Tue.Jul 30, 2024

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OKR-Centric Delivery Models for Engineering-Focused Enterprises

databricks

Introduction An organization adopting new technologies or on a modernization journey typically focuses on upcoming tools, their features and potential performance/cost improvements under.

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7 Steps to Master the Art of Data Storytelling

KDnuggets

Follow this 7 step recipe to mastering effective insight and information dissemination through compelling data story crafting.

Data 113
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New with Confluent Platform: Enhanced security with OAuth Support, Confluent Platform for Apache Flink® (LA), a new Connector, and More

Confluent

Confluent Platform 7.

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MarshMallow: The Sweetest Python Library for Data Serialization and Validation

KDnuggets

Stop debugging data mismatches and focus on your application logic when you let Marshmallow handle serialization, deserialization and validation for you.

Python 85
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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

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Polaris Catalog Is Now Open Source

Snowflake

In June 2024, Snowflake announced Polaris Catalog to provide organizations and the Iceberg community new levels of choice, flexibility and control over their data. It enables more open, secure lakehouse architectures with broad read-and-write interoperability and cross-engine access controls. Apache Iceberg™ has greatly improved data mobility by establishing a vast community around an open standard, and the next logical step is an open, community-driven catalog to complement Iceberg.

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How to Perform Matrix Operations with NumPy

KDnuggets

Learning how to perform several of the most basic matrix operations with NumPy.

Python 87

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The 6 Data Quality Dimensions with Examples

Monte Carlo

It’s clear that data quality is becoming more of a focus for more data teams. So why are there still so many questions like these: A quick search on subreddits for data engineers, data analysts, data scientists, and more can yield a plethora of users seeking data quality advice. And while the comment below may seem like the accepted way of doing data quality management… … there’s actually a much better way.

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If agile is the answer, what is the question? by Dave Ogle

Scott Logic

The other day a colleague asked this question on one of our internal Slack channels: “If you were writing headings in a document, and using the capitalisation style of capitalising the first letter of just the important words in a header, how would you capitalise ‘what we are trying to achieve?’” The answers were many and varied, ranging from serious answers with supporting documentation to tongue-in-cheek responses, the answer which won the day though was this: “Objective” Clever, isn’t it?

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How to Build RAG Applications Using Snowflake Cortex?

Hevo

GPT has become a go-to search engine for many. We often use it instead of Google to get a quick solution for any query. Given its popularity, why don’t you include a customer chatbot or a troubleshooting chatbot service in your business? Imagine having a brand-specific chatbot with expertise in answering your business related queries.

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Change Data Capture as the Backbone of RAG AI-Driven System Resilience Strategies

Striim

Ensuring system resilience is critical for maintaining a competitive edge in today’s data-driven world. As businesses rely on real-time data to fuel decision-making, it’s essential that their systems can withstand disruptions and maintain functionality. Change Data Capture (CDC) is a key player, particularly in AI-driven systems where real-time data integration and adaptive responses are crucial.

Systems 52
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Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

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Data Warehouse, Redefined

Towards Data Science

Rethinking data warehousing: Why redefinition is necessary even beyond Modern Data Warehouse (MDW) and Lakehouse Models Continue reading on Towards Data Science »

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Optimizing Data Warehouse Cost using Apache Iceberg

Hevo

Data warehouses bring phenomenal results from well-informed, data-driven decision-making for an organization. There were times when only companies with large capital, and substantial IT infrastructures invested time and effort, let alone money, in a data warehouse.