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Big Data Opportunity in Manufacturing

TDAN

The world now runs on Big Data. Defined as information sets too large for traditional statistical analysis, Big Data represents a host of insights businesses can apply towards better practices. But what exactly are the opportunities present in big data? In manufacturing, this means opportunity.

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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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Modern Data Architecture for Telecommunications

Cloudera

Data has continued to grow both in scale and in importance through this period, and today telecommunications companies are increasingly seeing data architecture as an independent organizational challenge, not merely an item on an IT checklist. Why telco should consider modern data architecture. The challenges.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

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The power of remote engine execution for ETL/ELT data pipelines

IBM Big Data Hub

Transformation styles like TETL (transform, extract, transform, load) and SQL Pushdown also synergies well with a remote engine runtime to capitalize on source/target resources and limit data movement, thus further reducing costs. With a multicloud data strategy, organizations need to optimize for data gravity and data locality.

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Empowering data-driven excellence: How the Bluestone Data Platform embraced data mesh for success

AWS Big Data

Four-layered data lake and data warehouse architecture – The architecture comprises four layers, including the analytical layer, which houses purpose-built facts and dimension datasets that are hosted in Amazon Redshift.

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How Amazon Finance Automation built a data mesh to support distributed data ownership and centralize governance

AWS Big Data

These inputs reinforced the need of a unified data strategy across the FinOps teams. We decided to build a scalable data management product that is based on the best practices of modern data architecture. Data source locations hosted by the producer are created within the producer’s AWS Glue Data Catalog.

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