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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

KDnuggets

Its key goals are to ensure data quality, consistency, and usability and align data with analytical models or reporting needs. Its key goals are to store data in a format that supports fast querying and scalability and to enable real-time or near-real-time access for decision-making. How often should dashboards update?

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Go vs. Python for Modern Data Workflows: Need Help Deciding?

KDnuggets

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Go vs. Python for Modern Data Workflows: Need Help Deciding?

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From Chaos to Clarity: How Data Lakehouses Empower AI at Scale

Data Science Connect

TL;DR – What you’ll learn Why lakehouses combine the flexibility of data lakes with the governance and performance of warehouses to cut friction in AI adoption. How modern file formats (Iceberg, Delta Lake) and open object storage enable real-time analytics, schema management, and engine interoperability.

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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

Flipboard

It enables different business units within an organization to create, share, and govern their own data assets, promoting self-service analytics and reducing the time required to convert data experiments into production-ready applications. We discuss this in more detail later in this post.

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What Is a Lakebase?

databricks

They sit outside the analytics and AI stack, require manual integration, and lack the flexibility needed for modern development workflows. At zero, the cost of the lakebase is just the cost of storing the data on cheap data lakes. As a result, there has been very little innovation in this space for decades.

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Data lakehouse

Dataconomy

Data Lakehouse has emerged as a significant innovation in data management architecture, bridging the advantages of both data lakes and data warehouses. By enabling organizations to efficiently store various data types and perform analytics, it addresses many challenges faced in traditional data ecosystems.

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How Data Intelligence is Accelerating IT/OT Convergence

databricks

Scalable Intelligence: The data lakehouse architecture supports scalable, real-time analytics, allowing industrials to monitor and improve key performance indicators, predict maintenance needs, and optimize production processes.