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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Data Integrity, the Basis for Reliable Insights

Sisense

We live in a world of data: There’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways data teams are tackling the challenges of this new world to help their companies and their customers thrive. What is data integrity?

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Apache Kafka and the Denodo Platform: Distributed Events Streaming Meets Logical Data Integration

Data Virtualization

Kafka is used when real-time data streaming and event-driven architectures with scalable data processing are essential.

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IBM named a leader in the 2022 Gartner® Magic Quadrant™ for Data Integration Tools

IBM Big Data Hub

The only question is, how do you ensure effective ways of breaking down data silos and bringing data together for self-service access? It starts by modernizing your data integration capabilities – ensuring disparate data sources and cloud environments can come together to deliver data in real time and fuel AI initiatives.

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Embracing Data Mesh: A Modern Approach to Data Management

Data Virtualization

Reading Time: 2 minutes In the ever-evolving landscape of data management, one concept has been garnering the attention of companies and challenging traditional centralized data architectures. This concept is known as “data mesh,” and it has the potential to revolutionize the way organizations handle.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Businesses are constantly evolving, and data leaders are challenged every day to meet new requirements. Customers are using AWS and Snowflake to develop purpose-built data architectures that provide the performance required for modern analytics and artificial intelligence (AI) use cases.

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How The Cloud Made ‘Data-Driven Culture’ Possible | Part 2: Cloud Adoption

BizAcuity

IaaS provides a platform for compute, data storage and networking capabilities. IaaS is mainly used for developing softwares (testing and development, batch processing), hosting web applications and data analysis. Analytics as a Service is almost a BI tool used for data analysis.and examples are restricted to the industry.