Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaHelena Edelson
Scala Days, Amsterdam, 2015: Lambda Architecture - Batch and Streaming with Spark, Cassandra, Kafka, Akka and Scala; Fault Tolerance, Data Pipelines, Data Flows, Data Locality, Akka Actors, Spark, Spark Cassandra Connector, Big Data, Asynchronous data flows. Time series data, KillrWeather, Scalable Infrastructure, Partition For Scale, Replicate For Resiliency, Parallelism
Isolation, Data Locality, Location Transparency
10 different Cassandra distributions and variants ranging from Cassandra / Cassandra Compliant Databases on JVM, Cassandra Compliant Databases on C++, Cassandra as a Service / Managed Cassandra Based on Open Source Cassandra, and Cassandra as a Service / Managed Cassandra Based on Proprietary Technology.
Feeding Cassandra with Spark-Streaming and KafkaDataStax Academy
In this session we will examine a sample application that simulates an IoT stream that is handled through Kafka, Spark Streaming, and into Cassandra. The session will discuss the implementation details including the Kafka design considerations, Spark Steaming functionality including working with windowing to achieve analytics and finally Cassandra Time series data model considerations. The example is based on OSS Kafka and Integrated Spark and Cassandra in DSE.
Apache Cassandra Lunch #78: Deploy Cassandra using DSE Operator to KubernetesAnant Corporation
In Cassandra Lunch #78, we will deploy Cassandra using DSE Operator to Kubernetes
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-78-cass-operator/
Accompanying YouTube: https://youtu.be/Cfvks4WBtKk
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaHelena Edelson
Scala Days, Amsterdam, 2015: Lambda Architecture - Batch and Streaming with Spark, Cassandra, Kafka, Akka and Scala; Fault Tolerance, Data Pipelines, Data Flows, Data Locality, Akka Actors, Spark, Spark Cassandra Connector, Big Data, Asynchronous data flows. Time series data, KillrWeather, Scalable Infrastructure, Partition For Scale, Replicate For Resiliency, Parallelism
Isolation, Data Locality, Location Transparency
10 different Cassandra distributions and variants ranging from Cassandra / Cassandra Compliant Databases on JVM, Cassandra Compliant Databases on C++, Cassandra as a Service / Managed Cassandra Based on Open Source Cassandra, and Cassandra as a Service / Managed Cassandra Based on Proprietary Technology.
Feeding Cassandra with Spark-Streaming and KafkaDataStax Academy
In this session we will examine a sample application that simulates an IoT stream that is handled through Kafka, Spark Streaming, and into Cassandra. The session will discuss the implementation details including the Kafka design considerations, Spark Steaming functionality including working with windowing to achieve analytics and finally Cassandra Time series data model considerations. The example is based on OSS Kafka and Integrated Spark and Cassandra in DSE.
Apache Cassandra Lunch #78: Deploy Cassandra using DSE Operator to KubernetesAnant Corporation
In Cassandra Lunch #78, we will deploy Cassandra using DSE Operator to Kubernetes
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-78-cass-operator/
Accompanying YouTube: https://youtu.be/Cfvks4WBtKk
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Muvr is a real-time personal trainer system. It must be highly available, resilient and responsive, and so it relies on heavily on Spark, Mesos, Akka, Cassandra, and Kafka—the quintuple also known as the SMACK stack. In this talk, we are going to explore the architecture of the entire muvr system, exploring, in particular, the challenges of ingesting very large volume of data, applying trained models on the data to provide real-time advice to our users, and training & evaluating new models using the collected data. We will specifically emphasize on how we have used Cassandra for consuming lots of fast incoming biometric data from devices and sensors, and how to securely access the big data sets from Cassandra in Spark to compute the models.
We will finish by showing the mechanics of deploying such a distributed application. You will get a clear understanding of how Mesos, Marathon, in conjunction with Docker, is used to build an immutable infrastructure that allows us to provide reliable service to our users and a great environment for our engineers.
Erkki Suurna, DW Developer at Pipedrive
Our AWS experience including following topics (but not limited to this list):
- VPC architecture
- S3 and Redshift ( + data visualization layer on top of it)
- Event collector (event producing, Kinesis event buffer, event consuming)
- Hadoop and Spark stack (HDFS, S3, Apache Spark, Apache Zeppelin)
Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time PersonalizationPatrick Di Loreto
The gambling industry has arguably been one of the most comprehensively affected by the internet revolution, and if an organization such as William Hill hadn't adapted successfully it would have disappeared. We call this, “Going Reactive.”
The company's latest innovations are very cutting edge platforms for personalization, recommendation, and big data, which are based on Akka, Scala, Play Framework, Kafka, Cassandra, Spark, and Mesos.
Typesafe & William Hill: Cassandra, Spark, and Kafka - The New Streaming Data...DataStax Academy
Typesafe did a survey of Spark usage last year and found that a large percentage of Spark users combine it with Cassandra and Kafka. This talk focuses on streaming data scenarios that demonstrate how these three tools complement each other for building robust, scalable, and flexible data applications. Cassandra provides resilient and scalable storage, with flexible data format and query options. Kafka provides durable, scalable collection of streaming data with message-queue semantics. Spark provides very flexible analytics, everything from classic SQL queries to machine learning and graph algorithms, running in a streaming model based on "mini-batches", offline batch jobs, or interactive queries. We'll consider best practices and areas where improvements are needed.
Cisco UCS Integrated Infrastructure for Big Data with CassandraDataStax Academy
With growing popularity of big data, it becomes imperative for enterprises to adopt the right platform for their workload, with efficient and user-friendly management of large scale clusters. In this session we will explore Cisco's revolutionary innovations that deliver leading-edge infrastructure, well suited for Cassandra like data base platforms, purpose built for performance and scalability. This enables our customers to unlock the intelligence in their data. Not only this provides a sustainable competitive advantage to their business, but also scales with their growing business needs.
Migrating from a Relational Database to Cassandra: Why, Where, When and HowAnant Corporation
Everything you need to know about moving from a relational database to Cassandra.
You may be very familiar with what Cassandra is, or the name might just be a buzzword you've heard used when discussing databases. Regardless of your familiarity with Cassandra, this database should be the first tool you consider when you need scalability and high availability without compromising performance.
Muvr is a real-time personal trainer system. It must be highly available, resilient and responsive, and so it relies on heavily on Spark, Mesos, Akka, Cassandra, and Kafka—the quintuple also known as the SMACK stack. In this talk, we are going to explore the architecture of the entire muvr system, exploring, in particular, the challenges of ingesting very large volume of data, applying trained models on the data to provide real-time advice to our users, and training & evaluating new models using the collected data. We will specifically emphasize on how we have used Cassandra for consuming lots of fast incoming biometric data from devices and sensors, and how to securely access the big data sets from Cassandra in Spark to compute the models.
We will finish by showing the mechanics of deploying such a distributed application. You will get a clear understanding of how Mesos, Marathon, in conjunction with Docker, is used to build an immutable infrastructure that allows us to provide reliable service to our users and a great environment for our engineers.
Erkki Suurna, DW Developer at Pipedrive
Our AWS experience including following topics (but not limited to this list):
- VPC architecture
- S3 and Redshift ( + data visualization layer on top of it)
- Event collector (event producing, Kinesis event buffer, event consuming)
- Hadoop and Spark stack (HDFS, S3, Apache Spark, Apache Zeppelin)
Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time PersonalizationPatrick Di Loreto
The gambling industry has arguably been one of the most comprehensively affected by the internet revolution, and if an organization such as William Hill hadn't adapted successfully it would have disappeared. We call this, “Going Reactive.”
The company's latest innovations are very cutting edge platforms for personalization, recommendation, and big data, which are based on Akka, Scala, Play Framework, Kafka, Cassandra, Spark, and Mesos.
Typesafe & William Hill: Cassandra, Spark, and Kafka - The New Streaming Data...DataStax Academy
Typesafe did a survey of Spark usage last year and found that a large percentage of Spark users combine it with Cassandra and Kafka. This talk focuses on streaming data scenarios that demonstrate how these three tools complement each other for building robust, scalable, and flexible data applications. Cassandra provides resilient and scalable storage, with flexible data format and query options. Kafka provides durable, scalable collection of streaming data with message-queue semantics. Spark provides very flexible analytics, everything from classic SQL queries to machine learning and graph algorithms, running in a streaming model based on "mini-batches", offline batch jobs, or interactive queries. We'll consider best practices and areas where improvements are needed.
Cisco UCS Integrated Infrastructure for Big Data with CassandraDataStax Academy
With growing popularity of big data, it becomes imperative for enterprises to adopt the right platform for their workload, with efficient and user-friendly management of large scale clusters. In this session we will explore Cisco's revolutionary innovations that deliver leading-edge infrastructure, well suited for Cassandra like data base platforms, purpose built for performance and scalability. This enables our customers to unlock the intelligence in their data. Not only this provides a sustainable competitive advantage to their business, but also scales with their growing business needs.
Migrating from a Relational Database to Cassandra: Why, Where, When and HowAnant Corporation
Everything you need to know about moving from a relational database to Cassandra.
You may be very familiar with what Cassandra is, or the name might just be a buzzword you've heard used when discussing databases. Regardless of your familiarity with Cassandra, this database should be the first tool you consider when you need scalability and high availability without compromising performance.
Containers, DevOps, Apache Mesos and Cloud - Reshaping how we develop and del...Marcelo Sousa Ancelmo
Presentation made at DevOpsDays Berlin 2015
Container technology are being evaluated by software developers and administrators with a great deal of interest. Developers want to focus on what they do best: Creating and coding new applications. That shouldn't have to change just because they need to deploy an application to a different environment. Administrators want the environment to stay reliable and stable, keeping changes at a minimum. By following a strategy that embraces good Architecture, use of Containers, DevOps philosophy, Apache Mesos and a Cloud based environment, developers and operators can create, consume and collaborate on the infrastructure configuration over the time, deploy Java EE applications and test your application infrastructure consistently regardless of the stage of the development life cycle.
Mario Cartia - SMACK is the new LAMP! - Codemotion Milan 2017Codemotion
SMACK è l'acronimo di Spark, Mesos, Akka, Cassandra e Kafka. Il titolo del talk "provocatoriamente" confronta lo stack di tecnologie per lo sviluppo di applicazioni Reactive con quello più comunemente utilizzato nell'ambito dello sviluppo web. Durante il talk verranno illustrati i concetti di base della Reactive programming, le differenze concettuali introdotte da questo paradigma rispetto all'approccio "classico" della programmazione web ed alcuni casi di successo legati all'utilizzo di queste tecnologie.
Containers, DevOps, Apache Mesos and Cloud - Reshaping how we develop and del...Marcelo Sousa Ancelmo
Presentation made at ApacheCon: Core Europe 2015
Container technology are being evaluated by software developers and administrators with a great deal of interest. Developers want to focus on what they do best: Creating and coding new applications. That shouldn't have to change just because they need to deploy an application to a different environment. Administrators want the environment to stay reliable and stable, keeping changes at a minimum. By following a strategy that embraces good Architecture, use of Containers, DevOps philosophy, Apache Mesos and a Cloud based environment, developers and operators can create, consume and collaborate on the infrastructure configuration over the time, deploy Java EE applications and test your application infrastructure consistently regardless of the stage of the development life cycle.
A brief into into using Apache Cassandra with Kubernetes. We also covered Docker, Docker Swarm, DC/OS and some open source tools to help you get started.
Apache Mesos is the first open source cluster manager that handles the workload efficiently in a distributed environment through dynamic resource sharing and isolation.
Microservices, Monoliths, SOA and How We Got HereLightbend
The Enterprise Architect’s Intro to Microservices - Part 1 of 3
**Find upcoming webinar details here: https://www.lightbend.com/community#filter:webinar**
If you’re tired of battling a monolithic enterprise system that’s difficult to scale and maintain––and even harder to understand––then this webinar series is for you. In these three expert sessions, we go over the details of why a microservice-based architecture that consists of small, independent services is far more flexible than the traditional all-in-one systems that continue to dominate today’s enterprise landscape.
In Part 1, Enterprise Advocate Kevin Webber will review a bit of history of application development, from the early days of monoliths and SOA to the emergence of Microservice architectures. We will review the drawbacks of heritage architectures and how the principles of Reactive can help you build isolated services that are scalable, resilient to failure, and combine with other services to form a cohesive whole.
In the next two webinars, we go deeper into the characteristics of Reactive Microservices, and the considerations how to build complete systems, presented by Lightbend CTO and Akka creator, Jonas Bonér.
Powering NLU Engine with Apache Spark to Communicate with the WorldRahul Kumar
Building natural language processing engines is really a complex work. It requires an architecture where we glue many algorithms, data processing, and data storage techniques together to solve a single most important problem—how effectively we can understand users query in the form of text, voice, or visual gestures fast and effectively and respond them with zero error. Identifying the best tools available in the market and how we can fit these tools and libraries in our pipelines makes a great edge to build these systems.
Reactive app using actor model & apache sparkRahul Kumar
Developing Application with Big Data is really challenging work, scaling, fault tolerance and responsiveness some are the biggest challenge. Realtime bigdata application that have self healing feature is a dream these days. Apache Spark is a fast in-memory data processing system that gives a good backend for realtime application.In this talk I will show how to use reactive platform, Actor model and Apache Spark stack to develop a system that have responsiveness, resiliency, fault tolerance and message driven feature.
Reactive dashboard’s using apache sparkRahul Kumar
Apache Spark's Tutorial talk, In this talk i explained how to start working with Apache spark, feature of apache spark and how to compose data platform with spark. This talk also explains about reactive platform, tools and framework like Play, akka.
2. “A distributed system is a collection of independent
computers that appears to its
users as a single coherent system.”
3. A distributed system application works independently
and communication through messages.
q Resource Sharing
q Openness
q Concurrency
q Scalability
q Fault Tolerance
q Transparency
4. Mesos Intro
“Apache Mesos abstracts CPU, memory, storage,
and other compute resources away from machines
(physical or virtual), enabling fault-tolerant and
elastic distributed systems to easily be built and
run effectively.”