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.
Building large-scale analytics platform with Storm, Kafka and Cassandra - NYC...Alexey Kharlamov
At Integral, we process heavy volumes of click-stream traffic. 50K QPS of ad impressions at peak and close to 200K QPS of all browser calls. We build analytics on this streams of data. There are two applications which require quite significant computational effort: 'sessionization' and fraud detection.
Sessionization implies linking a series of requests from same browser into single record. There can be 5 or more total requests spread over 15-30 minutes which we need to link to each other.
Fraud detection is a process looking at various signals in browser requests and at substantial historical evidence data classifying ad impression either as legitimate or as fraudulent.
We've been doing both (as well as all other analytics) in batch mode once an hour at best. Both processes, and, in particular, fraud detection, are time sensitive and much more meaningful if done in near-real-time.
This talk would be about our experience migrating a once-per-day offline batch processing of impression data using hadoop to in-memory stream processing using Kafka, Storm and Cassandra. We will touch upon our choices and our reasoning for selecting the products used for this solution.
Hadoop is no longer the only or always preferred option in Big Data space. In-memory stream processing may be more effective for time series data preparation and aggregation. Ability to scale at a significantly lower cost means more customers, better accuracy and better business practices: since only in-stream processing allows for low-latency data and insight delivery it opens entirely new opportunities. However, transitioning of non-trivial data pipelines raises a number of questions hidden previously within the offline nature of batch processing. How will you join several data feeds? How will you implement failure recovery? In addition to handling terabytes of data per day our streaming system has to be guided by the following considerations:
• Recovery time
• Time relativity and continuity
• Geographical distribution of data sources
• Limit on data loss
• Maintainability
The system produces complex cross-correlational analysis of several data feeds and aggregation for client analytics with input feed frequency of up to 100K msg/sec.
This presentation will benefit anyone interested in learning an alternate approach for big data analytics, especially the process of joining multiple streams in memory using Cassandra. Presentation will also highlight certain optimization patterns used those can be useful in similar situations.
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.
Building large-scale analytics platform with Storm, Kafka and Cassandra - NYC...Alexey Kharlamov
At Integral, we process heavy volumes of click-stream traffic. 50K QPS of ad impressions at peak and close to 200K QPS of all browser calls. We build analytics on this streams of data. There are two applications which require quite significant computational effort: 'sessionization' and fraud detection.
Sessionization implies linking a series of requests from same browser into single record. There can be 5 or more total requests spread over 15-30 minutes which we need to link to each other.
Fraud detection is a process looking at various signals in browser requests and at substantial historical evidence data classifying ad impression either as legitimate or as fraudulent.
We've been doing both (as well as all other analytics) in batch mode once an hour at best. Both processes, and, in particular, fraud detection, are time sensitive and much more meaningful if done in near-real-time.
This talk would be about our experience migrating a once-per-day offline batch processing of impression data using hadoop to in-memory stream processing using Kafka, Storm and Cassandra. We will touch upon our choices and our reasoning for selecting the products used for this solution.
Hadoop is no longer the only or always preferred option in Big Data space. In-memory stream processing may be more effective for time series data preparation and aggregation. Ability to scale at a significantly lower cost means more customers, better accuracy and better business practices: since only in-stream processing allows for low-latency data and insight delivery it opens entirely new opportunities. However, transitioning of non-trivial data pipelines raises a number of questions hidden previously within the offline nature of batch processing. How will you join several data feeds? How will you implement failure recovery? In addition to handling terabytes of data per day our streaming system has to be guided by the following considerations:
• Recovery time
• Time relativity and continuity
• Geographical distribution of data sources
• Limit on data loss
• Maintainability
The system produces complex cross-correlational analysis of several data feeds and aggregation for client analytics with input feed frequency of up to 100K msg/sec.
This presentation will benefit anyone interested in learning an alternate approach for big data analytics, especially the process of joining multiple streams in memory using Cassandra. Presentation will also highlight certain optimization patterns used those can be useful in similar situations.
Samza at LinkedIn: Taking Stream Processing to the Next LevelMartin Kleppmann
Slides from my talk at Berlin Buzzwords, 27 May 2014. Unfortunately Slideshare has screwed up the fonts. See https://speakerdeck.com/ept/samza-at-linkedin-taking-stream-processing-to-the-next-level for a version of the deck with correct fonts.
Stream processing is an essential part of real-time data systems, such as news feeds, live search indexes, real-time analytics, metrics and monitoring. But writing stream processes is still hard, especially when you're dealing with so much data that you have to distribute it across multiple machines. How can you keep the system running smoothly, even when machines fail and bugs occur?
Apache Samza is a new framework for writing scalable stream processing jobs. Like Hadoop and MapReduce for batch processing, it takes care of the hard parts of running your message-processing code on a distributed infrastructure, so that you can concentrate on writing your application using simple APIs. It is in production use at LinkedIn.
This talk will introduce Samza, and show how to use it to solve a range of different problems. Samza has some unique features that make it especially interesting for large deployments, and in this talk we will dig into how they work under the hood. In particular:
• Samza is built to support many different jobs written by different teams. Isolation between jobs ensures that a single badly behaved job doesn't affect other jobs. It is robust by design.
• Samza can handle jobs that require large amounts of state, for example joining multiple streams, augmenting a stream with data from a database, or aggregating data over long time windows. This makes it a very powerful tool for applications.
Distributed real time stream processing- why and howPetr Zapletal
In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more.
The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this.
Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution.
Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other.
Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work).
Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Amazon Web Services
"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."
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.
hbaseconasia2017: Building online HBase cluster of Zhihu based on KubernetesHBaseCon
Zhiyong Bai
As a high performance and scalable key value database, Zhihu use HBase to provide online data store system along with Mysql and Redis. Zhihu’s platform team had accumulated some experience in technology of container, and this time, based on Kubernetes, we build flexible platform of online HBase system, create multiple logic isolated HBase clusters on the shared physical cluster with fast rapid,and provide customized service for different business needs. Combined with Consul and DNS server, we implement high available access of HBase using client mainly written with Python. This presentation is mainly shared the architecture of online HBase platform in Zhihu and some practical experience in production environment.
hbaseconasia2017 hbasecon hbase
Functional Comparison and Performance Evaluation of Streaming FrameworksHuafeng Wang
A report covers the functional comparison and performance evaluation between Apache Flink, Apache Spark Streaming, Apache Storm and Apache Gearpump(incubating)
Big Data Streaming processing using Apache Storm - FOSSCOMM 2016Adrianos Dadis
Our presentation on FOSSCOMM conference (17 April 2016):
Agenda:
* Big Data concepts
* Batch & Streaming processing
* NoSQL persistence
* Apache Storm and Apache Kafka
* Streaming application demo
* Considerations for Big Data applications
Event: http://fosscomm.cs.unipi.gr/index.php/event/adrianos-dadis/?lang=en
Some of the biggest issues at the center of analyzing large amounts of data are query flexibility, latency, and fault tolerance. Modern technologies that build upon the success of “big data” platforms, such as Apache Hadoop, have made it possible to spread the load of data analysis to commodity machines, but these analyses can still take hours to run and do not respond well to rapidly-changing data sets.
A new generation of data processing platforms -- which we call “stream architectures” -- have converted data sources into streams of data that can be processed and analyzed in real-time. This has led to the development of various distributed real-time computation frameworks (e.g. Apache Storm) and multi-consumer data integration technologies (e.g. Apache Kafka). Together, they offer a way to do predictable computation on real-time data streams.
In this talk, we will give an overview of these technologies and how they fit into the Python ecosystem. As part of this presentation, we also released streamparse, a new Python that makes it easy to debug and run large Storm clusters.
Links:
* http://parse.ly/code
* https://github.com/Parsely/streamparse
* https://github.com/getsamsa/samsa
What's the time? ...and why? (Mattias Sax, Confluent) Kafka Summit SF 2019confluent
Data stream processing is built on the core concept of time. However, understanding time semantics and reasoning about time is not simple, especially if deterministic processing is expected. In this talk, we explain the difference between processing, ingestion, and event time and what their impact is on data stream processing. Furthermore, we explain how Kafka clusters and stream processing applications must be configured to achieve specific time semantics. Finally, we deep dive into the time semantics of the Kafka Streams DSL and KSQL operators, and explain in detail how the runtime handles time. Apache Kafka offers many ways to handle time on the storage layer, ie, the brokers, allowing users to build applications with different semantics. Time semantics in the processing layer, ie, Kafka Streams and KSQL, are even richer, more powerful, but also more complicated. Hence, it is paramount for developers, to understand different time semantics and to know how to configure Kafka to achieve them. Therefore, this talk enables developers to design applications with their desired time semantics, help them to reason about the runtime behavior with regard to time, and allow them to understand processing/query results.
Recently, the interest in highly scalable stream processing engines has risen, thus many projects have appeared. Apache Samza is a distributed stream-processing framework that uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, and resource management. It is one of the most popular stream processing engines out there used by many high-profile companies. On the other hand, we have Amazon Kinesis that is a fully managed service for real-time processing of streaming data which allows users to scale the amount of data ingested by Kinesis without worrying about the infrastructure details. This presentation gives a brief introduction about the very popular Samza-Kafka integration, then focuses on the new Samza-Kinesis integration, and explains users the new opportunities they have due to the new Samza-Kinesis integration.
Developing Java Streaming Applications with Apache StormLester Martin
Apache Storm, http://storm.apache.org, is a free and open source distributed real-time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. During this presentation, a simple Java-based streaming application will be built from scratch!
Code examples can be found at https://github.com/lestermartin/streaming-exploration.
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.
(Nina Hanzlikova, Zalando) Kafka Summit SF 2018
My team at Zalando fell in love with KStreams and their programming model straight out of the gate. However, as a small team of developers, building out and supporting our infrastructure while still trying to deliver solutions for our business has not always resulted in a smooth journey.
Can a small team of a couple of developers run their own Kafka infrastructure confidently and still spend most of their time developing code?
In this talk, we will dive into some of the problems we experienced while running Kafka brokers and Kafka Streams applications, as well as the consultations we had with other teams around this matter. We will outline some of the pragmatic decisions we made regarding backups, monitoring and operations to minimize our time spent administering our Kafka brokers and various stream applications.
Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...Flink Forward
Over 137 million members worldwide are enjoying TV series, feature films across a wide variety of genres and languages on Netflix. It leads to petabyte scale of user behavior data. At Netflix, our client logging platform collects and processes this data to empower recommendations, personalization and many other services to enhance user experience. Built with Apache Flink, this platform processes 100s of billion events and a petabyte data per day, 2.5 million events/sec in sub milliseconds latency. The processing involves a series of data transformations such as decryption and data enrichment of customer, geo, device information using microservices based lookups.
The transformed and enriched data is further used by multiple data consumers for a variety of applications such as improving user-experience with A/B tests, tracking application performance metrics, tuning algorithms. This causes redundant reads of the dataset by multiple batch jobs and incurs heavy processing costs. To avoid this, we have developed a config driven, centralized, managed platform, on top of Apache Flink, that reads this data once and routes it to multiple streams based on dynamic configuration. This has resulted in improved computation efficiency, reduced costs and reduced operational overhead.
Stream processing at scale while ensuring that the production systems are scalable and cost-efficient brings interesting challenges. In this talk, we will share about how we leverage Apache Flink to achieve this, the challenges we faced and our learnings while running one of the largest Flink application at Netflix.
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/
Samza at LinkedIn: Taking Stream Processing to the Next LevelMartin Kleppmann
Slides from my talk at Berlin Buzzwords, 27 May 2014. Unfortunately Slideshare has screwed up the fonts. See https://speakerdeck.com/ept/samza-at-linkedin-taking-stream-processing-to-the-next-level for a version of the deck with correct fonts.
Stream processing is an essential part of real-time data systems, such as news feeds, live search indexes, real-time analytics, metrics and monitoring. But writing stream processes is still hard, especially when you're dealing with so much data that you have to distribute it across multiple machines. How can you keep the system running smoothly, even when machines fail and bugs occur?
Apache Samza is a new framework for writing scalable stream processing jobs. Like Hadoop and MapReduce for batch processing, it takes care of the hard parts of running your message-processing code on a distributed infrastructure, so that you can concentrate on writing your application using simple APIs. It is in production use at LinkedIn.
This talk will introduce Samza, and show how to use it to solve a range of different problems. Samza has some unique features that make it especially interesting for large deployments, and in this talk we will dig into how they work under the hood. In particular:
• Samza is built to support many different jobs written by different teams. Isolation between jobs ensures that a single badly behaved job doesn't affect other jobs. It is robust by design.
• Samza can handle jobs that require large amounts of state, for example joining multiple streams, augmenting a stream with data from a database, or aggregating data over long time windows. This makes it a very powerful tool for applications.
Distributed real time stream processing- why and howPetr Zapletal
In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more.
The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this.
Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution.
Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other.
Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work).
Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Amazon Web Services
"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."
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.
hbaseconasia2017: Building online HBase cluster of Zhihu based on KubernetesHBaseCon
Zhiyong Bai
As a high performance and scalable key value database, Zhihu use HBase to provide online data store system along with Mysql and Redis. Zhihu’s platform team had accumulated some experience in technology of container, and this time, based on Kubernetes, we build flexible platform of online HBase system, create multiple logic isolated HBase clusters on the shared physical cluster with fast rapid,and provide customized service for different business needs. Combined with Consul and DNS server, we implement high available access of HBase using client mainly written with Python. This presentation is mainly shared the architecture of online HBase platform in Zhihu and some practical experience in production environment.
hbaseconasia2017 hbasecon hbase
Functional Comparison and Performance Evaluation of Streaming FrameworksHuafeng Wang
A report covers the functional comparison and performance evaluation between Apache Flink, Apache Spark Streaming, Apache Storm and Apache Gearpump(incubating)
Big Data Streaming processing using Apache Storm - FOSSCOMM 2016Adrianos Dadis
Our presentation on FOSSCOMM conference (17 April 2016):
Agenda:
* Big Data concepts
* Batch & Streaming processing
* NoSQL persistence
* Apache Storm and Apache Kafka
* Streaming application demo
* Considerations for Big Data applications
Event: http://fosscomm.cs.unipi.gr/index.php/event/adrianos-dadis/?lang=en
Some of the biggest issues at the center of analyzing large amounts of data are query flexibility, latency, and fault tolerance. Modern technologies that build upon the success of “big data” platforms, such as Apache Hadoop, have made it possible to spread the load of data analysis to commodity machines, but these analyses can still take hours to run and do not respond well to rapidly-changing data sets.
A new generation of data processing platforms -- which we call “stream architectures” -- have converted data sources into streams of data that can be processed and analyzed in real-time. This has led to the development of various distributed real-time computation frameworks (e.g. Apache Storm) and multi-consumer data integration technologies (e.g. Apache Kafka). Together, they offer a way to do predictable computation on real-time data streams.
In this talk, we will give an overview of these technologies and how they fit into the Python ecosystem. As part of this presentation, we also released streamparse, a new Python that makes it easy to debug and run large Storm clusters.
Links:
* http://parse.ly/code
* https://github.com/Parsely/streamparse
* https://github.com/getsamsa/samsa
What's the time? ...and why? (Mattias Sax, Confluent) Kafka Summit SF 2019confluent
Data stream processing is built on the core concept of time. However, understanding time semantics and reasoning about time is not simple, especially if deterministic processing is expected. In this talk, we explain the difference between processing, ingestion, and event time and what their impact is on data stream processing. Furthermore, we explain how Kafka clusters and stream processing applications must be configured to achieve specific time semantics. Finally, we deep dive into the time semantics of the Kafka Streams DSL and KSQL operators, and explain in detail how the runtime handles time. Apache Kafka offers many ways to handle time on the storage layer, ie, the brokers, allowing users to build applications with different semantics. Time semantics in the processing layer, ie, Kafka Streams and KSQL, are even richer, more powerful, but also more complicated. Hence, it is paramount for developers, to understand different time semantics and to know how to configure Kafka to achieve them. Therefore, this talk enables developers to design applications with their desired time semantics, help them to reason about the runtime behavior with regard to time, and allow them to understand processing/query results.
Recently, the interest in highly scalable stream processing engines has risen, thus many projects have appeared. Apache Samza is a distributed stream-processing framework that uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, and resource management. It is one of the most popular stream processing engines out there used by many high-profile companies. On the other hand, we have Amazon Kinesis that is a fully managed service for real-time processing of streaming data which allows users to scale the amount of data ingested by Kinesis without worrying about the infrastructure details. This presentation gives a brief introduction about the very popular Samza-Kafka integration, then focuses on the new Samza-Kinesis integration, and explains users the new opportunities they have due to the new Samza-Kinesis integration.
Developing Java Streaming Applications with Apache StormLester Martin
Apache Storm, http://storm.apache.org, is a free and open source distributed real-time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real-time processing what Hadoop did for batch processing. During this presentation, a simple Java-based streaming application will be built from scratch!
Code examples can be found at https://github.com/lestermartin/streaming-exploration.
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.
(Nina Hanzlikova, Zalando) Kafka Summit SF 2018
My team at Zalando fell in love with KStreams and their programming model straight out of the gate. However, as a small team of developers, building out and supporting our infrastructure while still trying to deliver solutions for our business has not always resulted in a smooth journey.
Can a small team of a couple of developers run their own Kafka infrastructure confidently and still spend most of their time developing code?
In this talk, we will dive into some of the problems we experienced while running Kafka brokers and Kafka Streams applications, as well as the consultations we had with other teams around this matter. We will outline some of the pragmatic decisions we made regarding backups, monitoring and operations to minimize our time spent administering our Kafka brokers and various stream applications.
Flink Forward San Francisco 2019: Massive Scale Data Processing at Netflix us...Flink Forward
Over 137 million members worldwide are enjoying TV series, feature films across a wide variety of genres and languages on Netflix. It leads to petabyte scale of user behavior data. At Netflix, our client logging platform collects and processes this data to empower recommendations, personalization and many other services to enhance user experience. Built with Apache Flink, this platform processes 100s of billion events and a petabyte data per day, 2.5 million events/sec in sub milliseconds latency. The processing involves a series of data transformations such as decryption and data enrichment of customer, geo, device information using microservices based lookups.
The transformed and enriched data is further used by multiple data consumers for a variety of applications such as improving user-experience with A/B tests, tracking application performance metrics, tuning algorithms. This causes redundant reads of the dataset by multiple batch jobs and incurs heavy processing costs. To avoid this, we have developed a config driven, centralized, managed platform, on top of Apache Flink, that reads this data once and routes it to multiple streams based on dynamic configuration. This has resulted in improved computation efficiency, reduced costs and reduced operational overhead.
Stream processing at scale while ensuring that the production systems are scalable and cost-efficient brings interesting challenges. In this talk, we will share about how we leverage Apache Flink to achieve this, the challenges we faced and our learnings while running one of the largest Flink application at Netflix.
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/
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.
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.
Stream, Stream, Stream: Different Streaming Methods with Spark and KafkaDataWorks Summit
At NMC (Nielsen Marketing Cloud) we provide our customers (marketers and publishers) real-time analytics tools to profile their target audiences.
To achieve that, we need to ingest billions of events per day into our big data stores, and we need to do it in a scalable yet cost-efficient manner.
In this session, we will discuss how we continuously transform our data infrastructure to support these goals.
Specifically, we will review how we went from CSV files and standalone Java applications all the way to multiple Kafka and Spark clusters, performing a mixture of Streaming and Batch ETLs, and supporting 10x data growth.
We will share our experience as early-adopters of Spark Streaming and Spark Structured Streaming, and how we overcame technical barriers (and there were plenty...).
We will present a rather unique solution of using Kafka to imitate streaming over our Data Lake, while significantly reducing our cloud services' costs.
Topics include :
* Kafka and Spark Streaming for stateless and stateful use-cases
* Spark Structured Streaming as a possible alternative
* Combining Spark Streaming with batch ETLs
* "Streaming" over Data Lake using Kafka
Monal Daxini - Beaming Flink to the Cloud @ NetflixFlink Forward
http://flink-forward.org/kb_sessions/beaming-flink-to-the-cloud-netflix/
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.
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.
Apache Flink 101 - the rise of stream processing and beyondBowen Li
Apache Flink is the most popular and widely adopted streaming processing framework, powering real time stream event computations at extremely large scale in companies like Uber, Lyft, AWS, Alibaba, Pinterest, Splunk, Yelp, etc.
In this talk, we will go over use cases and basic (yet hard to achieve!) requirements of stream processing, and how Flink fills the gaps and stands out with some of its unique core building blocks, like pipelined execution, native event time support, state support, and fault tolerance.
We will also take a look at how Flink is going beyond stream processing into areas like unified data processing, enterprise intergration, AI/machine learning (especially online ML), and serverless computation, and how Flink fits with its distinct value.
SPEAKER: Bowen Li
SPEAKER BIO: Bowen is a committer of Apache Flink, senior engineer at Alibaba, and host of Seattle Flink Meetup.
From Batch to Streaming ET(L) with Apache Apex at Berlin Buzzwords 2017Thomas Weise
https://berlinbuzzwords.de/17/session/batch-streaming-etl-apache-apex
Stream data processing is increasingly required to support business needs for faster actionable insight with growing volume of information from more sources. Apache Apex is a true stream processing framework for low-latency, high-throughput and reliable processing of complex analytics pipelines on clusters. Apex is designed for quick time-to-production, and is used in production by large companies for real-time and batch processing at scale.
This session will use an Apex production use case to walk through the incremental transition from a batch pipeline with hours of latency to an end-to-end streaming architecture with billions of events per day which are processed to deliver real-time analytical reports. The example is representative for many similar extract-transform-load (ETL) use cases with other data sets that can use a common library of building blocks. The transform (or analytics) piece of such pipelines varies in complexity and often involves business logic specific, custom components.
Topics include:
Pipeline functionality from event source through queryable state for real-time insights.
API for application development and development process.
Library of building blocks including connectors for sources and sinks such as Kafka, JMS, Cassandra, HBase, JDBC and how they enable end-to-end exactly-once results.
Stateful processing with event time windowing.
Fault tolerance with exactly-once result semantics, checkpointing, incremental recovery
Scalability and low-latency, high-throughput processing with advanced engine features for auto-scaling, dynamic changes, compute locality.
Recent project development and roadmap.
Following the session attendees will have a high level understanding of Apex and how it can be applied to use cases at their own organizations.
This slide deck explores trends in stream processing, how streaming SQL has become a standard, the advantages of streaming SQL and more.
View video: https://wso2.com/library/conference/2018/07/wso2con-usa-2018-the-rise-of-streaming-sql/
What's streaming processing? The evolution of streaming SQL. It's advantages & challenges, and how we can overcome them. Presented at WSO2 Con 2018 USA
Stream data processing is increasingly required to support business needs for faster actionable insight with growing volume of information from more sources. Apache Apex is a true stream processing framework for low-latency, high-throughput and reliable processing of complex analytics pipelines on clusters. Apex is designed for quick time-to-production, and is used in production by large companies for real-time and batch processing at scale.
This session will use an Apex production use case to walk through the incremental transition from a batch pipeline with hours of latency to an end-to-end streaming architecture with billions of events per day which are processed to deliver real-time analytical reports. The example is representative for many similar extract-transform-load (ETL) use cases with other data sets that can use a common library of building blocks. The transform (or analytics) piece of such pipelines varies in complexity and often involves business logic specific, custom components.
Topics include:
* Pipeline functionality from event source through queryable state for real-time insights.
* API for application development and development process.
* Library of building blocks including connectors for sources and sinks such as Kafka, JMS, Cassandra, HBase, JDBC and how they enable end-to-end exactly-once results.
* Stateful processing with event time windowing.
* Fault tolerance with exactly-once result semantics, checkpointing, incremental recovery
* Scalability and low-latency, high-throughput processing with advanced engine features for auto-scaling, dynamic changes, compute locality.
* Who is using Apex in production, and roadmap.
Following the session attendees will have a high level understanding of Apex and how it can be applied to use cases at their own organizations.
YOW2018 Cloud Performance Root Cause Analysis at NetflixBrendan Gregg
Keynote by Brendan Gregg for YOW! 2018. Video: https://www.youtube.com/watch?v=03EC8uA30Pw . Description: "At Netflix, improving the performance of our cloud means happier customers and lower costs, and involves root cause
analysis of applications, runtimes, operating systems, and hypervisors, in an environment of 150k cloud instances
that undergo numerous production changes each week. Apart from the developers who regularly optimize their own code
, we also have a dedicated performance team to help with any issue across the cloud, and to build tooling to aid in
this analysis. In this session we will summarize the Netflix environment, procedures, and tools we use and build t
o do root cause analysis on cloud performance issues. The analysis performed may be cloud-wide, using self-service
GUIs such as our open source Atlas tool, or focused on individual instances, and use our open source Vector tool, f
lame graphs, Java debuggers, and tooling that uses Linux perf, ftrace, and bcc/eBPF. You can use these open source
tools in the same way to find performance wins in your own environment."
Traveloka's data journey — Traveloka data meetup #2Traveloka
Discover the journey that Traveloka's Data Team have taken so far and learn from our struggles and triumphs in managing Traveloka's burgeoning data!
In this slide, you will learn more about the stories and lessons learned on building a scalable data pipeline at Traveloka.
Presenters of the slide:
Nisrina Luthfiyati - Data Engineer
Rendy B. Junior - Data System Architect
Wilson lauw - Lead Data Engineer
To follow our LinkedIn page, visit bit.ly/TravelokaLinkedInPage
Safe Harbor Statement
Our discussion may include predictions, estimates or other information that might be considered conclusive. While these conclusive statements represent our current judgment on the best practices, they are subject to risks and uncertainties that could cause actual results to differ materially. You are cautioned not to place undue reliance on our statements, which reflect our opinions only as of the date of this presentation. Please keep in mind that we are not obligating ourselves to revise or publicly release the results of any revision to these presentation materials in light of new information or future events.
Stream, Stream, Stream: Different Streaming Methods with Apache Spark and KafkaDatabricks
At NMC (Nielsen Marketing Cloud) we provide our customers (marketers and publishers) real-time analytics tools to profile their target audiences. To achieve that, we need to ingest billions of events per day into our big data stores, and we need to do it in a scalable yet cost-efficient manner.
In this session, we will discuss how we continuously transform our data infrastructure to support these goals. Specifically, we will review how we went from CSV files and standalone Java applications all the way to multiple Kafka and Spark clusters, performing a mixture of Streaming and Batch ETLs, and supporting 10x data growth We will share our experience as early-adopters of Spark Streaming and Spark Structured Streaming, and how we overcame technical barriers (and there were plenty). We will present a rather unique solution of using Kafka to imitate streaming over our Data Lake, while significantly reducing our cloud services’ costs. Topics include:
Kafka and Spark Streaming for stateless and stateful use-cases
Spark Structured Streaming as a possible alternative
Combining Spark Streaming with batch ETLs
”Streaming” over Data Lake using Kafka
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.
Similar to Real Time Data Streaming using Kafka & Storm (20)
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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/
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
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.
2. About me
● Technical Leader of Data Platform in LivePerson
● Bird watcher and amateur bird photographer
Pharaoh Eagle-Owl / Bubo ascalaphus
This is what the people from previous slide were looking at…
Amir Silberman
3. Agenda
● Why we chose Kafka + Storm
● How implementation was done
● Measures of success
● Two examples of use
● Tips from our experience
4. Data in LivePerson
Visitor in Site
Chat Window
Agent console
LivePerson SaaS Server
LoginMonitor
Rules,
Intelligence,
Decision
Chat
Chat
Invite
DATA
DATA DATA
BIG
DATA
5. Legacy Data flow in LivePerson
BI DWH
(Oracle)
RealTime
servers
ETL
Sessionize
Modeling
Schema
View
Real-Time data
Historical data
6. Why Kafka + Storm?
● Need to scale out and plan for future scale
○ Limit for scale should not be technology
○ Let the limit be cost of (commodity) hardware
● What Data platforms can be implemented quickly?
○ Open source - fast evolving and community
○ Micro-services - do only what you ought to do!
● Are there risks in this choice?
○ Yes! technology is not mature enough
○ But, there is no other mature technology that can
address our needs!
8. Legacy Data flow in LivePerson
BI DWH
(Oracle)
RealTime
servers
Customers
ETL
Sessionize
Modeling
Schema
View
9. 1st phase - move to Hadoop
ETL
Sessionize
Modeling
Schema
View
RealTime
servers
BI DWH
(Vertica)HDFS
Hadoop
MR Job transfers
data to BI DWH
Customers
10. 2. move to Kafka
6
RealTime
servers
HDFS
BI DWH
(Vertica)
Hadoop
MR Job transfers
data to BI DWH
Kafka
Topic-1
Customers
11. 3. Integrate with new producers
6
RealTime
servers
HDFS
BI DWH
(Vertica)
Hadoop
MR Job transfers
data to BI DWH
Kafka
Topic-1 Topic-2
New
RealTime
servers
Customers
12. 4. Add Real-time BI
6
Customers
RealTime
servers
HDFS
BI DWH
(Vertica)
Hadoop
MR Job transfers
data to BI DWH
Kafka
Topic-1 Topic-2
New
RealTime
servers
Storm
Topology
Analytics
DB
16. 1st Strom Use Case: “Visitors List”
Use case:
● Show list of visitors in the “Agent Console”
● Collect data about visitor in real time
● Visitor stickiness in streaming process
18. Selected Analytics DB - Couchbase
1st Strom Use Case: “Visitors List”
● Document Store - for complex documents
● Searchable - possible to search by different
attributes.
● High throughput - Read & Write
20. Visitors List - Storm considerations
● Complex calculations before sending to DB
○ Ignore delayed events
○ Reorder events before storing
● Document cached in memory
● Fields Grouping to bolt that writes to CouchBase
● High parallelism in bolt that writes to CouchBase
25. Selected Analytics DB - Cassandra
2nd Storm Use Case: “Agent State”
● Wide Column Store DB
● Highly Available w/o Single point of failure
● High throughput
● Optimized for counters
26. First Storm Topology – Visitor Feed
Storm Topology
Kafka Spout Analyze relevant
events
Send events
emit emit
Kafka events stream
Add
“Agent Status” Topology:
Analytics DB: Cassandra - Document store
Parse Avro into
tuple
emit
Data
visualization
using Highcharts
27. Agent Status - Storm considerations
● Counters stored by topology
● Calculations done after reading from DB
● Delayed events should not be ignored
● Order of events does not matter
● Using Highcharts for data visualization
29. 3rd Storm Use Case: Data Auditing
Use case:
● Needs to be able to tell whether events arrived
○ Where there any missing events?
○ Where there any duplicated events?
○ How long did it take for events to arrive?
● Data not important - only count of events
30. 3rd Storm Use Case: Data Auditing
Realtime server
Kafka
Topics
Auditing
Topic
Storm Sync
topology
Audit-loader
topology
MySql
Hadoop
HDFS
audit
job
kafka
1
3
4
2
Auditor
31. First Storm Topology – Visitor Feed
Storm Topology
Kafka Spout Analyze relevant
events
Send events
emit emit
Kafka events stream
Add
“Sync Audit” Topology:
Sync messages between two topics
Parse Avro into
tuple
emit
Kafka Audit topic
33. “Load Audit” Topology:
● Stores statistics of events count
● SQL type DB
● Used for Auditing and other statistics
● Requires metadata in events header
34. Challenges:
● High network traffic
● Writing to Kafka is faster than reading
● All topologies read all events
● How to avoid resource starvation in Storm
Subalpine Warbler / Sylvia cantillans
Amir Silberman
35. Optimizations of Kafka
● Increase Kafka consuming rate by adding partitions
● Run on physical machines with RAID
● Set retention to the proper need
● Monitor data flow!
36. Optimizations of Storm
● #of Kafka-Spouts = number of total partitions
● Set “Isolation mode” for important topologies
● Validate Network cards can carry network traffic
● Set Storm cluster on high CPU machines
● Monitor servers CPU & Memory (Graphite)
● Assess min. #Cores that topology needs
○ Use “top” -> “load” to find server load
37. Demo
● Agent Console - https://z1.le.liveperson.net/
71394613 / rans@liveperson.com
● My Site - http://birds-of-israel.weebly.com/