Remove Digital Transformation Remove Metadata Remove Structured Data Remove Visualization
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Why You Need End-to-End Data Lineage

erwin

But given the volume, velocity and variety of data (the three Vs of data) we generate today, producing and keeping up with end-to-end data linage is complex and time-consuming. Yet a consistent view of data and how it flows is paramount to the success of enterprise data governance and any data-driven initiative.

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Next-Gen Graph Technology: A CDO Matters Podcast with Ontotext’s CMO Doug Kimball

Ontotext

And the other thing is another way of displaying it or visualizing it, which is a little more node based or hierarchically based. Doug : You’ve got nodes that describe data and edges that describe the relationships between them. I’ve been given a mandate for digital transformation. Would you agree?

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

AWS Glue crawls both S3 bucket paths, populates the AWS Glue database tables based on the inferred schemas, and makes the data available to other analytics applications through the AWS Glue Data Catalog. Athena is used to run geospatial queries on the location data stored in the S3 buckets. Choose Run.

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Data Swamp, Data Lake, Data Lakehouse: What to Know

Alation

That dirty data then corrupts analyses and forces mistakes. A frequent and periodic data cleansing strategy is. Lack of metadata. A lack of organization is another sign of a data swamp, typically driven by bad or incomplete metadata.

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Shutterstock capitalizes on the cloud’s cutting edge

CIO Business Intelligence

We use Snowflake very heavily as our primary data querying engine to cross all of our distributed boundaries because we pull in from structured and non-structured data stores and flat objects that have no structure,” Frazer says. “We think we found a good balance there. Now that’s down to a number of hours.”

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

To ingest the data, smava uses a set of popular third-party customer data platforms complemented by custom scripts. After the data lands in Amazon S3, smava uses the AWS Glue Data Catalog and crawlers to automatically catalog the available data, capture the metadata, and provide an interface that allows querying all data assets.