Why And When Should We Consider Stream Processing In Our Solutions Teqnation 2023

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.

Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.

Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.

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.

This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.

This document provides an agenda and overview for the "Big MDM Part 2" meetup event. The agenda includes presentations on using graph databases for master data management (MDM) and relationship management. Speakers from Caserta Concepts, Neo Technology, and Pitney Bowes will discuss graph databases, MDM use cases, and modeling and managing data with graph databases. The meetup is sponsored by Caserta Concepts and hosted by Neo Technology. It will include networking, five presentations on graph databases and MDM topics, and a Q&A session.

REX How Jobteaser got rid of an old data dump job using change data capture (CDC) with Debezium and Kafka.

Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration. This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.

Watch this talk here: https://www.confluent.io/online-talks/bridge-to-cloud-apache-kafka-migrate-gcp Most companies start their cloud journey with a new use case, or a new application. Sometimes these applications can run independently in the cloud, but often times they need data from the on premises datacenter. Existing applications will slowly migrate, but will need a strategy and the technology to enable a multi-year migration. In this session, we will share how companies around the world are using Confluent Cloud, a fully managed Apache Kafka® service, to migrate to Google Cloud Platform. By implementing a central-pipeline architecture using Apache Kafka to sync on-prem and cloud deployments, companies can accelerate migration times and reduce costs. Register now to learn: -How to take the first step in migrating to GCP -How to reliably sync your on premises applications using a persistent bridge to cloud -How Confluent Cloud can make this daunting task simple, reliable and performant

This document discusses change data capture (CDC) and its components. CDC is an approach that identifies, captures, and delivers changes made to enterprise data sources. It feeds these changes into a central data stream that can be combined with other data sources in real-time. The document outlines Kafka Connect, Debezium, Schema Registry, and Apache Avro which are key parts of the CDC architecture. It also discusses future steps like supporting additional databases and improving deployment, as well as open issues around performance and compatibility with certain databases.

Microservices became the new black in enterprise architectures. APIs provide functions to other applications or end users. Even if your architecture uses another pattern than microservices, like SOA (Service-Oriented Architecture) or Client-Server communication, APIs are used between the different applications and end users. Apache Kafka plays a key role in modern microservice architectures to build open, scalable, flexible and decoupled real time applications. API Management complements Kafka by providing a way to implement and govern the full life cycle of the APIs. This session explores how event streaming with Apache Kafka and API Management (including API Gateway and Service Mesh technologies) complement and compete with each other depending on the use case and point of view of the project team. The session concludes exploring the vision of event streaming APIs instead of RPC calls. Understand how event streaming with Kafka and Confluent complements tools and frameworks such as Kong, Mulesoft, Apigee, Envoy, Istio, Linkerd, Software AG, TIBCO Mashery, IBM, Axway, etc. A Streaming API Data Exchangeprovides streaming replication between business units and companies. API Management with REST/HTTP is not appropriate for streaming data.

Nubank is the leading fintech in Latin America. Using bleeding-edge technology, design, and data, the company aims to fight complexity and empower people to take control of their finances. We are disrupting an outdated and bureaucratic system by building a simple, safe and 100% digital environment. In order to succeed, we need to constantly make better decisions in the speed of insight, and that’s what We aim when building Nubank’s Data Platform. In this talk we want to explore and share the guiding principles and how we created an automated, scalable, declarative and self-service platform that has more than 200 contributors, mostly non-technical, to build 8 thousand distinct datasets, ingesting data from 800 databases, leveraging Apache Spark expressiveness and scalability. The topics we want to explore are: – Making data-ingestion a no-brainer when creating new services – Reducing the cycle time to deploy new Datasets and Machine Learning models to production – Closing the loop and leverage knowledge processed in the analytical environment to take decisions in production – Providing the perfect level of abstraction to users You will get from this talk: – Our love for ‘The Log’ and how we use it to decouple databases from its schema and distribute the work to keep schemas up to date to the entire team. – How we made data ingestion so simple using Kafka Streams that teams stopped using databases for analytical data. – The huge benefits of relying on the DataFrame API to create datasets which made possible having tests end-to-end verifying that the 8000 datasets work without even running a Spark Job and much more. – The importance of creating the right amount of abstractions and restrictions to have the power to optimize.

Flink Forward San Francisco 2022. Being in the payments space, Stripe requires strict correctness and freshness guarantees. We rely on Flink as the natural solution for delivering on this in support of our Change Data Capture (CDC) infrastructure. We heavily rely on CDC as a tool for capturing data change streams from our databases without critically impacting database reliability, scalability, and maintainability. Data derived from these streams is used broadly across the business and powers many of our critical financial reporting systems totalling over $640 Billion in payment volume annually. We use many components of Flink’s flexible DataStream API to perform aggregations and abstract away the complexities of stream processing from our downstreams. In this talk, we’ll walk through our experience from the very beginning to what we have in production today. We’ll share stories around the technical details and trade-offs we encountered along the way. by Jeff Chao

Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse Analytics (Presented at Global Azure Norway on April 16th, 2021)

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.

Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need. There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.

Andreas Grabner maintains that most performance and scalability problems don’t need a large or long running performance test or the expertise of a performance engineering guru. Don’t let anybody tell you that performance is too hard to practice because it actually is not. You can take the initiative and find these often serious defects. Andreas analyzed and spotted the performance and scalability issues in more than 200 applications last year. He shares his performance testing approaches and explores the top problem patterns that you can learn to spot in your apps. By looking at key metrics found in log files and performance monitoring data, you will learn to identify most problems with a single functional test and a simple five-user load test. The problem patterns Andreas explains are applicable to any type of technology and platform. Try out your new skills in your current testing project and take the first step toward becoming a performance diagnostic hero.

This session takes an in-depth look at: - Trends in stream processing - How streaming SQL has become a standard - The advantages of Streaming SQL - Ease of development with streaming SQL: Graphical and Streaming SQL query editors - Business value of streaming SQL and its related tools: Domain-specific UIs - Scalable deployment of streaming SQL: Distributed processing

Most common Frontend & Backend Performance Problems. Automatically find them in your CI by looking at the right Metrics.

Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are available for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.

Designing a SharePoint 2010 Global Deployment. Drill down on understanding the Farm Trusts and Publishing and Consuming Service Apps.

SpringOne Tour 2018 by Pivotal Spring Boot & Spring Cloud Apps on Pivotal Application Service - Daniel Lavoie

SpringOne Tour by Pivotal Denver Spring Boot & Spring Cloud on Pivotal Application Service by Pieter Humphrey

Partner Tech Talk with Tinybird

This document discusses Spring and Pivotal Application Service (PAS). It notes that PAS provides market-leading support for Spring technologies and an ecosystem of services for Spring applications. It covers why developers use Spring and PAS, how PAS supports Spring features like Boot, Security, and Cloud, and the services available on PAS like MySQL, RabbitMQ, and Redis. It concludes with next steps around contacting an account team, trying hosted PAS software, and signing up for roadmap calls.

Why is Performance Important? What are the most common reasons applications dont scale and perform well. Which technical metrics to look at. How to check it automated in the pipeline

Presentation on complete Datasmith warehousing solutions offering, including Voice technology, middleware solutions, WMS (Warehouse Management System) and mobile store delivery application.

The document discusses using Fluentd to manage logs. It provides an overview of Fluentd, including how it can aggregate and route logs from multiple sources to various outputs like Elasticsearch. It also discusses approaches to scaling Fluentd in distributed environments like Kubernetes, including using sidecars. Real-world challenges with log management are addressed, such as the need to consolidate logs from many distributed services and support multiple analytics tools.

The document discusses how ChronoRace, a company that provides timing services for sports events, migrated their infrastructure to Windows Azure to handle unpredictable traffic bursts during large events. Key aspects covered include identifying current infrastructure limitations, migrating the VS2003 website and SQL database to Azure, implementing auto-scaling functionality, and addressing issues with video streaming and PDF generation. The migration allowed ChronoRace to scale their infrastructure as needed for events while reducing monthly costs compared to their previous setup.

The document discusses how ChronoRace, a company that provides timing services for sports events, migrated their infrastructure to Windows Azure to handle unpredictable traffic bursts during large events. Key points covered include identifying pitfalls of their current on-premise solution, migrating their website and database to Azure, implementing auto-scaling to dynamically scale resources during events, and testing the Azure-based solution at an upcoming large event. The migration overall was successful in addressing ChronoRace's needs, though one component requiring registry access could not be migrated and remains on-premise.

StreamSets Data Collector is an open source data integration tool that can ingest data from various sources in both batch and streaming modes. It uses a record-oriented approach to data processing which avoids issues caused by combinatorial explosion. Pipelines can be developed visually using an IDE interface, allowing non-technical users to build integrations. StreamSets originated from ex-Cloudera and Informatica employees and focuses on continuous open source development.

Presenter: Kenn Knowles, Software Engineer, Google & Apache Beam (incubating) PPMC member Apache Beam (incubating) is a programming model and library for unified batch & streaming big data processing. This talk will cover the Beam programming model broadly, including its origin story and vision for the future. We will dig into how Beam separates concerns for authors of streaming data processing pipelines, isolating what you want to compute from where your data is distributed in time and when you want to produce output. Time permitting, we might dive deeper into what goes into building a Beam runner, for example atop Apache Apex.

Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.

In this Meetup Arik Lerner – Liveperson Team lead of Java Automation, Performance & Resilience , will talk about How we measure our services, By End2End testing which become one of the most critical Monitor tool in LP . Over 200K tests runs per day providing statistics and insights into the problem as they happen. Arik will go through different topics and stages of the journey and share details that led to current results . Part of the menu topics are : The Awakens of the End2End Insights • How we measure our services using synthetic user experience • Measuring through analytics & insights • How we collect our data • How we debug our services? Hint: video recording, HAR (Http archive), KIbana , Dashboard analytics & insights • Future logs App correlation with End2End data • Our tools: Selenium, Jenkins and cutting edge technologies such as Kafka & ELK (Elastic search, Logstash and Kibana) In this Meetup, Arik will host Ali AbuAli- NOC Team Leader , who will talk about the e2e usage on his day 2 day work.

In this Meetup Arik Lerner – Liveperson Team lead of Java Automation, Performance & Resilience , will talk about How we measure our services, By End2End testing which become one of the most critical Monitor tool in LP . Over 200K tests runs per day providing statistics and insights into the problem as they happen. Arik will go through different topics and stages of the journey and share details that led to current results . Part of the menu topics are : The Awakens of the End2End Insights • How we measure our services using synthetic user experience • Measuring through analytics & insights • How we collect our data • How we debug our services? Hint: video recording, HAR (Http archive), KIbana , Dashboard analytics & insights • Future logs App correlation with End2End data • Our tools: Selenium, Jenkins and cutting edge technologies such as Kafka & ELK (Elastic search, Logstash and Kibana) In this Meetup, Arik will host Ali AbuAli- NOC Team Leader , who will talk about the e2e usage on his day 2 day work.

Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.

What do fleet managers do? What are their duties, responsibilities, and challenges? And what makes a fleet manager effective and successful? This blog answers all these questions.

Enhance the top 9 user pain points with effective visual design elements to improve user experience & satisfaction. Learn the best design strategies

Are you wondering how to migrate to the Cloud? At the ITB session, we addressed the challenge of managing multiple ColdFusion licenses and AWS EC2 instances. Discover how you can consolidate with just one EC2 instance capable of running over 50 apps using CommandBox ColdFusion. This solution supports both ColdFusion flavors and includes cb-websites, a GoLang binary for managing CommandBox websites.

Unlock the full potential of mobile monitoring with ONEMONITAR. Our advanced and discreet app offers a comprehensive suite of features, including hidden call recording, real-time GPS tracking, message monitoring, and much more. Perfect for parents, employers, and anyone needing a reliable solution, ONEMONITAR ensures you stay informed and in control. Explore the key features of ONEMONITAR and see why it’s the trusted choice for Android device monitoring. Share this infographic to spread the word about the ultimate mobile spy app!

Unlock the full potential of your data by effortlessly migrating from PostgreSQL to Snowflake, the leading cloud data warehouse. This comprehensive guide presents an easy-to-follow 8-step process using Estuary Flow, an open-source data operations platform designed to simplify data pipelines. Discover how to seamlessly transfer your PostgreSQL data to Snowflake, leveraging Estuary Flow's intuitive interface and powerful real-time replication capabilities. Harness the power of both platforms to create a robust data ecosystem that drives business intelligence, analytics, and data-driven decision-making. Key Takeaways: 1. Effortless Migration: Learn how to migrate your PostgreSQL data to Snowflake in 8 simple steps, even with limited technical expertise. 2. Real-Time Insights: Achieve near-instantaneous data syncing for up-to-the-minute analytics and reporting. 3. Cost-Effective Solution: Lower your total cost of ownership (TCO) with Estuary Flow's efficient and scalable architecture. 4. Seamless Integration: Combine the strengths of PostgreSQL's transactional power with Snowflake's cloud-native scalability and data warehousing features. Don't miss out on this opportunity to unlock the full potential of your data. Read & Download this comprehensive guide now and embark on a seamless data journey from PostgreSQL to Snowflake with Estuary Flow! Try it Free: https://dashboard.estuary.dev/register

AWS Cloud Practitioner Essentials (Second Edition) (Arabic) Course Introduction.pdf

Mindfire Solutions specializes in DevOps services, facilitating digital transformation through streamlined software development and operational efficiency. Their expertise enhances collaboration, accelerates delivery cycles, and ensures scalability using cloud-native technologies. Mindfire Solutions empowers businesses to innovate rapidly and maintain competitive advantage in dynamic market landscapes.

Splunk Presentation