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Supercharge your data strategy: Integrate and innovate today leveraging data integration

IBM Journey to AI blog

This process is known as data integration , one of the key components to improving the usability of data for AI and other use cases, such as business intelligence (BI) and analytics. Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models.

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How to Build ETL Data Pipeline in ML

The MLOps Blog

However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.

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Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

.   Request a live demo or start a proof of concept with Amazon RDS for Db2 Db2 Warehouse SaaS on AWS The cloud-native Db2 Warehouse fulfills your price and performance objectives for mission-critical operational analytics, business intelligence (BI) and mixed workloads.

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What is Integrated Business Planning (IBP)?

IBM Journey to AI blog

This blog explores the significance of IBP in today’s modern business landscape and highlights its key benefits and implementation considerations. Advanced analytics and business intelligence tools are utilized to analyze and interpret the data, uncovering insights and trends that drive informed decision-making.

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Fine-tune your data lineage tracking with descriptive lineage

IBM Journey to AI blog

Extraction, transformation and loading (ETL) tools dominated the data integration scene at the time, used primarily for data warehousing and business intelligence. Critical and quick bridges The demand for lineage extends far beyond dedicated systems such as the ETL example. This made things simple.

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Data architecture strategy for data quality

IBM Journey to AI blog

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Learn more about the benefits of data fabric and IBM Cloud Pak for Data.

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

To power AI and analytics workloads across your transactional and purpose-built databases, you must ensure they can seamlessly integrate with an open data lakehouse architecture without duplication or additional extract, transform, load (ETL) processes.

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