Migrate, Modernize and Build Java Web Apps on Azure: This live workshop will cover methods to enhance Java application development workflow.
Modern Digital Website Security: Prepare to face any form of malicious web activity and enable your sites to optimally serve your customers.
Kubernetes in the Enterprise: The latest expert insights on scaling, serverless, Kubernetes-powered AI, cluster security, FinOps, and more.
Principal Developer Advocate at Cloudera
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over a ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science
Enter the modern data stack: a technology stack designed and equipped with cutting-edge tools and services to ingest, store, and process data. No longer are we using data only to drive business decisions; we are entering a new era where cloud-based systems and tools are at the heart of data processing and analytics. Data-centric tools and techniques — like warehouses and lakes, ETL/ELT, observability, and real-time analytics — are democratizing the data we collect. The proliferation of and growing emphasis on data democratization results in increased and nuanced ways in which data platforms can be used. And of course, by extension, they also empower users to make data-driven decisions with confidence.In our 2023 Data Pipelines Trend Report, we further explore these shifts and improved capabilities, featuring findings from DZone-original research and expert articles written by practitioners from the DZone Community. Our contributors cover hand-picked topics like data-driven design and architecture, data observability, and data integration models and techniques.
Development at Scale
As organizations’ needs and requirements evolve, it’s critical for development to meet these demands at scale. The various realms in which mobile, web, and low-code applications are built continue to fluctuate. This Trend Report will further explore these development trends and how they relate to scalability within organizations, highlighting application challenges, code, and more.
In recent years, artificial intelligence has become less of a buzzword and more of an adopted process across the enterprise. With that, there is a growing need to increase operational efficiency as customer demands arise. AI platforms have become increasingly more sophisticated, and there has become the need to establish guidelines and ownership. In DZone’s 2022 Enterprise AI Trend Report, we explore MLOps, explainability, and how to select the best AI platform for your business. We also share a tutorial on how to create a machine learning service using Spring Boot, and how to deploy AI with an event-driven platform. The goal of this Trend Report is to better inform the developer audience on practical tools and design paradigms, new technologies, and the overall operational impact of AI within the business. This is a technology space that's constantly shifting and evolving. As part of our December 2022 re-launch, we've added new articles pertaining to knowledge graphs, a solutions directory for popular AI tools, and more.
Industry leaders discuss the latest trends in machine learning. We dive into using machine learning with microserivces, deploying machine learning models in real-life applications, and where the field is going over the next 12 months.