Remove Data Architecture Remove Data Quality Remove Data Warehouse
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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality.

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What is a Data Mesh?

DataKitchen

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. The past decades of enterprise data platform architectures can be summarized in 69 words. Introduction to Data Mesh. Source: Thoughtworks.

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

AWS Big Data

Amazon SageMaker Lakehouse , now generally available, unifies all your data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data. Having confidence in your data is key.

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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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Perform data parity at scale for data modernization programs using AWS Glue Data Quality

AWS Big Data

Today, customers are embarking on data modernization programs by migrating on-premises data warehouses and data lakes to the AWS Cloud to take advantage of the scale and advanced analytical capabilities of the cloud. Some customers build custom in-house data parity frameworks to validate data during migration.

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From data lakes to insights: dbt adapter for Amazon Athena now supported in dbt Cloud

AWS Big Data

This enables you to extract insights from your data without the complexity of managing infrastructure. dbt has emerged as a leading framework, allowing data teams to transform and manage data pipelines effectively. This feature reduces the amount of data scanned by Athena, resulting in faster query performance and lower costs.

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The future of data: A 5-pillar approach to modern data management

CIO Business Intelligence

They must also select the data processing frameworks such as Spark, Beam or SQL-based processing and choose tools for ML. Based on business needs and the nature of the data, raw vs structured, organizations should determine whether to set up a data warehouse, a Lakehouse or consider a data fabric technology.