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Data Engineer
Accelerate your Data Engineering career: Discover best practices, and learn the latest trends.
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
To stay competitive as a data engineer, it’s essential to master the latest trends in this constantly evolving field. Keeping up with new tools, technologies, and best practices helps you adapt and succeed amid rapid changes.
AI and machine learning innovations in CLAIRE® are driving significant advances in data management. Generative AI is central to digital transformation across industries, enhancing everyday life, enabling breakthroughs in life sciences, reducing fraud in finance, and personalizing customer experiences.
Discover, understand, trust, and access relevant data to generate business value
Data discovery enables your organization to identify, catalog, and classify business-critical and sensitive data, so you can govern it for meaningful purposes with increased transparency.
The guide shows nine steps to unlock your data’s potential, enable self-service access, and use AI/ML for predictive insights - helping you make smarter, data-driven business decisions.
Replicate and ingest data from applications, databases, streaming sources, and files using a simple, wizard-driven experience.
Cloud Data Ingestion and Replication (CDIR) simplifies data flow across cloud and on-premises platforms with a no-code interface, enabling data engineers to create scalable, low-maintenance pipelines cost-effectively. It enhances productivity and supports analytics, AI/ML, and business innovation.
Data drives digital transformation, but traditional methods create silos and don’t scale. Modern cloud data architectures offer better performance, flexibility, and unified insights, enabling organizations to adapt and innovate amid rapid change.
Build, deploy, and operationalize low-code data pipelines at scale
Data integration supports analytics and AI but managing servers is costly and complex. Going serverless boosts productivity and cuts costs. Download the ebook to learn key benefits, cost-saving examples, and how Informatica’s cloud-native solution works.
Enterprises moving to the cloud seek flexibility and cost savings but face challenges like cost overruns, resource limits, and complexity. To succeed, they need a cloud-native ETL/ELT solution that controls costs, boosts productivity, and scales seamlessly - ensuring efficient, modern cloud data integration for digital transformation.
Profile, cleanse, standardize, and enrich all data using an extensive set of prebuilt data quality rules.
To succeed in digital transformation, organizations must ensure business-focused data quality everywhere. Reliable data enables faster decisions, regulatory compliance, streamlined processes, and drives innovation through AI, IoT, and big data.
Informatica ModelServe offers easy, serverless AI/ML model deployment to speed up business value and decision-making. Ensuring data quality in the cloud data warehouse is key to reliable AI and ML success.
Open, embeddable, and extensible headless data management for data engineers
CDI-Free offers faster, cost-effective cloud data integration without extra infrastructure. Ensure business-focused data quality to drive better decisions and innovation. Download the data sheet to see how financial data management adds business value.
Build, deploy and monitor your AI/ML models at scale
Boost AI and ML productivity by operationalizing models with MLOps. This white paper explains why MLOps is essential, how to scale AI/ML automation, and offers a 5-step guide to success - plus how Informatica can support your data management.
Informatica ModelServe enables one-click, serverless deployment of virtually any AI/ML model for use in most applications. It simplifies and speeds up operationalizing models at scale, helping organizations unlock business value from AI faster while improving productivity and decision-making.
