Sat.Jan 18, 2025 - Fri.Jan 24, 2025

article thumbnail

How Meta discovers data flows via lineage at scale

Engineering at Meta

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

article thumbnail

How to Pick Between Data Science, Data Analytics, Data Engineering, ML Engineering, and SW…

Towards Data Science

Make the right choice for YOU Continue reading on Towards Data Science

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Data Quality Governance That Actually Works

Monte Carlo

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.

article thumbnail

What is Retrieval-Augmented Generation (RAG)?

Edureka

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

article thumbnail

Apache Airflow® 101 Essential Tips for Beginners

Apache Airflow® is the open-source standard to manage workflows as code. It is a versatile tool used in companies across the world from agile startups to tech giants to flagship enterprises across all industries. Due to its widespread adoption, Airflow knowledge is paramount to success in the field of data engineering.

article thumbnail

Strobelight: A profiling service built on open source technology

Engineering at Meta

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.

More Trending

article thumbnail

A Guide to Deploying Machine Learning Models to Production

KDnuggets

Lets learn how to move your model from development into production.

article thumbnail

The insertInto trap in Apache Spark SQL

Waitingforcode

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.

SQL 130
article thumbnail

The Data Engineering Toolkit: Essential Tools for Your Machine

Simon Späti

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

article thumbnail

Data Engineering Interview Series #2: System Design

Start Data Engineering

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.

Designing 130
article thumbnail

Apache Airflow® Best Practices: 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!

article thumbnail

Data Science Salaries & Job Market Analysis: From 2024 to 2025

KDnuggets

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.

article thumbnail

What Is PDFMiner And Should You Use It – How To Extract Data From PDFs

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.

IT 130
article thumbnail

Enhancing Neural Network Training at Yelp: Achieving 1,400x Speedup with WideAndDeep

Yelp Engineering

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

Datasets 104
article thumbnail

Revolutionizing Utility Outage Response

databricks

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.

article thumbnail

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.

article thumbnail

Learn Python for Data Science in 6 Weeks on DataCamp

KDnuggets

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.

article thumbnail

The Concepts Data Professionals Should Know in 2025: Part 1

Towards Data Science

From Data Lakehouses to Event-Driven Architecture — Master 12 data concepts and turn them into simple projects to stay ahead in IT.

article thumbnail

The AI Tipping Point: Key Insights for Telecom in 2025

Snowflake

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.

article thumbnail

The Three Levels of SQL Comprehension: What they are and why you need to know about them

dbt Developer Hub

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.

SQL 78
article thumbnail

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?

article thumbnail

10 Data Science Myths Debunked [Infographic]

KDnuggets

Our latest infographic breaks down 10 of the most common and enduring myths about data science, offering clarity on the misconceptions that often surround this rapidly evolving field.

article thumbnail

Modern Data And Application Engineering Breaks the Loss of Business Context

Towards Data Science

Here’s how your data retains its business relevance as it travels through your enterprise Continue reading on Towards Data Science

article thumbnail

Databricks Recognized as One of Glassdoor's Best Places to Work in 2025

databricks

Databricks has been recognized as one of the winners of the annual Glassdoor Employees Choice Awards, a list of the Best Places to.

75
article thumbnail

How to Make Maps Fast (Using Snow Data!)

ArcGIS

Learn how to make a map with SNODAS data in ArcGIS Pro and build a map quickly by planning your data and design early.

article thumbnail

Apache Airflow® Crash Course: From 0 to Running your Pipeline in the Cloud

With over 30 million monthly downloads, Apache Airflow is the tool of choice for programmatically authoring, scheduling, and monitoring data pipelines. Airflow enables you to define workflows as Python code, allowing for dynamic and scalable pipelines suitable to any use case from ETL/ELT to running ML/AI operations in production. This introductory tutorial provides a crash course for writing and deploying your first Airflow pipeline.

article thumbnail

How to Use groupby for Advanced Data Grouping and Aggregation in Pandas

KDnuggets

Learn how to perform advance grouping and aggregation in Pandas.

Data 116
article thumbnail

The Concepts Data Professionals Should Know in 2025: Part 2

Towards Data Science

From AI Agent to Human-In-The-Loop — Master 12 critical data concepts and turn them into simple projects to stay ahead in IT.

article thumbnail

Schema Evolution with Case Sensitivity Handling in Snowflake

Cloudyard

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.

article thumbnail

Allium and Confluent: How to Build a Foundational Data Platform for Blockchain

Confluent

Allium provides real-time, accessible blockchain data for analytics and business teams with the help of data streaming. Learn how here.

article thumbnail

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.

article thumbnail

Mastering Python’s Built-in Statistics Module: A Complete Guide to Essential Functions

KDnuggets

Let's have a look at the different functions included within the statistics module, and point to more in-depth tutorials on each of them individually.

115
115
article thumbnail

Flask Python: A Comprehensive Guide to Building Web Applications

Edureka

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.

Python 52
article thumbnail

Data Engineering Trends in 2025: Your Roadmap to Smarter Data Teams

Ascend.io

Data teams are under more pressure than ever before, with demands skyrocketing and technology outpacing teams ability to adapt. Understanding how your team stacks up against these challenges is crucialit could mean the difference between leading the charge and falling behind. Over the past five years, Ascend.io has conducted the industry-wide Pulse Survey to capture the current state of data teams.