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Fundamentals of Analytics on AWS-Part2: * Basics of data lakes * Basics of data warehousing * Overview of modern data architecture * Modern data architecture concepts * Best practices for building a modern data architecture * AWS services for common analytics use cases * Overview of reference architectures #aws #dataarchitecture #dataengineering #cloudengineering #datascience

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🎓 Excited to Share: Just Completed "Understanding Modern Data Architecture" Course! 🌟 I'm thrilled to announce that I've successfully completed the "Understanding Modern Data Architecture" course! 🚀 In today's data-driven world, staying updated with the latest trends and technologies in data architecture is crucial. This course provided a comprehensive overview of modern data architecture concepts, equipping me with the knowledge and skills needed to navigate through the complexities of managing and analyzing data effectively. Throughout the course, I delved into topics such as data modeling, data warehousing, data lakes, ETL processes, cloud data platforms, and much more. I gained valuable insights into designing scalable and efficient data systems that can adapt to the evolving needs of businesses in today's dynamic environment. #DataArchitecture #DataAnalytics #ContinuousLearning #DataDrivenDecisions #ProfessionalDevelopment

⭐ Data lakehouse is a modern data architecture that creates a single platform by combining the key benefits of data lakes and data warehouse. ⭐ Here is the Basic Architecture for your view & start building the Lakehouse. #lakehouse #databricks #azure #datafactory #datapipeline #azuresynapse #medallionArchitecture

More than 50% of data leaders say the complexity of their data architecture is a significant pain point in their enterprise. Companies hampered by legacy architectures are plagued by a high TCO, an inability to govern data, and a lack of scalability as data volumes grow. Discover how Marriott International modernised their data architecture with Snowflake.

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Fundamentals of Analytics on AWS – Part 2 by Amazon Web Services (AWS) Basics of data lakes Basics of data warehousing Overview of modern data architecture Modern data architecture concepts Best practices for building a modern data architecture AWS services for common analytics use cases Overview of reference architectures

Comparison of Different #DataPlatform Architectures and the #DataMesh   The structure, implementation and use of #datawarehouse, #datalake, #datalakehouse and data meshes are different. A data lake deals with raw data that can be structured, semi-structured and unstructured, while a data warehouse only deals with structured data. The type of repository in a data warehouse is less agile due to the stricter data requirements and the storage costs are much higher compared to the data lake. The general users of data lakes and data warehouses also differ, the data warehouse mainly targets professional business users while the data lake mainly targets data experts with relatively more data expertise and experience due to the complexity of unstructured data. The Data lake can be considered more effective and efficient to deal with heavy workloads, compared to the already established data warehouse. A data mesh architecture, on the other hand, does not limit the data platform to a single data warehouse. It is more an organizational approach towards data platform architectures than the technical implementation of a data platform. 👇🏾Comparison

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