximum performance
  • Data transformation optimizations such as cached lookups
  • Comprehensive monitoring of production data workflows via user-friendly tools that deliver operational confidence to catch and correct production problems early, which improves data quality and timeliness
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