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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Traditionally, such a legacy call center analytics platform would be built on a relational database that stores data from streaming sources. Data transformations through stored procedures and use of materialized views to curate datasets and generate insights is a known pattern with relational databases.

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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

Therefore, there are several roles that need to be filled, including: DQM Program Manager: The program manager role should be filled by a high-level leader who accepts the responsibility of general oversight for business intelligence initiatives. The program manager should lead the vision for quality data and ROI.

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What is Data Mapping?

Jet Global

This field guide to data mapping will explore how data mapping connects volumes of data for enhanced decision-making. 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.

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Data platform trinity: Competitive or complementary?

IBM Big Data Hub

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. A read-optimized platform that can integrate data from multiple applications emerged.

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Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

AWS Big Data

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

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Exploring the AI and data capabilities of watsonx

IBM Big Data Hub

foundation models to help users discover, augment, and enrich data with natural language. Watsonx.data is built on 3 core integrated components: multiple query engines, a catalog that keeps track of metadata, and storage and relational data sources which the query engines directly access.

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A Stitch in Time: How Jet Analytics Boosts Microsoft Fabric Time-to-Value

Jet Global

The solution offers data movement, data science, real-time analytics, and business intelligence within a single platform. Data Lineage and Documentation Jet Analytics simplifies the process of documenting data assets and tracking data lineage in Fabric.