Sat.Sep 14, 2024 - Fri.Sep 20, 2024

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How to build a data project with step-by-step instructions

Start Data Engineering

1. Introduction 2. Setup 3. Parts of data engineering 3.1. Requirements 3.1.1. Understand input datasets available 3.1.2. Define what the output dataset will look like 3.1.3. Define SLAs so stakeholders know what to expect 3.1.4. Define checks to ensure the output dataset is usable 3.2. Identify what tool to use to process data 3.3. Data flow architecture 3.

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Paying down tech debt: further learnings

The Pragmatic Engineer

This is a follow-up to the article Paying down tech debt , written by industry veteran Lou Franco. Lou has been in the software business for over 30 years as an engineer, EM, and executive. He’s also worked at four startups and the companies that later acquired them; most recently Atlassian as a Principal Engineer on the Trello iOS app. Later this year, he’s publishing a book on tech debt.

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Data Teams Survey 2020-2024 Analysis

Jesse Anderson

Survey Changes Over Time Between 2020 and 2024 (see 2020, 2023, and 2024 for each year’s information), I’ve been conducting a data teams survey. I wanted to dedicate an entire post to examining the change in data teams over time. Total Value Creation The most important question I ask each year concerns data team value creation. I break the question into two parts: “How successful would the business say your projects are?

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Partial Functions in Python: A Guide for Developers

KDnuggets

In Python, functions often require multiple arguments, and you may find yourself repeatedly passing the same values for certain parameters. This is where partial functions can help. Python’s built-in functools module allows you to create partial functions.

Python 148
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Apache Airflow® Best Practices for ETL and ELT Pipelines

Whether you’re creating complex dashboards or fine-tuning large language models, your data must be extracted, transformed, and loaded. ETL and ELT pipelines form the foundation of any data product, and Airflow is the open-source data orchestrator specifically designed for moving and transforming data in ETL and ELT pipelines. This eBook covers: An overview of ETL vs.

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Fine-tuning Llama 3.1 with Long Sequences

databricks

Mosaic AI Model Training now supports fine-tuning up to 131K context length for Llama 3.1 models. More efficient training at long sequence lengths is made possible by several optimizations highlighted in this post.

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How To Modernize Your Data Strategy And Infrastructure For 2025

Seattle Data Guy

We are still in the early days of data and the value it can add to companies. You’ll read plenty of statistics about how much value data can drive and how far behind companies that aren’t using data are. And as a data consultant, I have helped companies find that value in their data. It… Read more The post How To Modernize Your Data Strategy And Infrastructure For 2025 appeared first on Seattle Data Guy.

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5 YouTube Channels to Master LLMs

KDnuggets

Image by Author If you’re in the tech industry (or are attempting to transition into the field), LLMs are a must-learn. Companies have started integrating language models into their workflows to improve efficiencies and cut costs. Due to this, there have been a number of new AI job openings. New roles have begun to.

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Unleash Your Innovation: Announcing the Databricks Generative AI Startup Challenge with Over $1 Million in Credits, Prizes, and Potential Venture Funding

databricks

The Databricks Generative AI Startup Challenge offers $1M+ in prizes for innovative startups building Generative AI use cases on Databricks. Apply by November 1, 2024!

Building 138
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Inside Bento: Jupyter Notebooks at Meta

Engineering at Meta

This episode of the Meta Tech Podcast is all about Bento , Meta’s internal distribution of Jupyter Notebooks, an open-source web-based computing platform. Bento allows our engineers to mix code, text, and multimedia in a single document and serves a wide range of use cases at Meta from prototyping to complex machine learning workflows. Pascal Hartig ( @passy ) is joined by Steve, whose team has built several features on top of Jupyter, including scheduled notebooks , sharing with colleagues, and

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Data Modeling in the Brave New Lakehouse World

Confessions of a Data Guy

It is a Brave New World out there these days. The new tools and features come out faster than your mom on Sunday morning getting you ready for church. The same goes for the context and advice being produced on a myriad of platforms, the ole’ Like and Subscribe, and all that bit. It does […] The post Data Modeling in the Brave New Lakehouse World appeared first on Confessions of a Data Guy.

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Apache Airflow®: The Ultimate Guide to DAG Writing

Speaker: Tamara Fingerlin, Developer Advocate

In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!

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VoiceChat with Your LLMs using AlwaysReddy

KDnuggets

Rapid development is happening around us, and one of the most interesting aspects of this evolution is artificial intelligence's ability to communicate through natural language with humans. Suppose you want to communicate with some LLM running on your computer without switching between applications or windows, just by using a voice hotkey. This is exactly what.

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Introducing Databricks Assistant Quick Fix

databricks

Today, we're excited to introduce Databricks Assistant Quick Fix , a powerful new feature designed to automatically correct common, single-line errors such as.

Designing 122
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Introducing Netflix’s Key-Value Data Abstraction Layer

Netflix Tech

Vidhya Arvind , Rajasekhar Ummadisetty , Joey Lynch , Vinay Chella Introduction At Netflix our ability to deliver seamless, high-quality, streaming experiences to millions of users hinges on robust, global backend infrastructure. Central to this infrastructure is our use of multiple online distributed databases such as Apache Cassandra , a NoSQL database known for its high availability and scalability.

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Evaluating Data Observability Tools: A Comprehensive Guide

Data Engineering Weekly

Click the Link Below to Get Your Free Data Observability Buyers Guide: [link] The Buyer Guide for Data Observability is out. Please feel free to make a copy or comment to add more criteria. I w ant to extend my gratitude to the Data Heroes Community for their valuable insights and discussions, which served as the foundation for this piece. The points and thoughts shared here are largely drawn from the community's collective knowledge and contributions.

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Optimizing The Modern Developer Experience with Coder

Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

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How to Perform Data Aggregation Over Time Series Data with Pandas

KDnuggets

Image by Editor | Ideogram Let’s learn how to perform time series data aggregation in Pandas. Preparation We would need the Pandas and Numpy packages installed, so we can install them using the following code: pip install pandas numpy With the packages installed, let’s jump into the article. Time Series.

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Announcing GA of AI Model Sharing

databricks

Special thanks to Daniel Benito (CTO, Bitext), Antonio Valderrabanos(CEO, Bitext), Chen Wang (Lead Solution Architect, AI21 Labs), Robbin Jang (Alliance Manager, AI21 Labs).

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Introducing Confluent’s OEM Program: Deliver Data Streaming Faster and Unlock Revenue Growth

Confluent

Bring data streaming to your product or service quickly and confidently with unified Apache Kafka® and Apache Flink®, backed by the original creators of Kafka.

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Cloudera Evaluates Integrated Data and AI Exchange Business Line to Optimize Data-Driven Generative AI Use Cases

Cloudera

According to recent survey data from Cloudera, 88% of companies are already utilizing AI for the tasks of enhancing efficiency in IT processes, improving customer support with chatbots, and leveraging analytics for better decision-making. More and more enterprises are leveraging pre-trained models for various applications, from natural language processing to computer vision.

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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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How to Import Data into BigQuery

KDnuggets

Data come from everywhere, and the number of origins, sources, and formats under which valuable data may appear underscores the need for database management tools capable of loading data from multiple sources. This tutorial illustrates how to load datasets from different formats and sources into Google BigQuery. All the prerequisites we need are having registered.

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Security best practices for the Databricks Data Intelligence Platform

databricks

At Databricks, we know that data is one of your most valuable assets. Our product and security teams work together to deliver an enterprise-grade Data Intelligence Platform that enables you to defend against security risks and meet your compliance obligations. In this blog, we'll explain how you can leverage our platform's security features to establish a robust defense-in-depth posture that protects your data and AI assets from risks.

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Ensuring Even Ad Spend on the Zalando Homepage: How Our New Bidding Algorithm Maximizes Value for Advertisers and Shoppers

Zalando Engineering

Introduction Zalando Marketing Services (ZMS) is Zalando's advertising platform. It helps brands create and manage campaigns on Zalando, increasing their visibility and improving performance at every stage of the marketing funnel, from awareness to purchase, within the Zalando marketplace. At ZMS, we're constantly innovating to optimize the advertising experience on Zalando homepage.

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Snowflake Acquires Night Shift Development, Inc. to Accelerate Growth in US Public Sector

Snowflake

Data is increasingly becoming critical for the public sector — from guiding decisions in higher education to enhancing citizen services and streamlining government operations. Government agencies are overwhelmed with data, whether it be structured, like incident logs, or unstructured, like satellite images. Harnessing the vast amount of data can become a burden for any organization, yet the insights have the potential to significantly improve quality of life and strengthen national security.

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

Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali

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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Deep Learning Approaches in Medical Image Segmentation

KDnuggets

Medical imaging has been revolutionized by the adoption of deep learning techniques. The use of this branch of machine learning has ushered in a new era of precision and efficiency in medical image segmentation, a central analytical process in modern healthcare diagnostics and treatment planning. By harnessing neural networks, deep learning algorithms are able.

Medical 137
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Unifying Parameters Across Databricks

databricks

Today, we are excited to announce the support for named parameter markers in the SQL editor. This feature allows you to write parameterized.

SQL 119
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Building Cross-street Data Into Geocoding Locators

ArcGIS

Learn how to build cross-street data into a geocoding locator using ArcGIS Data Interoperability and its no-code approach.

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9 Ways AI Can Uplevel Your Business Right Now

Snowflake

As the frenzied hype around generative AI cools off and as we get into the year of ideation, earlier adopters of AI are starting to see the results of initial experimentation. And these conversations are increasingly shifting to a more problem-oriented mentality. A lot of people were understandably swept up in the excitement of all that AI can do, only to find that some use cases were too risky or that those problems could be solved with traditional methods that were less costly.

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How to Drive Cost Savings, Efficiency Gains, and Sustainability Wins with MES

Speaker: Nikhil Joshi, Founder & President of Snic Solutions

Is your manufacturing operation reaching its efficiency potential? A Manufacturing Execution System (MES) could be the game-changer, helping you reduce waste, cut costs, and lower your carbon footprint. Join Nikhil Joshi, Founder & President of Snic Solutions, in this value-packed webinar as he breaks down how MES can drive operational excellence and sustainability.

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3 Simple Ways to Merge Python Dictionaries

KDnuggets

When working with dictionaries in Python, you’ll sometimes have to merge them into a single dictionary for further processing. In this tutorial, we'll go over three common methods to merge Python dictionaries. Specifically, we’ll focus on merging dictionaries using: The update() method Dictionary unpacking The union operator Let’s get started. Note: You can find.

Python 134
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Establish your Generative AI expertise with the latest Databricks certification

databricks

The value of Generative AI, the deepened investment Databricks has made in the space, and how customers have benefited from the certification.

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AI Success – Powered by Data Governance and Quality

Precisely

Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust data governance for AI ensures data privacy, compliance, and ethical AI use. Proactive data quality measures are critical, especially in AI applications. Using AI systems to analyze and improve data quality both benefits and contributes to the generation of high-quality data.

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Run pandas on 1TB+ Enterprise Data Directly in Snowflake

Snowflake

As one of the most widely used libraries in the Python ecosystem, pandas helps developers analyze, load and transform data across data science, data engineering and machine learning. The flexibility and ease of use of the pandas API have driven rapid growth in popularity, with pandas being used by one in every five developers , according to the StackOverflow 2024 Developer Survey.

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The Cloud Development Environment Adoption Report

Cloud Development Environments (CDEs) are changing how software teams work by moving development to the cloud. Our Cloud Development Environment Adoption Report gathers insights from 223 developers and business leaders, uncovering key trends in CDE adoption. With 66% of large organizations already using CDEs, these platforms are quickly becoming essential to modern development practices.