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I’d like to share a story about an educational side project which could prove fruitful for a software engineer who’s seeking a new job. Juraj created a systems design explainer on how he built this project, and the technologies used: The systems design diagram for the Rides application The app uses: Node.js
While the technology continues to be a male-dominated industry, more women are pursuing careers in the space, driving meaningful change and innovation. To support this, the Precisely Women in Technology (PWIT) network, was created as a dedicated place for women to connect, share experiences, and learn from one another.
That can also be a risk when the community vision for the project doesn't align with your own goals. What are some of the other tools/technologies that can benefit from some or all of the pieces of the FDAP stack? If you've learned something or tried out a project from the show then tell us about it!
The goal is to touch on the common data engineering challenges and using promising new technologies, tools or frameworks, which most of them I wrote about in Business Intelligence meets Data Engineering with Emerging Technologies.
Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network
In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. Fortunately, digital tools now offer valuable insights to help mitigate these risks. That’s where data-driven construction comes in. You won’t want to miss this webinar!
Summary The dbt project has become overwhelmingly popular across analytics and data engineering teams. Dustin Dorsey and Cameron Cyr co-authored a practical guide to building your dbt project. In this episode they share their hard-won wisdom about how to build and scale your dbt projects. What was your path to adoption of dbt?
While there are numerous products available to provide that visibility, they all have different technologies and workflows that they focus on. To bring observability to dbt projects the team at Elementary embedded themselves into the workflow. Can you start by outlining what elements of observability are most relevant for dbt projects?
Datafold has invested a lot of time into integrating with the workflow of dbt projects to add early verification that the changes you are making are correct. What are some of the error conditions/failure modes that data-diff can help identify in a dbt project? What are the parallels to that in data projects?
Joshua is currently VP of Product & Strategy at VMware, a cloud computing and virtualization technology company. Joshua also writes an excellent Substack newsletter about how to design products which customers love, how to operate live services at scale, grow and optimize your technology orgs, and the history of the tech industry.
Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.
Natural Language Processing (NLP) has transformed technology by allowing machines to understand, decode, and generate human language. NLP plays a crucial role in multiple domains and NLP projects ranging from its automating customer service, improving search engines, or analyzing social media sentiments.
It begins with a clean state, and can ship something that works for, say, 90% of existing Node projects, and break the remaining 10%. I tip my hat to all volunteer open source contributors and maintainers — both for Node, and for other projects. Bun has no such constraint. If you are one of these people: thank you!
This project helped onboard me to the software, its structure, its build, and our issue tracking and version control workflows. My first project was supporting i18n (internationalization) in the app. They hired a manager who had done this kind of project before, and set a target date of nearly two years.
dbt Labs also develop dbt Cloud which is a cloud product that hosts and runs dbt Core projects. a dbt project — a dbt project is a folder that contains all the dbt objects needed to work. You can initialise a project with the CLI command: dbt init. In a dbt project you can define YAML file everywhere.
Speaker: Aindra Misra, Sr. Staff Product Manager of Data & AI at BILL (Previously PM Lead at Twitter/X)
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If you are a software professional or someone who is adept in the knowledge of the technology and the latest software development, then you have probably come across the term ‘Agile’ several times. Agile management ensures the quick and precise development of the target and the project status.
You can find the online PMP exam application on the Project Management Institute (PMI)® website. Check Project Management professional preparation course to get started with your PMP preparation. Work Experience Your project management expertise is questioned in the next area of the online form.
In close collaboration with the open source community, we shared knowledge, introduced new projects, and enhanced existing ones. In this post, we look at our portfolio of open source projects through numbers to give a better view of the scale of the community we interact with daily.
There may be a number of reasons why you’d want to bring this rising technology […] The post How to Choose a Machine Learning Consulting Firm in 2023? Introduction Artificial intelligence (AI) and machine learning (ML) are in the best swing to help businesses sharpen their edge over their competitors in the market.
Multiple open source projects and vendors have been working together to make this vision a reality. Contact Info LinkedIn dain on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? What do you have planned for the future of Trino/Starburst?
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Contact Info LinkedIn @devarispbrown on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? If you've learned something or tried out a project from the show then tell us about it! What do you have planned for the future of Meroxa?
Lastly, companies have historically collaborated using inefficient and legacy technologies requiring file retrieval from FTP servers, API scraping and complex data pipelines. To remain nimble and have the flexibility to use the best tools for different workstreams, customers should not be locked into a specific vendor or technology.
SQLMesh was designed as a unifying tool that is simple to work with but powerful enough for large-scale transformations and complex projects. Contact Info tobymao on GitHub @captaintobs on Twitter Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?
Tommy has built his own video games, consulted on a wide variety of game projects, and for a decade has taught game development at various universities. Each project typically takes several years to create, with shifting hardware specifications and emerging competitors and trends to anticipate and react to, during the process.
Align people, processes, and technology Successful data governance requires a holistic approach. Woods echoed this sentiment, emphasizing the importance of a roadmap for both data governance and quality initiatives: A roadmap should include technology, people, and processes.
Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Data projects are notoriously complex. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects.
The rapid evolution of technology and the increasing demand for data-driven insights have placed immense pressure on these teams. As we look towards 2025, it’s clear that data teams must evolve to meet the demands of evolving technology and opportunities. So, why do so many automation projects fail to deliver?
Free comprehensive teaching resources and a no-hassle setup: Teaching AI/ML, data science, apps and data cloud technologies shouldnt be bogged down by logistical challenges. Access to cutting-edge data technology: Snowflake is widely used by more than 10,000 enterprises across industries such as healthcare, financial services and retail.
Data teams are expected to juggle a combination of ad-hoc requests, big bet projects, migrations, etc. All while keeping up with the latest changes in technology. Running a successful data team is hard.
A long-term approach to your data strategy is key to success as business environments and technologies continue to evolve. The rapid pace of technological change has made data-driven initiatives more crucial than ever within modern business strategies. Overall, AI success truly depends on a business outcome-driven approach. “We
In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform. If you've learned something or tried out a project from the show then tell us about it!
Summary Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Operating it at scale, however, is notoriously challenging.
Nicola Askham found her way into data governance by accident, and stayed because of the benefit that she was able to provide by serving as a bridge between the technology and business. What impact has the evolution of data technologies had on the implementation of governance practices? Are there any trends that concern you?
They called it Office 365, and in 2010, this was a really exciting project to work on. After all, we already had open source technology to use – M3 – and we also had a clear path on what we wanted to build, similar to the roadmap the observability team at Uber used to have. We needed $11M to get started.
As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. Contact Info esammer on GitHub LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? How can you get the best results for your use case?
Consumers attention spans are at an all-time low, while competition for sports fan attention remains high as teams seek to secure a piece of the projected $8.12 Sports entity data teams are often mighty but small making complex technology solutions unrealistic to leverage. Personalization matters more than ever before.
I suspect most respondents use these registrars for side projects, and some use them at work. This service is ideal for saving money; for example, when buying domains for a side project. I asked on Twitter “what domain registrars would you recommend / do you use” , and more than 250 techies replied.
This massive event sprawled across half of the ExCeL centre, bringing together industry vendors, academics, and innovators across multiple technology domains. Not far off what weve been exploring through previous blog posts and my personal podcast, Architect Tomorrow , where sustainable technology solutions are a common topic.
With their launch of Dagster+ as the redesigned commercial companion to the open source project they are investing in that capability with a suite of new features. What are the notable enhancements beyond the Dagster Core project that this updated platform provides? What problems are you trying to solve with Dagster+?
Summary Data systems are inherently complex and often require integration of multiple technologies. In this episode Nick Schrock, creator of Dagster, shares his perspective on the state of data orchestration technology and its application to help inform its implementation in your environment.
And to create significant technology and team efficiencies, organizations need to consider opportunities to integrate LLM pipelines with existing structured data workflows. Being able to flexibly switch LLMs helps businesses optimize costs by right-sizing models for each use case and easily upgrading as models improve.
The scope of telecom services is growing in size and complexity, owing to technologies such as 5G, the Internet of Things (IoT), and cloud technology. And one technology that has potential to transform the telecom sector is Generative AI , or GAI, which lies in the focus of creating new things, be it content, ideas or solutions.
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