CCT is a project for the IT Project and Management exam's. It's an help for the employee to plan and organize the transfer and receive a refound in a short term. The principal operations are : Add transfer, Share on Telegram, View Details, Import Costs in way to semplify the PM's work. For develop this web app, I use Anaconda, Flask, Visual Studio and MySql.
Check Out our Rich Python Portfolio: Leaders in Python & DjangoZealous System
Zealous System is a top rated Python, Django web and application development company based in India. Specializing in Python Web Application Development for 10 Years. Zealous System python portfolio highlighting solutions that we have offered to our esteemed clients across the globe related to web technologies.
Event Streaming CTO Roundtable for Cloud-native Kafka ArchitecturesKai Wähner
Technical thought leadership presentation to discuss how leading organizations move to real-time architecture to support business growth and enhance customer experience. This is a forum to discuss use cases with your peers to understand how other digital-native companies are utilizing data in motion to drive competitive advantage.
Agenda:
- Data in Motion with Event Streaming and Apache Kafka
- Streaming ETL Pipelines
- IT Modernisation and Hybrid Multi-Cloud
- Customer Experience and Customer 360
- IoT and Big Data Processing
- Machine Learning and Analytics
Kafka Streams State Stores Being Persistentconfluent
Being Persistent: A Look Into Kafka Streams State Stores, Neil Buesing, Principal Solutions Architect, Rill Data
Meetup link: https://www.meetup.com/TwinCities-Apache-Kafka/events/284002062/
Building event-driven Microservices with Kafka EcosystemGuido Schmutz
This session will begin with a short recap of how we created systems over the past 20 years, up to the current idea of building systems, using a Microservices architecture. What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to integrate services with each each other in a Microservices Architecture? Or is it better to use a more loosely-coupled protocol? Answers to these and many other questions are provided. The talk will show how a distributed log (event hub) can help to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk shows the difference between a request-driven and event-driven communication and answers when to use which. It highlights how a modern stream processing system can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Stream me to the Cloud (and back) with Confluent & MongoDBconfluent
In this online talk, we’ll explore how and why companies are leveraging Confluent and MongoDB to modernize their architecture and leverage the scalability of the cloud and the velocity of streaming. Based upon a sample retail business scenario, we will explain how changes in an on-premise database are streamed via the Confluent Cloud to MongoDB Atlas and back.
Real-Time Market Data Analytics Using Kafka Streamsconfluent
(Lei Chen, Bloomberg, L.P.) Kafka Summit SF 2018
At Bloomberg, we are building a streaming platform with Apache Kafka, Kafka Streams and Spark Streaming to handle high volume, real-time processing with rapid derivative market data. In this talk, we’ll share the experience of how we utilize Kafka Streams Processor API to build pipelines that are capable of handling millions of market movements per second with ultra-low latency, as well as performing complex analytics like outlier detection, source confidence evaluation (scoring), arbitrage detection and other financial-related processing.
We’ll cover:
-Our system architecture
-Best practices of using the Processor API and State Store API
-Dynamic gap session implementation
-Historical data re-processing practice in KStreams app
-Chaining multiple KStreams apps with Spark Streaming job
Check Out our Rich Python Portfolio: Leaders in Python & DjangoZealous System
Zealous System is a top rated Python, Django web and application development company based in India. Specializing in Python Web Application Development for 10 Years. Zealous System python portfolio highlighting solutions that we have offered to our esteemed clients across the globe related to web technologies.
Event Streaming CTO Roundtable for Cloud-native Kafka ArchitecturesKai Wähner
Technical thought leadership presentation to discuss how leading organizations move to real-time architecture to support business growth and enhance customer experience. This is a forum to discuss use cases with your peers to understand how other digital-native companies are utilizing data in motion to drive competitive advantage.
Agenda:
- Data in Motion with Event Streaming and Apache Kafka
- Streaming ETL Pipelines
- IT Modernisation and Hybrid Multi-Cloud
- Customer Experience and Customer 360
- IoT and Big Data Processing
- Machine Learning and Analytics
Kafka Streams State Stores Being Persistentconfluent
Being Persistent: A Look Into Kafka Streams State Stores, Neil Buesing, Principal Solutions Architect, Rill Data
Meetup link: https://www.meetup.com/TwinCities-Apache-Kafka/events/284002062/
Building event-driven Microservices with Kafka EcosystemGuido Schmutz
This session will begin with a short recap of how we created systems over the past 20 years, up to the current idea of building systems, using a Microservices architecture. What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to integrate services with each each other in a Microservices Architecture? Or is it better to use a more loosely-coupled protocol? Answers to these and many other questions are provided. The talk will show how a distributed log (event hub) can help to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk shows the difference between a request-driven and event-driven communication and answers when to use which. It highlights how a modern stream processing system can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Stream me to the Cloud (and back) with Confluent & MongoDBconfluent
In this online talk, we’ll explore how and why companies are leveraging Confluent and MongoDB to modernize their architecture and leverage the scalability of the cloud and the velocity of streaming. Based upon a sample retail business scenario, we will explain how changes in an on-premise database are streamed via the Confluent Cloud to MongoDB Atlas and back.
Real-Time Market Data Analytics Using Kafka Streamsconfluent
(Lei Chen, Bloomberg, L.P.) Kafka Summit SF 2018
At Bloomberg, we are building a streaming platform with Apache Kafka, Kafka Streams and Spark Streaming to handle high volume, real-time processing with rapid derivative market data. In this talk, we’ll share the experience of how we utilize Kafka Streams Processor API to build pipelines that are capable of handling millions of market movements per second with ultra-low latency, as well as performing complex analytics like outlier detection, source confidence evaluation (scoring), arbitrage detection and other financial-related processing.
We’ll cover:
-Our system architecture
-Best practices of using the Processor API and State Store API
-Dynamic gap session implementation
-Historical data re-processing practice in KStreams app
-Chaining multiple KStreams apps with Spark Streaming job
More and more data sources today provide a constant data stream, from Internet of Things devices to Social Media streams. It is one thing to collect these events in the velocity they arrive, without losing any single message. An Event Hub and a data flow engine can help here. It’s another thing to do some (complex) analytics on the data. There is always the option to first store them in a data sink of choice, such as a data lake implemented with HDFS/object store, or in a database such as a NoSQL or even an RDBMS, if the volume of events is not too high. Storing a high-volume event stream is feasible and not such a challenge anymore. But doing it adds to the end-to-end latency and it’s a matter of minutes or hours until you can present some results of your analytics. If you need to react fast, you simply can't afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics directly on the data stream. This is called Stream Processing or Stream Analytics. In this talk I will present the important concepts, a Stream Processing solution should support and then dive into some of the most popular frameworks available on the market and how they compare.
Bridge to Cloud: Using Apache Kafka to Migrate to GCPconfluent
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
How to Quantify the Value of Kafka in Your Organization confluent
(Lyndon Hedderly, Confluent) Kafka Summit SF 2018
We all know real-time data has a value. But how do you quantify that value in order to create a business case for becoming more data, or event driven?
The first half of this talk will explore the value of data across a variety of organizations, starting with the five most valuable companies in the world: Apple, Alphabet (Google), Microsoft, Amazon and Facebook (based on stock prices July 2017). We will go on to discuss other digital natives: Uber, Ebay, Netflix and LinkedIn, before exploring more traditional companies across retail, finance and automotive. Next, we’ll look at non-businesses such as governments and lobbyists. Whether organizations are using data to create new business products and services, improve user experiences, increase productivity, manage risk or influencing global power, we’ll see that fast and interconnected data, or “event streaming” is increasingly important.
After showing that data value can be quantified, the second half of this talk will explain the five steps to creating a business case.
Most businesses focus on:
-Making more money or conferring competitive advantage to make more money
-Increasing efficiency to save money and/or
-Mitigating risk to the business to protect money
-We’ll walk through examples of real business cases, discuss how business cases have evolved over the years and show the power of a sound business case. If you’re interested in big money and big business, as well as big data, this talk is for you.
Kai Waehner [Confluent] | Real-Time Streaming Analytics with 100,000 Cars Usi...InfluxData
Kai Waehner [Confluent] | Real-Time Streaming Analytics with 100,000 Cars Using MQTT, Kafka and InfluxDB 2.0 on Kubernetes | InfluxDays Virtual Experience London 2020
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.
This talk will be about the reasons behind the new stream processing model, how it compare to the old batch model, what are their pros and cons, and a list of existing technologies implementing stream processing with their most prominent characteristics. It will contain details of one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map, beginning with an overview of all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform,showing a working demo implementation of it.
Data reply sneak peek: real time decision enginesconfluent
Events happen constantly in every business: a purchase in an online shop, a credit limit is hit, the mobile internet plan has been exhausted, users interact with a website. Events rule the business world. So why would you react to them hours or days later? Real-Time Decision Engines enable a variety of use cases, driving new products, increasing user experience, reducing costs and risks by reacting instantly to business events.
From personalized instantaneous marketing campaigns to reacting to user interactions, Real-Time is the key to open up a world of use cases that batch and scheduled processing cannot efficiently satisfy. In this talk, we are going to show some example use cases that Data Reply developed for some of its customers and how Real-Time Decision Engines had an impact on their businesses.
Top 5 Event Streaming Use Cases for 2021 with Apache KafkaKai Wähner
Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what the predicted TOP 5 Event Streaming Architectures and Use Cases for 2021 are? Check out the following presentation. Learn about edge deployments, hybrid and multi-cloud architectures, service mesh-based microservices, streaming machine learning, and cybersecurity.
On-demand video recording: https://videos.confluent.io/watch/XAjxV3j8hzwCcEKoZVErUJ
Event Broker (Kafka) in a Modern Data ArchitectureGuido Schmutz
Today's modern data architectures and the their implementations contain an Event Broker. What are the benefits of placing an Event Broker in a Modern Data (Analytics) Architecture? What exactly is an Event Broker and what capabilities should it provide? Why is Apache Kafka the most popular realisation of an Event Broker?
These and many other questions will be answered in this session. The talk will start with a vendor-neutral definition of the capabilities of an Event Broker.
Then the session will highlight the different architecture styles which can be supported using an Event Broker (Kafka), such as Streaming Data Integration, Stream Analytics and Decoupled Event-Driven Applications and how can these be combined into a unified architecture, making the Event Broker the central nervous system of an enterprise architecture. We will end with an overview of the Kafka ecosystem and a placement of the various components onto the Modern Data (Analytics) Architecture.
A guide through the Azure Messaging services - Update ConferenceEldert Grootenboer
https://www.updateconference.net/en/2019/session/a-guide-through-the-azure-messaging-services
A guide through the Azure Messaging services - Update Conference
Event: https://www.meetup.com/de-DE/Vienna-Kafka-meetup/events/262314643/
Speaker: Patrik Kleindl (patrik.kleindl@bearingpoint.com)
Slides of the introduction to Apache Kafka and some popular use cases.
Slides were provided by Confluent (confluent.io)
Use Apache Gradle to Build and Automate KSQL and Kafka Streams (Stewart Bryso...confluent
KSQL is an easy-to-use and easy-to-understand streaming SQL engine for Apache Kafka built on top of Kafka Streams. The ability to write streaming applications using only SQL makes Apache Kafka available to a whole range of new developers and potential use cases, either as a stand-alone solution, or as a single component to a broader Kafka Streams implementation. Inspired by a customer project now in production, experience the lifecycle of a streaming application developed using KSQL and Kafka Streams. With Apache Gradle as our build framework, we’ll explore the open-source Gradle plugin we built during this project to improve developer efficiency and automate the deployment of KSQL pipelines, user-defined functions, and Kafka Streams microservices.
We’ll demonstrate the deployment process live, and discuss design decisions around incorporating SQL-based processes into an overall streaming application.
Key Takeaways
1. KSQL is a natural choice for expressing data-driven applications, but it may not naturally fit into established DevOps processes and automations.
2. We built an open-source Gradle plugin to handle all aspects of deploying a Kafka-based streaming application: KSQL pipelines, KSQL user-defined functions, and Kafka Streams microservices.
3. KSQL pipelines can be deployed using either a server start script, or the KSQL REST API, and our Gradle plugin fully supports both options.
Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will dig into how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to
hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Bridge to Cloud: Using Apache Kafka to Migrate to AWSconfluent
Watch this talk here: https://www.confluent.io/online-talks/bridge-to-cloud-apache-kafka-migrate-aws
Speakers: Priya Shivakumar, Director of Product, Confluent + Konstantine Karantasis, Software Engineer, Confluent + Rohit Pujari, Partner Solutions Architect, AWS
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 AWS. By implementing a central-pipeline architecture using Apache Kafka to sync on-prem and cloud deployments, companies can accelerate migration times and reduce costs.
In this online talk we will cover:
•How to take the first step in migrating to AWS
•How to reliably sync your on premises applications using a persistent bridge to cloud
•Learn how Confluent Cloud can make this daunting task simple, reliable and performant
•See a demo of the hybrid-cloud and multi-region deployment of Apache Kafka
ChatGPT
The Big Data projects course includes five projects:
Data Engineering with PDF Summary Tool: Create a Streamlit app to summarize PDFs, comparing nougat and PyPDF libraries, and integrate architectural diagrams.
Large Language Models for SEC Document Summarization: Develop a tool for summarizing PDF documents, evaluating different libraries, and creating Jupyter notebooks and APIs for Streamlit integration.
Document Summarization with LLMs and RAG: Focus on automating embedding creation, data processing, and developing a client-facing application with secure login and search functionalities.
Data Engineering with Snowpark Python: Reproduce data pipeline steps, analyze datasets, design architectural diagrams, and integrate Streamlit with OpenAI for SQL query generation using natural language.
Project Redesign and Rearchitecture: Review existing architecture and redesign using open-source components and enterprise alternatives, focusing on flexible, scalable, and cost-effective solutions.
More and more data sources today provide a constant data stream, from Internet of Things devices to Social Media streams. It is one thing to collect these events in the velocity they arrive, without losing any single message. An Event Hub and a data flow engine can help here. It’s another thing to do some (complex) analytics on the data. There is always the option to first store them in a data sink of choice, such as a data lake implemented with HDFS/object store, or in a database such as a NoSQL or even an RDBMS, if the volume of events is not too high. Storing a high-volume event stream is feasible and not such a challenge anymore. But doing it adds to the end-to-end latency and it’s a matter of minutes or hours until you can present some results of your analytics. If you need to react fast, you simply can't afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics directly on the data stream. This is called Stream Processing or Stream Analytics. In this talk I will present the important concepts, a Stream Processing solution should support and then dive into some of the most popular frameworks available on the market and how they compare.
Bridge to Cloud: Using Apache Kafka to Migrate to GCPconfluent
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
How to Quantify the Value of Kafka in Your Organization confluent
(Lyndon Hedderly, Confluent) Kafka Summit SF 2018
We all know real-time data has a value. But how do you quantify that value in order to create a business case for becoming more data, or event driven?
The first half of this talk will explore the value of data across a variety of organizations, starting with the five most valuable companies in the world: Apple, Alphabet (Google), Microsoft, Amazon and Facebook (based on stock prices July 2017). We will go on to discuss other digital natives: Uber, Ebay, Netflix and LinkedIn, before exploring more traditional companies across retail, finance and automotive. Next, we’ll look at non-businesses such as governments and lobbyists. Whether organizations are using data to create new business products and services, improve user experiences, increase productivity, manage risk or influencing global power, we’ll see that fast and interconnected data, or “event streaming” is increasingly important.
After showing that data value can be quantified, the second half of this talk will explain the five steps to creating a business case.
Most businesses focus on:
-Making more money or conferring competitive advantage to make more money
-Increasing efficiency to save money and/or
-Mitigating risk to the business to protect money
-We’ll walk through examples of real business cases, discuss how business cases have evolved over the years and show the power of a sound business case. If you’re interested in big money and big business, as well as big data, this talk is for you.
Kai Waehner [Confluent] | Real-Time Streaming Analytics with 100,000 Cars Usi...InfluxData
Kai Waehner [Confluent] | Real-Time Streaming Analytics with 100,000 Cars Using MQTT, Kafka and InfluxDB 2.0 on Kubernetes | InfluxDays Virtual Experience London 2020
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.
This talk will be about the reasons behind the new stream processing model, how it compare to the old batch model, what are their pros and cons, and a list of existing technologies implementing stream processing with their most prominent characteristics. It will contain details of one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map, beginning with an overview of all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform,showing a working demo implementation of it.
Data reply sneak peek: real time decision enginesconfluent
Events happen constantly in every business: a purchase in an online shop, a credit limit is hit, the mobile internet plan has been exhausted, users interact with a website. Events rule the business world. So why would you react to them hours or days later? Real-Time Decision Engines enable a variety of use cases, driving new products, increasing user experience, reducing costs and risks by reacting instantly to business events.
From personalized instantaneous marketing campaigns to reacting to user interactions, Real-Time is the key to open up a world of use cases that batch and scheduled processing cannot efficiently satisfy. In this talk, we are going to show some example use cases that Data Reply developed for some of its customers and how Real-Time Decision Engines had an impact on their businesses.
Top 5 Event Streaming Use Cases for 2021 with Apache KafkaKai Wähner
Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what the predicted TOP 5 Event Streaming Architectures and Use Cases for 2021 are? Check out the following presentation. Learn about edge deployments, hybrid and multi-cloud architectures, service mesh-based microservices, streaming machine learning, and cybersecurity.
On-demand video recording: https://videos.confluent.io/watch/XAjxV3j8hzwCcEKoZVErUJ
Event Broker (Kafka) in a Modern Data ArchitectureGuido Schmutz
Today's modern data architectures and the their implementations contain an Event Broker. What are the benefits of placing an Event Broker in a Modern Data (Analytics) Architecture? What exactly is an Event Broker and what capabilities should it provide? Why is Apache Kafka the most popular realisation of an Event Broker?
These and many other questions will be answered in this session. The talk will start with a vendor-neutral definition of the capabilities of an Event Broker.
Then the session will highlight the different architecture styles which can be supported using an Event Broker (Kafka), such as Streaming Data Integration, Stream Analytics and Decoupled Event-Driven Applications and how can these be combined into a unified architecture, making the Event Broker the central nervous system of an enterprise architecture. We will end with an overview of the Kafka ecosystem and a placement of the various components onto the Modern Data (Analytics) Architecture.
A guide through the Azure Messaging services - Update ConferenceEldert Grootenboer
https://www.updateconference.net/en/2019/session/a-guide-through-the-azure-messaging-services
A guide through the Azure Messaging services - Update Conference
Event: https://www.meetup.com/de-DE/Vienna-Kafka-meetup/events/262314643/
Speaker: Patrik Kleindl (patrik.kleindl@bearingpoint.com)
Slides of the introduction to Apache Kafka and some popular use cases.
Slides were provided by Confluent (confluent.io)
Use Apache Gradle to Build and Automate KSQL and Kafka Streams (Stewart Bryso...confluent
KSQL is an easy-to-use and easy-to-understand streaming SQL engine for Apache Kafka built on top of Kafka Streams. The ability to write streaming applications using only SQL makes Apache Kafka available to a whole range of new developers and potential use cases, either as a stand-alone solution, or as a single component to a broader Kafka Streams implementation. Inspired by a customer project now in production, experience the lifecycle of a streaming application developed using KSQL and Kafka Streams. With Apache Gradle as our build framework, we’ll explore the open-source Gradle plugin we built during this project to improve developer efficiency and automate the deployment of KSQL pipelines, user-defined functions, and Kafka Streams microservices.
We’ll demonstrate the deployment process live, and discuss design decisions around incorporating SQL-based processes into an overall streaming application.
Key Takeaways
1. KSQL is a natural choice for expressing data-driven applications, but it may not naturally fit into established DevOps processes and automations.
2. We built an open-source Gradle plugin to handle all aspects of deploying a Kafka-based streaming application: KSQL pipelines, KSQL user-defined functions, and Kafka Streams microservices.
3. KSQL pipelines can be deployed using either a server start script, or the KSQL REST API, and our Gradle plugin fully supports both options.
Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will dig into how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to
hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Bridge to Cloud: Using Apache Kafka to Migrate to AWSconfluent
Watch this talk here: https://www.confluent.io/online-talks/bridge-to-cloud-apache-kafka-migrate-aws
Speakers: Priya Shivakumar, Director of Product, Confluent + Konstantine Karantasis, Software Engineer, Confluent + Rohit Pujari, Partner Solutions Architect, AWS
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 AWS. By implementing a central-pipeline architecture using Apache Kafka to sync on-prem and cloud deployments, companies can accelerate migration times and reduce costs.
In this online talk we will cover:
•How to take the first step in migrating to AWS
•How to reliably sync your on premises applications using a persistent bridge to cloud
•Learn how Confluent Cloud can make this daunting task simple, reliable and performant
•See a demo of the hybrid-cloud and multi-region deployment of Apache Kafka
ChatGPT
The Big Data projects course includes five projects:
Data Engineering with PDF Summary Tool: Create a Streamlit app to summarize PDFs, comparing nougat and PyPDF libraries, and integrate architectural diagrams.
Large Language Models for SEC Document Summarization: Develop a tool for summarizing PDF documents, evaluating different libraries, and creating Jupyter notebooks and APIs for Streamlit integration.
Document Summarization with LLMs and RAG: Focus on automating embedding creation, data processing, and developing a client-facing application with secure login and search functionalities.
Data Engineering with Snowpark Python: Reproduce data pipeline steps, analyze datasets, design architectural diagrams, and integrate Streamlit with OpenAI for SQL query generation using natural language.
Project Redesign and Rearchitecture: Review existing architecture and redesign using open-source components and enterprise alternatives, focusing on flexible, scalable, and cost-effective solutions.
Custom application development according to Oracle is primarily relevant for extending SaaS applications and creating customer experiences. The current recommended approach for building graphical user interface (on web and mobile) is through low code Visual Builder with high code JET injections when required. An alternative low code stack is available from Oracle in the form of APEX, This slide set discusses the above as well as ADF and Forms. It then introduces Digital Assistant, talks about the state and future of Java and concludes with CI/CD and DevOps. As presented on November 5th 2018 at AMIS HQ, Nieuwegein, The Netherlands.
The annual review session by the AMIS team on their findings, interpretations and opinions regarding news, trends, announcements and roadmaps around Oracle's product portfolio.
"Different software evolutions from Start till Release in PHP product" Oleksa...Fwdays
Ця розповідь розкриє підходи для вирішення багатьох проблем в PHP проєктах через: None-Breaking change development підхід, cross-stack контракти, Trunk Based development, еволюція з Polyrepo до Monorepo з компонентами на різних технологіях, Boilerplat’и компонентів, різні Architecture View, Continuous Testing & Quality, Infrastructure View, Infrastructure as a code як основний інструмент.
PHPFrameworkDay 2020 - Different software evolutions from Start till Release ...Alexandr Savchenko
https://fwdays.com/en/event/php-fwdays-2020
All of us think about many questions when we start a project, when we already have a product and when we release it. Here are some of them: which architecture and infrastructure to choose? what should be the repository structure? how to make the right evolution from one application to 100 microservices with success product release? how to distribute cross-stack commands as a whole? what development practices to use?
This story will expose approaches to solving these and many other problems in PHP projects through: None-Breaking change development approach, Cross-stack contacts, Trunk Based development, evolution from Polyrepo to Monorepo with components on different technologies, Boilerplates for components, different Architecture Views, Continuous Testing & Quality, Infrastructure View, Infrastructure as a code as the main tool.
This topic will appeal to everyone - from Software Developer to Architect, as many Tips & Tricks will be revealed.
Cloud Native Application Integration With APIsNirmal Fernando
Cloud native application architectures focus on building applications as microservices and running them on containers that run on dynamic orchestration platforms and utilize cloud computing functionalities. Agile DevOps and continuous delivery pipelines ensure agility and speed of application development and faster time to market. These systems follow a number of design principles to ensure they are built as loosely coupled services designed for cloud scale and performance.
A core design principle is the use of APIs for application integration. Underlying cloud orchestration layers provide certain functionalities for integration via APIs - be it RESTful or internal formats such as Protbuf, Thrift, gRPC, NATS, etc. APIs thus play an important role for both internal services communications as well as integration between composite apps. A cloud-native API gateway that also provides features of a full lifecycle API Management is key.
In this deep dive workshop, we look at the concepts of cloud-native app integration via APIs which utilize cloud-native API management. We focus on the architecture, design concepts followed by the implementation of API led microservices and then look at the runtime component which includes DevOps, CICD and hybrid clouds.
Building and deploying LLM applications with Apache AirflowKaxil Naik
Behind the growing interest in Generate AI and LLM-based enterprise applications lies an expanded set of requirements for data integrations and ML orchestration. Enterprises want to use proprietary data to power LLM-based applications that create new business value, but they face challenges in moving beyond experimentation. The pipelines that power these models need to run reliably at scale, bringing together data from many sources and reacting continuously to changing conditions.
This talk focuses on the design patterns for using Apache Airflow to support LLM applications created using private enterprise data. We’ll go through a real-world example of what this looks like, as well as a proposal to improve Airflow and to add additional Airflow Providers to make it easier to interact with LLMs such as the ones from OpenAI (such as GPT4) and the ones on HuggingFace, while working with both structured and unstructured data.
In short, this shows how these Airflow patterns enable reliable, traceable, and scalable LLM applications within the enterprise.
https://airflowsummit.org/sessions/2023/keynote-llm/
DevOps is focused on Agile development and in great demand.
GCP Supports DevOps in a manner similar to AWS.
Differences between API Gateway (CLI support and OpenAPI Support)
GCP uses a NGINX Proxy with Cloud Endpoints.
Rapid Web Development with Python for Absolute BeginnersFatih Karatana
This slide covers Python basics, Python key features, Web development basis, RESTful architecture key points, Agile Web Development, Python web framework basis and fundamentals.
Vertex AI: Pipelines for your MLOps workflowsMárton Kodok
In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated.
Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all standard resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner.
Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.
Enterprise guide to building a Data MeshSion Smith
Making Data Mesh simple, Open Source and available to all; without vendor lock-in, without complex tooling and to use an approach centered around ‘specifications’, existing tools and baking in a ‘domain’ model.
Continuous Lifecycle London 2018 Event KeynoteWeaveworks
Today it’s all about delivering velocity without compromising on quality, yet it’s becoming increasingly difficult for organisations to keep up with the challenges of current release management and traditional operations. The demand for developers to own the end-to-end delivery, including operational ownership, is increasing. A “you build it, you own it” development process requires tools that developers know and understand. So I’d like to introduce “GitOps”- an agile software lifecycle for modern applications.
In this session, I will discuss these industry challenges, including current CICD trends and how they’re converging with operations and monitoring. I’ll also illustrate the GitOps model, identify best practices and tools to use, and explain how you can benefit from adopting this methodology inherited from best practices going back 10-15 years.
SOLID Programming with Portable Class LibrariesVagif Abilov
Developers often don't pay attention to code portability until they need to target multiple platforms. However, large amount of non-portable code often hints about violation of clean code principles, so it is worth investigating which part of the source code base are platform-specific and for what reasons.
In this session we will give an overview of portable class libraries, show how to extract PCL components from a real-world application and go through typical challenges that are faced when writing portable code. We will present the original tool that analyzes assemblies for portability compliance and can be used as a guard to prevent mixing business logic with infrastructure-specific functionality. Finally we will demonstrate how PCLs help targeting platforms such as Windows Store, Android and iOS.
Cloud-Native Patterns for Data-Intensive ApplicationsVMware Tanzu
Are you interested in learning how to schedule batch jobs in container runtimes?
Maybe you’re wondering how to apply continuous delivery in practice for data-intensive applications? Perhaps you’re looking for an orchestration tool for data pipelines?
Questions like these are common, so rest assured that you’re not alone.
In this webinar, we’ll cover the recent feature improvements in Spring Cloud Data Flow. More specifically, we’ll discuss data processing use cases and how they simplify the overall orchestration experience in cloud runtimes like Cloud Foundry and Kubernetes.
Please join us and be part of the community discussion!
Presenters :
Sabby Anandan, Product Manager
Mark Pollack, Software Engineer, Pivotal
This report describes and detects the analysis pages performed on a data set provided by the site http://stat-computing.org/. Data comes from
Research and Innovative Technology Administration (RITA). Data includes 22 years from the year 1987 to the year 2007 with a total of 123 million
observations and 29 different variables. I highlight the main variables used with the related description.
CCT is a Web App developed for an IT Project Management exam. This Web App hasbeen developed using various tools including Python, MySQL, Flask, Anaconda, and VisualStudio Code. An app aimed at companies able to manage the transfers of a worker withinthe company, taking into account all the costs that he faces and allowing him to updatethem through this webApp. Finally, to make understanding easier, there was a graphicalinterface dedicated to understanding costs, transfers through plots.
CCT is a web app developed to help the project manager to have an overview of the transfers made by his team. It is a web app developed entirely in Python, HTML, css. I also used Flask to connect to the server.
CCT allowed to: add new transfers, show charts related to the types of costs and value, produce a PDF document to download, automatically calculate the sum of the costs made and look for new users on Github to cover missing skills.
SLEM is a modular and also embedded solution to manage warehouses’s issues. Is
is composed of a series of smaller apps developed for Android because of the needs
to use an open NFC protocol and a Barcode scanner.
It has been developed using Javascript, JQuery, HTML and CSS through the Apache
Cordova technology.
The business plan and the pitch for an idea to attend to Management and Control
system’s class exam.
The idea is to let a mirror to be smart, also introducing the augmented reality concept.
Value Proposition: CoolMi, the easiest way to buy brand new quality clothes, the
fastest to try them, the smartest to wear them.
Mission: Radiate the best in class clothes suitable to working-class customers through
a revolutionary shopping experience.
Vision: To attain market leadership through unmatched shopping experiences, best
in class clothes quality suitable for a group of customers thanks to a phenomenal
team that has the highest ethical and professional standards.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
4. CLOSING PHASE
It include:
• Completed project scope
• Customer acceptance document
• Lesson learned document
• Final project report
The planning phase starts when the project
charter is approved.
It include:
• Business requirements
• RTM
• RAM
• Quality plan
• Communication plan
• Project organisational structure
• Project schedule
• Project budget
• Work breakdown structure (WBS)
Project closure processes ensure the successful completion
of the projectPLANNING PHASE
5. Flask is considered more
Pythonic than the Django web
framework because in common
situations the equivalent Flask
web application is more explicit.
Flask uses multiple folders to have a general order of different
files:
Templates -> .html
Static -> assets -> .css, .img, .js
6. CODE
Create an instance of the Flask
class for the web app
When the script is executed, it
obtains the string main value
“__name__” will be the same as
“__main__” and the script will
be executed
7. PANDAS
Pandas is a Python package
providing fast, flexible, and
expressive data structures
designed to make working
with “relational” or “labeled”
data both easy and intuitive.
SERIES DATAFRAME
8. Series…
Dataframe…
Database…
SERIES
Series is a one-dimensional
labeled array capable of
holding any data type
(integers, strings, floating
point numbers, Python
objects, etc.). The axis
labels are collectively
referred to as the index
DATAFRAME
DataFrame is a 2-
dimensional labeled data
structure with columns of
potentially different types. You
can think of it like a
spreadsheet or SQL table, or
a dict of Series objects. It is
generally the most commonly
used pandas object.
DATABASE
Database is a collection of
data, structured to control
data management.
10. Matplotlib is a Python 2D plotting library which
produces publication quality figures in a variety of
hardcopy formats and interactive environments
across platforms. Matplotlib can be used in Python
scripts, the Python and IPython shells, the Jupyter
notebook, web application servers, and four graphical
user interface toolkits.
13. PyGithub is a Python library to use the Github API v3.
With it, you can manage your Github resources
(repositories, user profiles, organizations, etc.) from
Python scripts.
17. CODE conn: it connects to a specific
data base and returns an
object (conn)
cursor: uses the connection
object to manipulate the
query
.execute: is a function called
by the variable cursor to
perform the query
.commit: save all changes to
the current transaction
18. CCT
Check and Calculate Transfer
CCT stands for Check and Calculate Transfer is a web app that
was born to help and simplify the PM's work to calculate the
employee transfer's, to create the final report and to add the
transfers to assign to Github user's
Import Costs
View Details
Share and Link on
Telegram
Help PM
Add Transfer
Create automatically PDF