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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. Business/Data Analyst: The business analyst is all about the “meat and potatoes” of the business.

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The Modern Data Stack Explained: What The Future Holds

Alation

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

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How Alation’s Data Team Uses the Modern Data Stack to Power Insights

Alation

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. But what does this mean from a practitioner perspective?

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

Content includes reports, documents, articles, presentations, visualizations, video, and audio representations of the insights and knowledge that have been extracted from data. This is where SAP Datasphere (the next generation of SAP Data Warehouse Cloud) comes in.

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Birst automates the creation of data warehouses in Snowflake

Birst BI

Managing large-scale data warehouse systems has been known to be very administrative, costly, and lead to analytic silos. The good news is that Snowflake, the cloud data platform, lowers costs and administrative overhead. The result is a lower total cost of ownership and trusted data and analytics.

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

To speed up the self-service analytics and foster innovation based on data, a solution was needed to provide ways to allow any team to create data products on their own in a decentralized manner. To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse.

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What is Data Mapping?

Jet Global

Why Data Mapping is Important Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation. Data mapping is important for several reasons.