DC Spark bake off - Realtime TCP Packet Analysis using Spark and Azure Event Hubs
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Slides for my entry to the DC Apache Spark Meetup, Spark Bake Off. I built a demo of a distributed, real-time TCP packet analysis system with Apache Spark, Azure Event Hubs, and Power BI
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This document discusses Hopsworks, a Spark-as-a-Service platform built on Hops Hadoop. It provides:
- Secure multi-tenant Spark and Kafka clusters hosted on-premise using YARN.
- Project-based access control and quotas for storage and compute.
- Simplified development of secure Spark Streaming applications with Kafka using automatically distributed certificates.
- Support for Zeppelin notebooks, automated installation, and tools like DrElephant for job monitoring.
Databricks Meetup @ Los Angeles Apache Spark User Group
This document summarizes a presentation on Apache Spark and Spark Streaming. It provides an overview of Spark, describing it as an in-memory cluster computing framework. It then discusses Spark Streaming, explaining that it runs streaming computations as small batch jobs to provide low latency processing. Several use cases for Spark Streaming are presented, including from companies like Stratio, Pearson, Ooyala, and Sharethrough. The presentation concludes with a demonstration of Python Spark Streaming code.
On-premise Spark as a Service with YARN
On Premise Spark-as-a-Service on YARN provides Spark-as-a-service in Sweden using Hopsworks, which was built on Hops Hadoop and provides multi-tenant Spark/Kafka/Flink jobs as a service. Hopsworks uses X.509 certificates for authentication instead of Kerberos and provides project-based access control and quotas. It simplifies writing secure Spark Streaming applications with Kafka.
Seattle Spark Meetup Mobius CSharp API
Mobius talk in Seattle Spark Meetup (Feb 2106). Mobius adds C# language binding to Apache Spark, enabling the implementation of Spark driver code and data processing operations in C#. More info @ https://github.com/Microsoft/Mobius. Tweet to @MobiusForSpark.
Seattle spark-meetup-032317
The document discusses the design and implementation of Spark Streaming connectors for real-time data sources like Azure Event Hubs. It covers key aspects like connecting Event Hubs to Spark Streaming, designing the connector to minimize resource usage, ensuring fault tolerance through checkpointing and recovery, and managing message offsets and processing rates in a distributed manner. The connector design addresses challenges like long-running receivers, extra resource requirements, and data loss during failures. Lessons from the initial receiver-based approach informed the design of a more efficient solution.
Create a Varnish cluster in Kubernetes for Drupal caching - DrupalCon North A...
Varnish is a caching proxy usually used for high profile Drupal sites. However, configuring Varnish is not an easy task that requires a lot of work. It is even more difficult when it comes to creating a scalable cluster of Varnish nodes.
Fortunately, there is a solution. I’ve been working on kube-httpcache project (https://github.com/mittwald/kube-httpcache) that takes care of many things such as routing, scaling, broadcasting, config-reloading, etc...
If you need to run more than one instance of Varnish, this session is for you. You will learn how to:
* Launch a single instance of Varnish in Kubernetes.
* Configure Varnish for Drupal.
* Scale Varnish from 1 to N nodes as part of the cluster.
* Make your Varnish cluster resilient.
* Reload Varnish configs on the fly.
* Properly invalidate cache for multiple Varnish nodes.
This session requires some basic understanding of Docker and Kubernetes; however, I will provide some intro if you are new to it.
Join this session and enjoy!
Overview of Apache Flink: the 4G of Big Data Analytics Frameworks
This document provides an overview of Apache Flink and discusses why it is suitable for real-world streaming analytics. The document contains an agenda that covers how Flink is a multi-purpose big data analytics framework, why streaming analytics are emerging, why Flink is suitable for real-world streaming analytics, novel use cases enabled by Flink, who is using Flink, and where to go from here. Key points include Flink innovations like custom memory management, its DataSet API, rich windowing semantics, and native iterative processing. Flink's streaming features that make it suitable for real-world use include its pipelined processing engine, stream abstraction, performance, windowing support, fault tolerance, and integration with Hadoop.
Overview of Apache Fink: the 4 G of Big Data Analytics Frameworks
Slides of my talk at the Hadoop Summit Europe in Dublin, Ireland on April 13th, 2016. The talk introduces Apache Flink as both a multi-purpose Big Data analytics framework and real-world streaming analytics framework. It is focusing on Flink's key differentiators and suitability for streaming analytics use cases. It also shows how Flink enables novel use cases such as distributed CEP (Complex Event Processing) and querying the state by behaving like a key value data store.
Overview of Apache Fink: The 4G of Big Data Analytics Frameworks
This document provides an overview of Apache Flink and discusses why it is suitable for real-world streaming analytics. The document contains an agenda that covers how Flink is a multi-purpose big data analytics framework, why streaming analytics are emerging, why Flink is suitable for real-world streaming analytics, novel use cases enabled by Flink, who is using Flink, and where to go from here. Key points include Flink innovations like custom memory management, its DataSet API, rich windowing semantics, and native iterative processing. Flink's streaming features that make it suitable for real-world use include its pipelined processing engine, stream abstraction, performance, windowing support, fault tolerance, and integration with Hadoop.
Highlights and Challenges from Running Spark on Mesos in Production by Morri ...
This document discusses AppsFlyer's experience running Spark on Mesos in production for retention data processing and analytics. Key points include:
- AppsFlyer processes over 30 million installs and 5 billion sessions daily for retention reporting across 18 dimensions using Spark, Mesos, and S3.
- Challenges included timeouts and errors when using Spark's S3 connectors due to the eventual consistency of S3, which was addressed by using more robust connectors and configuration options.
- A coarse-grained Mesos scheduling approach was found to be more stable than fine-grained, though it has limitations like static core allocation that future Mesos improvements may address.
- Tuning jobs for coarse-
Teaching Apache Spark: Demonstrations on the Databricks Cloud Platform
Yao Yao Mooyoung Lee
https://github.com/yaowser/learn-spark/tree/master/Final%20project
https://www.youtube.com/watch?v=IVMbSDS4q3A
https://www.academia.edu/35646386/Teaching_Apache_Spark_Demonstrations_on_the_Databricks_Cloud_Platform
https://www.slideshare.net/YaoYao44/teaching-apache-spark-demonstrations-on-the-databricks-cloud-platform-86063070/
Apache Spark is a fast and general engine for big data analytics processing with libraries for SQL, streaming, and advanced analytics
Cloud Computing, Structured Streaming, Unified Analytics Integration, End-to-End Applications
H2O PySparkling Water
Michal Malohlava talks about the PySparkling Water package for Spark and Python users.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
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This document summarizes a presentation about bridging the gap between RESTful APIs and Linked Data using GitHub and SPARQL queries. It discusses how grlc maps GitHub repositories of SPARQL queries to Swagger API specifications and endpoints to provide RESTful access to Linked Data without having to code and maintain separate APIs. Features like content negotiation, pagination, caching and containerization are described to improve the usability and performance of the generated APIs. The presentation concludes by demonstrating how grlc allows flexible organization of SPARQL queries and separation of query curation from client applications.
Headaches and Breakthroughs in Building Continuous Applications
At SpotX, we have built and maintained a portfolio of Spark Streaming applications -- all of which process records in the millions per minute. From pure data ingestion, to ETL, to real-time reporting, to live customer-facing products and features, continuous applications are in our DNA. Come along with us as we outline our journey from square one to present in the world of Spark Streaming. We'll detail what we've learned about efficient processing and monitoring, reliability and stability, and long term support of a streaming app. Come learn from our mistakes, and leave with some handy settings and designs you can implement in your own streaming apps.
Running Apache Spark on Kubernetes: Best Practices and Pitfalls
Since initial support was added in Apache Spark 2.3, running Spark on Kubernetes has been growing in popularity
JConWorld_ Continuous SQL with Kafka and Flink
JConWorld: Continuous SQL with Kafka and Flink
In this talk, I will walk through how someone can setup and run continous SQL queries against Kafka topics utilizing Apache Flink. We will walk through creating Kafka topics, schemas and publishing data.
We will then cover consuming Kafka data, joining Kafka topics and inserting new events into Kafka topics as they arrive. This basic over view will show hands-on techniques, tips and examples of how to do this.
Tim Spann is the Principal Developer Advocate for Data in Motion @ Cloudera where he works with Apache Kafka, Apache Flink, Apache NiFi, Apache Iceberg, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science. https://www.datainmotion.dev/p/about-me.html https://dzone.com/users/297029/bunkertor.html
https://www.youtube.com/channel/UCDIDMDfje6jAvNE8DGkJ3_w?view_as=subscriber
Spark + AI Summit 2019: Headaches and Breakthroughs in Building Continuous Ap...
At SpotX, we have built and maintained a portfolio of Spark Streaming applications -- all of which process records in the millions per minute. From pure data ingestion, to ETL, to real-time reporting, to live customer-facing products and features, continuous applications are in our DNA. Come along with us as we outline our journey from square one to present in the world of Spark Streaming. We'll detail what we've learned about efficient processing and monitoring, reliability and stability, and long term support of a streaming app. Come learn from our mistakes, and leave with some handy settings and designs you can implement in your own streaming apps.
Presented by Landon Robinson and Jack Chapa
Developing apache spark jobs in .net using mobius
Slides used for the talk "Developing Apache Spark Jobs in .NET using Mobius" at dotnetfringe 20016 (http://lanyrd.com/2016/netfringe/sfcxpx).
Apache Spark is an open source data processing framework built for big data processing and analytics. Ease of programming and high performance relative to the traditional big data tools and platforms and a unified API to solve a diverse set of complex data problems drove the rapid adoption of Spark in the industry. Apache Spark APIs in Scala, Java, Python and R cater to a wide range of big data professionals and a variety of functional roles. Mobius is an open source project that aims to bring Spark's rich set of capabilities to the .NET community. Mobius project added C# as another first-class programming language for Apache Spark and currently supports RDD, DataFrame and Streaming API. With Mobius, developers can build Spark jobs in C# and reuse their existing .NET libraries with Apache Spark. Mobius is open-sourced at http://github.com/Microsoft/Mobius. This project has received great support from the .NET community and positive feedback from the Spark enthusiasts
Jump Start with Apache Spark 2.0 on Databricks
This document provides an agenda for a 3+ hour workshop on Apache Spark 2.x on Databricks. It includes introductions to Databricks, Spark fundamentals and architecture, new features in Spark 2.0 like unified APIs, and workshops on DataFrames/Datasets, Spark SQL, and structured streaming concepts. The agenda covers lunch and breaks and is divided into hour and half hour segments.
03 2014 Apache Spark Serving: Unifying Batch, Streaming, and RESTful Serving
We present Spark Serving, a new spark computing mode that enables users to deploy any Spark computation as a sub-millisecond latency web service backed by any Spark Cluster. Attendees will explore the architecture of Spark Serving and discover how to deploy services on a variety of cluster types like Azure Databricks, Kubernetes, and Spark Standalone. We will also demonstrate its simple yet powerful API for RESTful SparkSQL, SparkML, and Deep Network deployment with the same API as batch and streaming workloads. In addition, we will explore the "dual architecture": HTTP on Spark. This architecture converts any spark cluster into a distributed web client with the familiar and pipelinable SparkML API. These two contributions provide the fundamental spark communication primitives to integrate and deploy any computation framework into the Spark Ecosystem. We will explore how Microsoft has used this work to leverage Spark as a fault-tolerant microservice orchestration engine in addition to an ETL and ML platform. And will walk through two examples drawn from Microsoft's ongoing work on Cognitive Service composition, and unsupervised object detection for Snow Leopard recognition.
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Databricks Meetup @ Los Angeles Apache Spark User Group
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Overview of Apache Flink: the 4G of Big Data Analytics Frameworks
Overview of Apache Flink: the 4G of Big Data Analytics Frameworks
Overview of Apache Fink: the 4 G of Big Data Analytics Frameworks
Overview of Apache Fink: the 4 G of Big Data Analytics Frameworks
Overview of Apache Fink: The 4G of Big Data Analytics Frameworks
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Highlights and Challenges from Running Spark on Mesos in Production by Morri ...
Teaching Apache Spark: Demonstrations on the Databricks Cloud Platform
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grlc: Bridging the Gap Between RESTful APIs and Linked Data
grlc: Bridging the Gap Between RESTful APIs and Linked Data
Headaches and Breakthroughs in Building Continuous Applications
Headaches and Breakthroughs in Building Continuous Applications
Running Apache Spark on Kubernetes: Best Practices and Pitfalls
Running Apache Spark on Kubernetes: Best Practices and Pitfalls
Spark + AI Summit 2019: Headaches and Breakthroughs in Building Continuous Ap...
Spark + AI Summit 2019: Headaches and Breakthroughs in Building Continuous Ap...
03 2014 Apache Spark Serving: Unifying Batch, Streaming, and RESTful Serving
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Consistent toolbox talks are critical for maintaining workplace safety, as they provide regular opportunities to address specific hazards and reinforce safe practices.
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DC Spark bake off - Realtime TCP Packet Analysis using Spark and Azure Event Hubs
- 1. Washington DC Area Apache Spark Interactive Spark Bake-off Team Name: Silvio Fiorito Solution Title: Real-time Packet Analysis using Spark
- 2. Spark Bake-off Page: 2 Team Introductions Silvio Fiorito – Background in development and app security – Started working with Hadoop in 2012 – Started using Spark at v0.6 in early 2013 – Built a few prototypes for low-latency query services with Spark/Shark and then SparkSQL – Twitter: @granturing
- 3. Spark Bake-off Page: 3 Solution Overview Real-time TCP packet analysis of geographically distributed hosts – Must support high throughput from many hosts – 3 demo VMs ( 2 x Azure & 1 x AWS) Local Flume agent pushes events to Azure Event Hub Events are partitioned and persisted up to 7 days Spark Streaming app ingests streams – Reconstruct packets – Lookups for geo-ip and port description – Clusters using pre-trained k-means model – Saves data to Azure Table Storage and publishes on Service Bus Topic
- 4. Spark Bake-off Page: 4 Solution Overview
- 5. Spark Bake-off Page: 5 Sample Dashboard with Power BI
- 6. Spark Bake-off Page: 6 Final Comments & Questions With more time – Add true anomaly detection with MLLib – Test on hosts with real traffic – Wire up end-to-end with d3.js viz and SparkSQL backend – Integrate with existing IDS/IPS rules – Bad IPs lookup