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Week 1: 10/9/20 - 10/16/20 In my quest to further improve my overall data science skills, I pulled the trigger on October 9th, 2020, and enrolled in a Data Engineering boot camp lead by Andreas Kretz. First a little bit about myself. I have a background in Aerospace Engineering and have been in the industry for close to 15 years now. A little more than a year ago, I decided to pivot to Machine Learning and Data Science.
It’s all about the Customer. Customers today expect services to be highly personalized. In a digital world tuned to understand your likes, dislikes, interests and preferences we expect a similar level of customization in all aspects of our lives. Insurance is no different. Insurance is not something the average consumer thinks about every day but when a life changing event happens, insurance becomes extremely important.
How viewers are able to watch their favorite show on Netflix while the infrastructure self-recovers from a system failure By Manuel Correa , Arthur Gonigberg , and Daniel West Getting stuck in traffic is one of the most frustrating experiences for drivers around the world. Everyone slows to a crawl, sometimes for a minor issue or sometimes for no reason at all.
Summary Data lakes are gaining popularity due to their flexibility and reduced cost of storage. Along with the benefits there are some additional complexities to consider, including how to safely integrate new data sources or test out changes to existing pipelines. In order to address these challenges the team at Treeverse created LakeFS to introduce version control capabilities to your storage layer.
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
Week 2: 10/16/20 - 10/23/20 Week 2 of the course consists of Modules 3 & 4. If you have not read my first blog go here. Module 3 focuses on creating a professional LinkedIn profile. Your LinkedIn profile is the world's access to you and how you want to be seen professionally. Below is a screenshot. So here, I have a professionally taken photograph, what I am interested in below, and the 'About' section that summarizes Me.in a professional sense.
Typically, running smooth and successful internship programs requires in-person interactions with high touchpoints. From onboarding and regular meetings to coffee chats and welcome events to meet the team – it takes a lot to integrate a new intern. They’re not only new to the organization but new to the workforce, after all. . Yet, with most tech companies going fully remote, Early Talent teams had to consider their options.
“Persistent” queries have historically formed the basis of ksqlDB applications, which continuously transform, enrich, aggregate, materialize, and join your Apache Kafka® data using a familiar SQL interface. ksqlDB continuously executes […].
“Persistent” queries have historically formed the basis of ksqlDB applications, which continuously transform, enrich, aggregate, materialize, and join your Apache Kafka® data using a familiar SQL interface. ksqlDB continuously executes […].
This "how-to" guide will help you to connect Teradata Vantage using the Native Object Store feature to query Salesforce data sourced by Microsoft Azure Data Factory.
Users today are asking ever more from their data warehouse. This is resulting in advancements of what is provided by the technology, and a resulting shift in the art of the possible. As an example of this, in this post we look at Real Time Data Warehousing (RTDW), which is a category of use cases customers are building on Cloudera and which is becoming more and more common amongst our customers.
Today, the ability to capture and harness the value of data in real time is critical for businesses to remain competitive in a data-driven world. Apache Kafka®, a scalable, open-source, […].
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.
In a recent post , my teammate Jennifer Xia outlined our motivation and initial direction for tracking XRP liquidity in support of RippleNet’s On-Demand Liquidity (ODL) service. ODL leverages the digital asset XRP to facilitate cross-border payments by sourcing destination currencies right at the time of payment. Jennifer’s post introduces the concept of order books and defines the implied FX rate or the FX rate implied by a pair of trades bridged through XRP.
So, what is a Power BI Template App? A Power BI Template App is a published Power BI solution that can be used by any company that has the data platform for which the Template App was created. Wouldn’t it be nice to pick your entire Power BI Solution off the shelf - one crafted for your specific business needs and your specific data structure. Power BI Template Apps are designed to be such an out-of-the-box solution and this blog post is an example of such for a Power BI Solution for Salesforce.
Featuring: Jerry Green, World Wide Open Source Sales and Strategy Leader at IBM. IBM and Cloudera joined forces to bring the best of both companies to enterprises seeking advanced data and AI solutions. Jerry Green, World Wide Open Source Sales and Strategy Leader at IBM, has been instrumental with the relationship since its inception. We wanted to probe deeper into the man, the myth, the legend, Jerry Green!
Part 1 — Rebuilding at Scale Authors: Jonathan Parks, Vaughn Quoss, Paul Ellwood Introduction At Airbnb, we’ve always had a data-driven culture. We’ve assembled top-notch data science and engineering teams, built industry-leading data infrastructure, and launched numerous successful open source projects, including Apache Airflow and Apache Superset.
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.
Since the 2008 financial crisis the CFO's role has turned inward, & they have lost influence. What role should they play in the Bank of the Future, and can data be their savior?
Many organizations struggle to meet growing and variable data warehouse demands. No matter how much they pad their annual IT budgets, there never seems to be enough capacity to cover unexpected business requests. This leads to resource restrictions for the various business units that use the platform. . When business units are not well served by central IT, “shadow IT” emerges.
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
BioData World Congress 2020 is next week, and I am looking forward to the opportunity to meet with decision makers and thought leaders working in omics, diagnostics and R&D from across Europe and beyond. Cloudera’s work with BioPharma organizations helps them link clinical and business knowledge with analytics expertise to drive patient-level insights and operational decision making in a dynamic environment.
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