Remove Business Objectives Remove Data Analytics Remove Data Lake
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Data architecture strategy for data quality

IBM Big Data Hub

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Pharmaceutical IT leaders: Speed drug discovery and development with data lakes

CIO Business Intelligence

Without a unified data foundation, this vital data remains siloed and fragmented, hindering collaboration, innovation, and insights in drug discovery and development. A cloud data lake is a secure, central repository for vast amounts of disparate data in their original form.

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Build a real-time analytics solution with Apache Pinot on AWS

AWS Big Data

In essence, it’s the foundation for user-centric data analysis in modern apps, because it’s the layer that translates technical assets into business-friendly terms that enable users to extract actionable insights from data. Ismail Makhlouf is a Senior Specialist Solutions Architect for Data Analytics at AWS.

OLAP 91
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Migrate Amazon Redshift from DC2 to RA3 to accommodate increasing data volumes and analytics demands

AWS Big Data

With the ever-increasing volume of data available, Dafiti faces the challenge of effectively managing and extracting valuable insights from this vast pool of information to gain a competitive edge and make data-driven decisions that align with company business objectives. We started with 115 dc2.large

Data Lake 105
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Havmor’s VP IT Dhaval Mankad on ‘melting’ hurdles with a scoop of digital innovation

CIO Business Intelligence

Currently, we have not implemented any full-fledged AI solutions, but internal discussions with the management are underway to develop dashboard solutions with data analytics. We need to define our business objective before adopting those new tools, because AI is simply algorithm.

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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

A successful migration can be accomplished through proactive planning, continuous monitoring, and performance fine-tuning, thereby aligning with and delivering on business objectives. This requires a dedicated team of 3–7 members building a serverless data lake for all data sources. Vijay Bagur is a Sr.

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

AWS Big Data

The AWS modern data architecture shows a way to build a purpose-built, secure, and scalable data platform in the cloud. Learn from this to build querying capabilities across your data lake and the data warehouse. About the Authors Ismail Makhlouf is a Senior Specialist Solutions Architect for Data Analytics at AWS.