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5 Critical Factors to Consider When Choosing a Data Warehouse Solution

Dataversity

We have seen an unprecedented increase in modern data warehouse solutions among enterprises in recent years. Experts believe that this trend will continue: The global data warehousing market is projected to reach $51.18 The reason is pretty obvious – businesses want to leverage the power of data […].

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5 Data Governance Mistakes to Avoid

Alation

It’d be difficult to exaggerate the importance of data in today’s global marketplace, especially for firms which are going through digital transformation (DT). Using bad data, or the incorrect data can generate devastating results. And the rise in data valuation has been compared to that of oil during the 19th century.

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5 Data Governance Mistakes to Avoid

Alation

It’d be difficult to exaggerate the importance of data in today’s global marketplace, especially for firms which are going through digital transformation (DT). Using bad data, or the incorrect data can generate devastating results. And the rise in data valuation has been compared to that of oil during the 19th century.

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How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

Flipboard

These datasets are often a mix of numerical and text data, at times structured, unstructured, or semi-structured. In this post, we discuss a Q&A bot use case that Q4 has implemented, the challenges that numerical and structured datasets presented, and how Q4 concluded that using SQL may be a viable solution.

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Mainframe Optimization: 5 Best Practices to Implement Now

Precisely

Migrating applications and data are potentially expensive, time-consuming, and fraught with risk. Today’s cloud systems excel at high-volume data storage, powerful analytics, AI, and software & systems development. For decades, mainframes have provided a powerful and secure backbone for enterprise computing.

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Definite Guide to Building a Machine Learning Platform

The MLOps Blog

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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8 Best Practices for On-Premises to Cloud Migration

Alation

Many things have driven the rise of the cloud data warehouse. The cloud can deliver myriad benefits to data teams, including agility, innovation, and security. More users can access, query, and learn from data, contributing to a greater body of knowledge for the organization. What is On-Premises to Cloud Migration?