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1. Introduction 2. Key parts of data systems: 2.1. Requirements 2.2. Data flow design 2.3. Orchestrator and scheduler 2.4. Data processing design 2.5. Code organization 2.6. Data storage design 2.7. Monitoring & Alerting 2.9. Infrastructure 3. Conclusion 1. Introduction If you are trying to break into (or land a new) data engineering job, you will inevitably encounter a slew of data engineering tools.
We are working in partnership with others to scale inclusive solutions that support the transition to a zero-carbon economy and help create a healthier planet for all.
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.
Data teams spend way too much time troubleshooting issues, applying patches, and restarting failed workloads. It's not uncommon for engineers to spend their.
Over the many years I’ve been pounding my keyboard … Perl, PHP, Python, C#, Rust … whatever … I, like most programmers, built up a certain disdain for what is called Low Code / No Code solutions. In my rush to worship at the feet of the code we create, I failed, in the beginning, […] The post Streaming Postgres data to Databricks Delta Lake in Unity Catalog appeared first on Confessions of a Data Guy.
Our team had previously built a tool to investigate code quality from PR data. Building on this work, we set about finding a method to detect AI-written code, so we could investigate any potential differences in code quality between human and AI-written code. During our time on this project, we learnt some important lessons, including just how hard it can be to detect AI-written code, and the importance of good-quality data when conducting research.
Our team had previously built a tool to investigate code quality from PR data. Building on this work, we set about finding a method to detect AI-written code, so we could investigate any potential differences in code quality between human and AI-written code. During our time on this project, we learnt some important lessons, including just how hard it can be to detect AI-written code, and the importance of good-quality data when conducting research.
Polars is the hot new Rust based Python Dataframe tool that is taking over the world and destryoing Pandas even as we speak. You want the quick and dirty introduction to Polars? Look no farther. The post Introduction to Polars in 2 Minutes appeared first on Confessions of a Data Guy.
Recommender systems (RecSys) have become an integral part of modern digital experiences, powering personalized content suggestions across various platforms. These sophisticated systems and.
A data warehouse is a centralized system that stores, integrates, and analyzes large volumes of structured data from various sources. It is predicted that more than 200 zettabytes of data will be stored in the global cloud by 2025.
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.
According to the Women in Tech Network , women make up about 35 percent of the tech workforce. While this number has grown over the years, it still indicates that technology is a male-dominated industry. Precisely is committed to creating a supportive environment for women to build their careers so that this number can continue growing. As a result, the Precisely Women in Technology (PWIT) network was developed.
The world is generating an astonishing amount of data every second of every day. It reached 64.2 zettabytes in 2020, and is projected to mushroom to over 180 zettabytes by 2025, according to Statista. Modern problems require modern solutions — which is why businesses across industries are moving away from batch processing and towards real-time data streams, or streaming data.
Picture this: You’re leading a meeting, ready to present the latest sales figures. But, as you start sharing the numbers, someone points out a glaring inconsistency. Suddenly, the room is filled with doubt—about the data, the insights, and, let’s face it, even your judgment. A data quality dashboard is your safety net in these situations. It’s more than a tool—it’s a real-time report card on the health of your data.
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
This article was co-written by Lynda Chao & Tess Newkold With the growing interest in AI-powered analytics, ThoughtSpot stands out as a leader among legacy BI solutions known for its self-service search-driven analytics capabilities. ThoughtSpot offers AI-powered and lightning-fast analytics, a user-friendly semantic engine that is easy to learn, and the ability to empower users across any organization to quickly search and answer data questions.
Deciding which path to follow between Application Development vs Web Development is a decision that should not be taken lightly. They are both relevant and promising. In both areas, there are numerous opportunities. This guide will help to introduce each one, to state what it is that they do, the skills required, and how they may differ in terms someone with little or no background in sociology can understand.
In this fast-changing world of data analytics, choosing the right tool for data transformation is one of the keys. Grown in this sector, dbt, or what is popularly known as the data build tool, is a significant solution for SQL-based data transformations, keeping workflows properly and well-documented by data teams inside data warehouses.
Currently, data management is a continually developing field that requires careful consideration when deciding which solution should be implemented to store, process, and analyze data effectively. There are two forms that are frequently selected: data warehouse vs data lake.
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
Data is the new currency in today’s world, helping industries make decisions and innovations. To use data to its full potential, organizations require powerful tools to manage, transform, and analyze vast amounts of it. Various tools are available, among which Alteryx and Matillion stand out as two of the leading ETL solutions.
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