Apache Pulsar Development 101 with Python PS2022_Ecosystem_v0.0
There is always the fear a speaker cannot make it. So just in case, since I was the MC for the ecosystem track I put together a talk just in case.
Here it is. Never seen or presented.
Building a Streaming Microservice Architecture: with Apache Spark Structured ...Databricks
As we continue to push the boundaries of what is possible with respect to pipeline throughput and data serving tiers, new methodologies and techniques continue to emerge to handle larger and larger workloads
In this session, we will start with the importance of monitoring of services and infrastructure. We will discuss about Prometheus an opensource monitoring tool. We will discuss the architecture of Prometheus. We will also discuss some visualization tools which can be used over Prometheus. Then we will have a quick demo for Prometheus and Grafana.
Introducing Exactly Once Semantics in Apache Kafka with Matthias J. SaxDatabricks
Apache Kafka’s rise in popularity as a streaming platform has demanded a revisit of its traditional at least once message delivery semantics. In this talk, we present the recent additions to Apache Kafka to achieve exactly once semantics. We shall discuss the newly introduced transactional APIs and use Kafka Streams as an example to show how these APIs are leveraged for streams tasks.
Both Apache Pulsar and Apache Flink share a similar view on how the data and the computation level of an application can be “streaming-first” with batch as a special case streaming. With Apache Pulsar’s Segmented-Stream storage and Apache Flink’s steps to unify batch and stream processing workloads under one framework, there are numerous ways of integrating the two technologies to provide elastic data processing at massive scale, and build a real streaming warehouse.
In this talk, Sijie Guo from Apache Pulsar community will given an overview of Apache Pulsar and how it provides the unified data view to fully leverage Apache Flink unified computation runtime for elastic data processing. He will share the latest integrations between Apache Pulsar and Apache Flink, especially around effectively-once processing and schema integration.
Deep Dive into Stateful Stream Processing in Structured Streaming with Tathag...Databricks
Stateful processing is one of the most challenging aspects of distributed, fault-tolerant stream processing. The DataFrame APIs in Structured Streaming make it easy for the developer to express their stateful logic, either implicitly (streaming aggregations) or explicitly (mapGroupsWithState). However, there are a number of moving parts under the hood which makes all the magic possible. In this talk, I will dive deep into different stateful operations (streaming aggregations, deduplication and joins) and how they work under the hood in the Structured Streaming engine.
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
Building a Streaming Microservice Architecture: with Apache Spark Structured ...Databricks
As we continue to push the boundaries of what is possible with respect to pipeline throughput and data serving tiers, new methodologies and techniques continue to emerge to handle larger and larger workloads
In this session, we will start with the importance of monitoring of services and infrastructure. We will discuss about Prometheus an opensource monitoring tool. We will discuss the architecture of Prometheus. We will also discuss some visualization tools which can be used over Prometheus. Then we will have a quick demo for Prometheus and Grafana.
Introducing Exactly Once Semantics in Apache Kafka with Matthias J. SaxDatabricks
Apache Kafka’s rise in popularity as a streaming platform has demanded a revisit of its traditional at least once message delivery semantics. In this talk, we present the recent additions to Apache Kafka to achieve exactly once semantics. We shall discuss the newly introduced transactional APIs and use Kafka Streams as an example to show how these APIs are leveraged for streams tasks.
Both Apache Pulsar and Apache Flink share a similar view on how the data and the computation level of an application can be “streaming-first” with batch as a special case streaming. With Apache Pulsar’s Segmented-Stream storage and Apache Flink’s steps to unify batch and stream processing workloads under one framework, there are numerous ways of integrating the two technologies to provide elastic data processing at massive scale, and build a real streaming warehouse.
In this talk, Sijie Guo from Apache Pulsar community will given an overview of Apache Pulsar and how it provides the unified data view to fully leverage Apache Flink unified computation runtime for elastic data processing. He will share the latest integrations between Apache Pulsar and Apache Flink, especially around effectively-once processing and schema integration.
Deep Dive into Stateful Stream Processing in Structured Streaming with Tathag...Databricks
Stateful processing is one of the most challenging aspects of distributed, fault-tolerant stream processing. The DataFrame APIs in Structured Streaming make it easy for the developer to express their stateful logic, either implicitly (streaming aggregations) or explicitly (mapGroupsWithState). However, there are a number of moving parts under the hood which makes all the magic possible. In this talk, I will dive deep into different stateful operations (streaming aggregations, deduplication and joins) and how they work under the hood in the Structured Streaming engine.
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Stephan Ewen - Experiences running Flink at Very Large ScaleVerverica
This talk shares experiences from deploying and tuning Flink steam processing applications for very large scale. We share lessons learned from users, contributors, and our own experiments about running demanding streaming jobs at scale. The talk will explain what aspects currently render a job as particularly demanding, show how to configure and tune a large scale Flink job, and outline what the Flink community is working on to make the out-of-the-box for experience as smooth as possible. We will, for example, dive into - analyzing and tuning checkpointing - selecting and configuring state backends - understanding common bottlenecks - understanding and configuring network parameters
This is a talk on how you can monitor your microservices architecture using Prometheus and Grafana. This has easy to execute steps to get a local monitoring stack running on your local machine using docker.
Getting Started with Confluent Schema Registryconfluent
Getting started with Confluent Schema Registry, Patrick Druley, Senior Solutions Engineer, Confluent
Meetup link: https://www.meetup.com/Cleveland-Kafka/events/272787313/
Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안SANG WON PARK
Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다.
다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다.
[Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안]
Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 …
[Apache Kafka 모니터링을 위한 Metrics 이해]
Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다.
이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다.
모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임
[Apache Kafka 성능 Configuration 최적화]
성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다.
튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.
This talk provides an in-depth overview of the key concepts of Apache Calcite. It explores the Calcite catalog, parsing, validation, and optimization with various planners.
Apache Spark Data Source V2 with Wenchen Fan and Gengliang WangDatabricks
As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements.
1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
Practical learnings from running thousands of Flink jobsFlink Forward
Flink Forward San Francisco 2022.
Task Managers constantly running out of memory? Flink job keeps restarting from cryptic Akka exceptions? Flink job running but doesn’t seem to be processing any records? We share practical learnings from running thousands of Flink Jobs for different use-cases and take a look at common challenges they have experienced such as out-of-memory errors, timeouts and job stability. We will cover memory tuning, S3 and Akka configurations to address common pitfalls and the approaches that we take on automating health monitoring and management of Flink jobs at scale.
by
Hong Teoh & Usamah Jassat
Using Delta Lake to Transform a Legacy Apache Spark to Support Complex Update...Databricks
The convergence of big data technology towards traditional database domain has became an industry trend. At present, open source big data processing engines, such as Apache Spark, Apache Hadoop, Apache Flink, etc., already support SQL interfaces, and the usage of SQL basically occupies a dominant position. Companies use above open source software to build their own ETL framework and OLAP technology. However, in terms of OLTP technology, it is still a strong point of traditional databases. One of the main reasons is the support of ACID by traditional databases.
Designing Structured Streaming Pipelines—How to Architect Things RightDatabricks
"Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark's built-in functions make it easy for developers to express complex computations. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem needs to be solved.
What are you trying to consume? Single source? Joining multiple streaming sources? Joining streaming with static data?
What are you trying to produce? What is the final output that the business wants? What type of queries does the business want to run on the final output?
When do you want it? When does the business want to the data? What is the acceptable latency? Do you really want to millisecond-level latency?
How much are you willing to pay for it? This is the ultimate question and the answer significantly determines how feasible is it solve the above questions.
These are the questions that we ask every customer in order to help them design their pipeline. In this talk, I am going to go through the decision tree of designing the right architecture for solving your problem."
Like many other messaging systems, Kafka has put limit on the maximum message size. User will fail to produce a message if it is too large. This limit makes a lot of sense and people usually send to Kafka a reference link which refers to a large message stored somewhere else. However, in some scenarios, it would be good to be able to send messages through Kafka without external storage. At LinkedIn, we have a few use cases that can benefit from such feature. This talk covers our solution to send large message through Kafka without additional storage.
GraphQL is a query language for APIs and a runtime for fulfilling those queries. It gives clients the power to ask for exactly what they need, which makes it a great fit for modern web and mobile apps. In this talk, we explain why GraphQL was created, introduce you to the syntax and behavior, and then show how to use it to build powerful APIs for your data. We will also introduce you to AWS AppSync, a GraphQL-powered serverless backend for apps, which you can use to host GraphQL APIs and also add real-time and offline capabilities to your web and mobile apps. You can follow along if you have an AWS account – no GraphQL experience required!
Level: Beginner
Speaker: Rohan Deshpande - Sr. Software Dev Engineer, AWS Mobile Applications
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
Deep Dive into the New Features of Apache Spark 3.0Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.0 extends its scope with more than 3000 resolved JIRAs. We will talk about the exciting new developments in the Spark 3.0 as well as some other major initiatives that are coming in the future.
Fast Streaming into Clickhouse with Apache PulsarTimothy Spann
https://github.com/tspannhw/SpeakerProfile/tree/main/2022/talks
Fast Streaming into Clickhouse with Apache Pulsar
https://github.com/tspannhw/FLiPC-FastStreamingIntoClickhouseWithApachePulsar
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup/events/285271332/
Fast Streaming into Clickhouse with Apache Pulsar - Meetup 2022
StreamNative - Apache Pulsar - Stream to Altinity Cloud - Clickhouse
May the 4th Be With You!
04-May-2022 Clickhosue Meetup
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runtime String,
host String,
filename String,
host_name String,
macaddress String,
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CREATE TABLE iotjetsonjson ON CLUSTER '{cluster}' AS iotjetsonjson_local
ENGINE = Distributed('{cluster}', default, iotjetsonjson_local, rand());
select uuid, top1pct, top1, gputempf, cputempf
from iotjetsonjson
where toFloat32OrZero(top1pct) > 40
order by toFloat32OrZero(top1pct) desc, systemtime desc
select uuid, systemtime, networktime, te, top1pct, top1, cputempf, gputempf, cpu, diskusage, memory,filename
from iotjetsonjson
order by systemtime desc
select top1, max(toFloat32OrZero(top1pct)), max(gputempf), max(cputempf)
from iotjetsonjson
group by top1
select top1, max(toFloat32OrZero(top1pct)) as maxTop1, max(gputempf), max(cputempf)
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group by top1
order by maxTop1
Tim Spann
Developer Advocate
StreamNative
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Stephan Ewen - Experiences running Flink at Very Large ScaleVerverica
This talk shares experiences from deploying and tuning Flink steam processing applications for very large scale. We share lessons learned from users, contributors, and our own experiments about running demanding streaming jobs at scale. The talk will explain what aspects currently render a job as particularly demanding, show how to configure and tune a large scale Flink job, and outline what the Flink community is working on to make the out-of-the-box for experience as smooth as possible. We will, for example, dive into - analyzing and tuning checkpointing - selecting and configuring state backends - understanding common bottlenecks - understanding and configuring network parameters
This is a talk on how you can monitor your microservices architecture using Prometheus and Grafana. This has easy to execute steps to get a local monitoring stack running on your local machine using docker.
Getting Started with Confluent Schema Registryconfluent
Getting started with Confluent Schema Registry, Patrick Druley, Senior Solutions Engineer, Confluent
Meetup link: https://www.meetup.com/Cleveland-Kafka/events/272787313/
Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안SANG WON PARK
Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다.
다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다.
[Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안]
Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 …
[Apache Kafka 모니터링을 위한 Metrics 이해]
Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다.
이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다.
모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임
[Apache Kafka 성능 Configuration 최적화]
성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다.
튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.
This talk provides an in-depth overview of the key concepts of Apache Calcite. It explores the Calcite catalog, parsing, validation, and optimization with various planners.
Apache Spark Data Source V2 with Wenchen Fan and Gengliang WangDatabricks
As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements.
1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
Practical learnings from running thousands of Flink jobsFlink Forward
Flink Forward San Francisco 2022.
Task Managers constantly running out of memory? Flink job keeps restarting from cryptic Akka exceptions? Flink job running but doesn’t seem to be processing any records? We share practical learnings from running thousands of Flink Jobs for different use-cases and take a look at common challenges they have experienced such as out-of-memory errors, timeouts and job stability. We will cover memory tuning, S3 and Akka configurations to address common pitfalls and the approaches that we take on automating health monitoring and management of Flink jobs at scale.
by
Hong Teoh & Usamah Jassat
Using Delta Lake to Transform a Legacy Apache Spark to Support Complex Update...Databricks
The convergence of big data technology towards traditional database domain has became an industry trend. At present, open source big data processing engines, such as Apache Spark, Apache Hadoop, Apache Flink, etc., already support SQL interfaces, and the usage of SQL basically occupies a dominant position. Companies use above open source software to build their own ETL framework and OLAP technology. However, in terms of OLTP technology, it is still a strong point of traditional databases. One of the main reasons is the support of ACID by traditional databases.
Designing Structured Streaming Pipelines—How to Architect Things RightDatabricks
"Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark's built-in functions make it easy for developers to express complex computations. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem needs to be solved.
What are you trying to consume? Single source? Joining multiple streaming sources? Joining streaming with static data?
What are you trying to produce? What is the final output that the business wants? What type of queries does the business want to run on the final output?
When do you want it? When does the business want to the data? What is the acceptable latency? Do you really want to millisecond-level latency?
How much are you willing to pay for it? This is the ultimate question and the answer significantly determines how feasible is it solve the above questions.
These are the questions that we ask every customer in order to help them design their pipeline. In this talk, I am going to go through the decision tree of designing the right architecture for solving your problem."
Like many other messaging systems, Kafka has put limit on the maximum message size. User will fail to produce a message if it is too large. This limit makes a lot of sense and people usually send to Kafka a reference link which refers to a large message stored somewhere else. However, in some scenarios, it would be good to be able to send messages through Kafka without external storage. At LinkedIn, we have a few use cases that can benefit from such feature. This talk covers our solution to send large message through Kafka without additional storage.
GraphQL is a query language for APIs and a runtime for fulfilling those queries. It gives clients the power to ask for exactly what they need, which makes it a great fit for modern web and mobile apps. In this talk, we explain why GraphQL was created, introduce you to the syntax and behavior, and then show how to use it to build powerful APIs for your data. We will also introduce you to AWS AppSync, a GraphQL-powered serverless backend for apps, which you can use to host GraphQL APIs and also add real-time and offline capabilities to your web and mobile apps. You can follow along if you have an AWS account – no GraphQL experience required!
Level: Beginner
Speaker: Rohan Deshpande - Sr. Software Dev Engineer, AWS Mobile Applications
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
Deep Dive into the New Features of Apache Spark 3.0Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.0 extends its scope with more than 3000 resolved JIRAs. We will talk about the exciting new developments in the Spark 3.0 as well as some other major initiatives that are coming in the future.
Fast Streaming into Clickhouse with Apache PulsarTimothy Spann
https://github.com/tspannhw/SpeakerProfile/tree/main/2022/talks
Fast Streaming into Clickhouse with Apache Pulsar
https://github.com/tspannhw/FLiPC-FastStreamingIntoClickhouseWithApachePulsar
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup/events/285271332/
Fast Streaming into Clickhouse with Apache Pulsar - Meetup 2022
StreamNative - Apache Pulsar - Stream to Altinity Cloud - Clickhouse
May the 4th Be With You!
04-May-2022 Clickhosue Meetup
CREATE TABLE iotjetsonjson_local
(
uuid String,
camera String,
ipaddress String,
networktime String,
top1pct String,
top1 String,
cputemp String,
gputemp String,
gputempf String,
cputempf String,
runtime String,
host String,
filename String,
host_name String,
macaddress String,
te String,
systemtime String,
cpu String,
diskusage String,
memory String,
imageinput String
)
ENGINE = MergeTree()
PARTITION BY uuid
ORDER BY (uuid);
CREATE TABLE iotjetsonjson ON CLUSTER '{cluster}' AS iotjetsonjson_local
ENGINE = Distributed('{cluster}', default, iotjetsonjson_local, rand());
select uuid, top1pct, top1, gputempf, cputempf
from iotjetsonjson
where toFloat32OrZero(top1pct) > 40
order by toFloat32OrZero(top1pct) desc, systemtime desc
select uuid, systemtime, networktime, te, top1pct, top1, cputempf, gputempf, cpu, diskusage, memory,filename
from iotjetsonjson
order by systemtime desc
select top1, max(toFloat32OrZero(top1pct)), max(gputempf), max(cputempf)
from iotjetsonjson
group by top1
select top1, max(toFloat32OrZero(top1pct)) as maxTop1, max(gputempf), max(cputempf)
from iotjetsonjson
group by top1
order by maxTop1
Tim Spann
Developer Advocate
StreamNative
[March sn meetup] apache pulsar + apache nifi for cloud data lakeTimothy Spann
https://www.meetup.com/new-york-city-apache-pulsar-meetup/events/283837865/
Learn how to use Apache Pulsar and Apache NiFi to Stream to your Data Lake
Discover how to stream data to and from your data lake or data mart using Apache Pulsar™ and Apache NiFi®. Learn how these cloud-native, scalable open-source projects built for streaming data pipelines work together to enable you to quickly build applications with minimal coding.
|WHAT THE SESSION WILL COVER|
Best Practices for using Pulsar and NiFi
A deep dive on Apache NiFi's Pulsar connector and demos
Building an End-to-End Application in the Hybrid Cloud
Attend for a chance to win a We <3 Pulsar t-shirt! The first 50 registrants who register through here [https://hubs.ly/Q013LTpn0] will be entered in a drawing!
—------------------------
|AGENDA|
6:00 - 7:00 PM EST: Presentation - Tim Spann, StreamNative Developer Advocate
7:00 - 8:00 PM EST: Presentation - John Kuchmek, Cloudera Principal Solutions Engineer
8:00 - 8:30 PM EST: Q&A + Networking
—------------------------
|ABOUT THE SPEAKERS|
John Kuchmek is a Principal Solutions Engineer for Cloudera. Before joining Cloudera, John transitioned to the Autonomous Intelligence team where he was in charge of integrating the platforms to allow data scientists to work with various types of data.
Tim Spann is a Developer Advocate for StreamNative. He works with StreamNative Cloud, Apache Pulsar™, Apache Flink®, Flink® SQL, Big Data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science. He is currently working on a book about the FLiP Stack.
Python web conference 2022 apache pulsar development 101 with python (f li-...Timothy Spann
Python web conference 2022 apache pulsar development 101 with python (FLiP-Py)
What is Apache Pulsar?
Python 3 Coding
Python Consumers
Python Producers
Python via MQTT, Web Sockets, Kafka
Python for Pulsar Functions
Schemas
Python Web Conference 2022 - Apache Pulsar Development 101 with Python (FLiP-Py)Timothy Spann
Python Web Conference 2022 - Apache Pulsar Development 101 with Python (FLiP-Py)
https://sixfeetup.com/company/news/90-talks-and-tutorials-from-2022-python-web-conference-released
https://www.youtube.com/playlist?list=PLt4L3V8wVnF7PJ3wfq1rdJWX4ziasHMHl
https://www.youtube.com/watch?v=H88re4p-DoU&list=PLt4L3V8wVnF7PJ3wfq1rdJWX4ziasHMHl&index=62
We will start off with a gentle introduction to Apache Pulsar and setting up your first easy standalone cluster. We will then l show you how to produce and consume message to Pulsar using several different Python libraries including Python client, websockets, MQTT and even Kafka.
After this session you will be building real-time streaming and messaging applications with Python.
#PWC2022 attracted nearly 375 attendees from 36 countries and 21 time zones making it the biggest and best year yet. The highly engaging format featured 90 speakers, 6 tracks (including 80 talks and 4 tutorials) and took place virtually on March 21-25, 2022 on LoudSwarm by Six Feet Up.
Apache Pulsar
Apache Flink
Apache Kafka
MQTT
AMQP/RabbitMQ
WebSockets
Python3
bigdata 2022_ FLiP Into Pulsar Apps
In this session, Timothy will introduce you to the world of Apache Pulsar and how to build real-time messaging and streaming applications with a variety of OSS libraries, schemas, languages, frameworks, and tools.
FLiP Into Pulsar Apps
08:30 – 09:15
•
23 Nov, 2022
In this session, Timothy will introduce you to the world of Apache Pulsar and how to build real-time messaging and streaming applications with a variety of OSS libraries, schemas, languages, frameworks, and tools.
The Dream Stream Team for Pulsar and SpringTimothy Spann
THE DREAM STREAM TEAM FOR PULSAR AND SPRING
TIM SPANN - STREAMNATIVE
For building Java application, Spring is the universal answer as it supplies all the connectors and integrations one could want. The same is true for Apache Pulsar as it provides connectors, integration and flexibility to any use case. Apache Pulsar has a robust native Java library to use with Spring as well as other protocol options.
ApachePulsar provides a cloud native, geo-replicated unified messaging platform that allows for many messaging paradims. This lends it self well to upgrading existing applications as Pulsar supports using libraries for WebSockets, MQTT, Kafka, JMS, AMQP and RocketMQ. In this talk I will build some example applications utilizing several different protocols for building a variety of applications from IoT to Microservices to Log Analytics.
https://2022.springio.net/sessions/the-dream-stream-team-for-pulsar-and-spring
SPRING I/O 2022
THE CONFERENCE
Spring I/O is the leading european conference focused on the Spring Framework ecosystem.
Join us in our 9th in-person edition!
May 26/27, 2022 Barcelona, Spain
JConf.dev 2022 - Apache Pulsar Development 101 with JavaTimothy Spann
JConf.dev 2022 - Apache Pulsar Development 101 with Java
https://2022.jconf.dev/
In this session I will get you started with real-time cloud native streaming programming with Java. We will start off with a gentle introduction to Apache Pulsar and setting up your first easy standalone cluster. We will then l show you how to produce and consume message to Pulsar using several different Java libraries including native Java client, AMQP/RabbitMQ, MQTT and even Kafka. After this session you will building real-time streaming and messaging applications with Java. We will also touch on Apache Spark and Apache Flink.
Timothy Spann
Tim Spann is a Developer Advocate @ StreamNative where he works with Apache Pulsar, Apache Flink, Apache NiFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science. https://www.datainmotion.dev/p/about-me.html https://dzone.com/users/297029/bunkertor.html https://conferences.oreilly.com/strata/strata-ny-2018/public/schedule/speaker/185963
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache PulsarTimothy Spann
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache Pulsar
In this session I will get you started with real-time cloud native streaming programming with Java, Golang, Python and Apache NiFi. If there’s a preferred language that the attendees pick, we will focus only on that one. I will start off with an introduction to Apache Pulsar and setting up your first easy standalone cluster in docker. We will then go into terms and architecture so you have an idea of what is going on with your events. I will then show you how to produce and consume messages to and from Pulsar topics. As well as using some of the command line and REST interfaces to monitor, manage and do CRUD on things like tenants, namespaces and topics. We will discuss Functions, Sinks, Sources, Pulsar SQL, Flink SQL and Spark SQL interfaces. We also discuss why you may want to add protocols such as MoP (MQTT), AoP (AMQP/RabbitMQ) or KoP (Kafka) to your cluster. We will also look at WebSockets as a producer and consumer. I will demonstrate a simple web page that sends and receives Pulsar messages with basic JavaScript. After this session you will be able to build simple real-time streaming and messaging applications with your chosen language or tool of your choice.
apache pulsar
tim spann developer advocate
streamnative
datainmotion.dev
Apache Pulsar with MQTT for Edge Computing - Pulsar Summit Asia 2021StreamNative
Today we will span from edge to any and all clouds to support data collection, real-time streaming, sensor ingest, edge computing, IoT use cases and edge AI. Apache Pulsar allows us to build computing at the edge and produce and consume messages at scale in any IoT, hybrid or cloud environment. Apache Pulsar supports MoP which allows for MQTT protocol to be used for high speed messaging.
We will teach you to quickly build scalable open source streaming applications regardless of if you are running in containers, pods, edge devices, VMs, on-premise servers, moving vehicles and any cloud.
DBCC 2021 - FLiP Stack for Cloud Data LakesTimothy Spann
DBCC 2021 - FLiP Stack for Cloud Data Lakes
With Apache Pulsar, Apache NiFi, Apache Flink. The FLiP(N) Stack for Event processing and IoT. With StreamNative Cloud.
DBCC International – Friday 15.10.2021
Powered by Apache Pulsar, StreamNative provides a cloud-native, real-time messaging and streaming platform to support multi-cloud and hybrid cloud strategies.
CODEONTHEBEACH_Streaming Applications with Apache PulsarTimothy Spann
CODEONTHEBEACH_Streaming Applications with Apache Pulsar
https://www.codeonthebeach.com/schedule
Deep Dive into Building Streaming Applications with Apache Pulsar - Timothy Spann
In this session I will get you started with real-time cloud native streaming programming with Java, Golang, Python and Apache NiFi. I will start off with an introduction to Apache Pulsar and setting up your first easy standalone cluster in docker. We will then go into terms and architecture so you have an idea of what is going on with your events. I will then show you how to produce and consume messages to and from Pulsar topics. As well as using some of the command line and REST interfaces to monitor, manage and do CRUD on things like tenants, namespaces and topics.
We will discuss Functions, Sinks, Sources, Pulsar SQL, Flink SQL and Spark SQL interfaces. We also discuss why you may want to add protocols such as MoP (MQTT), AoP (AMQP/RabbitMQ) or KoP (Kafka) to your cluster. We will also look at WebSockets as a producer and consumer. I will demonstrate a simple web page that sends and receives Pulsar messages with basic JavaScript.
After this session you will be able to build simple real-time streaming and messaging applications with your chosen language or tool of your choice.
Princeton Dec 2022 Meetup_ NiFi + Flink + PulsarTimothy Spann
Princeton Dec 2022 Meetup_ NiFi + Flink + Pulsar
Streaming Data Platform for cloud-native event-driven applications
https://github.com/tspannhw/pulsar-csp-ce/blob/main/weather.md
https://github.com/tspannhw/create-nifi-pulsar-flink-apps
https://medium.com/@tspann/using-apache-pulsar-with-cloudera-sql-builder-apache-flink-b518aa9eadff
https://www.meetup.com/new-york-city-apache-pulsar-meetup/events/289674210/
For non-locals, we will Broadcast Live via Youtube. Sign up and we will send out the link.
Location:
TigerLabs in Princeton on the 2nd floor, walk up and the door will be open. Same that we were using for the old Future of Data - Princeton events 2016-2019.
Parking at the school is free. street parking nearby is free. there are meters on some streets, and a few blocks away is a paid parking garage.
We are joining forces with our friends Cloudera again on a FLiPN amazing journey into Real-Time Streaming Applications with Apache Flink, Apache NiFi, and Apache Pulsar.
Discover how to stream data to and from your data lake or data mart using Apache Pulsar™ and Apache NiFi®. Learn how these cloud-native, scalable open-source projects built for streaming data pipelines work together to enable you to quickly build applications with minimal coding.
|WHAT THE SESSION WILL COVER|
Apache NiFi
Apache Pulsar
Apache Flink
Flink SQL
We will show you how to build apps, so download beforehand to Docker, K8, your Laptop, or the cloud.
Cloudera CSP Setup
Getting Started with Cloudera Stream Processing Community Edition
You may download CSP-CE here:
Cloudera Stream Processing Community Edition
The Cloudera CDP User's page:
CDP Resources Page
https://youtu.be/s80sz3NWwHo
https://docs.cloudera.com/csp-ce/latest/index.html
https://www.cloudera.com/downloads/cdf/csp-community-edition.html
Apache Pulsar
https://pulsar.apache.org/docs/getting-started-standalone/
or
https://streamnative.io/free-cloud/
Cloudera + Pulsar
https://community.cloudera.com/t5/Cloudera-Stream-Processing-Forum/Using-Apache-Pulsar-with-SQL-Stream-Builder/m-p/349917
https://community.cloudera.com/t5/Community-Articles/Using-Apache-NiFi-with-Apache-Pulsar-for-Streaming/ta-p/337891
|AGENDA|
6:00 - 6:30 PM EST: Food, Drink, and Networking!!!
6:30 - 7:15 PM EST: Presentation - Tim Spann, StreamNative Developer Advocate
7:15 - 8:00 PM EST: Presentation - John Kuchmek, Cloudera Principal Solutions Engineer
8:00 - 8:30 PM EST: Round Table on Real-Time Streaming, Q&A
|ABOUT THE SPEAKERS|
John Kuchmek is a Principal Solutions Engineer for Cloudera. Before joining Cloudera, John transitioned to the Autonomous Intelligence team where he was in charge of integrating the platforms to allow data scientists to work with various types of data.
Tim Spann is a Developer Advocate for StreamNative. He works with StreamNative Cloud, Apache Pulsar™, Apache Flink®, Flink® SQL, Big Data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, dist
Serverless Event Streaming Applications as Functionson K8Timothy Spann
Serverless Event Streaming Applications as Functionson K8
Data on Kubernetes Day @ Kubecon / CloudNativeCon EU 2022
We will walk through how to build serverless event streaming applications as functions running in a function mesh on kubernetes with cloud native messaging via Apache Pulsar.
In this talk, you will deploy ML functions to transform real-time data on Kubernets.
DOK
Unified Messaging and Data Streaming 101Timothy Spann
https://budapestdata.hu/2022/en/speakers/timothy-spann/
Unified Messaging and Data Streaming 101.pdf
Unified Messaging and Data Streaming 101
Democratizing the ability to build streaming data pipelines will help turn everyone who needs streaming data to be able to do it themselves. By utilizing the open source streaming platform Apache Pulsar enhanced with Apache Flink, we can build messages and data streaming applications utilizing the one unified data platform.
Apache Pulsar supports multiple protocols including Pulsar, AMQP, Kafka, MQTT and RocketMQ. It also supports a variety of message subscription models to support all your workloads from batch, work queues, exactly once streaming and more. With the ability to run cloud native on all your K8, VM, container and cloud platforms. Pulsar has built-in geo-replication, scalability, separate of compute and storage, function support and high reliability. Let’s get streaming.
I will also touch of utilizing Apache Spark and Apache NiFi with Apache Pulsar.
Timothy Spann
Developer Advocate, StreamNative
Tim Spann is a Developer Advocate for StreamNative. He works with StreamNative Cloud, Apache Pulsar, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, Big Data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming.
Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark.
Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
Flink + Pulsar + Spark + NiFi
FLiPNs
Building Modern Data Streaming Apps with PythonTimothy Spann
Building Modern Data Streaming Apps with Python
https://www.conf42.com/DevOps_2023_Tim_Spann_modern_data_streaming_apps
Share
In my session, I will show you some best practices I have discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In my modern approach, we utilize several open-source frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar. From there, we build streaming ETL with Apache Spark and enhance events with Pulsar Functions for ML and enrichment. We build continuous queries against our topics with Flink SQL. We will stream data into various data stores. We use Python for microservices as Functions and stand-alone apps. We use the best streaming tools for the current applications with FLiPN. https://www.flipn.app/
Timothy Spann
Developer Advocate
https://datainmotion.dev/
Similar to Apache Pulsar Development 101 with Python (20)
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNKTimothy Spann
Building Real-Time Pipelines With FLaNK
Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp
The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making.
Apache NiFi
Apache Kafka
Apache Flink
Apache Iceberg
LLM
Generative AI
Slack
Postgresql
Generative AI on Enterprise Cloud with NiFi and MilvusTimothy Spann
Gen AI on Enterprise Cloud
Apache NiFi
Milvus
Apache Kafka
Apache Flink
Cloudera Machine Learning
Cloudera DataFlow
https://medium.com/@tspann/building-a-milvus-connector-for-nifi-34372cb3c7fa
https://www.meetup.com/futureofdata-princeton/events/300737266/
https://lu.ma/q7pcfyjn?source=post_page-----34372cb3c7fa--------------------------------&tk=TTyakY
If you're interested in working with Generative AI on the cloud, this virtual workshop is for you.
Tim Spann from Cloudera and Yujian Tang from Zilliz will cover how you can implement your own GenAI workflows on the cloud at enterprise scale.
9:00 - 9:05: Intro
9:05 - 9:15: What is Milvus
9:15 - 9:25: Cloudera Development Platform
9:25 - 10:00: Demo
Location
https://www.youtube.com/watch?v=IfWIzKsoHnA
https://github.com/tspannhw/SpeakerProfile
https://www.linkedin.com/in/yujiantang/
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesTimothy Spann
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
https://www.youtube.com/watch?v=Yeua8NlzQ3Y
https://www.conf42.com/Large_Language_Models_LLMs_2024_Tim_Spann_generative_ai_streaming
Adding Generative AI to Real-Time Streaming Pipelines
Abstract
Let’s build streaming pipelines that convert streaming events into prompts, call LLMs, and process the results.
Summary
Tim Spann: My talk is adding generative AI to real time streaming pipelines. I'm going to discuss a couple of different open source technologies. We'll touch on Kafka, Nifi, Flink, Python, Iceberg. All the slides, all the code and GitHub are out there.
Llm, if you didn't know, is rapidly evolving. There's a lot of different ways to interact with models. That enrichment, transformation, processing really needs tools. The amount of models and projects and software that are available is massive.
Nifi supports hundreds of different inputs and can convert them on the fly. Great way to distribute your data quickly to whoever needs it without duplication, without tight coupling. Fun to find new things to integrate into.
So what we can do is, well, I want to get a meetup chat going. I have a processor here that just listens for events as they come from slack. And then I'm going to clean it up, add a couple fields and push that out to slack. Every model is a little bit of different tweaking.
Nifi acts as a whole website. And as you see here, it can be get, post, put, whatever you want. We send that response back to flink and it shows up here. Thank you for attending this talk. I'm going to be speaking at some other events very shortly.
Transcript
This transcript was autogenerated. To make changes, submit a PR.
Hi, Tim Spann here. My talk is adding generative AI to real time streaming pipelines, and we're here for the large language model conference at Comp 42, which is always a nice one, great place to be. I'm going to discuss a couple of different open source technologies that work together to enable you to build real time pipelines using large language models. So we'll touch on Kafka, Nifi, Flink, Python, Iceberg, and I'll show you a little bit of each one in the demos. I've been working with data machine learning, streaming IoT, some other things for a number of years, and you could contact me at any of these places, whether Twitter or whatever it's called, some different blogs, or in person at my meetups and at different conferences around the world. I do a weekly newsletter, cover streaming ML, a lot of LLM, open source, Python, Java, all kinds of fun stuff, as I mentioned, do a bunch of different meetups. They are not just in the east coast of the US, they are available virtually live, and I also put them on YouTube, and if you need them somewhere else, let me know. We publish all the slides, all the code and GitHub. Everything you need is out there. Let's get into the talk. Llm, if you didn't know, is rapidly evolving. While you're typing down the things that you use, it
2024 XTREMEJ_ Building Real-time Pipelines with FLaNK_ A Case Study with Tra...Timothy Spann
2024 XTREMEJ_ Building Real-time Pipelines with FLaNK_ A Case Study with Transit Data
https://xtremej.dev/2023/schedule/
Building Real-time Pipelines with FLaNK: A Case Study with Transit Data
Overview of the problem, the application (code walkthru and running), overview of FLaNK, introduction to NiFi, introduction to Kafka, and introduction to Flink.
28March2024-Codeless-Generative-AI-Pipelines
https://www.meetup.com/futureofdata-princeton/events/299440871/
https://www.meetup.com/real-time-analytics-meetup-ny/events/299290822/
******Note*****
The event is seat-limited, therefore please complete your registration here. Only people completing the form will be able to attend.
-----------------------
We're excited to invite you to join us in-person, for a Real-Time Analytics exploration!
Join us for an evening of insights, networking as we delve into the OSS technologies shaping the field!
Agenda:
05:30-06:00: Pizza and friends
06:00- 06:40: Codeless GenAI Pipelines with Flink, Kafka, NiFi
06:40- 07:20 Real-Time Analytics in the Corporate World: How Apache Pinot® Powers Industry Leaders
07:20-07:30 QNA
Codeless GenAI Pipelines with Flink, Kafka, NiFi | Tim Spann, Cloudera
Explore the power of real-time streaming with GenAI using Apache NiFi. Learn how NiFi simplifies data engineering workflows, allowing you to focus on creativity over technical complexities. I'll guide you through practical examples, showcasing NiFi's automation impact from ingestion to delivery. Whether you're a seasoned data engineer or new to GenAI, this talk offers valuable insights into optimizing workflows. Join us to unlock the potential of real-time streaming and witness how NiFi makes data engineering a breeze for GenAI applications!
Real-Time Analytics in the Corporate World: How Apache Pinot® Powers Industry Leaders | Viktor Gamov, StarTree
Explore how industry leaders like LinkedIn, Uber Eats, and Stripe are mastering real-time data with Viktor as your guide. Discover how Apache Pinot transforms data into actionable insights instantly. Viktor will showcase Pinot's features, including the Star-Tree Index, and explain why it's a game-changer in data strategy. This session is for everyone, from data geeks to business gurus, eager to uncover the future of tech. Join us and be wowed by the power of real-time analytics with Apache Pinot!
-------
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera.
He works with Apache NiFi, Apache Kafka, Apache Pulsar, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more.
TCFPro24 Building Real-Time Generative AI PipelinesTimothy Spann
https://princetonacm.acm.org/tcfpro/
18th Annual IEEE IT Professional Conference (ITPC)
Armstrong Hall at The College of New Jersey
Friday, March 15th, 2024 | 10:00 AM to 5:00 PM
IT Professional Conference at Trenton Computer Festival
IEEE Information Technology Professional Conference on Friday, March 15th, 2024
TCFPro24 Building Real-Time Generative AI Pipelines
Building Real-Time Generative AI Pipelines
In this talk, Tim will delve into the exciting realm of building real-time generative AI pipelines with streaming capabilities. The discussion will revolve around the integration of cutting-edge technologies to create dynamic and responsive systems that harness the power of generative algorithms.
From leveraging streaming data sources to implementing advanced machine learning models, the presentation will explore the key components necessary for constructing a robust real-time generative AI pipeline. Practical insights, use cases, and best practices will be shared, offering a comprehensive guide for developers and data scientists aspiring to design and implement dynamic AI systems in a streaming environment.
Tim will show a live demo showing we can use Apache NiFi to provide a live chat between a person in Slack and several LLM models all orchestrated with Apache NiFi, Apache Kafka and Python. We will use RAG against Chroma and Pinecone vector data stores, Hugging Face and WatsonX.AI LLM, and add additional context with NiFi lookups of stocks, weather and other data streams in real-time.
Timothy Spann
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming.
Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark.
Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...Timothy Spann
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipelines
https://www.meetup.com/futureofdata-newyork/events/298660453/
Unlocking Financial Data with Real-Time Pipelines
(Flink Analytics on Stocks with SQL )
By Timothy Spann
Financial institutions thrive on accurate and timely data to drive critical decision-making processes, risk assessments, and regulatory compliance. However, managing and processing vast amounts of financial data in real-time can be a daunting task. To overcome this challenge, modern data engineering solutions have emerged, combining powerful technologies like Apache Flink, Apache NiFi, Apache Kafka, and Iceberg to create efficient and reliable real-time data pipelines. In this talk, we will explore how this technology stack can unlock the full potential of financial data, enabling organizations to make data-driven decisions swiftly and with confidence.
Introduction: Financial institutions operate in a fast-paced environment where real-time access to accurate and reliable data is crucial. Traditional batch processing falls short when it comes to handling rapidly changing financial markets and responding to customer demands promptly. In this talk, we will delve into the power of real-time data pipelines, utilizing the strengths of Apache Flink, Apache NiFi, Apache Kafka, and Iceberg, to unlock the potential of financial data. I will be utilizing NiFi 2.0 with Python and Vector Databases.
Timothy Spann
Principal Developer Advocate, Cloudera
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Kafka, Apache Pulsar, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
https://twitter.com/PaaSDev
https://www.linkedin.com/in/timothyspann/
https://medium.com/@tspann
https://github.com/tspannhw/FLiPStackWeekly/
Conf42-Python-Building Apache NiFi 2.0 Python Processors
https://www.conf42.com/Python_2024_Tim_Spann_apache_nifi_2_processors
Building Apache NiFi 2.0 Python Processors
Abstract
Let’s enhance real-time streaming pipelines with smart Python code. Adding code for vector databases and LLM.
Summary
Tim Spann: I'm going to be talking today, be building Apache 9520 Python processors. One of the main purposes of supporting Python in the streaming tool Apache Nifi is to interface with new machine learning and AI and Gen AI. He says Python is a real game changer for Cloudera.
You're just going to add some metadata around it. It's a great way to pass a file along without changing it too substantially. We really need you to have Python 310 and again JDK 21 on your machine. You got to be smart about how you use these models.
There are a ton of python processors available. You can use them in multiple ways. We're still in the early world of Python processors, so now's the time to start putting yours out there. Love to see a lot of people write their own.
When we are parsing documents here, again, this is the Python one I'm picking PDF. Lots of different things you could do. If you're interested on writing your own python code for Apache Nifi, definitely reach out and thank.
Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg with Stock Data and LLM
Abstract
In this talk, we’ll discuss how to use Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg to process and analyze stock data. We demonstrated the ingestion, processing, and analysis of stock data. Additionally, we illustrated how to use an LLM to generate predictions from the analyzed data.
Karin Wolok
Developer Relations, Dev Marketing, and Community Programming @ Project Elevate
Karin Wolok's LinkedIn account Karin Wolok's twitter account
Tim Spann
Principal Developer Advocate @ Cloudera
Tim Spann's LinkedIn account Tim Spann's twitter account
https://www.conf42.com/Python_2024_Karin_Wolok_Tim_Spann_nifi__kafka_risingwave_iceberg_llm
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI PipelinesTimothy Spann
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines
https://www.aicamp.ai/event/eventdetails/W2024022214
apache nifi
llm
generative ai
gen ai
ml
dl
machine learning
apache kafka
apache flink
postgresql
python
AI Meetup (NYC): GenAI, LLMs, ML and Data
Feb 22, 05:30 PM EST
Welcome to the monthly in-person AI meetup in New York City, in collaboration with Microsoft. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers
Agenda:
* 5:30pm~6:00pm: Checkin, Food/drink and networking
* 6:00pm~6:10pm: Welcome/community update
* 6:10pm~8:30pm: Tech talks
* 8:30pm: Q&A, Open discussion
Tech Talk: Searching and Reasoning Over Multimedia Data with Vector Databases and LMMs
Speaker: Zain Hasan (Weaviate LinkedIn)
Abstract: In this talk, Zain Hasan will discuss how we can use open-source multimodal embedding models in conjunction with large generative multimodal models that can that can see, hear, read, and feel data(!), to perform cross-modal search(searching audio with images, videos with text etc.) and multimodal retrieval augmented generation (MM-RAG) at the billion-object scale with the help of open source vector databases. I will also demonstrate, with live code demos, how being able to perform this cross-modal retrieval in real-time can enables users to use LLMs that can reason over their enterprise multimodal data. This talk will revolve around how we can scale the usage of multimodal embedding and generative models in production.
Tech Talk: Codeless Generative AI Pipelines
Speaker: Timothy Spann (Cloudera LinkedIn)
Abstract: Join us for an insightful talk on leveraging the power of real-time streaming tools, specifically Apache NiFi, to revolutionize GenAI data engineering. In this session, we’ll explore how the integration of Apache NiFi can automate the entire process of prompt building, making it a seamless and efficient task.
Speakers/Topics:
Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics
Sponsors:
We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will have the chance to speak at the meetups, receive prominent recognition, and gain exposure to our extensive membership base of 20,000+ local or 300K+ developers worldwide.
Venue:
Microsoft NYC - Times Square, 11 Times Square, New York, NY 10036
Room Name: Central Park West 6501
Community on Slack/Discord
- Event chat: chat and connect with speakers and attendees
- Sharing blogs, events, job openings, projects collaborations
Join Slack (search and join the #newyork channel) | Join Discord
DBA Fundamentals Group: Continuous SQL with Kafka and FlinkTimothy Spann
DBA Fundamentals Group: Continuous SQL with Kafka and Flink
20-Feb-2024
In this talk, I will walk through how someone can set up and run continuous SQL queries against Kafka topics utilizing Apache Flink. We will walk through creating Kafka topics, schemas, and publishing data.
We will then cover consuming Kafka data, joining Kafka topics, and inserting new events into Kafka topics as they arrive. This basic overview will show hands-on techniques, tips, and examples of how to do this.
Tim Spann
Tim Spann is the Principal Developer Advocate for Data in Motion @ Cloudera where he works with Apache Kafka, Apache Flink, Apache NiFi, Apache Iceberg, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
OSACon 2023_ Unlocking Financial Data with Real-Time PipelinesTimothy Spann
OSACon 2023_ Unlocking Financial Data with Real-Time Pipelines
Unlocking Financial Data with Real-Time Pipelines
Financial institutions thrive on accurate and timely data to drive critical decision-making processes, risk assessments, and regulatory compliance. However, managing and processing vast amounts of financial data in real-time can be a daunting task. To overcome this challenge, modern data engineering solutions have emerged, combining powerful technologies like Apache Flink, Apache NiFi, Apache Kafka, and Iceberg to create efficient and reliable real-time data pipelines. In this talk, we will explore how this technology stack can unlock the full potential of financial data, enabling organizations to make data-driven decisions swiftly and with confidence.
Introduction: Financial institutions operate in a fast-paced environment where real-time access to accurate and reliable data is crucial. Traditional batch processing falls short when it comes to handling rapidly changing financial markets and responding to customer demands promptly. In this talk, we will delve into the power of real-time data pipelines, utilizing the strengths of Apache Flink, Apache NiFi, Apache Kafka, and Iceberg, to unlock the potential of financial data.
Key Points to be Covered:
Introduction to Real-Time Data Pipelines: a. The limitations of traditional batch processing in the financial domain. b. Understanding the need for real-time data processing.
Apache Flink: Powering Real-Time Stream Processing: a. Overview of Apache Flink and its role in real-time stream processing. b. Use cases for Apache Flink in the financial industry. c. How Flink enables fast, scalable, and fault-tolerant processing of streaming financial data.
Apache Kafka: Building Resilient Event Streaming Platforms: a. Introduction to Apache Kafka and its role as a distributed streaming platform. b. Kafka's capabilities in handling high-throughput, fault-tolerant, and real-time data streaming. c. Integration of Kafka with financial data sources and consumers.
Apache NiFi: Data Ingestion and Flow Management: a. Overview of Apache NiFi and its role in data ingestion and flow management. b. Data integration and transformation capabilities of NiFi for financial data. c. Utilizing NiFi to collect and process financial data from diverse sources.
Iceberg: Efficient Data Lake Management: a. Understanding Iceberg and its role in managing large-scale data lakes. b. Iceberg's schema evolution and table-level metadata capabilities. c. How Iceberg simplifies data lake management in financial institutions.
Real-World Use Cases: a. Real-time fraud detection using Flink, Kafka, and NiFi. b. Portfolio risk analysis with Iceberg and Flink. c. Streamlined regulatory reporting leveraging all four technologies.
Best Practices and Considerations: a. Architectural considerations when building real-time financial data pipelines. b. Ensuring data integrity, security, and compliance in real-time pipelines. c. Scalability an
Building Real-time Travel Alerts
In this session, we will walk through how to build a complete streaming application to send alerts based on travel advisories from public data. We will also join in other data sources of relevance and push out alerts.
We will show you how to build this streaming application with Apache NiFi, Apache Kafka, and Apache Flink and show you when/why/how, and what to build to maximize performance, productivity, and ease of development.
Let's get streaming.
Apache Flink
Apache Kafka
Apache NiFi
FLaNK Stack
Tim Spann
Big Data Conference Europe 2023
JConWorld_ Continuous SQL with Kafka and FlinkTimothy Spann
JConWorld: Continuous SQL with Kafka and Flink
In this talk, I will walk through how someone can setup and run continous SQL queries against Kafka topics utilizing Apache Flink. We will walk through creating Kafka topics, schemas and publishing data.
We will then cover consuming Kafka data, joining Kafka topics and inserting new events into Kafka topics as they arrive. This basic over view will show hands-on techniques, tips and examples of how to do this.
Tim Spann is the Principal Developer Advocate for Data in Motion @ Cloudera where he works with Apache Kafka, Apache Flink, Apache NiFi, Apache Iceberg, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science. https://www.datainmotion.dev/p/about-me.html https://dzone.com/users/297029/bunkertor.html
https://www.youtube.com/channel/UCDIDMDfje6jAvNE8DGkJ3_w?view_as=subscriber
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
Tim Combridge from Sensible Giraffe and Salesforce Ben presents some important tips that all developers should know when dealing with Flows in Salesforce.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
How Does XfilesPro Ensure Security While Sharing Documents in Salesforce?XfilesPro
Worried about document security while sharing them in Salesforce? Fret no more! Here are the top-notch security standards XfilesPro upholds to ensure strong security for your Salesforce documents while sharing with internal or external people.
To learn more, read the blog: https://www.xfilespro.com/how-does-xfilespro-make-document-sharing-secure-and-seamless-in-salesforce/
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
1. Pulsar Summit
San Francisco
Hotel Nikko
August 18 2022
Timothy Spann
Developer Advocate, StreamNative
Apache Pulsar
Development 101
with Python
2. Tim Spann
Developer Advocate
StreamNative
FLiP(N) Stack = Flink, Pulsar and NiFi Stack
Streaming Systems & Data Architecture Expert
Experience
15+ years of experience with streaming
technologies including Pulsar, Flink, Spark, NiFi, Big
Data, Cloud, MXNet, IoT, Python and more.
Today, he helps to grow the Pulsar community
sharing rich technical knowledge and experience at
both global conferences and through individual
conversations.
https://streamnative.io/pulsar-python/
4. FLiP Stack Weekly
This week in Apache Flink, Apache Pulsar, Apache
NiFi, Apache Spark and open source friends.
https://bit.ly/32dAJft
5. Python Application for ADS-B Data
Diagram
Python App REST CALL
LOGGING
ANALYTICS
SEND TO
PULSAR
https://github.com/tspannhw/FLiP-Py-ADS-B
6. Apache Pulsar Training
● Instructor-led courses
○ Pulsar Fundamentals
○ Pulsar Developers
○ Pulsar Operations
● On-demand learning with labs
● 300+ engineers, admins and architects trained!
StreamNative Academy
Now Available
FREE On-Demand
Pulsar Training
Academy.StreamNative.io
7. What is Apache Pulsar?
Unified
Messaging
Platform
Guaranteed
Message Delivery Resiliency Infinite
Scalability
8. ● “Bookies”
● Stores messages and cursors
● Messages are grouped in
segments/ledgers
● A group of bookies form an
“ensemble” to store a ledger
● “Brokers”
● Handles message routing and
connections
● Stateless, but with caches
● Automatic load-balancing
● Topics are composed of
multiple segments
●
● Stores metadata for both
Pulsar and BookKeeper
● Service discovery
Store
Messages
Metadata &
Service Discovery
Metadata &
Service Discovery
Pulsar Cluster
Metadata Store
(ZK, RocksDB, etcd, …)
9. Pulsar’s Publish-Subscribe model
Broker
Subscription
Consumer 1
Consumer 2
Consumer 3
Topic
Producer 1
Producer 2
● Producers send messages.
● Topics are an ordered, named channel that
producers use to transmit messages to
subscribed consumers.
● Messages belong to a topic and contain an
arbitrary payload.
● Brokers handle connections and routes
messages between producers /
consumers.
● Subscriptions are named configuration
rules that determine how messages are
delivered to consumers.
● Consumers receive messages.
10. Subscription Modes
Different subscription modes have
different semantics:
Exclusive/Failover - guaranteed
order, single active consumer
Shared - multiple active consumers,
no order
Key_Shared - multiple active
consumers, order for given key
Producer 1
Producer 2
Pulsar Topic
Subscription D
Consumer D-1
Consumer D-2
Key-Shared
<
K
1,
V
10
>
<
K
1,
V
11
>
<
K
1,
V
12
>
<
K
2
,V
2
0
>
<
K
2
,V
2
1>
<
K
2
,V
2
2
>
Subscription C
Consumer C-1
Consumer C-2
Shared
<
K
1,
V
10
>
<
K
2,
V
21
>
<
K
1,
V
12
>
<
K
2
,V
2
0
>
<
K
1,
V
11
>
<
K
2
,V
2
2
>
Subscription A Consumer A
Exclusive
Subscription B
Consumer B-1
Consumer B-2
In case of failure in
Consumer B-1
Failover
11. Messages - the Basic Unit of Pulsar
Component Description
Value / data payload The data carried by the message. All Pulsar messages contain raw bytes, although message data
can also conform to data schemas.
Key Messages are optionally tagged with keys, used in partitioning and also is useful for things like
topic compaction.
Properties An optional key/value map of user-defined properties.
Producer name The name of the producer who produces the message. If you do not specify a producer name, the
default name is used.
Sequence ID Each Pulsar message belongs to an ordered sequence on its topic. The sequence ID of the
message is its order in that sequence.
13. ● Consume messages from one
or more Pulsar topics.
● Apply user-supplied
processing logic to each
message.
● Publish the results of the
computation to another topic.
● Support multiple
programming languages (Java,
Python, Go)
● Can leverage 3rd-party
libraries
Pulsar Functions
15. Function Mesh
Pulsar Functions, along with Pulsar IO/Connectors, provide a powerful API for ingesting,
transforming, and outputting data.
Function Mesh, another StreamNative project, makes it easier for developers to create entire
applications built from sources, functions, and sinks all through a declarative API.
19. Spark + Pulsar
https://pulsar.apache.org/docs/en/adaptors-spark/
val dfPulsar = spark.readStream.format("
pulsar")
.option("
service.url", "pulsar://pulsar1:6650")
.option("
admin.url", "http://pulsar1:8080
")
.option("
topic", "persistent://public/default/airquality").load()
val pQuery = dfPulsar.selectExpr("*")
.writeStream.format("
console")
.option("truncate", false).start()
____ __
/ __/__ ___ _____/ /__
_ / _ / _ `/ __/ '_/
/___/ .__/_,_/_/ /_/_ version 3.2.0
/_/
Using Scala version 2.12.15
(OpenJDK 64-Bit Server VM, Java 11.0.11)
20. ● Unified computing engine
● Batch processing is a special case of stream processing
● Stateful processing
● Massive Scalability
● Flink SQL for queries, inserts against Pulsar Topics
● Streaming Analytics
● Continuous SQL
● Continuous ETL
● Complex Event Processing
● Standard SQL Powered by Apache Calcite
Apache Flink?
21. SQL
select aqi, parameterName, dateObserved, hourObserved, latitude,
longitude, localTimeZone, stateCode, reportingArea from
airquality
select max(aqi) as MaxAQI, parameterName, reportingArea from
airquality group by parameterName, reportingArea
select max(aqi) as MaxAQI, min(aqi) as MinAQI, avg(aqi) as
AvgAQI, count(aqi) as RowCount, parameterName, reportingArea
from airquality group by parameterName, reportingArea
23. Schema Registry
Schema Registry
schema-1 (value=Avro/Protobuf/JSON) schema-2 (value=Avro/Protobuf/JSON) schema-3
(value=Avro/Protobuf/JSON)
Schema
Data
ID
Local Cache
for Schemas
+
Schema
Data
ID +
Local Cache
for Schemas
Send schema-1
(value=Avro/Protobuf/JSON) data
serialized per schema ID
Send (register)
schema (if not in
local cache)
Read schema-1
(value=Avro/Protobuf/JSON) data
deserialized per schema ID
Get schema by ID (if
not in local cache)
Producers Consumers
25. Pulsar Functions
● Lightweight computation
similar to AWS Lambda.
● Specifically designed to use
Apache Pulsar as a message
bus.
● Function runtime can be
located within Pulsar Broker.
● Python Functions
A serverless event streaming
framework
26. ● Consume messages from one or
more Pulsar topics.
● Apply user-supplied processing
logic to each message.
● Publish the results of the
computation to another topic.
● Support multiple programming
languages (Java, Python, Go)
● Can leverage 3rd-party libraries
to support the execution of ML
models on the edge.
Pulsar Functions
28. Run a Local Standalone Bare Metal
wget
https://archive.apache.org/dist/pulsar/pulsar-2.9.1/apache-pulsar-2.9.1-bi
n.tar.gz
tar xvfz apache-pulsar-2.9.1-bin.tar.gz
cd apache-pulsar-2.9.1
bin/pulsar standalone
(For Pulsar SQL Support)
bin/pulsar sql-worker start
https://pulsar.apache.org/docs/en/standalone/
29. <or> Run in StreamNative Cloud
Scan the QR code to earn
$200 in cloud credit
30. Building Tenant, Namespace, Topics
bin/pulsar-admin tenants create conference
bin/pulsar-admin namespaces create conference/pythonweb
bin/pulsar-admin tenants list
bin/pulsar-admin namespaces list conference
bin/pulsar-admin topics create persistent://conference/pythonweb/first
bin/pulsar-admin topics list conference/pythonweb
31. Install Python 3 Pulsar Client
pip3 install pulsar-client=='2.9.1[all]'
# Depending on Platform May Need to Build C++ Client
For Python on Pulsar on Pi https://github.com/tspannhw/PulsarOnRaspberryPi
https://pulsar.apache.org/docs/en/client-libraries-python/
32. Building a Python 3 Producer
import pulsar
client = pulsar.Client('pulsar://localhost:6650')
producer = client.create_producer('persistent://conference/pythonweb/first')
producer.send(('Simple Text Message').encode('utf-8'))
client.close()