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Set up alerts and orchestrate data quality rules with AWS Glue Data Quality

AWS Big Data

Alerts and notifications play a crucial role in maintaining data quality because they facilitate prompt and efficient responses to any data quality issues that may arise within a dataset. This proactive approach helps mitigate the risk of making decisions based on inaccurate information.

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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

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As insurers look to be more agile, data mesh strategies take centerstage

CIO Business Intelligence

These domain data leaders often cite the diminishing returns and significant effort of central data team engagement. Additionally, data silos and fragmentation often occur inorganically as in the case of merger or acquisition scenarios. Data Center, Data Management, Digital Transformation

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8 data strategy mistakes to avoid

CIO Business Intelligence

Building a successful data strategy at scale goes beyond collecting and analyzing data,” says Ryan Swann, chief data analytics officer at financial services firm Vanguard. Creating data silos Denying business users access to information because of data silos has been a problem for years.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

It does this by helping teams handle the T in ETL (extract, transform, and load) processes. It allows users to write data transformation code, run it, and test the output, all within the framework it provides. As part of their cloud modernization initiative, they sought to migrate and modernize their legacy data platform.

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Drive Growth with Data-Driven Strategies: Introducing Zenia Graph’s Salesforce Accelerator

Ontotext

Traditional data integration methods struggle to bridge these gaps, hampered by high costs, data quality concerns, and inconsistencies. Studies reveal that businesses lose significant time and opportunities due to missing integrations and poor data quality and accessibility.

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An AI Chat Bot Wrote This Blog Post …

DataKitchen

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. Overall, DataOps is an essential component of modern data-driven organizations. Query> DataOps.