Sat.Jun 08, 2024 - Fri.Jun 14, 2024

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Data Engineering Projects

Start Data Engineering

1. Introduction 2. Run Data Pipelines 2.1. Run on codespaces 2.2. Run locally 3. Projects 3.1. Projects from least to most complex 3.2. Batch pipelines 3.3. Stream pipelines 3.4. Event-driven pipelines 3.5. LLM RAG pipelines 4. Conclusion 1. Introduction Whether you are new to data engineering or have been in the data field for a few years, one of the most challenging parts of learning new frameworks is setting them up!

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Unpacking the Latest Streaming Announcements: A Comprehensive Analysis

Jesse Anderson

This video covers the latest announcements from StreamNative, Confluent, and WarpStream. We discuss communication protocols, how they’re used, and what they mean for you. We also discuss the various systems using Kafka’s protocol. Finally, we discuss the announcements about writing to Iceberg and DeltaLake directly from the broker and what that means for costs and operational ease.

Kafka 147
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X-Ray Vision For Your Flink Stream Processing With Datorios

Data Engineering Podcast

Summary Streaming data processing enables new categories of data products and analytics. Unfortunately, reasoning about stream processing engines is complex and lacks sufficient tooling. To address this shortcoming Datorios created an observability platform for Flink that brings visibility to the internals of this popular stream processing system. In this episode Ronen Korman and Stav Elkayam discuss how the increased understanding provided by purpose built observability improves the usefulness

Process 147
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How FactSet Implemented an Enterprise Generative AI Platform with Databricks and MLflow

databricks

“FactSet’s mission is to empower clients to make data-driven decisions and supercharge their workflows and productivity. To deliver AI-driven solutions across our entire.

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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 Meta trains large language models at scale

Engineering at Meta

As we continue to focus our AI research and development on solving increasingly complex problems, one of the most significant and challenging shifts we’ve experienced is the sheer scale of computation required to train large language models (LLMs). Traditionally, our AI model training has involved a training massive number of models that required a comparatively smaller number of GPUs.

Algorithm 130
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Building Open-Source Python Packages – SparklePop

Confessions of a Data Guy

One of the things I love about Python is its flexibility and huge community, a community that puts out a never-ending stream of useful packages for the average Software Engineer. In a show of solidarity to the open-source community, I thought I would publish a PYPI package that will probably be used by 5 people […] The post Building Open-Source Python Packages – SparklePop appeared first on Confessions of a Data Guy.

Python 100

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Mosaic AI: Build and deploy production-quality Compound AI Systems

databricks

Over the last year, we have seen a surge of commercial and open-source foundation models showing strong reasoning abilities on general knowledge tasks.

Systems 144
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Maintaining large-scale AI capacity at Meta

Engineering at Meta

Meta is currently operating many data centers with GPU training clusters across the world. Our data centers are the backbone of our operations, meticulously designed to support the scaling demands of compute and storage. A year ago, however, as the industry reached a critical inflection point due to the rise of artificial intelligence (AI), we recognized that to lead in the generative AI space we’d need to transform our fleet.

Utilities 127
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Accelerate Development and Productivity with DevOps in Snowflake 

Snowflake

Today’s data-driven world requires an agile approach. Modern data teams are constantly under pressure to deliver innovative solutions faster than ever before. Fragmented tooling across data engineering, application development and AI/ML development creates a significant bottleneck, hindering the speed of value delivery required to stay competitive.

Python 106
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Understanding Data Privacy in the Age of AI

KDnuggets

Data privacy has been a long-standing issue that continues to challenge the data industry. Let’s understand how rapid developments in the world of AI have elevated data privacy concerns.

Data 113
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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 Databricks LakeFlow: A unified, intelligent solution for data engineering

databricks

Today, we are excited to announce Databricks LakeFlow, a new solution that contains everything you need to build and operate production data pipelines.

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Addressing the Elephant in the Room – Welcome to Today’s Cloudera

Cloudera

Hadoop. The first time that I really became familiar with this term was at Hadoop World in New York City some ten or so years ago. There were thousands of attendees at the event – lining up for book signings and meetings with recruiters to fill the endless job openings for developers experienced with MapReduce and managing Big Data. This was the gold rush of the 21st century, except the gold was data.

Hadoop 102
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Observability in Snowflake: A New Era with Snowflake Trail

Snowflake

Discovering and surfacing telemetry traditionally can be a tedious and challenging process, especially when it comes to pinpointing specific issues for debugging. However, as applications and pipelines grow in complexity, understanding what’s happening beneath the surface becomes increasingly crucial. A lack of visibility hinders the development and maintenance of high-quality applications and pipelines, ultimately impacting customer experience.

Python 103
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Data Engineering Weekly #175

Data Engineering Weekly

Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. Learn More → Cube Research: Crystallizing Snowflake Summit 2024 We should officially call the first week of June the data engineering week, as two major data companies are running their developer conference.

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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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Open Sourcing Unity Catalog

databricks

We are excited to announce that we are open sourcing Unity Catalog, the industry’s first open source catalog for data and AI governance.

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Step-by-Step Tutorial to Building Your First Machine Learning Model

KDnuggets

Machine Learning model is an exciting project. Learn how to develop your first model that the company would want to use.

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Serverless Jupyter Notebooks at Meta

Engineering at Meta

At Meta, Bento , our internal Jupyter notebooks platform, is a popular tool that allows our engineers to mix code, text, and multimedia in a single document. Use cases run the entire spectrum from what we call “lite” workloads that involve simple prototyping to heavier and more complex machine learning workflows. However, even though the lite workflows require limited compute, users still have to go through the same process of reserving and provisioning remote compute – a process that takes time

SQL 100
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Top 5 Tips for Styling Published Layers and Maps

ArcGIS

The Living Atlas team publishes a lot of web layers. Here's some of our favorite tips and tricks for customizing your layers and maps.

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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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Introducing AI/BI: Intelligent Analytics for Real-World Data

databricks

Today, we are excited to announce Databricks AI/BI , a new type of business intelligence product built from the ground up to deeply.

BI 142
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Using SQL with Python: SQLAlchemy and Pandas

KDnuggets

A simple tutorial on how to connect to databases, execute SQL queries, and analyze and visualize data.

SQL 137
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Fueling Enterprise Generative AI with Data: The Cornerstone of Differentiation

Cloudera

More than two-thirds of companies are currently using Generative AI (GenAI) models, such as large language models (LLMs), which can understand and generate human-like text, images, video, music, and even code. However, the true power of these models lies in their ability to adapt to an enterprise’s unique context. By leveraging an organization’s proprietary data, GenAI models can produce highly relevant and customized outputs that align with the business’s specific needs and ob

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Snowflake ML Now Supports Expanded MLOps Capabilities for Streamlined Management of Features and Models 

Snowflake

Bringing machine learning (ML) models into production is often hindered by fragmented MLOps processes that are difficult to scale with the underlying data. Many enterprises stitch together a complex mix of various MLOps tools to build an end-to-end ML pipeline. The friction of having to set up and manage separate environments for features and models creates operational complexity that can be costly to maintain and difficult to use.

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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.

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What’s New with Databricks Unity Catalog at Data + AI Summit 2024

databricks

In an era marked by rapid advancements in artificial intelligence and an explosion of data and Gen AI tools, enterprises face fragmented data.

Data 127
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5 Free University Courses to Learn Coding for Data Science

KDnuggets

Learn programming for free from top-tier universities like Harvard and MIT.

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How Wild is the Land?

ArcGIS

Explore the Global Wildland-Urban Interface (WUI) dataset to find out how close you are to areas prone to wildfires. Understand the intermixing of human activity with wildland vegetation.

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Ingest Data Faster, Easier and Cost-Effectively with New Connectors and Product Updates

Snowflake

The journey toward achieving a robust data platform that secures all your data in one place can seem like a daunting one. But at Snowflake, we’re committed to making the first step the easiest — with seamless, cost-effective data ingestion to help bring your workloads into the AI Data Cloud with ease. Snowflake is launching native integrations with some of the most popular databases, including PostgreSQL and MySQL.

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Business Intelligence 101: How To Make The Best Solution Decision For Your Organization

Speaker: Evelyn Chou

Choosing the right business intelligence (BI) platform can feel like navigating a maze of features, promises, and technical jargon. With so many options available, how can you ensure you’re making the right decision for your organization’s unique needs? 🤔 This webinar brings together expert insights to break down the complexities of BI solution vetting.

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Announcing General Availability of Predictive Optimization

databricks

We're excited to announce the General Availability of Databricks Predictive Optimization. This capability intelligently optimizes your table data layouts for faster queries and.

Data 139
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FastAPI Tutorial: Build APIs with Python in Minutes

KDnuggets

Want to build APIs with Python? Learn how to do so using FastAPI with this step-by-step tutorial.

Python 132
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Maxar’s Precision3D in Esri’s World Elevation 3D and Hillshade Layers

ArcGIS

Maxar's Precision 3D (P3D) DTM's now integrated into Esri's World Elevation 3D and Hillshade Layers, providing accurate and detailed elevation data for your GIS projects.

Project 87
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Unlocking the power of mixed reality devices with MobileConfig

Engineering at Meta

MobileConfig enables developers to centrally manage a mobile app’s configuration parameters in our data centers. Once a parameter value is changed on our central server, billions of app devices automatically fetch and apply the new value without app updates. These remotely managed configuration parameters serve various purposes such as A/B testing, feature rollout, and app personalization.

Java 88
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Driving Responsible Innovation: How to Navigate AI Governance & Data Privacy

Speaker: Aindra Misra, Senior Manager, Product Management (Data, ML, and Cloud Infrastructure) at BILL

Join us for an insightful webinar that explores the critical intersection of data privacy and AI governance. In today’s rapidly evolving tech landscape, building robust governance frameworks is essential to fostering innovation while staying compliant with regulations. Our expert speaker, Aindra Misra, will guide you through best practices for ensuring data protection while leveraging AI capabilities.