Remove Data Analytics Remove Data Architecture Remove Data Lake Remove Reporting
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Data architecture strategy for data quality

IBM Big Data Hub

Several factors determine the quality of your enterprise data like accuracy, completeness, consistency, to name a few. But there’s another factor of data quality that doesn’t get the recognition it deserves: your data architecture. How the right data architecture improves 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. First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt.

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.

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The New Normal for FP&A: Data Analytics

Jedox

The term “data analytics” refers to the process of examining datasets to draw conclusions about the information they contain. Data analysis techniques enhance the ability to take raw data and uncover patterns to extract valuable insights from it. Data analytics is not new. Inability to get data quickly.

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Detect, mask, and redact PII data using AWS Glue before loading into Amazon OpenSearch Service

AWS Big Data

Solution overview The following diagram illustrates the high-level solution architecture. We have defined all layers and components of our design in line with the AWS Well-Architected Framework Data Analytics Lens. Amazon AppFlow can be used to transfer data from different SaaS applications to a data lake.

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DataOps For Business Analytics Teams

DataKitchen

Analytics Hub and Spoke. The data analytics function in large enterprises is generally distributed across departments and roles. For example, teams working under the VP/Directors of Data Analytics may be tasked with accessing data, building databases, integrating data, and producing reports.

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Using Snowflake and Denodo to Reduce Data Modernization Downtime

CDW Research Hub

Cloud-based solutions are promising, but some organizations are reluctant to migrate from legacy systems because it could result in costly downtime and many unknown data architecture and migration issues. The user can then consume the data through reports, web applications, mobile apps, or a data platform of their choice.