Remove Data Quality Remove Metadata Remove Optimization Remove Structured Data
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What is data governance? Best practices for managing data assets

CIO Business Intelligence

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. The program must introduce and support standardization of enterprise data.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. They should also provide optimal performance with low or no tuning. The source data is usually in either structured or semi-structured formats, which are highly and loosely formatted, respectively.

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Success Stories: Applications and Benefits of Knowledge Graphs in Financial Services

Ontotext

This shift of both a technical and an outcome mindset allows them to establish a centralized metadata hub for their data assets and effortlessly access information from diverse systems that previously had limited interaction. There are four groups of data that are naturally siloed: Structured data (e.g.,

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Throwing Your Data Into the Ocean

Ontotext

That means removing errors, filling in missing information and harmonizing the various data sources so that there is consistency. Once that is done, data can be transformed and enriched with metadata to facilitate analysis. You can apply graph optimizations or operations such as traversals and transformations.

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The Role of AI and ML in Model Governance

Alation

A data catalog is a central hub for XAI and understanding data and related models. While “operational exhaust” arrived primarily as structured data, today’s corpus of data can include so-called unstructured data. These methods and their results need to be captured, but how? Other Technologies. Conclusion.

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How SumUp made digital analytics more accessible using AWS Glue

AWS Big Data

In this post we showcase how we used AWS Glue to move siloed digital analytics data, with inconsistent arrival times, to AWS S3 (our Data Lake) and our central data warehouse (DWH), Snowflake. AWS Glue gave us a cost-efficient option to migrate the data and we further optimized storage cost by pruning cold data.

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. We get this question regularly. million users.