DataKitchen

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Your Generative AI LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers

DataKitchen

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure. LLMs have the potential to revolutionize how we interact with data, automate processes, and extract insights.

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DataKitchen Resource Guide To Data Journeys & Data Observability & DataOps

DataKitchen

DataKitchen Resource Guide To Data Journeys & Data Observability & DataOps Data (and Analytic) Observability & Data Journey – Ideas and Background Data Journey Manifesto and Why the Data Journey Manifesto? Five Pillars of Data Journeys Data Journey First DataOps “You Complete Me,” said Data Lineage to Data Journeys. Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability The Need For Personalized Data Journeys for Your Data Consumers Data Te

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The Art of Data Buck-Passing 101: Mastering the Blame Game in Data and Analytic Teams

DataKitchen

The Art of Data Buck-Passing 101: Mastering the Blame Game in Data and Analytic Teams Welcome, dear readers, to the hallowed halls of Data Buck-Passing University, where the motto is “ Per Alios Culpa Transfertur ” (Blame is Transferred to Others). In the world of data and analytics, one skill stands timeless and universal: the art of blaming someone else when things go sideways.

Analytics 173
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ON DEMAND WEBINAR: Beyond Data Observability

DataKitchen

Do you have data quality issues, a complex technical environment, and a lack of visibility into production systems? These challenges lead to poor quality analytics and frustrated end users. Getting your data reliable is a start, but many other problems arise even if your data could be better. And your customers don't care where the problem is in your toolchain.

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Navigating the Chaos of Unruly Data: Solutions for Data Teams

DataKitchen

The Perilous State of Today’s Data Environments Data teams often navigate a labyrinth of chaos within their databases. The core issue plaguing many organizations is the presence of out-of-control databases or data lakes characterized by: Unrestrained Data Changes: Numerous users and tools incessantly alter data, leading to a tumultuous environment. Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team.

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ON DEMAND WEBINAR: Data Observability Demo Day

DataKitchen

This webinar discusses how to make embarrassing data errors a thing of the past. We will start with how data engineers do not understand their data and have difficulty identifying problematic data records. We will also discuss how the vast majority of data engineers are so busy that they don’t know, or have time to write, tests to write to find data errors.

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The Need For Personalized Data Journeys for Your Data Consumers

DataKitchen

In today’s data-driven landscape, Data and Analytics Teams i ncreasingly face a unique set of challenges presented by Demanding Data Consumers who require a personalized level of Data Observability. As opposed to receiving one-size-fits-all status updates, these key stakeholders desire real-time, granular insights into the status of their specific data as it traverses the complicated data production pipeline.

Insurance 182