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With burnout and mental stress at every level of our lives, I find my Personal Knowledge Management (PKM) system even more valuable. As a human, I forget lots of things. As a dad, I have more responsibilities with remembering all things related to my kid. As a developer and knowledge worker, I re-use code snippets or create new things. That’s why a PKM system such as a Second Brain to store all of it in a sustainable way is crucial to me.
In this article, we will have a look at five distinct data careers, and hopefully provide some advice on how to get one's feet wet in this convoluted field.
This blog post was written by Dean Bubley , industry analyst, as a guest author for Cloudera. . Communications service providers (CSPs) are rethinking their approach to enterprise services in the era of advanced wireless connectivity and 5G networks, as well as with the continuing maturity of fibre and Software-Defined Wide Area Network (SD-WAN) portfolios. .
Introduction Data lakes and data warehouses Data lake Data warehouse Criteria to choose lake and warehouse tools Conclusion Further reading References Introduction With the data ecosystem growing fast, new terms are coming up every week. Some of the most popular ones include “data lakes” and “data warehouses” If you are Trying to understand the differences between a data lake and a data warehouse Frustrated by vendor marketing content aimed at selling their lake/warehouse
In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate
DAG Dependencies in Apache Airflow might be one of the most popular topics. I received countless questions about DAG dependencies, is it possible? How? What are the best practices? and the list goes on. It’s funny because it comes naturally to wonder how to do that even when we are beginners. Do we like to complexify things by nature? Maybe, but that’s another question 😉 At the end of this article, you will be able to spot when you need to create DAG Dependencies, which metho
It’s not difficult to get started with Apache Kafka®. Learning resources can be found all over the internet, especially on the Confluent Developer site. If you are new to Kafka, […].
Data mesh is quickly becoming a way for companies to roll out their data strategy. If you haven’t already learned about data mesh , I suggest doing so. It comes with organizational and technical changes. I think a crucial part of your data mesh revolves around the choice of publish/subscribe technologies. At the crux of data mesh is a desire for flexibility.
Data mesh is quickly becoming a way for companies to roll out their data strategy. If you haven’t already learned about data mesh , I suggest doing so. It comes with organizational and technical changes. I think a crucial part of your data mesh revolves around the choice of publish/subscribe technologies. At the crux of data mesh is a desire for flexibility.
Solving the Python coding interview questions is the best way to get ready for an interview. That’s why we’ll lead you through 15 examples and five concepts these questions cover.
The term “AI-first” has received its share of attention lately, especially in the boardroom where strategies to gain a competitive advantage are always welcome. But before a company embarks on an AI-first strategy, it pays to understand what it is and how it will transform the organization. If you’re AI-first, that means you have figured out how to leverage artificial intelligence to boost organizational agility so you can continuously adapt operational processes to deliver the right business ou
By Alex Hutter , Falguni Jhaveri and Senthil Sayeebaba Over the past few years Content Engineering at Netflix has been transitioning many of its services to use a federated GraphQL platform. GraphQL federation enables domain teams to independently build and operate their own Domain Graph Services (DGS) and, at the same time, connect their domain with other domains in a unified GraphQL schema exposed by a federated gateway.
Summary There are very few tools which are equally useful for data engineers, data scientists, and machine learning engineers. WhyLogs is a powerful library for flexibly instrumenting all of your data systems to understand the entire lifecycle of your data from source to productionized model. In this episode Andy Dang explains why the project was created, how you can apply it to your existing data systems, and how it functions to provide detailed context for being able to gain insight into all o
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.
Our everyday digital experiences are in the midst of a revolution. Customers increasingly expect their online experiences to be interactive, immersive, and real time by default. The need to satisfy […].
Understanding the fundamentals of statistics is a core capability for becoming a Data Scientist. Review these essential ideas that will be pervasive in your work and raise your expertise in the field.
This blog post was written by Elizabeth Howell, Ph.D as a guest author for Cloudera. . At a distance of a million miles from Earth, the James Webb Space Telescope is pushing the edge of data transfer capabilities. The observatory launched Dec. 25 2021 on a mission to look at the early universe, at exoplanets, and at other objects of celestial interest.
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 systems (will) take decisions that have ethical grounds and consequences.”. Prof. Dr. Virginia Dignum from Umeå University. On March 23, 2016, Microsoft released its AI-based chatbot Tay via Twitter. The bot was trained to generate its responses based on interactions with users. But there was a catch. Various users started posting offensive tweets toward the bot, resulting in Tay making replies in the same language.
Summary A huge amount of effort goes into modeling and shaping data to make it available for analytical purposes. This is often due to the need to simplify the final queries so that they are performant for visualization or limited exploration. In order to cut down the level of effort involved in making data usable, Matthew Halliday and his co-founders created Incorta as an end-to-end, in-memory analytical engine that removes barriers to insights on your data.
We’re pleased to share a new multi-year partnership between Confluent and Microsoft to accelerate enterprises’ journey to cloud data streaming on Azure. Today’s announcement builds upon the partnership agreement we […].
Digitization is necessary, but not sufficient to meet evolving customer demands & create the bank of the future. Use data analytics to help customers achieve their goals not deliver better apps.
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
The pandemic changed our healthcare behaviors. Planned hospital and doctor visits were reduced while telemedicine, for physical and mental health, increased. As healthcare providers and insurers /payers worked through mass amounts of new data, our health insurance practice was there to help. I have noticed a growing excitement with health insurers around the world exploring these data driven types of capabilities, and I am looking forward to experiencing more of this in my personal life while I
Summary The next paradigm shift in computing is coming in the form of quantum technologies. Quantum procesors have gained significant attention for their speed and computational power. The next frontier is in quantum networking for highly secure communications and the ability to distribute across quantum processing units without costly translation between quantum and classical systems.
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.
Data streaming is a new category of technology that is reshaping the way businesses operate, but there hasn’t been a place for everyone in the ecosystem to come together and […].
Managing the new class of emerging risks requires infusing the principles of resiliency and efficient risk analytics into traditional risk management frameworks.
Approaches to data sampling, modeling, and analysis can vary based on the distribution of your data, and so determining the best fit theoretical distribution can be an essential step in your data exploration process.
This week I participated in an informative event that Cloudera hosted with TechCrunch: Data and the Culture Transformation. The event was moderated by tech industry analyst Maribel Lopez, and we were joined by Shirley Collie, chief health analytics actuary at Discovery Health in South Africa. The conversations focused on how company data cultures are rapidly evolving and delivering new levels of value to businesses with the emergence of data ecosystems.
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?
Summary Putting machine learning models into production and keeping them there requires investing in well-managed systems to manage the full lifecycle of data cleaning, training, deployment and monitoring. This requires a repeatable and evolvable set of processes to keep it functional. The term MLOps has been coined to encapsulate all of these principles and the broader data community is working to establish a set of best practices and useful guidelines for streamlining adoption.
It’s official: Kafka Summit is back! Technically, it never went away—it just went online. But this week in London, Kafka Summit returned in all its glory to welcome over 1,200 […].
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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
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