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Data lineage is an instrumental part of Metas Privacy Aware Infrastructure (PAI) initiative, a suite of technologies that efficiently protect user privacy. It is a critical and powerful tool for scalable discovery of relevant data and data flows, which supports privacy controls across Metas systems. This allows us to verify that our users everyday interactions are protected across our family of apps, such as their religious views in the Facebook Dating app, the example well walk through in this
Here’s a thing people say all the time: Bad data costs businesses millions of dollars. This is usually followed by an earnest pitch for data quality governance – you know, the whole apparatus of rules and systems meant to keep your data clean and trustworthy. The logic goes something like: messy data lost money need governance problem solved.
Large language models (LLMs) work better when they can reach a specific knowledge base instead of just their general training data. This is called retrieval-augmented generation (RAG). Because they are trained on huge datasets and have billions of factors. LLMs are great at answering questions, translating, and filling in blanks in text. RAG improves this feature even more by letting LLMs get information from a reliable outside source, like an organization’s own data before they write repl
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Were sharing details about Strobelight, Metas profiling orchestrator. Strobelight combines several technologies, many open source, into a single service that helps engineers at Meta improve efficiency and utilization across our fleet. Using Strobelight, weve seen significant efficiency wins, including one that has resulted in an estimated 15,000 servers worth of annual capacity savings.
Hi, this is Gergely with a bonus issue of the Pragmatic Engineer Newsletter. In every issue, I cover topics related to Big Tech and startups through the lens of engineering managers and senior engineers. This article is one out of five sections from The Pulse #119. Full subscribers received this issue a week and a half ago. To get articles like this in your inbox, subscribe here.
PDF files are one of the most popular file formats today. Because they can preserve the visual layout of documents and are compatible with a wide range of devices and operating systems, PDFs are used for everything from business forms and educational material to creative designs. However, PDF files also present multiple challenges when it… Read more The post What Is PDFMiner And Should You Use It – How To Extract Data From PDFs appeared first on Seattle Data Guy.
PDF files are one of the most popular file formats today. Because they can preserve the visual layout of documents and are compatible with a wide range of devices and operating systems, PDFs are used for everything from business forms and educational material to creative designs. However, PDF files also present multiple challenges when it… Read more The post What Is PDFMiner And Should You Use It – How To Extract Data From PDFs appeared first on Seattle Data Guy.
At Yelp, we encountered challenges that prompted us to enhance the training time of our ad-revenue generating models, which use a Wide and Deep Neural Network architecture for predicting ad click-through rates (pCTR). These models handle large tabular datasets with small parameter spaces, requiring innovative data solutions. This blog post delves into our journey of optimizing training time using TensorFlow and Horovod, along with the development of ArrowStreamServer, our in-house library for lo
We are entering a new era for marketing and advertising agencies. From evolving consumer expectations and increasingly stringent privacy regulations to the rise of AI, the landscape is shifting rapidly. To remain competitive, agencies need to reimagine how they operate. The winners will be those that adopt forward-thinking data strategies, build trust with partners and clients, and leverage AI to deliver real-time insights and personalized campaigns.
Ever since dbt Labs acquired SDF Labs last week , I've been head-down diving into their technology and making sense of it all. The main thing I knew going in was "SDF understands SQL". It's a nice pithy quote, but the specifics are fascinating. For the next era of Analytics Engineering to be as transformative as the last, dbt needs to move beyond being a string preprocessor and into fully comprehending SQL.
Read Time: 6 Minute, 6 Second In modern data pipelines, handling data in various formats such as CSV, Parquet, and JSON is essential to ensure smooth data processing. However, one of the most common challenges faced by data engineers is the evolution of schemas as new data comes in. Schema evolution refers to the ability of a system to adapt to changes in the structure of incoming data without breaking existing workflows.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
AI is proving that its here to stay. While 2023 brought wonder, and 2024 saw widespread experimentation, 2025 will be the year that telecommunications enterprises get serious about AI's applications. But its complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AIs biggest cheerleaders are turning a bit dour.
Even though Apache Spark SQL provides an API for structured data, the framework sometimes behaves unexpectedly. It's the case of an insertInto operation that can even lead to some data quality issues. Why? Let's try to understand in this short article.
To be proficient as a data engineer, you need to know various toolkitsfrom fundamental Linux commands to different virtual environments and optimizing efficiency as a data engineer. This article focuses on the building blocks of data engineering work, such as operating systems, development environments, and essential tools. We’ll start from the ground upexploring crucial Linux commands, containerization with Docker, and the development environments that make modern data engineering possibl
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
1. Introduction 2. Guide the interviewer through the process 2.1. [Requirements gathering] Make sure you clearly understand the requirements & business use case 2.2. [Understand source data] Know what you have to work with 2.3. [Model your data] Define data models for historical analytics 2.4. [Pipeline design] Design data pipelines to populate your data models 2.5.
It is imperative to have backend tools in order to develop web applications that are scalable, efficient, and robust. One of the most popular choices among developers is Flask, a Python framework that is both lightweight and flexible. Flask, which is renowned for its modularity and simplicity, enables developers to rapidly construct web applications without the need for excessive complexity.
Data science is still among the best careers to choose from in terms of compensation, with data scientists earning higher than the average salary. Lets see what data professionals stand to earn in 2025.
Data is the new currency, and nowhere is that more evident than at the Gartner Data & Analytics Summit an event that gathers industry leaders, practitioners, and tech enthusiasts to discuss the latest in data-driven strategies and cutting-edge analytics solutions. If youre attending the Gartner Data & Analytics Summit and youre ready to learn how your leading data team can leverage data observability to get AI-ready in 2025, then read on for what you can expect, why it matters, and how t
With Airflow being the open-source standard for workflow orchestration, knowing how to write Airflow DAGs has become an essential skill for every data engineer. This eBook provides a comprehensive overview of DAG writing features with plenty of example code. You’ll learn how to: Understand the building blocks DAGs, combine them in complex pipelines, and schedule your DAG to run exactly when you want it to Write DAGs that adapt to your data at runtime and set up alerts and notifications Scale you
In today's fast-paced world, utility companies face numerous challenges when it comes to outage response and restoration, especially during severe weather events. The.
Imagine having a record of daily expenditures and wishing to investigate how they would eventually pile up over time—the places this concept of running totals will take you. With running totals, Power BI gives you a mighty insight into the cumulative value you build while traversing through your data. It allows you to analyze the trend of sales and investigate inventory and financial metrics by showing a rise over time.
Whether youre starting from scratch or building on existing skills, this hands-on program teaches you how to import, clean, and visualize data from day one using libraries like pandas, Seaborn, and Matplotlib. Plus, earn an industry-recognized certification to showcase your expertise and stand out in the job market.
Welcome to the fourth blog in WeCloudDatas Prompt Engineering Series! In the previous blog we explored basic prompt engineering techniques, such as zero-shot prompting and few-shot prompting. These techniques are effective in helping large language models to produce contextually relevant output. This blog is an introduction to a more advanced technique known as chain of […] The post Chain of Thought Prompting (CoT) appeared first on WeCloudData.
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!
Python is a sophisticated predictive analytics platform that uses libraries such as Pandas, NumPy, and Scikit-learn for data manipulation, analysis, and modeling. Businesses can use it to predict trends, find patterns, and make choices based on data. Python’s machine learning techniques can use past data to guess what will happen in the future.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
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