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.

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.

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.

Whether you’re creating complex dashboards or fine-tuning large language models, your data must be extracted, transformed, and loaded. ETL and ELT pipelines form the foundation of any data product, and Airflow is the open-source data orchestrator specifically designed for moving and transforming data in ETL and ELT pipelines. This eBook covers: An overview of ETL vs.

Explore how enterprises can enhance developer productivity and onboarding by adopting self-hosted Cloud Development Environments (CDEs). This whitepaper highlights the simplicity and flexibility of cloud-based development over traditional setups, demonstrating how large teams can leverage economies of scale to boost efficiency and developer satisfaction.

Cloud Development Environments (CDEs) are changing how software teams work by moving development to the cloud. Our Cloud Development Environment Adoption Report gathers insights from 223 developers and business leaders, uncovering key trends in CDE adoption. With 66% of large organizations already using CDEs, these platforms are quickly becoming essential to modern development practices.

Leverage the Cloud Development Environment Maturity Model to elevate your software development practices with scalable, secure cloud-based workspaces. This model offers a structured approach to modernizing development, aligning technology, developer experience, security, and workflows. By implementing Cloud Development Environments (CDEs), teams can boost efficiency, improve security, and streamline operations through centralized governance.

In the fast-moving manufacturing sector, delivering mission-critical data insights to empower your end users or customers can be a challenge. Traditional BI tools can be cumbersome and difficult to integrate - but it doesn't have to be this way. Logi Symphony offers a powerful and user-friendly solution, allowing you to seamlessly embed self-service analytics, generative AI, data visualization, and pixel-perfect reporting directly into your applications.

In the rapidly evolving healthcare industry, delivering data insights to end users or customers can be a significant challenge for product managers, product owners, and application team developers. The complexity of healthcare data, the need for real-time analytics, and the demand for user-friendly interfaces can often seem overwhelming. But with Logi Symphony, these challenges become opportunities.

The complexity of financial data, the need for real-time insight, and the demand for user-friendly visualizations can seem daunting when it comes to analytics - but there is an easier way. With Logi Symphony, we aim to turn these challenges into opportunities. Our platform empowers you to seamlessly integrate advanced data analytics, generative AI, data visualization, and pixel-perfect reporting into your applications, transforming raw data into actionable insights.

Adding high-quality entity resolution capabilities to enterprise applications, services, data fabrics or data pipelines can be daunting and expensive. Organizations often invest millions of dollars and years of effort to achieve subpar results. This guide will walk you through the requirements and challenges of implementing entity resolution. By the end, you'll understand what to look for, the most common mistakes and pitfalls to avoid, and your options.

Organizations look to embedded analytics to provide greater self-service for users, introduce AI capabilities, offer better insight into data, and provide customizable dashboards that present data in a visually pleasing, easy-to-access format. To better understand the factors behind the decision to build or buy analytics, insightsoftware partnered with Hanover Research to survey IT, software development, and analytics professionals on why they make the embedded analytics choices they do.

In the rapidly-evolving world of embedded analytics and business intelligence, one important question has emerged at the forefront: How can you leverage artificial intelligence (AI) to enhance your application’s analytics capabilities? Imagine having an AI tool that answers your user’s questions with a deep understanding of the context in their business and applications, nuances of their industry, and unique challenges they face.

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

Just by embedding analytics, app owners can charge 24% more for their product. How much value could you add? This framework from Software Pricing Partners explains how application enhancements can extend your product offerings. You’ll learn: How to take a disciplined approach to pricing The three elements of the Packaging Decision Framework Ways to structure your new embedded analytics offering Download the White Paper to learn about How To Package & Price Embedded Analytics.