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Success
Manage your Success Plans and Engagements, gain key insights into your implementation journey, and collaborate with your CSMsSuccessAccelerate your Purchase to Value engaging with Informatica Architects for Customer SuccessAll your Engagements at one place
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Communities
A collaborative platform to connect and grow with like-minded Informaticans across the globeCommunitiesConnect and collaborate with Informatica experts and championsHave a question? Start a Discussion and get immediate answers you are looking forCustomer-organized groups that meet online and in-person. Join today to network, share ideas, and get tips on how to get the most out of Informatica
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Knowledge Center
Troubleshooting documents, product guides, how to videos, best practices, and moreKnowledge CenterOne-stop self-service portal for solutions, FAQs, Whitepapers, How Tos, Videos, and moreVideo channel for step-by-step instructions to use our products, best practices, troubleshooting tips, and much moreInformation library of the latest product documentsBest practices and use cases from the Implementation team
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Learn
Rich resources to help you leverage full capabilities of our productsLearnRole-based training programs for the best ROIGet certified on Informatica products. Free, Foundation, or ProfessionalFree and unlimited modules based on your expertise level and journeySelf-guided, intuitive experience platform for outcome-focused product capabilities and use cases
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Resources
Library of content to help you leverage the best of Informatica productsResourcesMost popular webinars on product architecture, best practices, and moreProduct Availability Matrix statements of Informatica productsMonthly support newsletterInformatica Support Guide and Statements, Quick Start Guides, and Cloud Product Description ScheduleEnd of Life statements of Informatica products
- Success Portal
- Data Engineer
Data engineering is the process of discovering, designing, and building the data infrastructure to help data owners and data users use and analyze raw data from multiple sources and formats. This allows businesses to use the data to make critical business decisions. You can revisit the definition of data engineering here. If you want to dig a little deep we recommend taking a look at the four fundamentals of data engineering:
- Data discovery and lineage
- Data ingestion
- Data processing
- Data quality
Explore the different components of Data Engineering and stay up to date with the latest trends, technologies, troubleshooting techniques, and best practices.
Unleash the power of data by building an AI-powered modern data architecture
5 Data Engineering Trends You Must Master to Stay Competitive
Artificial Intelligence for the Data-Driven Intelligent Enterprise
Discover, understand, trust, and access relevant data to generate business value
What is Data Discovery and Why Does it Matter?
Setting Your Sights on Data Intelligence
Replicate and ingest data from applications, databases, streaming sources, and files using a simple, wizard-driven experience.
Cloud Mass Ingestion Data Sheet
Artificial Intelligence for the Data-Driven Intelligent Enterprise
Build, deploy, and operationalize low-code data pipelines at scale
5 Reasons to go Serverless to Achieve Your Cloud Data Integration Needs
Faster, More Cost-Effective Cloud Data Integration CDI-Free
Profile, cleanse, standardize, and enrich all data using an extensive set of prebuilt data quality rules.
Informatica Cloud Data Quality and Cloud Data Integration – Solution Brief
Data Quality in the Cloud Data Warehouse
Open, embeddable, and extensible headless data management for data engineers
Faster, More Cost-Effective Cloud Data Integration CDI-Free
Build, deploy and monitor your AI/ML models at scale
MLOps: How to Operationalize Machine Learning Models in 5 Steps
Informatica ModelServe: Put AI into Action