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Modern Data Engineering with MAGE: Empowering Efficient Data Processing

Analytics Vidhya

Introduction In today’s data-driven world, organizations across industries are dealing with massive volumes of data, complex pipelines, and the need for efficient data processing.

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Event time skew in stream processing

Waitingforcode

As a data engineer you're certainly familiar with data skew. Turns out, stream processing also has its skew but more related to time. Yes, this bad phenomena where one task takes considerably more input than the others and often causes unexpected latency or failures.

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Reading and Processing JSON with Rust vs Python.

Confessions of a Data Guy

When it comes to debugging […] The post Reading and Processing JSON with Rust vs Python. appeared first on Confessions of a Data Guy. I’ve found I like being explicit and verbose when writing code, rather than being vague in what I’m doing most of the time.

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5 Real-Time Data Processing and Analytics Technologies – And Where You Can Implement Them

Seattle Data Guy

Real-time data can help you do just that. Real-time data processing can satisfy the ever-increasing demand for… Read more The post 5 Real-Time Data Processing and Analytics Technologies – And Where You Can Implement Them appeared first on Seattle Data Guy.

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Drive Better Decision-Making with Data Storytelling

Storytelling is more than just data visualization. Storytelling provides an organized approach for conveying data insights through visuals and narrative. Data-driven storytelling could be used to influence user actions, and ensure they understand what data matters the most.

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Apache Druid’s Architecture – How Druid Processes Data In Real Time At Scale

Seattle Data Guy

Everything from its various nodes and processes that each have unique… Read more The post Apache Druid’s Architecture – How Druid Processes Data In Real Time At Scale appeared first on Seattle Data Guy. Apache Druid has several unique features that allow it to be used as a real-time OLAP.

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Fueling Data-Driven Decision-Making with Data Validation and Enrichment Processes

Precisely

77% of data and analytics professionals say data-driven decision-making is the top goal for their data programs. Data-driven decision-making and initiatives are certainly in demand, but their success hinges on … well, the data that supports them. More specifically, the quality and integrity of that data.

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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.

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How to Package and Price Embedded Analytics

Just by embedding analytics, application owners can charge 24% more for their product. How much value could you add? This framework explains how application enhancements can extend your product offerings. Brought to you by Logi Analytics.

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New Study: 2018 State of Embedded Analytics Report

Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.

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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Greg Loughnane and Chris Alexiuk in this exciting webinar to learn all about: How to design and implement production-ready systems with guardrails, active monitoring of key evaluation metrics beyond latency and token count, managing prompts, and understanding the process for continuous improvement Best practices for setting up the proper mix of open- (..)