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Architect’s Open-Source Guide for a Data Mesh Architecture

Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh? In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry. The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems. This session is targeted for architects, decision-makers, data-engineers, and system designers.

Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.

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Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.

Azure Synapse Analytics

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Modern Data architecture Design

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Azure data platform overview

Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.

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Microsoft Fabric: How to Accelerate AI with Data

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Learn how to harness the power of data through Microsoft Fabric to accelerate your AI capabilities for growth and scale. What You'll Learn Introduction to Microsoft Fabric: Understand the basics of Microsoft Fabric and its role in revolutionizing data management and analytics. Exploring Microsoft Fabric's Features: Dive into the core functionalities and components that make Microsoft Fabric a powerful tool in data management. Microsoft Fabric in Business: Discover how Microsoft Fabric transforms business processes, enhancing efficiency and decision-making with advanced data solutions. Evolution of Data Management: Learn about the shift from traditional data management to dynamic, integrated solutions offered by Microsoft Fabric. Microsoft Fabric Live in Action: Experience Microsoft Fabric firsthand through a live demo, illustrating its practical applications in real-world business scenarios.

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Databricks for Dummies

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Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;) After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters. This is part 1 of an 8 part Data Science for Dummies series: Databricks for dummies Titanic survival prediction with Databricks + Python + Spark ML Titanic with Azure Machine Learning Studio Titanic with Databricks + Azure Machine Learning Service Titanic with Databricks + MLS + AutoML Titanic with Databricks + MLFlow Titanic with DataRobot Deployment, DevOps/MLops and Operationalization

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Introduction to Azure Databricks

Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.

Data Mesh Part 4 Monolith to Mesh

Jeffrey T. Pollock

This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems. Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/) Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.

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Learn how to choose which #Azure services to use so that you can start "Jumping Clouds" with confidence :) Watch the recording at https://youtu.be/34U1hUJmCUc and for more forward-looking #Software #Developerment topics, join http://ServerlessToronto.org User Group LINKS FROM THE MEETUP & CHAT https://www.askyourdeveloper.com/ http://youtube.serverlesstoronto.org https://youtu.be/Ivcndg9pTpk?t=1390 https://www.meetup.com/Serverless-Toronto/events/276721419/ https://www.meetup.com/Serverless-Toronto/events/275256767/ https://www.meetup.com/Serverless-Toronto/events/276752609/ https://developerweeklypodcast.com/ https://channel9.msdn.com/Shows/Azure-Friday https://www.pluralsight.com/paths/microsoft-azure-compute-for-developers https://azureoverview.com/ https://build5nines.com/ https://azure.microsoft.com/en-us/updates/ https://azure.microsoft.com/en-us/blog/ https://docs.microsoft.com/en-us/azure/architecture/ https://www.mssqltips.com/sqlservertip/5144/sql-server-temporal-tables-vs-change-data-capture-vs-change-tracking--part-3/ https://azure.microsoft.com/en-us/pricing/details/synapse-analytics/ https://www.manning.com/books/azure-data-engineering https://www.manning.com/books/azure-storage-streaming-and-batch-analytics https://docs.microsoft.com/en-us/azure/azure-functions/durable/durable-functions-overview?tabs=csharp https://cloudevents.io/ https://docs.microsoft.com/en-us/azure/architecture/patterns/ https://www.linkedin.com/pulse/you-asking-your-team-design-perfect-solution-daniel-zivkovic/ https://youtu.be/GBTdnfD6s5Q https://www.linkedin.com/company/serverless-toronto/

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Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.

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Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate. This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo. Agenda: What is Microsoft Fabric? Workspaces and capacities OneLake Lakehouse Data Warehouse ADF Power BI / DirectLake Resources

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