Sat.Nov 16, 2024 - Fri.Nov 22, 2024

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8 Essential Data Pipeline Design Patterns You Should Know

Monte Carlo

Let’s set the scene: your company collects data, and you need to do something useful with it. Whether it’s customer transactions, IoT sensor readings, or just an endless stream of social media hot takes, you need a reliable way to get that data from point A to point B while doing something clever with it along the way. That’s where data pipeline design patterns come in.

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Challenges You Will Face When Parsing PDFs With Python – How To Parse PDFs With Python

Seattle Data Guy

Scraping data from PDFs is a right of passage if you work in data. Someone somewhere always needs help getting invoices parsed, contracts read through, or dozens of other use cases. Most of us will turn to Python and our trusty list of Python libraries and start plugging away. Of course, there are many challenges… Read more The post Challenges You Will Face When Parsing PDFs With Python – How To Parse PDFs With Python appeared first on Seattle Data Guy.

Python 130
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DuckDB … reading from s3 … with AWS Credentials and more.

Confessions of a Data Guy

In my never-ending quest to plumb the most boring depths of every single data tool on the market, I found myself annoyed when recently using DuckDB for a benchmark that was reading parquet files from s3. What was not clear, or easy, was trying to figure out how DuckDB would LIKE to read default AWS […] The post DuckDB … reading from s3 … with AWS Credentials and more. appeared first on Confessions of a Data Guy.

AWS 113
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Celebrating Innovation: Announcing the Finalists of the Databricks Generative AI Startup Challenge

databricks

We are thrilled to unveil the finalists for the Databricks Generative AI Startup Challenge , a competition designed to spotlight innovative early-stage startups.

Designing 107
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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have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth.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 Implement Named Entity Recognition with Hugging Face Transformers

KDnuggets

Let's take a look at how we can perform NER using that Swiss army knife of NLP and LLM libraries, Hugging Face's Transformers.

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From IC to Data Leader: Key Strategies for Managing and Growing Data Teams

Seattle Data Guy

There are plenty of statistics about the speed at which we are creating data in today’s modern world. On the flip side of all that data creation is a need to manage all of that data and thats where data teams come in. But leading these data teams is challenging and yet many new data… Read more The post From IC to Data Leader: Key Strategies for Managing and Growing Data Teams appeared first on Seattle Data Guy.

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How to present and share your Notebook insights in AI/BI Dashboards

databricks

We’re excited to announce a new integration between Databricks Notebooks and AI/BI Dashboards, enabling you to effortlessly transform insights from your notebooks into.

BI 93
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Exploring Ethics and Morality Through Machine Intelligence

KDnuggets

This article examines the challenges of aligning machine behavior with human values, and the role of ethical frameworks in shaping responsible AI.

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GHC's wasm backend now supports Template Haskell and ghci

Tweag

Two years ago I wrote a blog post to announce that the GHC wasm backend had been merged upstream. I’ve been too lazy to write another blog post about the project since then, but rest assured, the project hasn’t stagnated. A lot of improvements have happened after the initial merge, including but not limited to: Many, many bugfixes in the code generator and runtime, witnessed by the full GHC testsuite for the wasm backend in upstream GHC CI pipelines.

Coding 89
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Elevating Productivity: Cloudera Data Engineering Brings External IDE Connectivity to Apache Spark

Cloudera

As advanced analytics and AI continue to drive enterprise strategy, leaders are tasked with building flexible, resilient data pipelines that accelerate trusted insights. AI pioneer Andrew Ng recently underscored that robust data engineering is foundational to the success of data-centric AI —a strategy that prioritizes data quality over model complexity.

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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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Introducing an exclusively Databricks-hosted Assistant

databricks

We’re excited to announce that the Databricks Assistant , now fully hosted and managed within Databricks, is available in public preview! This version.

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10 Python Libraries Every Data Analyst Should Know

KDnuggets

Interested in data analytics? Here's a list of Python libraries you cannot do without.

Python 127
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Expert Insights for Your 2025 Data, Analytics, and AI Initiatives

Precisely

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. A long-term approach to your data strategy is key to success as business environments and technologies continue to evolve. The rapid pace of technological change has made data-driven initiatives more crucial than ever within modern business strategies.

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Sequence learning: A paradigm shift for personalized ads recommendations

Engineering at Meta

AI plays a fundamental role in creating valuable connections between people and advertisers within Meta’s family of apps. Meta’s ad recommendation engine, powered by deep learning recommendation models (DLRMs) , has been instrumental in delivering personalized ads to people. Key to this success was incorporating thousands of human-engineered signals or features in the DLRM-based recommendation system.

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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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Introducing Predictive Optimization for Statistics

databricks

We are excited to introduce the gated Public Preview of Predictive Optimization for statistics. Announced at the Data + AI Summit, Predictive Optimization.

Data 83
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7 Advanced SQL Techniques for Data Manipulation in Data Science

KDnuggets

Can SQL be used for advanced data manipulation in data science? It sure can with these seven techniques.

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Composable CDPs for Travel: Personalizing Guest Experiences with AI

Snowflake

As travelers increasingly expect personalized experiences, brands in the travel and hospitality industry must find innovative ways to leverage data in their marketing and product experiences. That said, managing vast, complex data sets across multiple brands, loyalty programs and guest touchpoints presents unique challenges for companies in this industry.

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Rewiring My Career: How I Transitioned from Electrical Engineering to Data Engineering

Towards Data Science

Data is booming. It comes in vast volumes and variety and this explosion comes with a plethora of job opportunities too. Is it worth switching to a data career now? My honest opinion: absolutely! It is worth mentioning that this article comes from an Electrical and Electronic Engineer graduate who went all the way and spent almost 8 years in academia learning about the Energy sector (and when I say all the way, I mean from a bachelor degree to a PhD and postdoc).

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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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Automating Unity Catalog Upgrade Workflows with UCX

databricks

As organizations increasingly leverage the Databricks Data Intelligence Platform for data and AI needs, upgrading to Unity Catalog is a key step in.

Data 79
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Exploring Python’s Ellipsis (…) : More than Just Syntax Sugar

KDnuggets

Ever wondered what the three dots (.) in Python are used for? Discover how this powerful operator can simplify your code!

Python 94
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Snowflake Will Automatically Disable Passwords Detected on the Dark Web

Snowflake

Security has been an integral part of Snowflake’s platform since the company was founded. Through the security capabilities of Snowflake Horizon Catalog , we empower security admins and CISO’s to better protect their environments. As part of our continued efforts to help customers secure their accounts, and in line with our pledge to align with CISA’s Secure By Design principles, we are announcing the general availability of Snowflake Leaked Password Protection (LPP).

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The No-Panic Guide to Building a Data Engineering Pipeline That Actually Scales

Monte Carlo

Your data engineering pipeline started simple: a few CSV exports, some Python scripts, and manual updates every week. Back then, it worked just fine for your small team and handful of customers. But now? Your user base has quintupled, analytics requests are piling up, and that trusty Python script crashes more often than it runs. You’re left wondering if there’s a breaking point where your DIY data solution won’t cut it anymore—and honestly, you might be there already.

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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.

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From Data Warehousing to Data Intelligence: How Data Took Over

databricks

While GenAI is the focus today, most enterprises have been working for a decade or longer to make data intelligence a reality within.

Data 81
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5 Essential Resources for Learning R

KDnuggets

Learn R from top institutions like Harvard, Stanford, and Codecademy.

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9 Best Practices for Transitioning From On-Premises to Cloud

Snowflake

On a day-to-day basis, Snowflake teams identify opportunities and help customers implement recommended best practices that ease the migration process from on-premises to the cloud. They also monitor potential challenges and advise on proven patterns to help ensure a successful data migration. This article highlights nine key areas to watch out for and plan around in order to accelerate a smooth transition to the cloud.

Cloud 60
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Understanding Databricks Architecture

Hevo

Do you have a fascination with Databricks architecture but you get lost with all the terms being used out there? Let’s break it down simply! If you are just getting familiar with cloud computing or just need a refresher, in this blog, let’s try distilling the key aspects of Databricks architecture in simple, easy-to-understand concepts.

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Improving the Accuracy of Generative AI Systems: A Structured Approach

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.

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Databricks training invests in closing the data + AI skills gap across enterprises

databricks

The Data + AI Skills Gap The “skills gap” has been a concern for CEOs and leaders for many years, and the gap.

Data 79
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Integrating Language Models into Existing Software Systems

KDnuggets

Improving existing software systems, making them more robust and capable of solving complex contemporary problems.

Systems 84
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Expert Insights for Your 2025 Data, Analytics, and AI Initiatives

Precisely

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. A long-term approach to your data strategy is key to success as business environments and technologies continue to evolve. The rapid pace of technological change has made data-driven initiatives more crucial than ever within modern business strategies.

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How Skyscanner Enabled Data & AI Governance with Monte Carlo

Monte Carlo

For over 20 years, Skyscanner has been helping travelers plan and book trips with confidence— including airfare, hotels, and car rentals. As digital natives, the organization is no stranger to staggering volume. Over the years, Skyscanner has grown organically to include a vast network of high-volume data producers and consumers, including: Serving over 110 million monthly users Partnering with hundreds of travel providers Operating in 30+ languages and 180 countries An fulfilling over 5,000

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The Ultimate Guide To Data-Driven Construction: Optimize Projects, Reduce Risks, & Boost Innovation

Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network

In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. Fortunately, digital tools now offer valuable insights to help mitigate these risks. However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in.