Covers how to use Avro to save records to disk. This can be used later to use Avro with Kafka Schema Registry. This provides background on Avro which gets used with Hadoop and Kafka.
Kafka Intro With Simple Java Producer ConsumersJean-Paul Azar
Introduction to Kafka streaming platform. Covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example. Lastly, we added some simple Java client examples for a Kafka Producer and a Kafka Consumer.
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Jean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.
Simplifying Distributed Transactions with Sagas in Kafka (Stephen Zoio, Simpl...confluent
Microservices are seen as the way to simplify complex systems, until you need to coordinate a transaction across services, and in that instant, the dream ends. Transactions involving multiple services can lead to a spaghetti web of interactions. Protocols such as two-phase commit come with complexity and performance bottlenecks. The Saga pattern involves a simplified transactional model. In sagas, a sequence of actions are executed, and if any action fails, a compensating action is executed for each of the actions that have already succeeded. This is particularly well suited to long-running and cross-microservice transactions. In this talk we introduce the new Simple Sagas library (https://github.com/simplesourcing/simplesagas). Built using Kafka streams, it provides a scalable fault tolerance event-based transaction processing engine. We walk through a use case of coordinating a sequence of complex financial transactions. We demonstrate the easy to use DSL, show how the system copes with failure, and discuss this overall approach to building scalable transactional systems in an event-driven streaming context.
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
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.
Kafka Intro With Simple Java Producer ConsumersJean-Paul Azar
Introduction to Kafka streaming platform. Covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example. Lastly, we added some simple Java client examples for a Kafka Producer and a Kafka Consumer.
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Jean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.
Simplifying Distributed Transactions with Sagas in Kafka (Stephen Zoio, Simpl...confluent
Microservices are seen as the way to simplify complex systems, until you need to coordinate a transaction across services, and in that instant, the dream ends. Transactions involving multiple services can lead to a spaghetti web of interactions. Protocols such as two-phase commit come with complexity and performance bottlenecks. The Saga pattern involves a simplified transactional model. In sagas, a sequence of actions are executed, and if any action fails, a compensating action is executed for each of the actions that have already succeeded. This is particularly well suited to long-running and cross-microservice transactions. In this talk we introduce the new Simple Sagas library (https://github.com/simplesourcing/simplesagas). Built using Kafka streams, it provides a scalable fault tolerance event-based transaction processing engine. We walk through a use case of coordinating a sequence of complex financial transactions. We demonstrate the easy to use DSL, show how the system copes with failure, and discuss this overall approach to building scalable transactional systems in an event-driven streaming context.
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
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.
Kafka Connect & Streams - the ecosystem around KafkaGuido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Apache Kafka vs RabbitMQ: Fit For Purpose / Decision TreeSlim Baltagi
Kafka as a streaming data platform is becoming the successor to traditional messaging systems such as RabbitMQ. Nevertheless, there are still some use cases where they could be a good fit. This one single slide tries to answer in a concise and unbiased way where to use Apache Kafka and where to use RabbitMQ. Your comments and feedback are much appreciated.
Kafka Tutorial - introduction to the Kafka streaming platformJean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka?
Introduction to Kafka streaming platform. Covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example. Lastly, we added some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have started to expand on the Java examples to correlate with the design discussion of Kafka. We have also expanded on the Kafka design section and added references.
Hello, kafka! (an introduction to apache kafka)Timothy Spann
Hello ApacheKafka
An Introduction to Apache Kafka with Timothy Spann and Carolyn Duby Cloudera Principal engineers.
We also demo Flink SQL, SMM, SSB, Schema Registry, Apache Kafka, Apache NiFi and Public Cloud - AWS.
Kafka and Avro with Confluent Schema RegistryJean-Paul Azar
Covers how to use Kafka/Avro to send Records with support for schema and Avro serialization. Covers how to use Avro with Kafka and the confluent Schema Registry.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra...HostedbyConfluent
Active-Active, Active-Passive, and stretch clusters are hallmark patterns that have been the gold standard in Apache Kafka® disaster recovery architectures for years. Moving to Kubernetes requires unpacking these patterns and choosing a configuration that allows you to meet the same RTO and RPO requirements.
In this talk, we will cover how Active-Active/Active-Passive modes for disaster recovery have worked in the past and how the architecture evolves with deploying Apache Kafka on Kubernetes. We'll also look at how stretch clusters sitting on this architecture give a disaster recovery solution that's built-in!
Armed with this information, you will be able to architect your new Apache Kafka Kubernetes deployment (or retool your existing one) to achieve the resilience you require.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Troubleshooting Kerberos in Hadoop: Taming the BeastDataWorks Summit
Kerberos is the ubiquitous authentication mechanism when it comes to secure any Hadoop Services. With recent updates in Hadoop core and various Apache Hadoop components, inherent Kerberos support has matured and has come a long way.
Understanding & configuring Kerberos is still a challenge but even more painful & frustrating is troubleshooting a Kerberos issue. There are lot of things (small & big) that can go wrong (and will go wrong!). This talk covers the Kerberos debugging part in detail and discusses the tools & tricks that can be used to narrow down any Kerberos issue.
Rather than discussing the issues and their resolution, we will focus on how to approach a Kerberos problem and do's / dont's in Kerberos scene. This talk will provide a step by step guide that will equip the audience for troubleshooting future Kerberos problems.
Agenda is to discuss:
- Systematic approach to Kerberos troubleshooting
- Kerberos Tools available in Hadoop arsenal
- Tips & Tricks to narrow down Kerberos issues quickly
- Some nasty Kerberos issues from Support trenches
Some prior knowledge on Kerberos basics will be appreciated but is not a prerequisite.
Speaker:
Vipin Rathor, Sr. Product Specialist (HDP Security), Hortonworks
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
Streaming all over the world Real life use cases with Kafka Streamsconfluent
Streaming all over the world Real life use cases with Kafka Streams, Dr. Benedikt Linse, Senior Solutions Architect, Confluent
https://www.meetup.com/Apache-Kafka-Germany-Munich/events/281819704/
Building Streaming Data Applications Using Apache KafkaSlim Baltagi
Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing.
In this talk you will learn more about:
1. A quick introduction to Kafka Core, Kafka Connect and Kafka Streams: What is and why?
2. Code and step-by-step instructions to build an end-to-end streaming data application using Apache Kafka
Kafka Tutorial - Introduction to Apache Kafka (Part 2)Jean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.
Kafka Tutorial: Streaming Data ArchitectureJean-Paul Azar
Kafka tutorial covers Java examples for Producers and Consumers. Also covers why Kafka is important and what Kafka is. Takes a look at the whole ecosystem around Kafka. Discusses low-level details about Kafka needed for successful deploys and performance tuning like batching, compression, partitioning, and replication.
Kafka Connect & Streams - the ecosystem around KafkaGuido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Apache Kafka vs RabbitMQ: Fit For Purpose / Decision TreeSlim Baltagi
Kafka as a streaming data platform is becoming the successor to traditional messaging systems such as RabbitMQ. Nevertheless, there are still some use cases where they could be a good fit. This one single slide tries to answer in a concise and unbiased way where to use Apache Kafka and where to use RabbitMQ. Your comments and feedback are much appreciated.
Kafka Tutorial - introduction to the Kafka streaming platformJean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka?
Introduction to Kafka streaming platform. Covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example. Lastly, we added some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have started to expand on the Java examples to correlate with the design discussion of Kafka. We have also expanded on the Kafka design section and added references.
Hello, kafka! (an introduction to apache kafka)Timothy Spann
Hello ApacheKafka
An Introduction to Apache Kafka with Timothy Spann and Carolyn Duby Cloudera Principal engineers.
We also demo Flink SQL, SMM, SSB, Schema Registry, Apache Kafka, Apache NiFi and Public Cloud - AWS.
Kafka and Avro with Confluent Schema RegistryJean-Paul Azar
Covers how to use Kafka/Avro to send Records with support for schema and Avro serialization. Covers how to use Avro with Kafka and the confluent Schema Registry.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra...HostedbyConfluent
Active-Active, Active-Passive, and stretch clusters are hallmark patterns that have been the gold standard in Apache Kafka® disaster recovery architectures for years. Moving to Kubernetes requires unpacking these patterns and choosing a configuration that allows you to meet the same RTO and RPO requirements.
In this talk, we will cover how Active-Active/Active-Passive modes for disaster recovery have worked in the past and how the architecture evolves with deploying Apache Kafka on Kubernetes. We'll also look at how stretch clusters sitting on this architecture give a disaster recovery solution that's built-in!
Armed with this information, you will be able to architect your new Apache Kafka Kubernetes deployment (or retool your existing one) to achieve the resilience you require.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Troubleshooting Kerberos in Hadoop: Taming the BeastDataWorks Summit
Kerberos is the ubiquitous authentication mechanism when it comes to secure any Hadoop Services. With recent updates in Hadoop core and various Apache Hadoop components, inherent Kerberos support has matured and has come a long way.
Understanding & configuring Kerberos is still a challenge but even more painful & frustrating is troubleshooting a Kerberos issue. There are lot of things (small & big) that can go wrong (and will go wrong!). This talk covers the Kerberos debugging part in detail and discusses the tools & tricks that can be used to narrow down any Kerberos issue.
Rather than discussing the issues and their resolution, we will focus on how to approach a Kerberos problem and do's / dont's in Kerberos scene. This talk will provide a step by step guide that will equip the audience for troubleshooting future Kerberos problems.
Agenda is to discuss:
- Systematic approach to Kerberos troubleshooting
- Kerberos Tools available in Hadoop arsenal
- Tips & Tricks to narrow down Kerberos issues quickly
- Some nasty Kerberos issues from Support trenches
Some prior knowledge on Kerberos basics will be appreciated but is not a prerequisite.
Speaker:
Vipin Rathor, Sr. Product Specialist (HDP Security), Hortonworks
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
Streaming all over the world Real life use cases with Kafka Streamsconfluent
Streaming all over the world Real life use cases with Kafka Streams, Dr. Benedikt Linse, Senior Solutions Architect, Confluent
https://www.meetup.com/Apache-Kafka-Germany-Munich/events/281819704/
Building Streaming Data Applications Using Apache KafkaSlim Baltagi
Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing.
In this talk you will learn more about:
1. A quick introduction to Kafka Core, Kafka Connect and Kafka Streams: What is and why?
2. Code and step-by-step instructions to build an end-to-end streaming data application using Apache Kafka
Kafka Tutorial - Introduction to Apache Kafka (Part 2)Jean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.
Kafka Tutorial: Streaming Data ArchitectureJean-Paul Azar
Kafka tutorial covers Java examples for Producers and Consumers. Also covers why Kafka is important and what Kafka is. Takes a look at the whole ecosystem around Kafka. Discusses low-level details about Kafka needed for successful deploys and performance tuning like batching, compression, partitioning, and replication.
Kafka Tutorial, Kafka ecosystem with clustering examplesJean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This Introduction to Kafka streaming platform covers Kafka Architecture with many small examples from the command line. Then we expand on this with a multi-server example. We walk you through Consumer failover. Then we walk you through clustering and Kafka Broker failover. It covers consumers, producers, and clustering basics.
Kafka Tutorial - basics of the Kafka streaming platformJean-Paul Azar
Introduction to Kafka streaming platform. Covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example. Lastly, we added some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have started to expand on the Java examples to correlate with the design discussion of Kafka. We have also expanded on the Kafka design section and added references.
Amazon AWS basics needed to run a Cassandra Cluster in AWSJean-Paul Azar
There is a lot of advice on how to configure a Cassandra cluster on AWS. Not every configuration meets every use case.
Best way to know how to deploy Cassandra on AWS is to know the basics of AWS. Part 1: We start covering AWS (as it applies to Cassandra). Later we go into detail with AWS Cassandra specifics.
Amazon Cassandra Basics & Guidelines for AWS/EC2/VPC/EBSJean-Paul Azar
A comprehensive guide to deploying and configuring Cassandra on AWS/EC2. This guide is accurate and up to date as of 2017. There is a lot of information out there, and some of it is old or just wrong. Examples come from working code. This guide covers Ec2MultiRegionSnitch and EC2Snitch, broadcast address, using KMS to encrypt EBS, SSL config which is required for Ec2MultiRegionSnitch, Ansible, SSH Config, and setting up bastions so you can deploy your cluster on private subnets (NatGateway, how to setup routes, security groups, etc.). We also cover multi-region, multi-DC Cassandra deployments using VPN. We include the advantages and set up for enhanced networking and cluster placement groups in EC2.
We even cover how to setup and use the new EBS elastic volumes and how they benefit Cassandra deploys on AWS. We also cover how to setup Cassandra with systemd, and how to enable CloudWatch monitoring for Cassandra and the Linux OS for metrics and log aggregation.
From NAT setup to how to configure the GC and which EC2 instances to pick, this is the most comprehensive guide to deploying Cassandra on AWS/EC2/VPC.
In this slide deck we show how to implement custom Kafka Serializer for Producer. We then show how failover works configuring when broker/topic config min.insync.replicas, and Producer config acks (0, 1, -1, none, leader, all).
Then tutorial show how to implement Kafka producer batching and compression. Then use Producer metrics API to see how batching and compression improves throughput. Then this tutorial covers using retires and timeouts, and tested that it works. It explains how the setup of max inflight messages and retry back off work and when to use and not use inflight messaging.
It goes on to who how to implement a ProducerInterceptor. Then lastly, it shows how to implement a custom Kafka partitioner to implement a priority queue for important records. Through many of the step by step examples, this tutorial shows how to use some of the Kafka tools to do replication verification, and inspect the topic partition leadership status.
Covers using Kafka MirrorMaker for disaster recovery, scaling reads, and to isolate mission critical clusters. Starts out with a description of MirrorMaker and how to use. Then walks through a thorough introduction and example. Step by Step.
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
** Apache Cassandra Certification Training: https://www.edureka.co/cassandra **
In this PPT, you will get a detailed introduction to NoSQL and Apache Cassandra Questions and Answers required to crack any Interview. Brush up your Knowledge of Cassandra, It's various database elements and how to configure the database.
Streaming Microservices With Akka Streams And Kafka StreamsLightbend
One of the most frequent questions that we get asked at Lightbend is “what’s the difference between Akka Streams and Kafka Streams?” After all, there is only a 1 letter difference between these two technologies, so how different could they be?
Well, as we see in this presentation, they are actually quite different. Both tools are part of the streaming Fast Data stack, but were created with entirely different technological approaches in mind. For example, While Akka Streams emerged as a dataflow-centric abstraction for the Akka Actor model, designed for general-purpose microservices, very low-latency event processing, and supports a wider class of application problems and third-party integrations via Alpakka, Kafka Streams is purpose-built for reading data from Kafka topics, processing it, and writing the results to new topics in a Kafka-centric way.
In this webinar by Dr. Dean Wampler, VP of Fast Data Engineering at Lightbend, we will:
* Discuss the strengths and weaknesses of Kafka Streams and Akka Streams for particular design needs in data-centric microservices
* Contrast them with Spark Streaming and Flink, which provide richer analytics over potentially huge data sets
* Help you map these streaming engines to your specific use cases, so you confidently pick the right ones for your jobs
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
MongoDB is one of the fastest growing NoSQL workloads on AWS due to its simplicity and scalability, and recent product additions by the AWS team have only improved those traits. In this session, we’ll talk about various AWS offerings and how they fit together with MongoDB -- including CloudFormation, Elastic MapReduce, Route53, Elastic Beanstalk, Elastic Load Balancing, and more -- and how they can be leveraged to enhance your MongoDB experience.
Similar to Avro Tutorial - Records with Schema for Kafka and Hadoop (20)
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Avro Tutorial - Records with Schema for Kafka and Hadoop
1. ™
Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
Avro
Avro Apache Avro Data
Serialization
2. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Apache Avro
❖ Data serialization system
❖ Data structures
❖ Binary data format
❖ Container file format to store persistent data
❖ RPC capabilities
❖ Does not require code generation to use
3. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Avro Schemas
❖ Supports schemas for defining data structure
❖ Serializing and deserializing data, uses schema
❖ File schema
❖ Avro files store data with its schema
❖ RPC Schema
❖ RPC protocol exchanges schemas as part of the
handshake
❖ Schemas written in JSON
4. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Avro compared to…
❖ Similar to Thrift, Protocol Buffers, JSON, etc.
❖ Does not require code generation
❖ Avro needs less encoding as part of the data since it
stores names and types in the schema
❖ It supports evolution of schemas.
5. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Avro Schema
Avro schema stored in src/main/avro by default.
6. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Code Generation
7. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Employee Code Generation
8. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Using Generated Avro class
9. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Writing employees to an
Avro File
10. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Reading employees From a
File
11. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Using GenericRecord
12. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Writing Generic Records
13. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Reading using Generic
Records
14. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Avro Schema Validation
15. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Avro supported types
❖ Records
❖ Arrays
❖ Enums
❖ Unions
❖ Maps
❖ Strings, Int, Boolean, Decimal, Timestamp, Date
16. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Fuller example Avro Schema
17. Cassandra / Kafka Support in EC2/AWS. Kafka Training, Kafka
Consulting
™
Avro
❖ Fast data serialization
❖ Supports data structures
❖ Supports Records, Maps, Array, and basic types
❖ You can use it direct or use Code Generation
❖ Read more
❖ Kafka Training
❖ Kafka Consulting
Editor's Notes
Apache Avro™ is a data serialization system.
Avro provides data structures, binary data format, container file format to store persistent data and RPC capabilities. Avro does not require code generation to use.
Integrates well with JavaScript, Python, Ruby and Java.
Avro data format is defined by Avro schemas. When deserializing data, the schema is used. Data is serialized based on the schema, and schema is sent with data. Avro data plus schema is fully self-describing.
When Avro files store data with its schema. Avro RPC is also based on schema. Part of the RPC protocol exchanges schemas as part of the handshake.
When Avro is used in RPC, the client and server exchange schemas in the connection handshake.
Avro schemas are written in JSON.
Avro is similar to Thrift, Protocol Buffers, JSON, etc. Avro does not require code generation. Avro needs less encoding as part of the data since it stores names and types in the schema. It supports evolution of schemas.
There are plugins for Maven and Gradle to generate code based on Avro schemas.
This gradle-avro-plugin is a Gradle plugin that uses Avro tools to do Java code generation for Apache Avro. This plugin supports Avro schema files (avsc), and Avro RPC IDL (avdl). For Kafka you only need avsc. Notice that we did not generate setter methods. This makes the instances somewhat immutable.
The plugin generates the files and puts them under build/generated-main-avro-java.
The Employee class has a constructor and has a builder.
The above shoes serializing an Employee list to disk. In Kafka, we will not be writing to disk directly.
We are just showing how so you have a way to test Avro serialization, which is helpful when debugging schema incompatibilities.
Note we create a DatumWriter, which converts Java instance into an in-memory serialized format. SpecificDatumWriter is used with generated classes like Employee.
DataFileWriter writes the serialized records to the employee.avro file.
The above deserializes employees from the employees.avro file.
Deserializing is similar to serializing but in reverse. We create a SpecificDatumReader to converts in-memory serialized items into instances of our generated Employee class.
The DatumReader reads records from the file by calling next.
Another way to read is using forEach as follows:
final DataFileReader<Employee> dataFileReader = new DataFileReader<>(file, empReader); dataFileReader.forEach(employeeList::add);
You can use a generic record instead of using generated code.
You can write to Avro files using Generic records as well.
You can read from Avro files using generic records as well.
Avro will validate the data types when it serializes and deserializes the data.
The document https://avro.apache.org/docs/current/spec.html#Protocol+Declaration describes all of the supported types.
The above has examples of default values, arrays, primitive types, Records within records, enums, and more.
