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Data integrity vs. data quality: Is there a difference?

IBM Big Data Hub

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Big Data Hub

When effectively implemented, a data democracy simplifies the data stack, eliminates data gatekeepers, and makes the company’s comprehensive data platform easily accessible by different teams via a user-friendly dashboard. Then, it applies these insights to automate and orchestrate the data lifecycle.

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Extracting key insights from Amazon S3 access logs with AWS Glue for Ray

AWS Big Data

This blog post presents an architecture solution that allows customers to extract key insights from Amazon S3 access logs at scale. We will partition and format the server access logs with Amazon Web Services (AWS) Glue , a serverless data integration service, to generate a catalog for access logs and create dashboards for insights.

Metadata 101
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SAP enhances Datasphere and SAC for AI-driven transformation

CIO Business Intelligence

“SAP is executing on a roadmap that brings an important semantic layer to enterprise data, and creates the critical foundation for implementing AI-based use cases,” said analyst Robert Parker, SVP of industry, software, and services research at IDC. We are also seeing customers bringing in other data assets from other apps or data sources.

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

Over the years, data lakes on Amazon Simple Storage Service (Amazon S3) have become the default repository for enterprise data and are a common choice for a large set of users who query data for a variety of analytics and machine leaning use cases. Analytics use cases on data lakes are always evolving.

Data Lake 104
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SAP Datasphere review: turning data from a technical problem to a business data product.

Jen Stirrup

However, to turn data into a business problem, organizations need support to move away from technical issues to start getting value as quickly as possible. SAP Datasphere simplifies data integration, cataloging, semantic modeling, warehousing, federation, and virtualization through a unified interface. Why is this interesting?

Metadata 121
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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Then, you transform this data into a concise format.