Simon Späti

JULY 28, 2024

A Vim-Inspired Approach to Efficient Note Management with Obsidian and Markdown

Management 361

Management 361

The Pragmatic Engineer

APRIL 21, 2024

See a longer version of this article here: Scaling ChatGPT: Five Real-World Engineering Challenges. Sometimes the best explanations of how a technology solution works come from the software engineers who built it. To explain how ChatGPT (and other large language models) operate, I turned to the ChatGPT engineering team. "How does ChatGPT work, under the hood?

Engineering 351

Engineering Software Engineer Software Engineering Programming 351

Data Engineering Podcast

MAY 18, 2024

Summary The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. Unfortunately this often turns into an exercise in frustration for everyone involved due to complex workflows and hard-to-understand dashboards. The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data.

Building 278

Building Data Lake High Quality Data Business Intelligence 278

Christophe Blefari

MARCH 15, 2024

Mountains I hope this e-mail finds you well, wherever you are. I'd like to thank you for the excellent comments you sent me last week after the publication of the first version of the Recommendations. This is just the beginning! This week I've added a subscribe button in the Recommendations page in order for you to opt-in for the weekly recommendation email—every Tuesday.

Metadata 272

Metadata Datasets Data Data Warehouse 272

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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.

Architecture

Start Data Engineering

SEPTEMBER 18, 2024

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.

Project 240

Project Building Datasets Architecture 240

Confessions of a Data Guy

JUNE 20, 2024

After 10 years of Data Engineering work, I think it’s time to hang up the proverbial hat and ride off into the sunset, never to be seen again. I wish. Everything has changed in 10 years, yet nothing has changed in 10 years, how is that even possible? Sometimes I wonder if I’ve learned anything […] The post What I’ve Learned After A Decade Of Data Engineering appeared first on Confessions of a Data Guy.

Data Engineer 223

Data Engineer Data Engineering Engineering Data 223

KDnuggets

DECEMBER 2, 2024

Learn reinforcement learning using free resources, including books, frameworks, courses, tutorials, example code, and projects.

Coding 154

Coding Project 154

Jesse Anderson

SEPTEMBER 18, 2024

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?

Data Warehouse 162

Data Warehouse Data Big Data Project 162

Engineering at Meta

DECEMBER 3, 2024

Meta releases a Request for Proposals (RFP) to identify nuclear energy developers to support AI innovation and clean and renewable energy goals.

Engineering 145

Engineering Data 145

Tweag

NOVEMBER 20, 2024

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 137

Coding Programming Project Process 137

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!

Data

Seattle Data Guy

FEBRUARY 10, 2024

Photo by Tiger Lily Data warehouses and data lakes play a crucial role for many businesses. It gives businesses access to the data from all of their various systems. As well as often integrating data so that end-users can answer business critical questions. But if we take a step back and only focus on the… Read more The post Data Warehousing Essentials: A Guide To Data Warehousing appeared first on Seattle Data Guy.

Data Lake 162

Data Lake Data Warehouse Data Accessibility 162

The Pragmatic Engineer

OCTOBER 18, 2024

The below was originally published in The Pragmatic Engineer. To get timely analysis on the tech industry like this, on a weekly basis: sign up to The Pragmatic Engineer Newsletter. If you are into podcasts, check out The Pragmatic Engineer Podcast. Imagine Apple decided Spotify was a big enough business threat that it had to take unfair measures to limit Spotify’s growth on the App Store.

Government 326

Government Engineering Project AWS 326

Data Engineering Podcast

FEBRUARY 18, 2024

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Multiple open source projects and vendors have been working together to make this vision a reality. In this episode Dain Sundstrom, CTO of Starburst, explains how the combination of the Trino query engine and the Iceberg table format offer the ease of use and execution speed of data warehouses with the infinite storage and sc

Data Lake 262

Data Lake High Quality Data Data Warehouse Google Cloud 262

Christophe Blefari

MARCH 2, 2024

Mistral ( credits ) Hello all, this is the Data News, this week edition might be smaller than usual in term of comments as I'm working on a Data News related project that takes me a bit of time, which will probably lead to a series of articles. Before I forget I've appeared on The Joe Reis Show , we chatted with Joe about data engineering teaching, why it is hard and about generative AI that will change education for ever.

Data 162

Data Python Database BI 162

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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.

Cloud

Start Data Engineering

JUNE 14, 2024

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!

Data Engineer 239

Data Engineer Data Engineering Project Engineering 239

Confessions of a Data Guy

MAY 30, 2024

Of all the duties that Data Engineers take on during the regular humdrum of business and work, it’s usually filled with the same old, same old. Build new pipeline, update pipeline, new data model, fix bug, etc, etc. It’s never-ending. It’s a constant stream of data, new and old, spilling into our Data Warehouses and […] The post Building Data Platforms (from scratch) appeared first on Confessions of a Data Guy.

Building 184

Building Data Warehouse Data Data Engineer 184

Analytics Vidhya

FEBRUARY 28, 2024

Introduction Data is fuel for the IT industry and the Data Science Project in today’s online world. IT industries rely heavily on real-time insights derived from streaming data sources. Handling and processing the streaming data is the hardest work for Data Analysis. We know that streaming data is data that is emitted at high volume […] The post Kafka to MongoDB: Building a Streamlined Data Pipeline appeared first on Analytics Vidhya.

MongoDB 217

MongoDB Data Pipeline Kafka Building 217

KDnuggets

OCTOBER 16, 2024

Where can you find projects dealing with advanced ML topics? GitHub is a perfect source with its many repositories. I’ve selected ten to talk about in this article.

Machine Learning 159

Machine Learning Project IT 159

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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?

Business Intelligence

Jesse Anderson

AUGUST 28, 2024

In the spring of 2024, I ran a new survey to gather more data for my Data Teams book and update my 2023 and 2020 surveys. In total, we had 81 respondents. This survey was designed to get information about how management uses data teams, the value they’re creating, and how they’re creating it. The survey asked about the best and worst practices that teams are using or experiencing.

Data 147

Data Consulting Big Data Data Engineer 147

databricks

OCTOBER 8, 2024

Summary Databricks Apps, a new way to build and deploy internal data and AI applications, is now available in Public Preview on AWS.

AWS 144

AWS Building Data 144

ArcGIS

AUGUST 23, 2024

With a new GIS mapping tool you can map the most visited national parks (and much more!) to explore your spatial data even further.

Designing 145

Designing Data 145

Seattle Data Guy

DECEMBER 12, 2024

Document Intelligence Studio is a data extraction tool that can pull unstructured data from diverse documents, including invoices, contracts, bank statements, pay stubs, and health insurance cards. The cloud-based tool from Microsoft Azure comes with several prebuilt models designed to extract data from popular document types. However, you can also use labeled datasets to train… Read more The post Alternatives to Azure Document Intelligence Studio: Exploring Powerful Document Analysis Tool

Insurance 130

Insurance Unstructured Data Banking Datasets 130

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.

Data

The Pragmatic Engineer

OCTOBER 10, 2024

Automattic, creator of Wordpress, is being sued by one of the largest WordPress hosting providers. The conflict fits into a trend of billion-dollar companies struggling to effectively monetize open source, and are changing tactics to limit their competition and increase their revenue. This article was originally published a week ago, on 3 October 2024, in The Pragmatic Engineer.

Consulting 306

Consulting AWS Engineering Software Engineer 306

Data Engineering Podcast

APRIL 21, 2024

Summary Generative AI has rapidly transformed everything in the technology sector. When Andrew Lee started work on Shortwave he was focused on making email more productive. When AI started gaining adoption he realized that he had even more potential for a transformative experience. In this episode he shares the technical challenges that he and his team have overcome in integrating AI into their product, as well as the benefits and features that it provides to their customers.

Data Lake 182

Data Lake High Quality Data Data Pipeline Machine Learning 182

Christophe Blefari

JUNE 21, 2024

Welcome to the snow world ( credits ) Every year, the competition between Snowflake and Databricks intensifies, using their annual conferences as a platform for demonstrating their power. This year, the Snowflake Summit was held in San Francisco from June 2 to 5, while the Databricks Data+AI Summit took place 5 days later, from June 10 to 13, also in San Francisco.

Metadata 147

Metadata Data Warehouse BI MySQL 147

Start Data Engineering

APRIL 22, 2024

1. Introduction 2. Docker concepts 2.1. Define the OS and its configurations with an image 2.2. Use the image to run containers 2.2.1. Communicate between containers and local OS 2.2.2. Start containers with docker CLI or compose 3. Conclusion 1. Introduction Docker can be overwhelming to start with. Most data projects use Docker to set up the data infra locally (and often in production).

Data Engineer 225

Data Engineer Data Engineering Engineering Data 225

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.

Manufacturing

Confessions of a Data Guy

JULY 24, 2024

I’ve had something rattling around in the old noggin for a while; it’s just another strange idea that I can’t quite shake out. We all keep hearing about Arrow this and Arrow that … seems every new tool built today for Data Engineering seems to be at least partly based on Arrow’s in-memory format. So, […] The post PyArrow vs Polars (vs DuckDB) for Data Pipelines. appeared first on Confessions of a Data Guy.

Data Pipeline 162

Data Pipeline Data Engineer Data Engineering Data 162

Analytics Vidhya

AUGUST 7, 2024

Introduction Apache Airflow is a crucial component in data orchestration and is known for its capability to handle intricate workflows and automate data pipelines. Many organizations have chosen it due to its flexibility and strong scheduling capabilities. Yet, as data requirements change, Airflow’s lack of scalability, real-time processing capabilities, and setup complexity may lead to […] The post Airflow Alternatives for Data Orchestration appeared first on Analytics Vidhya.

Data Pipeline 187

Data Pipeline Data Process Data Workflow 187

KDnuggets

OCTOBER 30, 2024

Each project, from beginner tasks like Image Classification to advanced ones like Anomaly Detection, includes a link to the dataset and source code for easy access and implementation.

Project 153

Project Datasets Coding Accessibility 153

Jesse Anderson

JUNE 12, 2024

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

Kafka Data Lake Architecture Cloud 147

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.

Systems