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Digital Transformation is a Data Journey From Edge to Insight

Cloudera

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

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Data Pipeline Observability: A Model For Data Engineers

Databand.ai

This might include processes like data extraction from different sources, data cleansing, data transformation (like aggregation), and loading the data into a database or a data warehouse. Data storage and delivery : Observability continues into the storage and delivery phase.

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Accenture’s Smart Data Transition Toolkit Now Available for Cloudera Data Platform

Cloudera

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

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Top Data Lake Vendors (Quick Reference Guide)

Monte Carlo

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. See our post: Data Lakes vs. Data Warehouses.

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8 Data Ingestion Tools (Quick Reference Guide)

Monte Carlo

At the heart of every data-driven decision is a deceptively simple question: How do you get the right data to the right place at the right time? The growing field of data ingestion tools offers a range of answers, each with implications to ponder. Fivetran Image courtesy of Fivetran.

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The Pros and Cons of Leading Data Management and Storage Solutions

The Modern Data Company

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

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The Pros and Cons of Leading Data Management and Storage Solutions

The Modern Data Company

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.