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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.

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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

Amazon Redshift is a cloud data warehousing service that provides high-performance analytical processing based on a massively parallel processing (MPP) architecture. Building and maintaining data pipelines is a common challenge for all enterprises. The table refresh can be full or incremental based on the configuration.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

As with all AWS services, Amazon Redshift is a customer-obsessed service that recognizes there isn’t a one-size-fits-all for customers when it comes to data models, which is why Amazon Redshift supports multiple data models such as Star Schemas, Snowflake Schemas and Data Vault.

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What is business intelligence? Transforming data into business insights

CIO Business Intelligence

Business intelligence definition Business intelligence (BI) is a set of strategies and technologies enterprises use to analyze business information and transform it into actionable insights that inform strategic and tactical business decisions. BI aims to deliver straightforward snapshots of the current state of affairs to business managers.

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

AWS Big Data

Solution overview Typically, you have multiple accounts to manage and provision resources for your data pipeline. Although it’s possible to configure the AWS CDK template to work with GitHub, GitHub Enterprise, or Bitbucket, for this post, we use CodeCommit. rename_field('id', 'org_id').rename_field('name',

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. Unlike migrate or snapshot, add_files can import files from a specific partition or partitions and doesn’t create a new Iceberg table. Supported formats are Avro, Parquet, and ORC.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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