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

datapine

Benefits Of Big Data In Logistics Before we look at our selection of practical examples and applications, let’s look at the benefits of big data in logistics – starting with the (not so) small matter of costs. Use our 14-days free trial today & transform your supply chain! Now’s the time to strike.

Big Data 275
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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Key performance indicators (KPIs) of interest for a call center from a near-real-time platform could be calls waiting in the queue, highlighted in a performance dashboard within a few seconds of data ingestion from call center streams. Visualize KPIs of call center performance in near-real time through OpenSearch Dashboards.

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End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

Plan In the planning phase, developers collect requirements from stakeholders such as end-users to define a data requirement. Every time the business requirement changes (such as adding data sources or changing data transformation logic), you make changes on the AWS Glue app stack and re-provision the stack to reflect your changes.

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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

Initially, Tricentis defines these dashboards and charts to enable insight on test runs, test traceability with requirements, and many other pre-defined use cases that can be valuable to customers. As the files are created, another process is triggered to load the data from each customer on their schema or table on Amazon Redshift.

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How SafetyCulture scales unpredictable dbt Cloud workloads in a cost-effective manner with Amazon Redshift

AWS Big Data

A source of unpredictable workloads is dbt Cloud , which SafetyCulture uses to manage data transformations in the form of models. SafetyCulture also successfully ran its dbt project with all seeds, models, and snapshots materialized into the serverless instance via run commands from the dbt Cloud IDE and dbt Cloud CI jobs.

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Build incremental data pipelines to load transactional data changes using AWS DMS, Delta 2.0, and Amazon EMR Serverless

AWS Big Data

Data ingestion – Steps 1 and 2 use AWS DMS, which connects to the source database and moves full and incremental data (CDC) to Amazon S3 in Parquet format. Data transformation – Steps 3 and 4 represent an EMR Serverless Spark application (Amazon EMR 6.9 Let’s refer to this S3 bucket as the raw layer.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

If you ask an engineer to show how they operate the application in production, they will likely show containers and operational dashboards—not unlike any other software service. To manage the dynamism, we can resort to taking snapshots that represent immutable points in time: of models, of data, of code, and of internal state.

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