Remove Data Warehouse Remove Digital Transformation Remove Metadata
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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

This is where we dispel an old “big data” notion (heard a decade ago) that was expressed like this: “we need our data to run at the speed of business.” Instead, what we really need is for our business to run at the speed of data. This is where SAP Datasphere (the next generation of SAP Data Warehouse Cloud) comes in.

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Constructing A Digital Transformation Strategy: Putting the Data in Digital Transformation

erwin

Having a clearly defined digital transformation strategy is an essential best practice for successful digital transformation. But what makes a viable digital transformation strategy? Constructing A Digital Transformation Strategy: Data Enablement.

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Data Governance and Metadata Management: You Can’t Have One Without the Other

erwin

When an organization’s data governance and metadata management programs work in harmony, then everything is easier. Data governance is a complex but critical practice. There’s always more data to handle, much of it unstructured; more data sources, like IoT, more points of integration, and more regulatory compliance requirements.

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7 Benefits of Metadata Management

erwin

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. What Is Metadata? Harvest data.

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How Metadata Makes Data Meaningful

erwin

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

An extract, transform, and load (ETL) process using AWS Glue is triggered once a day to extract the required data and transform it into the required format and quality, following the data product principle of data mesh architectures. This process is shown in the following figure.

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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

With improved access and collaboration, you’ll be able to create and securely share analytics and AI artifacts and bring data and AI products to market faster. This innovation drives an important change: you’ll no longer have to copy or move data between data lake and data warehouses.