We will talk about how we are migrating our Presto clusters from AWS EMR to Docker using production-grade orchestrators
considering cluster management, configuration and monitoring. We will discuss between Hashicorp Nomad and Kubernetes as a base solution
#ApacheKafkaTLV: Building distributed, fault-tolerant processing apps with Kafka Streams - use case
The second part of the #2 Meetup, delivered by Anatoly Tichonov - Mentory. Hosted by WeWork Sarona TLV,
It's Time To Stop Using Lambda Architecture | Yaroslav Tkachenko, ShopifyHostedbyConfluent
Lambda Architecture has been a common way to build data pipelines for a long time, despite difficulties in maintaining two complex systems. An alternative, Kappa Architecture, was proposed in 2014, but many companies are still reluctant to switch to Kappa. And there is a reason for that: even though Kappa generally provides a simpler design and similar or lower latency, there are a lot of practical challenges in areas like exactly-once delivery, late-arriving data, historical backfill and reprocessing.
In this talk, I want to show how you can solve those challenges by embracing Apache Kafka as a foundation of your data pipeline and leveraging modern stream-processing frameworks like Apache Kafka Streams and Apache Flink.
Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder.
Real time infrastructure powers critical pieces of Uber (think Surge) and in this talk we will discuss our architecture, technical challenges, learnings and how a blend of open source infrastructure (Apache Kafka and Samza) and in-house technologies have helped Uber scale.
Analyzing Petabyte Scale Financial Data with Apache Pinot and Apache Kafka | ...HostedbyConfluent
At Stripe, we operate a general ledger modeled as double-entry bookkeeping for all financial transactions. Warehousing such data is challenging due to its high volume and high cardinality of unique accounts.
aFurthermore, it is financially critical to get up-to-date, accurate analytics over all records. Due to the changing nature of real time transactions, it is impossible to pre-compute the analytics as a fixed time series. We have overcome the challenge by creating a real time key-value store inside Pinot that can sustain half million QPS with all the financial transactions.
We will talk about the details of our solution and the interesting technical challenges faced.
The need for gleaning answers from data in real-time is moving from nicety to a necessity. There are few options to analyze the never-ending stream of unbounded data at scale. Let’s compare and contrast the core principles and technologies the different open source solutions available to help with this endeavor, and where in the future processing engines need to evolve to solve processing needs at scale. These findings are based on the experience of continuing to build a scalable solution in the cloud to process over 700 billion events at Netflix, and how we are embarking on the next journey to evolve unbounded data processing engines.
Kafka Tiered Storage | Satish Duggana and Sriharsha Chintalapani, UberHostedbyConfluent
Kafka is a vital part of data infrastructure in many organizations. When the Kafka cluster grows and more data is stored in Kafka for a longer duration, several issues related to scalability, efficiency, and operations become important to address. Kafka cluster storage is typically scaled by adding more broker nodes to the cluster. But this also adds needless memory and CPUs to the cluster making overall storage cost less efficient compared to storing the older data in external storage.
Tiered storage is introduced to extend Kafka's storage beyond the local storage available on the Kafka cluster by retaining the older data in cheaper stores, such as HDFS, S3, Azure or GCS with minimal impact on the internals of Kafka.
We will talk about
- How tiered storage addresses the above problems and also brings several other advantages.
- High level architecture of tiered storage
- Future work planned as part of tiered storage.
Beaming flink to the cloud @ netflix ff 2016-monal-daxiniMonal Daxini
Netflix is a data driven company and we process over 700 billion streaming events per day with at-least once processing semantics in the cloud. To enable extracting intelligence from this unbounded stream easily we are building Stream Processing as a Service (SPaaS) infrastructure so that the user can focus on extracting value and not have to worry about boilerplate infrastructure and scale.
We will share our experience in building a scalable SPaaS using Flink, Apache Beam and Kafka as the foundation layer to process over 1.3 PB of event data without service disruption.
#ApacheKafkaTLV: Building distributed, fault-tolerant processing apps with Kafka Streams - use case
The second part of the #2 Meetup, delivered by Anatoly Tichonov - Mentory. Hosted by WeWork Sarona TLV,
It's Time To Stop Using Lambda Architecture | Yaroslav Tkachenko, ShopifyHostedbyConfluent
Lambda Architecture has been a common way to build data pipelines for a long time, despite difficulties in maintaining two complex systems. An alternative, Kappa Architecture, was proposed in 2014, but many companies are still reluctant to switch to Kappa. And there is a reason for that: even though Kappa generally provides a simpler design and similar or lower latency, there are a lot of practical challenges in areas like exactly-once delivery, late-arriving data, historical backfill and reprocessing.
In this talk, I want to show how you can solve those challenges by embracing Apache Kafka as a foundation of your data pipeline and leveraging modern stream-processing frameworks like Apache Kafka Streams and Apache Flink.
Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder.
Real time infrastructure powers critical pieces of Uber (think Surge) and in this talk we will discuss our architecture, technical challenges, learnings and how a blend of open source infrastructure (Apache Kafka and Samza) and in-house technologies have helped Uber scale.
Analyzing Petabyte Scale Financial Data with Apache Pinot and Apache Kafka | ...HostedbyConfluent
At Stripe, we operate a general ledger modeled as double-entry bookkeeping for all financial transactions. Warehousing such data is challenging due to its high volume and high cardinality of unique accounts.
aFurthermore, it is financially critical to get up-to-date, accurate analytics over all records. Due to the changing nature of real time transactions, it is impossible to pre-compute the analytics as a fixed time series. We have overcome the challenge by creating a real time key-value store inside Pinot that can sustain half million QPS with all the financial transactions.
We will talk about the details of our solution and the interesting technical challenges faced.
The need for gleaning answers from data in real-time is moving from nicety to a necessity. There are few options to analyze the never-ending stream of unbounded data at scale. Let’s compare and contrast the core principles and technologies the different open source solutions available to help with this endeavor, and where in the future processing engines need to evolve to solve processing needs at scale. These findings are based on the experience of continuing to build a scalable solution in the cloud to process over 700 billion events at Netflix, and how we are embarking on the next journey to evolve unbounded data processing engines.
Kafka Tiered Storage | Satish Duggana and Sriharsha Chintalapani, UberHostedbyConfluent
Kafka is a vital part of data infrastructure in many organizations. When the Kafka cluster grows and more data is stored in Kafka for a longer duration, several issues related to scalability, efficiency, and operations become important to address. Kafka cluster storage is typically scaled by adding more broker nodes to the cluster. But this also adds needless memory and CPUs to the cluster making overall storage cost less efficient compared to storing the older data in external storage.
Tiered storage is introduced to extend Kafka's storage beyond the local storage available on the Kafka cluster by retaining the older data in cheaper stores, such as HDFS, S3, Azure or GCS with minimal impact on the internals of Kafka.
We will talk about
- How tiered storage addresses the above problems and also brings several other advantages.
- High level architecture of tiered storage
- Future work planned as part of tiered storage.
Beaming flink to the cloud @ netflix ff 2016-monal-daxiniMonal Daxini
Netflix is a data driven company and we process over 700 billion streaming events per day with at-least once processing semantics in the cloud. To enable extracting intelligence from this unbounded stream easily we are building Stream Processing as a Service (SPaaS) infrastructure so that the user can focus on extracting value and not have to worry about boilerplate infrastructure and scale.
We will share our experience in building a scalable SPaaS using Flink, Apache Beam and Kafka as the foundation layer to process over 1.3 PB of event data without service disruption.
___________________________________________
Meetup#7 | Session 2 | 21/03/2018 | Taboola
_____________________________________________
In this talk, we will present our multi-DC Kafka architecture, and discuss how we tackle sending and handling 10B+ messages per day, with maximum availability and no tolerance for data loss.
Our architecture includes technologies such as Cassandra, Spark, HDFS, and Vertica - with Kafka as the backbone that feeds them all.
Presented at Stream Processing Meetup (7/19/2018)(https://www.meetup.com/Stream-Processing-Meetup-LinkedIn/events/251481797/).
At Uber, we operate 20+ Kafka clusters to collect system and application logs as well as event data from rider and driver apps. We need a Kafka replication solution to replicate data between Kafka clusters across multiple data centers for different purposes. This talk will introduce the history behind uReplicator and the high level architecture. As the original uReplicator ran into scalability challenges and operational overhead as the scale of Kafka clusters increased, we built the Federated uReplicator which addressed above issues and provide an extensible architecture for further scaling.
Easily Build a Smart Pulsar Stream Processor_Simon CrosbyStreamNative
For organizations with boundless data sources, it is important to analyze, learn, predict and even respond in real time – directly from streaming data. This is important when:
•Data volumes are large, or moving raw data is expensive,
•Data is generated by widely distributed assets (eg: mobile devices),
•Data is of ephemeral value and analysis can’t wait, or
•It is critical to always have the latest insight and extrapolation won’t do.
Use cases include prediction of failures on assembly lines, prediction of traffic flows in cities, predicting demand placed in power grids, detection of hackers, and understanding connection quality in mobile networks. They are characterized by a need to know – now – and require real-time processing of streaming data. Our goal is to enable real-time stream processing for Apache Pulsar in which analysis, learning and prediction are done on-the-fly, with continuous insights streamed back to the broker.
The need for gleaning answers from unbounded data streams is moving from nicety to a necessity. Netflix is a data driven company, and has a need to process over 1 trillion events a day amounting to 3 PB of data to derive business insights.
To ease extracting insight, we are building a self-serve, scalable, fault-tolerant, multi-tenant "Stream Processing as a Service" platform so the user can focus on data analysis. I'll share our experience using Flink to help build the platform.
Use ksqlDB to migrate core-banking processing from batch to streaming | Mark ...HostedbyConfluent
Core banking systems are batch oriented: typically with heavy overnight batch cycles before business opens each morning. In this talk I will explain some of the common interface points between core-banking infrastructure and event streaming systems. Then I will focus on how to do stream processing using ksqlDB for core-banking shaped data: showing how to do common operation using various ksqlDB functions. The key features are avro-record keys and multi-key joins (ksqlDB 0.15), schema management and state store planning.
A talk given on 2018-06-16 in HK Open Source Conference 2018.
The rise of the Apache Kafka starts a new generation of data pipeline - the stream-processing pipeline.
In this talk, Dr. Mole Wong will walk you through the concept of the stream-processing data pipeline, and how this data pipeline can be set up. He will also discuss the use cases of such a data pipeline.
Netflix Keystone Pipeline at Samza Meetup 10-13-2015Monal Daxini
Netflix Keystone Pipeline processing 600 billion events a day, and detailed treatise on the modification of and use of Samza for real time routing of events including docker.
Netflix Keystone - How Netflix Handles Data Streams up to 11M Events/SecPeter Bakas
Talk on Netflix Keystone by Peter Bakas at SF Data Engineering Meetup on 2/23/2016.
Topics covered:
- Architectural design and principles for Keystone
- Technologies that Keystone is leveraging
- Best practices
http://www.meetup.com/SF-Data-Engineering/events/228293610/
ApacheCon2019 Talk: Kafka, Cassandra and Kubernetes at Scale – Real-time Ano...Paul Brebner
Apache Kafka, Apache Cassandra and Kubernetes are open source big data technologies enabling applications and business operations to scale massively and rapidly. While Kafka and Cassandra underpins the data layer of the stack providing capability to stream, disseminate, store and retrieve data at very low latency, Kubernetes is a container orchestration technology that helps in automated application deployment and scaling of application clusters. In this presentation, we will reveal how we architected a massive scale deployment of a streaming data pipeline with Kafka and Cassandra to cater to an example Anomaly detection application running on a Kubernetes cluster and generating and processing massive amount of events. Anomaly detection is a method used to detect unusual events in an event stream. It is widely used in a range of applications such as financial fraud detection, security, threat detection, website user analytics, sensors, IoT, system health monitoring, etc. When such applications operate at massive scale generating millions or billions of events, they impose significant computational, performance and scalability challenges to anomaly detection algorithms and data layer technologies. We will demonstrate the scalability, performance and cost effectiveness of Apache Kafka, Cassandra and Kubernetes, with results from our experiments allowing the Anomaly detection application to scale to 19 Billion anomaly checks per day.
Disaster Recovery for Multi-Region Apache Kafka Ecosystems at Uberconfluent
Speaker: Yupeng Fu, Staff Engineer, Uber
High availability and reliability are important requirements to Uber services, and the services shall tolerate datacenter failures in a region and fail over to another region. In this talk, we will present the active-active Apache Kafka® at Uber and how it facilitates disaster discovery across regions for Uber services. In particular, we will highlight the key components including topic replication, topic aggregation, offsets sync and then walk through several use cases of their disaster recovery strategy using active-active Kafka. Lastly, we will present several interesting challenges and the future work planned.
Yupeng Fu is a staff engineer in Uber Data Org leading the streaming data platform. Previously, he worked at Alluxio and Palantir, building distributed data analysis and storage platforms. Yupeng holds a B.S. and an M.S. from Tsinghua University and did his Ph.D. research on databases at UCSD.
Netflix Keystone Pipeline at Big Data Bootcamp, Santa Clara, Nov 2015Monal Daxini
Keystone - Processing over Half a Trillion events per day with 8 million events & 17 GB per second peaks, and at-least once processing semantics. We will explore in detail how we employ Kafka, Samza, and Docker at scale to implement a multi-tenant pipeline. We will also look at the evolution to its current state and where the pipeline is headed next in offering a self-service stream processing infrastructure atop the Kafka based pipeline and support Spark Streaming.
http://www.oreilly.com/pub/e/3764
Keystone processes over 700 billion events per day (1 peta byte) with at-least-once processing semantics in the cloud. Monal Daxini details how they used Kafka, Samza, Docker, and Linux at scale to implement a multi-tenant pipeline in AWS cloud within a year. He'll also share plans on offering a Stream Processing as a Service for all of Netflix use.
Scalable complex event processing on samza @UBERShuyi Chen
The Marketplace data team at Uber has built a scalable complex event processing platform to solve many challenging real time data needs for various Uber products. This platform has been in production for almost a year and it has proven to be very flexible to solve many use cases. In this talk, we will share in detail the design and architecture of the platform, and how we employ Samza, Kafka, and Siddhi at scale.
This slides was presented at Stream Processing Meetup @ LinkedIn on June 15 2016.
Hadoop summit - Scaling Uber’s Real-Time Infra for Trillion Events per DayAnkur Bansal
Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder.
Real time infrastructure powers critical pieces of Uber (think Surge) and in this talk we will discuss our architecture, technical challenges, learnings and how a blend of open source infrastructure (Apache Kafka and Samza) and in-house technologies have helped Uber scale.
Slides from a presentation by Monal Daxini at Disney, Glendale CA about Netflix Open Source Software, Cloud Data Persistence, and Cassandra best Practices
From a kafkaesque story to The Promised LandRan Silberman
LivePerson moved from an ETL based data platform to a new data platform based on emerging technologies from the Open Source community: Hadoop, Kafka, Storm, Avro and more.
This presentation tells the story and focuses on Kafka.
The Netflix Way to deal with Big Data ProblemsMonal Daxini
Netflix is a data driven company with a unique culture. Come take a holistic tour of the Big Data ecosystem, and how Netflix culture catalyzes the development of systems. Then ogle at how we quickly evolved and scaled the event pipeline to a 1 trillion events per day and over 1.4 PB of event data without service disruption, and a small team.
Introduction to Streaming Distributed Processing with StormBrandon O'Brien
Contact:
https://www.linkedin.com/in/brandonjobrien
@hakczar
Introducing streaming data concepts, Storm cluster architecture, Storm topology architecture, and demonstrate working example of a WordCount topology for SIGKDD Seattle chapter meetup.
Presented by Brandon O'Brien
Code example: https://github.com/OpenDataMining/brandonobrien
Meetup: http://www.meetup.com/seattlesigkdd/events/222955114/
Netflix Open Source Meetup Season 4 Episode 2aspyker
In this episode, we will take a close look at 2 different approaches to high-throughput/low-latency data stores, developed by Netflix.
The first, EVCache, is a battle-tested distributed memcached-backed data store, optimized for the cloud. You will also hear about the road ahead for EVCache it evolves into an L1/L2 cache over RAM and SSDs.
The second, Dynomite, is a framework to make any non-distributed data-store, distributed. Netflix's first implementation of Dynomite is based on Redis.
Come learn about the products' features and hear from Thomson and Reuters, Diego Pacheco from Ilegra and other third party speakers, internal and external to Netflix, on how these products fit in their stack and roadmap.
___________________________________________
Meetup#7 | Session 2 | 21/03/2018 | Taboola
_____________________________________________
In this talk, we will present our multi-DC Kafka architecture, and discuss how we tackle sending and handling 10B+ messages per day, with maximum availability and no tolerance for data loss.
Our architecture includes technologies such as Cassandra, Spark, HDFS, and Vertica - with Kafka as the backbone that feeds them all.
Presented at Stream Processing Meetup (7/19/2018)(https://www.meetup.com/Stream-Processing-Meetup-LinkedIn/events/251481797/).
At Uber, we operate 20+ Kafka clusters to collect system and application logs as well as event data from rider and driver apps. We need a Kafka replication solution to replicate data between Kafka clusters across multiple data centers for different purposes. This talk will introduce the history behind uReplicator and the high level architecture. As the original uReplicator ran into scalability challenges and operational overhead as the scale of Kafka clusters increased, we built the Federated uReplicator which addressed above issues and provide an extensible architecture for further scaling.
Easily Build a Smart Pulsar Stream Processor_Simon CrosbyStreamNative
For organizations with boundless data sources, it is important to analyze, learn, predict and even respond in real time – directly from streaming data. This is important when:
•Data volumes are large, or moving raw data is expensive,
•Data is generated by widely distributed assets (eg: mobile devices),
•Data is of ephemeral value and analysis can’t wait, or
•It is critical to always have the latest insight and extrapolation won’t do.
Use cases include prediction of failures on assembly lines, prediction of traffic flows in cities, predicting demand placed in power grids, detection of hackers, and understanding connection quality in mobile networks. They are characterized by a need to know – now – and require real-time processing of streaming data. Our goal is to enable real-time stream processing for Apache Pulsar in which analysis, learning and prediction are done on-the-fly, with continuous insights streamed back to the broker.
The need for gleaning answers from unbounded data streams is moving from nicety to a necessity. Netflix is a data driven company, and has a need to process over 1 trillion events a day amounting to 3 PB of data to derive business insights.
To ease extracting insight, we are building a self-serve, scalable, fault-tolerant, multi-tenant "Stream Processing as a Service" platform so the user can focus on data analysis. I'll share our experience using Flink to help build the platform.
Use ksqlDB to migrate core-banking processing from batch to streaming | Mark ...HostedbyConfluent
Core banking systems are batch oriented: typically with heavy overnight batch cycles before business opens each morning. In this talk I will explain some of the common interface points between core-banking infrastructure and event streaming systems. Then I will focus on how to do stream processing using ksqlDB for core-banking shaped data: showing how to do common operation using various ksqlDB functions. The key features are avro-record keys and multi-key joins (ksqlDB 0.15), schema management and state store planning.
A talk given on 2018-06-16 in HK Open Source Conference 2018.
The rise of the Apache Kafka starts a new generation of data pipeline - the stream-processing pipeline.
In this talk, Dr. Mole Wong will walk you through the concept of the stream-processing data pipeline, and how this data pipeline can be set up. He will also discuss the use cases of such a data pipeline.
Netflix Keystone Pipeline at Samza Meetup 10-13-2015Monal Daxini
Netflix Keystone Pipeline processing 600 billion events a day, and detailed treatise on the modification of and use of Samza for real time routing of events including docker.
Netflix Keystone - How Netflix Handles Data Streams up to 11M Events/SecPeter Bakas
Talk on Netflix Keystone by Peter Bakas at SF Data Engineering Meetup on 2/23/2016.
Topics covered:
- Architectural design and principles for Keystone
- Technologies that Keystone is leveraging
- Best practices
http://www.meetup.com/SF-Data-Engineering/events/228293610/
ApacheCon2019 Talk: Kafka, Cassandra and Kubernetes at Scale – Real-time Ano...Paul Brebner
Apache Kafka, Apache Cassandra and Kubernetes are open source big data technologies enabling applications and business operations to scale massively and rapidly. While Kafka and Cassandra underpins the data layer of the stack providing capability to stream, disseminate, store and retrieve data at very low latency, Kubernetes is a container orchestration technology that helps in automated application deployment and scaling of application clusters. In this presentation, we will reveal how we architected a massive scale deployment of a streaming data pipeline with Kafka and Cassandra to cater to an example Anomaly detection application running on a Kubernetes cluster and generating and processing massive amount of events. Anomaly detection is a method used to detect unusual events in an event stream. It is widely used in a range of applications such as financial fraud detection, security, threat detection, website user analytics, sensors, IoT, system health monitoring, etc. When such applications operate at massive scale generating millions or billions of events, they impose significant computational, performance and scalability challenges to anomaly detection algorithms and data layer technologies. We will demonstrate the scalability, performance and cost effectiveness of Apache Kafka, Cassandra and Kubernetes, with results from our experiments allowing the Anomaly detection application to scale to 19 Billion anomaly checks per day.
Disaster Recovery for Multi-Region Apache Kafka Ecosystems at Uberconfluent
Speaker: Yupeng Fu, Staff Engineer, Uber
High availability and reliability are important requirements to Uber services, and the services shall tolerate datacenter failures in a region and fail over to another region. In this talk, we will present the active-active Apache Kafka® at Uber and how it facilitates disaster discovery across regions for Uber services. In particular, we will highlight the key components including topic replication, topic aggregation, offsets sync and then walk through several use cases of their disaster recovery strategy using active-active Kafka. Lastly, we will present several interesting challenges and the future work planned.
Yupeng Fu is a staff engineer in Uber Data Org leading the streaming data platform. Previously, he worked at Alluxio and Palantir, building distributed data analysis and storage platforms. Yupeng holds a B.S. and an M.S. from Tsinghua University and did his Ph.D. research on databases at UCSD.
Netflix Keystone Pipeline at Big Data Bootcamp, Santa Clara, Nov 2015Monal Daxini
Keystone - Processing over Half a Trillion events per day with 8 million events & 17 GB per second peaks, and at-least once processing semantics. We will explore in detail how we employ Kafka, Samza, and Docker at scale to implement a multi-tenant pipeline. We will also look at the evolution to its current state and where the pipeline is headed next in offering a self-service stream processing infrastructure atop the Kafka based pipeline and support Spark Streaming.
http://www.oreilly.com/pub/e/3764
Keystone processes over 700 billion events per day (1 peta byte) with at-least-once processing semantics in the cloud. Monal Daxini details how they used Kafka, Samza, Docker, and Linux at scale to implement a multi-tenant pipeline in AWS cloud within a year. He'll also share plans on offering a Stream Processing as a Service for all of Netflix use.
Scalable complex event processing on samza @UBERShuyi Chen
The Marketplace data team at Uber has built a scalable complex event processing platform to solve many challenging real time data needs for various Uber products. This platform has been in production for almost a year and it has proven to be very flexible to solve many use cases. In this talk, we will share in detail the design and architecture of the platform, and how we employ Samza, Kafka, and Siddhi at scale.
This slides was presented at Stream Processing Meetup @ LinkedIn on June 15 2016.
Hadoop summit - Scaling Uber’s Real-Time Infra for Trillion Events per DayAnkur Bansal
Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder.
Real time infrastructure powers critical pieces of Uber (think Surge) and in this talk we will discuss our architecture, technical challenges, learnings and how a blend of open source infrastructure (Apache Kafka and Samza) and in-house technologies have helped Uber scale.
Slides from a presentation by Monal Daxini at Disney, Glendale CA about Netflix Open Source Software, Cloud Data Persistence, and Cassandra best Practices
From a kafkaesque story to The Promised LandRan Silberman
LivePerson moved from an ETL based data platform to a new data platform based on emerging technologies from the Open Source community: Hadoop, Kafka, Storm, Avro and more.
This presentation tells the story and focuses on Kafka.
The Netflix Way to deal with Big Data ProblemsMonal Daxini
Netflix is a data driven company with a unique culture. Come take a holistic tour of the Big Data ecosystem, and how Netflix culture catalyzes the development of systems. Then ogle at how we quickly evolved and scaled the event pipeline to a 1 trillion events per day and over 1.4 PB of event data without service disruption, and a small team.
Introduction to Streaming Distributed Processing with StormBrandon O'Brien
Contact:
https://www.linkedin.com/in/brandonjobrien
@hakczar
Introducing streaming data concepts, Storm cluster architecture, Storm topology architecture, and demonstrate working example of a WordCount topology for SIGKDD Seattle chapter meetup.
Presented by Brandon O'Brien
Code example: https://github.com/OpenDataMining/brandonobrien
Meetup: http://www.meetup.com/seattlesigkdd/events/222955114/
Netflix Open Source Meetup Season 4 Episode 2aspyker
In this episode, we will take a close look at 2 different approaches to high-throughput/low-latency data stores, developed by Netflix.
The first, EVCache, is a battle-tested distributed memcached-backed data store, optimized for the cloud. You will also hear about the road ahead for EVCache it evolves into an L1/L2 cache over RAM and SSDs.
The second, Dynomite, is a framework to make any non-distributed data-store, distributed. Netflix's first implementation of Dynomite is based on Redis.
Come learn about the products' features and hear from Thomson and Reuters, Diego Pacheco from Ilegra and other third party speakers, internal and external to Netflix, on how these products fit in their stack and roadmap.
Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...javier ramirez
QuestDB es una base de datos open source de alto rendimiento. Mucha gente nos comentaba que les gustaría usarla como servicio, sin tener que gestionar las máquinas. Así que nos pusimos manos a la obra para desarrollar una solución que nos permitiese lanzar instancias de QuestDB con provisionado, monitorización, seguridad o actualizaciones totalmente gestionadas.
Unos cuantos clusters de Kubernetes más tarde, conseguimos lanzar nuestra oferta de QuestDB Cloud. Esta charla es la historia de cómo llegamos ahí. Hablaré de herramientas como Calico, Karpenter, CoreDNS, Telegraf, Prometheus, Loki o Grafana, pero también de retos como autenticación, facturación, multi-nube, o de a qué tienes que decir que no para poder sobrevivir en la nube.
Processing 19 billion messages in real time and NOT dying in the processJampp
Here is an introduction in the Jampp architecture for data processing. We walk through our journey of migrating to systems that allows us to process more data in real time
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
NetflixOSS Meetup S3 E1, covering latest components in Distributed Databases, Telemetry systems, Big Data tools and more. Speakers from Netflix, IBM Watson, Pivotal and Nike Digital
AWS Big Data Demystified #1: Big data architecture lessons learned Omid Vahdaty
AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company
The video: https://youtu.be/l5KmaZNQxaU
dont forget to subcribe to the youtube channel
The website: https://amazon-aws-big-data-demystified.ninja/
The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/
The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/
Netflix keystone streaming data pipeline @scale in the cloud-dbtb-2016Monal Daxini
Keystone processes over 700 billion events per day (1 peta byte) with at-least once processing semantics in the cloud. We will explore in detail how we leverage Kafka, Samza, Docker, and Linux at scale to implement a multi-tenant pipeline in AWS cloud within a year. We will also share our plans on offering a Stream Processing as a Service for all of Netflix use.
USENIX LISA15: How TubeMogul Handles over One Trillion HTTP Requests a MonthNicolas Brousse
TubeMogul grew from few servers to over two thousands servers and handling over one trillion http requests a month, processed in less than 50ms each. To keep up with the fast growth, the SRE team had to implement an efficient Continuous Delivery infrastructure that allowed to do over 10,000 puppet deployment and 8,500 application deployment in 2014. In this presentation, we will cover the nuts and bolts of the TubeMogul operations engineering team and how they overcome challenges.
Building a real-time, scalable and intelligent programmatic ad buying platformJampp
After a brief introduction to programmatic ads and RTB we go through the evolution of Jampp's data platform to handle the enormous about of data we need to process.
FOSDEM 2019: M3, Prometheus and Graphite with metrics and monitoring in an in...Rob Skillington
The world in which we monitor software is growing more complex every year. There are increasingly more ways to run server-side software, with many more independent services and more points of failures, the list goes on! On the plus side, there’s a lot of great tools and patterns being developed to try and make things simple to assess and understand. This talk covers how metrics and monitoring can be leveraged in a variety of different ways, auto-discovering applications and their usage of databases, caches, load balancers, etc, setting up and tearing down dashboards and monitoring automatically for services and instances, and more.
We’ll also talk about how you can accomplish all this with a global view of your systems using both Prometheus and Graphite with M3, our open source metrics platform. We’ll take a deep dive look at how we use M3DB, distributed aggregation with the M3 aggregator and the M3 Kubernetes operator to horizontally scale a metrics platform in a way that doesn’t cost outrageous amounts to run with a system that’s still sane to operate with petabytes of metrics data.
Amazon aws big data demystified | Introduction to streaming and messaging flu...Omid Vahdaty
amazon aws big data demystified meetup:
https://www.meetup.com/AWS-Big-Data-Demystified/
Introduction to streaming and messaging flume kafka sqs kinesis
Functioning incessantly of Data Science Platform with Kubeflow - Albert Lewan...GetInData
Did you like it? Check out our blog to stay up to date: https://getindata.com/blog
The talk is focused on administration, development and monitoring platform with Apache Spark, Apache Flink and Kubeflow in which the monitoring stack is based on Prometheus stack.
Author: Albert Lewandowski
Linkedin: https://www.linkedin.com/in/albert-lewandowski/
___
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
OSMC 2018 | Learnings, patterns and Uber’s metrics platform M3, open sourced ...NETWAYS
At Uber we use high cardinality monitoring to observe and detect issues with our 4,000 microservices running on Mesos and across our infrastructure systems and servers. We’ll cover how we put the resulting 6 billion plus time series to work in a variety of different ways, auto-discovering services and their usage of other systems at Uber, setting up and tearing down alerts automatically for services, sending smart alert notifications that rollup different failures into individual high level contextual alerts, and more. We’ll also talk about how we accomplish all this with a global view of our systems with M3, our open source metrics platform. We’ll take a deep dive look at how we use M3DB, now available as an open source Prometheus long term storage backend, to horizontally scale our metrics platform in a cost efficient manner with a system that’s still sane to operate with petabytes of metrics data.
Zero Downtime Architectures based on JEE platform. Almost every big enterprise with online business tries to design its applications in a way that they are always online. But is it also the case when we upgrade the database cluster? When we switch the whole data center? Based on a customer project we try to present common architecture principles that enable you to do all this without any service interruption and the most important: without any stress.
Building Analytic Apps for SaaS: “Analytics as a Service”Amazon Web Services
TIBCO Jaspersoft® for AWS is a business intelligence suite that helps you deliver stunning interactive reports and dashboards inside your app that make it easy for your customers to get answers. Purpose-built for AWS, our reporting and analytics server quickly and easily connects to Amazon Relational Database Service (RDS), Amazon Redshift, and Amazon EMR. It includes ad-hoc reporting, dashboards, data analysis, data visualization, and data blending. In less than 10 minutes, you can be analyzing and reporting on your data. You get a full Cloud BI server starting at less than $1/hour, with no user or data limits and no additional fees.
This webinar deck shows how embeddable analytics with TIBCO Jaspersoft for AWS gives you the power to create the experience your end users demand and how to scale and manage that experience across your customer base with AWS.
Triangle Devops Meetup covering Netflix open source, cloud architecture, and what Andrew did in his first year working as a senior software engineer in the cloud platform group.
Similar to Big data Argentina meetup 2020-09: Intro to presto on docker (20)
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
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
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.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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.
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/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
6. Real time bidding (RTB)
Ad exchange
ad spot available
impression
< 70ms
Max Latency
Win!
bid
Participant 1
Participant N
.
.
.
auction
7. Auction Bid Impression Click
Install /
Event
Ad-Tech funnel
■ Each step decreases volume by an order of magnitude
■ The data criticality increases with each step.
■ We can sample auctions to optimize costs but under no circumstances can we lose
clicks, installs or events.
■ Each table has different access patterns and needs a different partitioning scheme.
10. Our pipeline operational unit
■ One pipeline per event type.
■ Focused on modularity and separation of concerns.
■ Having them separated allows us to optimize for cost without fear of losing critical
messages.
Internal
Data Source
11. ETLs and data insertion
■ Spark and Hive are very reliable for ETLs and insertion.
■ We use the Hive Metastore as the main interface between engines.
■ Airflow is an amazing tool for scheduling and orchestration.
■ Storing data on S3 allows us to decouple compute from storage
12. Presto is the main interface with our Data Warehouse
Through the years it became the main method of
interacting with the Data Warehouse for every team in
the company.
● Feeding our Machine Learning algorithms
● Building automatic audience segments
● Ad-Hoc queries through Apache Superset
● Templated reports through a custom UI
● Monitoring data quality
Presto
13. AWS EMR clusters
■ 1 ETL cluster (Spark/Hive/Tez)
■ 2 or 3 Presto clusters
■ Data stored on S3, we don’t use
HDFS
■ Each cluster is auto scalable
depending the load
■ Shared EMRFS on DynamoDB table
■ Shared Hive Metastore on RDS
14. The bad
● Troublesome interaction between YARN (Hive, Spark) and non YARN
apps (Presto).
● Low update frequency for fast pacing applications.
● Limited Presto support (i.e: no monitoring, no autoscaling on fleets)
The good
● Provisions out of the box many popular Big Data tools.
● Flexibility to tune applications and shape clusters as needed.
● Mainstream applications are frequently added to the app catalog, like
PrestoSQL v338!
AWS Elastic
MapReduce The ugly
● They upgraded the OS to Amazon Linux 2 without EMR version change
16. What?
Why?
● Why self-managed and Docker?
○ Lower costs (no EMR fees, no cluster overhead)
○ Quicker version upgrades
○ Local/ci environments just like prod/stg
○ Simpler configuration management
● Why PrestoSQL?
○ Community and user focused
○ Growing at a faster pace, more active contributors
○ Some known bugs already fixed (like hive bucketed tables)
○ Improved features like Cost Based Optimizer (CBO) and Security
● We decided to do two mayor changes:
○ Switch from PrestoDB to PrestoSQL
○ Take ownership of cluster provisioning and maintenance
17. Building our
docker image
● Based on the offical PrestoSQL image
● Dynamic configuration
○ Presto config and catalog files with templated values
○ Parameters and secrets stored on AWS SSM Parameter
store
○ Segmentio’s chamber to load parameters as env vars on
runtime
○ Unix’s envsubs to render final config files
● Additional tools like java agent for monitoring
19. Orchestrator
candidates
● The Tao of HashiCorp
● Orchestration with low complexity
● Support for non-container workloads
● Limited community - less known
● We already have it running
● Great community and tool ecosystem
● Industry-standard solution and battle tested
● High complexity, lot of internal “moving parts”
● Simple to spin-up using EKS/GKE/AKS
20. Presto setup on Nomad:
Infra level
■ Elastic autoscaling group
for each component
■ Consul: Service discovery
+ Distributed KV
■ Control plane with Consul
& Nomad
■ Traefik as API Gateway /
HTTP Proxy
22. Extra
Features
● Nomad job templating with Hashicorp Levant
○ Terraform-like workflow using a single template and a variable
file per cluster/environment
● Autoscaling:
○ Application level: Nomad native support (CPU based)
○ Cluster level: Nomad official autoscaler agent
● Graceful scale-in of Presto workers
○ Autoscaling group hooks
○ Local node script
○ Put new status on presto node state endpoint /v1/info/state
28. Monitoring stack
● We expose low level metrics with JMX java agent for Prometheus.
● Developed a custom exporter to get user level usage metrics from
/v1/query endpoint
● Prometheus stack collects mbeans attributes.
● Grafana for dashboards and custom searches.
HTTP
Scraper
29. Low level (JMX)
● Memory pools, Heap usage.
● Garbage collection frequency and duration.
● Cluster size and nodes status.
● Active, Pending and Blocked queries.
User level (HTTP API)
● Finished, canceled and failed queries per user.
● Normalized query analytics to detect usage patterns.
Monitoring
relevant
metrics
30. ● Leverage CBO to improve query performance.
● Evaluate the usage of a Presto gateway to manage query
routing to multiple clusters.
● Enable autoscaling from Prometheus metrics.
● Define SLI’s and SLO’s to measure reliability.
● Evaluate Presto on k8s + AWS Fargate (serverless containers)
Next
steps
