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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications. Did you know?

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

datapine

No, its ultimate goal is to increase return on investment (ROI) for those business segments that depend upon data. With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. The 5 Pillars of Data Quality Management.

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Best BI Tools For 2024 You Need to Know

FineReport

In 2024, business intelligence (BI) software has undergone significant advancements, revolutionizing data management and decision-making processes. Harnessing the power of advanced APIs, automation, and AI, these tools simplify data compilation, organization, and visualization, empowering users to extract actionable insights effortlessly.

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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.

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

AWS Big Data

Organizations with legacy, on-premises, near-real-time analytics solutions typically rely on self-managed relational databases as their data store for analytics workloads. Traditionally, such a legacy call center analytics platform would be built on a relational database that stores data from streaming sources.

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

The data volume is in double-digit TBs with steady growth as business and data sources evolve. smava’s Data Platform team faced the challenge to deliver data to stakeholders with different SLAs, while maintaining the flexibility to scale up and down while staying cost-efficient.