Wed.Oct 23, 2024

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Cloudera and Snowflake Partner to Deliver the Most Comprehensive Open Data Lakehouse

Cloudera

In August, we wrote about how in a future where distributed data architectures are inevitable, unifying and managing operational and business metadata is critical to successfully maximizing the value of data, analytics, and AI. One of the most important innovations in data management is open table formats, specifically Apache Iceberg , which fundamentally transforms the way data teams manage operational metadata in the data lake.

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Keras vs. JAX: A Comparison

KDnuggets

This comparison analyzes and compares two salient frameworks for architecting deep learning solutions.

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Databricks Migration Strategy - lessons learned

databricks

Migrating your data warehouse workloads is one of the most challenging yet essential tasks for any organization. Whether the motivation is the growth.

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Creating Interactive Dashboards with D3.js

KDnuggets

Learn how to build interactive dashboards where users can interact with data in greater detail, explore trends and patterns dynamically using D3.js.

Building 124
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A Guide to Debugging Apache Airflow® DAGs

In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

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Supercharging R&D in Life Sciences

Snowflake

Imagine a biotech company successfully integrating AI into its research and development (R&D) processes. Using AI algorithms, users in every division of the company can perform advanced analytics, predictive modeling and simulation studies. These capabilities allow them to quickly identify therapeutic targets, design more efficient clinical trials and enhance drug development.

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Resource Management with Apache YuniKorn™ for Apache Spark™ on AWS EKS at Pinterest

Pinterest Engineering

Yongjun Zhang; Staff Software Engineer | William Tom; Staff Software Engineer | Sandeep Kumar; Software Engineer | Monarch, Pinterest’s Batch Processing Platform, was initially designed to support Pinterest’s ever-growing number of Apache Spark and MapReduce workloads at scale. During Monarch’s inception in 2016, the most dominant batch processing technology around to build the platform was Apache Hadoop YARN.

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Announcing the Confluent for Startups AI Accelerator Program: Empowering the First Generation of Real-Time AI Startups

Confluent

Join the Confluent AI Accelerator Program to help AI startups harness real-time AI and data streaming for innovation, growth, and cutting-edge AI-powered applications.

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How Roche Built Trust in the Data Mesh with Data Observability

Monte Carlo

Since Zhamak Deghani introduced the concept of the data mesh in 2019, this decentralized approach to data architecture has generated an enormous amount of buzz. But what does it actually look like to implement a data mesh at scale? The data team at Roche has the answer. Headquartered in Switzerland, Roche is a leading, global pharmaceutical and diagnostics company creating and leveraging massive amounts of organizational data.

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Trends in AI and Data Analytics for Renewable Energy

RandomTrees

The world is currently in the middle of a transition towards renewable energy sources such as wind, solar, and hydroelectricity from fossil fuels. The importance of Artificial Intelligence (AI) and Data Analytics in renewable energy is increasing with the accelerating pace of this transition. These technologies are also crucial to address challenges that exist within the energy industry, optimize operations, and enable renewable energy to satisfy rising global demand.