This document summarizes a training presentation on Azure Data Explorer (Kusto). The presentation covered:
1. An introduction to Kusto as a new way to analyze big data and logs that is fast, easy to use, and helps understand services quickly.
2. Examples of different Kusto query types including counting, filtering, aggregating, rendering graphs, and combining queries.
3. How Kusto is used at Taboola to analyze HTTP logs from their CDN, including database sizes and architecture.
4. Additional features like dashboards, alerts, notebooks, and community resources for learning more.
5. A question and answer session addressing common questions about Kusto.
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems.
There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
Delta Lake delivers reliability, security and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture.
Building Cloud-Native App Series - Part 3 of 11
Microservices Architecture Series
AWS Kinesis Data Streams
AWS Kinesis Firehose
AWS Kinesis Data Analytics
Apache Flink - Analytics
Radical Speed for SQL Queries on Databricks: Photon Under the HoodDatabricks
Join this session to hear from the Photon product and engineering team talk about the latest developments with the project.
As organizations embrace data-driven decision-making, it has become imperative for them to invest in a platform that can quickly ingest and analyze massive amounts and types of data. With their data lakes, organizations can store all their data assets in cheap cloud object storage. But data lakes alone lack robust data management and governance capabilities. Fortunately, Delta Lake brings ACID transactions to your data lakes – making them more reliable while retaining the open access and low storage cost you are used to.
Using Delta Lake as its foundation, the Databricks Lakehouse platform delivers a simplified and performant experience with first-class support for all your workloads, including SQL, data engineering, data science & machine learning. With a broad set of enhancements in data access and filtering, query optimization and scheduling, as well as query execution, the Lakehouse achieves state-of-the-art performance to meet the increasing demands of data applications. In this session, we will dive into Photon, a key component responsible for efficient query execution.
Photon was first introduced at Spark and AI Summit 2020 and is written from the ground up in C++ to take advantage of modern hardware. It uses the latest techniques in vectorized query processing to capitalize on data- and instruction-level parallelism in CPUs, enhancing performance on real-world data and applications — all natively on your data lake. Photon is fully compatible with the Apache Spark™ DataFrame and SQL APIs to ensure workloads run seamlessly without code changes. Come join us to learn more about how Photon can radically speed up your queries on Databricks.
Lambda architecture is a popular technique where records are processed by a batch system and streaming system in parallel. The results are then combined during query time to provide a complete answer. Strict latency requirements to process old and recently generated events made this architecture popular. The key downside to this architecture is the development and operational overhead of managing two different systems.
There have been attempts to unify batch and streaming into a single system in the past. Organizations have not been that successful though in those attempts. But, with the advent of Delta Lake, we are seeing lot of engineers adopting a simple continuous data flow model to process data as it arrives. We call this architecture, The Delta Architecture.
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks
Recently, a set of modern table formats such as Delta Lake, Hudi, Iceberg spring out. Along with Hive Metastore these table formats are trying to solve problems that stand in traditional data lake for a long time with their declared features like ACID, schema evolution, upsert, time travel, incremental consumption etc.
Delta Lake delivers reliability, security and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture.
Building Cloud-Native App Series - Part 3 of 11
Microservices Architecture Series
AWS Kinesis Data Streams
AWS Kinesis Firehose
AWS Kinesis Data Analytics
Apache Flink - Analytics
Radical Speed for SQL Queries on Databricks: Photon Under the HoodDatabricks
Join this session to hear from the Photon product and engineering team talk about the latest developments with the project.
As organizations embrace data-driven decision-making, it has become imperative for them to invest in a platform that can quickly ingest and analyze massive amounts and types of data. With their data lakes, organizations can store all their data assets in cheap cloud object storage. But data lakes alone lack robust data management and governance capabilities. Fortunately, Delta Lake brings ACID transactions to your data lakes – making them more reliable while retaining the open access and low storage cost you are used to.
Using Delta Lake as its foundation, the Databricks Lakehouse platform delivers a simplified and performant experience with first-class support for all your workloads, including SQL, data engineering, data science & machine learning. With a broad set of enhancements in data access and filtering, query optimization and scheduling, as well as query execution, the Lakehouse achieves state-of-the-art performance to meet the increasing demands of data applications. In this session, we will dive into Photon, a key component responsible for efficient query execution.
Photon was first introduced at Spark and AI Summit 2020 and is written from the ground up in C++ to take advantage of modern hardware. It uses the latest techniques in vectorized query processing to capitalize on data- and instruction-level parallelism in CPUs, enhancing performance on real-world data and applications — all natively on your data lake. Photon is fully compatible with the Apache Spark™ DataFrame and SQL APIs to ensure workloads run seamlessly without code changes. Come join us to learn more about how Photon can radically speed up your queries on Databricks.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
"The common use cases of Spark SQL include ad hoc analysis, logical warehouse, query federation, and ETL processing. Spark SQL also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning, and GraphFrame for graph-parallel computation. For boosting the speed of your Spark applications, you can perform the optimization efforts on the queries prior employing to the production systems. Spark query plans and Spark UIs provide you insight on the performance of your queries. This talk discloses how to read and tune the query plans for enhanced performance. It will also cover the major related features in the recent and upcoming releases of Apache Spark.
"
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Apache Spark on K8S Best Practice and Performance in the CloudDatabricks
Kubernetes As of Spark 2.3, Spark can run on clusters managed by Kubernetes. we will describes the best practices about running Spark SQL on Kubernetes upon Tencent cloud includes how to deploy Kubernetes against public cloud platform to maximum resource utilization and how to tune configurations of Spark to take advantage of Kubernetes resource manager to achieve best performance. To evaluate performance, the TPC-DS benchmarking tool will be used to analysis performance impact of queries between configurations set.
Speakers: Junjie Chen, Junping Du
Vous apprendrez également à :
• Créer plus rapidement des produits et fonctionnalités à l’aide d’une suite complète de connecteurs et d’outils de gestion des flux, et à connecter vos environnements à des pipelines de données
• Protéger vos données et charges de travail les plus critiques grâce à des garanties intégrées en matière de sécurité, de gouvernance et de résilience
• Déployer Kafka à grande échelle en quelques minutes tout en réduisant les coûts et la charge opérationnelle associés
Apache Iceberg: An Architectural Look Under the CoversScyllaDB
Data Lakes have been built with a desire to democratize data - to allow more and more people, tools, and applications to make use of data. A key capability needed to achieve it is hiding the complexity of underlying data structures and physical data storage from users. The de-facto standard has been the Hive table format addresses some of these problems but falls short at data, user, and application scale. So what is the answer? Apache Iceberg.
Apache Iceberg table format is now in use and contributed to by many leading tech companies like Netflix, Apple, Airbnb, LinkedIn, Dremio, Expedia, and AWS.
Watch Alex Merced, Developer Advocate at Dremio, as he describes the open architecture and performance-oriented capabilities of Apache Iceberg.
You will learn:
• The issues that arise when using the Hive table format at scale, and why we need a new table format
• How a straightforward, elegant change in table format structure has enormous positive effects
• The underlying architecture of an Apache Iceberg table, how a query against an Iceberg table works, and how the table’s underlying structure changes as CRUD operations are done on it
• The resulting benefits of this architectural design
Real-time Analytics with Trino and Apache PinotXiang Fu
Trino summit 2021:
Overview of Trino Pinot Connector, which bridges the flexibility of Trino's full SQL support to the power of Apache Pinot's realtime analytics, giving you the best of both worlds.
Traditionally database systems were optimized either for OLAP either for OLTP workloads. Such mainstream DBMSes like Postgres,MySQL,... are mostly used for OLTP, while Greenplum, Vertica, Clickhouse, SparkSQL,... are oriented on analytic queries. But right now many companies do not want to have two different data stores for OLAP/OLTP and need to perform analytic queries on most recent data. I want to discuss which features should be added to Postgres to efficiently handle HTAP workload.
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
New Directions in pySpark for Time Series Analysis: Spark Summit East talk by...Spark Summit
Whether it’s Internet of Things (IoT), analysis of Financial Data, or Adtech, the arrival of events in time order requires tools and techniques that are noticeably missing from the Pandas and pySpark software stack.
In this talk, we’ll cover Two Sigma’s contribution to time series analysis for Spark, our work with Pandas, and propose a roadmap for to future-proof pySpark and establish Python as a first class language in the Spark Ecosystem.
Presentation for Pervasive Systems class lectured by prof. Ioannis Chatzigiannakis, a.y. 2015-16, about the No-SQL database InfluxDB. The course is intended for students of MS in Engineering in Computer Science at Sapienza - University of Rome.
The complete code for the demo is available on Github:
https://github.com/RobGaud/PervasiveSystemsPersonal
You can also find me on LinkedIn:
https://www.linkedin.com/in/roberto-gaudenzi-4b0422116
Standing on the Shoulders of Open-Source Giants: The Serverless Realtime Lake...HostedbyConfluent
"Unlike just a few years ago, today the lakehouse architecture is an established data platform embraced by all major cloud data companies such as AWS, Azure, Google, Oracle, Microsoft, Snowflake and Databricks.
This session kicks off with a technical, no-nonsense introduction to the lakehouse concept, dives deep into the lakehouse architecture and recaps how a data lakehouse is built from the ground up with streaming as a first-class citizen.
Then we focus on serverless for streaming use cases. Serverless concepts are well-known from developers triggering hundreds of thousands of AWS Lambda functions at a negligible cost. However, the same concept becomes more interesting when looking at data platforms.
We have all heard about the principle ""It runs best on Powerpoint"", so I decided to skip slides here and bring a serverless demo instead:
A hands-on, fun, and interactive serverless streaming use case example where we ingest live events from hundreds of mobile devices (don't miss out - bring your phone and be part of it!!). Based on this use case I will critically explore how much of a modern lakehouse is serverless and how we implemented that at Databricks (spoiler alert: serverless is everywhere from data pipelines, workflows, optimized Spark APIs, to ML).
TL;DR benefits for the Data Practitioners:
-Recap the OSS foundation of the Lakehouse architecture and understand its appeal
- Understand the benefits of leveraging a lakehouse for streaming and what's there beyond Spark Structured Streaming.
- Meat of the talk: The Serverless Lakehouse. I give you the tech bits beyond the hype. How does a serverless lakehouse differ from other serverless offers?
- Live, hands-on, interactive demo to explore serverless data engineering data end-to-end. For each step we have a critical look and I explain what it means, e.g for you saving costs and removing operational overhead."
Building Robust ETL Pipelines with Apache SparkDatabricks
Stable and robust ETL pipelines are a critical component of the data infrastructure of modern enterprises. ETL pipelines ingest data from a variety of sources and must handle incorrect, incomplete or inconsistent records and produce curated, consistent data for consumption by downstream applications. In this talk, we’ll take a deep dive into the technical details of how Apache Spark “reads” data and discuss how Spark 2.2’s flexible APIs; support for a wide variety of datasources; state of art Tungsten execution engine; and the ability to provide diagnostic feedback to users, making it a robust framework for building end-to-end ETL pipelines.
Log Management
Log Monitoring
Log Analysis
Need for Log Analysis
Problem with Log Analysis
Some of Log Management Tool
What is ELK Stack
ELK Stack Working
Beats
Different Types of Server Logs
Example of Winlog beat, Packetbeat, Apache2 and Nginx Server log analysis
Mimikatz
Malicious File Detection using ELK
Practical Setup
Conclusion
Interconnection Automation For All - Extended - MPS 2023Chris Grundemann
Matt "Grizz" Griswold and Chris Grundemann are both IX founders, internetworking experts, and automation proponents. With over 4 decades of combined experience they are now turning to sharing what they've learned about automating BGP and interconnection through a set of open source tools, along with support and services for those that need it.
This talk will share what they have learned both from personal experience as well as through dozens of recent interviews with IX operators and interconnection engineers over the past several months. Including common challenges, productive methodologies, and best practices.
The highlight of the talk will be announcing and describing two open source automation tools built to make interconnection and BGP easier for everyone. One is ixCtl, which is built to automate the most common and problematic tasks involved in running an internet exchange point, particularly configuring and managing secure route servers. The other is PeerCtl, which is built to automate the most common and problematic tasks involved in interconnecting an AS; from bilateral and multilateral peering to PNI and also transit connections.
Code for both (along with several other tools) is available on GitHub: https://github.com/fullctl.
Speaker: Chris Grundemann
Speaker: Matt Griswold
Monitoring Big Data Systems Done "The Simple Way" - Demi Ben-Ari - Codemotion...Codemotion
Once you start working with Big Data systems, you discover a whole bunch of problems you won’t find in monolithic systems. Monitoring all of the components becomes a big data problem itself. In the talk, we’ll mention all of the aspects that you should take into consideration when monitoring a distributed system using tools like Web Services, Spark, Cassandra, MongoDB, AWS. Not only the tools, what should you monitor about the actual data that flows in the system? We’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
"The common use cases of Spark SQL include ad hoc analysis, logical warehouse, query federation, and ETL processing. Spark SQL also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning, and GraphFrame for graph-parallel computation. For boosting the speed of your Spark applications, you can perform the optimization efforts on the queries prior employing to the production systems. Spark query plans and Spark UIs provide you insight on the performance of your queries. This talk discloses how to read and tune the query plans for enhanced performance. It will also cover the major related features in the recent and upcoming releases of Apache Spark.
"
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Apache Spark on K8S Best Practice and Performance in the CloudDatabricks
Kubernetes As of Spark 2.3, Spark can run on clusters managed by Kubernetes. we will describes the best practices about running Spark SQL on Kubernetes upon Tencent cloud includes how to deploy Kubernetes against public cloud platform to maximum resource utilization and how to tune configurations of Spark to take advantage of Kubernetes resource manager to achieve best performance. To evaluate performance, the TPC-DS benchmarking tool will be used to analysis performance impact of queries between configurations set.
Speakers: Junjie Chen, Junping Du
Vous apprendrez également à :
• Créer plus rapidement des produits et fonctionnalités à l’aide d’une suite complète de connecteurs et d’outils de gestion des flux, et à connecter vos environnements à des pipelines de données
• Protéger vos données et charges de travail les plus critiques grâce à des garanties intégrées en matière de sécurité, de gouvernance et de résilience
• Déployer Kafka à grande échelle en quelques minutes tout en réduisant les coûts et la charge opérationnelle associés
Apache Iceberg: An Architectural Look Under the CoversScyllaDB
Data Lakes have been built with a desire to democratize data - to allow more and more people, tools, and applications to make use of data. A key capability needed to achieve it is hiding the complexity of underlying data structures and physical data storage from users. The de-facto standard has been the Hive table format addresses some of these problems but falls short at data, user, and application scale. So what is the answer? Apache Iceberg.
Apache Iceberg table format is now in use and contributed to by many leading tech companies like Netflix, Apple, Airbnb, LinkedIn, Dremio, Expedia, and AWS.
Watch Alex Merced, Developer Advocate at Dremio, as he describes the open architecture and performance-oriented capabilities of Apache Iceberg.
You will learn:
• The issues that arise when using the Hive table format at scale, and why we need a new table format
• How a straightforward, elegant change in table format structure has enormous positive effects
• The underlying architecture of an Apache Iceberg table, how a query against an Iceberg table works, and how the table’s underlying structure changes as CRUD operations are done on it
• The resulting benefits of this architectural design
Real-time Analytics with Trino and Apache PinotXiang Fu
Trino summit 2021:
Overview of Trino Pinot Connector, which bridges the flexibility of Trino's full SQL support to the power of Apache Pinot's realtime analytics, giving you the best of both worlds.
Traditionally database systems were optimized either for OLAP either for OLTP workloads. Such mainstream DBMSes like Postgres,MySQL,... are mostly used for OLTP, while Greenplum, Vertica, Clickhouse, SparkSQL,... are oriented on analytic queries. But right now many companies do not want to have two different data stores for OLAP/OLTP and need to perform analytic queries on most recent data. I want to discuss which features should be added to Postgres to efficiently handle HTAP workload.
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
New Directions in pySpark for Time Series Analysis: Spark Summit East talk by...Spark Summit
Whether it’s Internet of Things (IoT), analysis of Financial Data, or Adtech, the arrival of events in time order requires tools and techniques that are noticeably missing from the Pandas and pySpark software stack.
In this talk, we’ll cover Two Sigma’s contribution to time series analysis for Spark, our work with Pandas, and propose a roadmap for to future-proof pySpark and establish Python as a first class language in the Spark Ecosystem.
Presentation for Pervasive Systems class lectured by prof. Ioannis Chatzigiannakis, a.y. 2015-16, about the No-SQL database InfluxDB. The course is intended for students of MS in Engineering in Computer Science at Sapienza - University of Rome.
The complete code for the demo is available on Github:
https://github.com/RobGaud/PervasiveSystemsPersonal
You can also find me on LinkedIn:
https://www.linkedin.com/in/roberto-gaudenzi-4b0422116
Standing on the Shoulders of Open-Source Giants: The Serverless Realtime Lake...HostedbyConfluent
"Unlike just a few years ago, today the lakehouse architecture is an established data platform embraced by all major cloud data companies such as AWS, Azure, Google, Oracle, Microsoft, Snowflake and Databricks.
This session kicks off with a technical, no-nonsense introduction to the lakehouse concept, dives deep into the lakehouse architecture and recaps how a data lakehouse is built from the ground up with streaming as a first-class citizen.
Then we focus on serverless for streaming use cases. Serverless concepts are well-known from developers triggering hundreds of thousands of AWS Lambda functions at a negligible cost. However, the same concept becomes more interesting when looking at data platforms.
We have all heard about the principle ""It runs best on Powerpoint"", so I decided to skip slides here and bring a serverless demo instead:
A hands-on, fun, and interactive serverless streaming use case example where we ingest live events from hundreds of mobile devices (don't miss out - bring your phone and be part of it!!). Based on this use case I will critically explore how much of a modern lakehouse is serverless and how we implemented that at Databricks (spoiler alert: serverless is everywhere from data pipelines, workflows, optimized Spark APIs, to ML).
TL;DR benefits for the Data Practitioners:
-Recap the OSS foundation of the Lakehouse architecture and understand its appeal
- Understand the benefits of leveraging a lakehouse for streaming and what's there beyond Spark Structured Streaming.
- Meat of the talk: The Serverless Lakehouse. I give you the tech bits beyond the hype. How does a serverless lakehouse differ from other serverless offers?
- Live, hands-on, interactive demo to explore serverless data engineering data end-to-end. For each step we have a critical look and I explain what it means, e.g for you saving costs and removing operational overhead."
Building Robust ETL Pipelines with Apache SparkDatabricks
Stable and robust ETL pipelines are a critical component of the data infrastructure of modern enterprises. ETL pipelines ingest data from a variety of sources and must handle incorrect, incomplete or inconsistent records and produce curated, consistent data for consumption by downstream applications. In this talk, we’ll take a deep dive into the technical details of how Apache Spark “reads” data and discuss how Spark 2.2’s flexible APIs; support for a wide variety of datasources; state of art Tungsten execution engine; and the ability to provide diagnostic feedback to users, making it a robust framework for building end-to-end ETL pipelines.
Log Management
Log Monitoring
Log Analysis
Need for Log Analysis
Problem with Log Analysis
Some of Log Management Tool
What is ELK Stack
ELK Stack Working
Beats
Different Types of Server Logs
Example of Winlog beat, Packetbeat, Apache2 and Nginx Server log analysis
Mimikatz
Malicious File Detection using ELK
Practical Setup
Conclusion
Interconnection Automation For All - Extended - MPS 2023Chris Grundemann
Matt "Grizz" Griswold and Chris Grundemann are both IX founders, internetworking experts, and automation proponents. With over 4 decades of combined experience they are now turning to sharing what they've learned about automating BGP and interconnection through a set of open source tools, along with support and services for those that need it.
This talk will share what they have learned both from personal experience as well as through dozens of recent interviews with IX operators and interconnection engineers over the past several months. Including common challenges, productive methodologies, and best practices.
The highlight of the talk will be announcing and describing two open source automation tools built to make interconnection and BGP easier for everyone. One is ixCtl, which is built to automate the most common and problematic tasks involved in running an internet exchange point, particularly configuring and managing secure route servers. The other is PeerCtl, which is built to automate the most common and problematic tasks involved in interconnecting an AS; from bilateral and multilateral peering to PNI and also transit connections.
Code for both (along with several other tools) is available on GitHub: https://github.com/fullctl.
Speaker: Chris Grundemann
Speaker: Matt Griswold
Monitoring Big Data Systems Done "The Simple Way" - Demi Ben-Ari - Codemotion...Codemotion
Once you start working with Big Data systems, you discover a whole bunch of problems you won’t find in monolithic systems. Monitoring all of the components becomes a big data problem itself. In the talk, we’ll mention all of the aspects that you should take into consideration when monitoring a distributed system using tools like Web Services, Spark, Cassandra, MongoDB, AWS. Not only the tools, what should you monitor about the actual data that flows in the system? We’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Monitoring Big Data Systems "Done the simple way" - Demi Ben-Ari - Codemotion...Demi Ben-Ari
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Real Time Processing Using Twitter Heron by Karthik RamasamyData Con LA
Abstract:- Today's enterprises are not only producing data in high volume but also at high velocity. With velocity comes the need to process the data in real time. To meet the real time needs, we developed and deployed Heron, the next generation streaming engine at Twitter. Heron processes billions and billions of events per day at Twitter and has been in production for nearly 3 years. Heron provides unparalleled performance at large scale and has been successfully meeting Twitter's strict performance requirements for various streaming and iOT applications. Heron is a open source project with several major contributors from various institutions. As the project, we identified and implemented several optimizations that improved throughput by additional 5x and further reduce latency by 50-60%. In this talk, we will describe Heron in detail, how the detailed profiling indicated the performance bottleneck areas such as multiple serializations/deserialization and immutable data structures. After mitigating these costs, we were able to show much higher throughput and latencies as low as 12ms.
Monitoring Big Data Systems Done "The Simple Way" - Codemotion Berlin 2017Demi Ben-Ari
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Monitoring Big Data Systems - "The Simple Way"Demi Ben-Ari
Once you start working with distributed Big Data systems, you start discovering a whole bunch of problems you won’t find in monolithic systems.
All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
And we’ll cover the simplest solution with your day to day open source tools, the surprising thing, that it comes not from an Ops Guy.
Demi Ben-Ari is a Co-Founder and CTO @ Panorays.
Demi has over 9 years of experience in building various systems both from the field of near real time applications and Big Data distributed systems.
Describing himself as a software development groupie, Interested in tackling cutting edge technologies.
Demi is also a co-founder of the “Big Things” Big Data community: http://somebigthings.com/big-things-intro/
Cloud Dataflow - A Unified Model for Batch and Streaming Data ProcessingDoiT International
Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
Presenter: Kenn Knowles, Software Engineer, Google & Apache Beam (incubating) PPMC member
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Application Monitoring using Open Source: VictoriaMetrics - ClickHouseVictoriaMetrics
Monitoring is the key to successful operation of any software service, but commercial solutions are complex, expensive, and slow. Let us show you how to build monitoring that is simple, cost-effective, and fast using open source stacks easily accessible to any developer.
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Application Monitoring using Open Source - VictoriaMetrics & Altinity ClickHo...Altinity Ltd
Application Monitoring using Open Source - VictoriaMetrics & Altinity ClickHouse Webinar Slides
Monitoring is the key to the successful operation of any software service, but commercial solutions are complex, expensive, and slow. Let us show you how to build monitoring that is simple, cost-effective, and fast using open-source stacks easily accessible to any developer.
We’ll start with the elements of monitoring systems: data ingest, query engine, visualization, and alerting. We’ll then explain and contrast two implementation approaches. The first uses VictoriaMetrics, a fast-growing, high-performance time series database that uses PromQL for queries. The second is based on ClickHouse, a popular real-time analytics database that speaks SQL. Fast, affordable monitoring is within reach. This webinar provides designs and working code to get you there.
Presented by:
Roman Khavronenko, Co-Founder at VictoriaMetrics
Robert Hodges, CEO at Altinity
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014Austin Ogilvie
Applied Data Science: Building a Beer Recommender | Data Science MD - Oct 2014
-----------
Slides from a talk by Greg Lamp, CTO of Yhat, about building recommendation systems using Python and deploying them to production.
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In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more.
The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this.
Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution.
Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...Soroosh Khodami
Session Recording on Youtube
https://www.youtube.com/watch?v=uWPZQ_HMy10
- Session Description
Do you find yourself bombarded with buzzwords and overwhelmed by the rapid emergence of new technologies? "Stream Processing" is a tech buzzword that has been around for some time but is still unfamiliar to many. Join this session to discover its potential in software systems. I will share insights from Apache Flink, Apache Beam, Google Dataflow, and my experiences at Bol.com (the biggest e-commerce platform in the Netherlands) as we cover:
- Stream Processing overview: main concepts and features
- Apache Beam vs. Spring Boot comparison
- Key Considerations for Using Stream Processing
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Monitoring Big Data Systems Done "The Simple Way" - Codemotion Milan 2017 - D...Demi Ben-Ari
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All of a sudden to monitor all of the components becomes a big data problem itself.
In the talk we’ll mention all of the aspects that you should take in consideration when monitoring a distributed system once you’re using tools like:
Web Services, Apache Spark, Cassandra, MongoDB, Amazon Web Services.
Not only the tools, what should you monitor about the actual data that flows in the system?
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Similar to Kusto (Azure Data Explorer) Training for R&D - January 2019 (20)
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
top nidhi software solution freedownloadvrstrong314
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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.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
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How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
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AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
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In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
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Kusto (Azure Data Explorer) Training for R&D - January 2019
1. Kusto
Azure Data Explorer
For Taboola LA R&D
Monitoring in Production
Maher Odeh (Taboola Production IT), Adi Eldar (Microsoft), Tal Bar Zvi (Taboola R&D) 1
https://youtu.be/iWay1PeoGhg
Click here to watch
the recorded session
8. Kusto is...
8
A new way to look at data / logs
What is it actually?What is it actually?
It’s a new, innovative thing
Developed by Microsoft
We are one of the first to use it
It helps us to get the picture of our service in a few
seconds
What is it actually?
It’s a new, innovative thing
Developed by Microsoft
We are one of the first to use it
It helps us to get the picture of our service in a few
seconds
Wow, sounds cool
10. Now Really Let’s begin
10
✓ Big Data
✓ Database
✓ Tables
✓ Functions
✓ Scripting
✓ Join
✓ Union
✓ Fast Search
✓ Graphs
✓ Dashboards
✓ Alerts
✓ HTTP Logs (for now)
✓ Notebooks
✓ Python
11. Why Kusto?
● Kibana-Fastly replacement
● It has a WOW effect
● It is easy to use and learn
11
It is new, for all,
we learn it together.
(This is Rare!)
12. 12
Different payment model.
Kusto is already paid - flat.
Queries do not* cost extra money.
*prod-it are gonna hate me after this slide
✓ Credits: Shaked Zychlinski
17. Architecture
17
Log files - from Fastly (CDN)
Kusto
Web interface
● Query
● Graphs
● Dashboards
API
● Alerts (Sensu)
● Scripting
● Jupyter
● Programming
18. What is a CDN (Fastly & Akamai for example)?
18
50 server farms
7 Data Centers
Caching our HTTP responses
HTTP Logs
CDN = Content Delivery Network
19. Kusto Database Sizes (as of Jan 2019)
19
Database Size RETENTION
COLD / HOT (CACHED)
fastly-
backstage
15 GB 60 days (31 days 🔥)
fastly-c3 10 TB 30 days (3 days 🔥)
fastly-trc 250 TB 30 days (3 days 🔥)
20. SLIDE | 20
Take Away
Messages No. 1
20
1. Kusto is BigData database
1. It holds our HTTP requests
1. Hot vs. Cold
26. Example No. 1 of 7
26
● trc_access | count
Hot vs. Cold...
27. Example No. 1 of 7 - corrected
27
● trc_access | where timestamp > ago(1d) | count
28. Example No. 2 of 7 - by publisher
28
trc_access |
where timestamp > ago(1d) |
where publisher_name == ‘msn-msn’ |
count
29. Example No. 3 of 7 - take (like “limit”)
29
trc_access |
where timestamp > ago(5m) |
where publisher_name == ‘msn-msn’ |
take 5Geo Referrer Time Action URL
30. Example No. 4 of 7 - summarize & top
30
trc_access | where timestamp > ago(1h) |
summarize count() by geo_country_code |
top 5 by count_ desc;
trc_access | where timestamp > ago(1h) |
summarize count() by action |
top 5 by count_ desc
; Semicolon
31. Example No. 5 of 7 - render
31
trc_access | where timestamp > ago(1h) |
summarize count() by geo_country_code |
top 5 by count_ desc; | render piechart
WOW
36. Exmple - HTTP errors, where? what?
36
● Step 1 - See HTTP error increased
● Step 2 - Summarize by data center
● Step 3 - Summarize by action
● Step 4 - Union with normal traffic
HTTP Error Spike
Step 1
NJ & CH are
suffering
Step 2Step 2`Step 3
Found the actionsUnion
Project
Alias
Low
errors
Normal
Traffic
Both Normal
and Errors rise
Errors
Gone
37. SLIDE | 37
Take Away
Messages No. 2
37
1. Kusto has fast query capacities
1. It can create graphs
1. Can aggregate and create fields on-the-fly
1. Helps in:
a. Find root cause
b. Traffic sampling
c. Insights & trends
d. Integration validations
54. Use make-series (it’s fast)
To see the HTTP error
spike
Remember the example from 15 min. ago?
54
Use autocluster to find
similar error characteristics
DC is CH
Newsroom
affected
This is
the host
Using diffpatterns to find
clues
DC is CH Newsroom
affected
This is
the host
57. Summary
57
1. You know where to find me (tal.b@taboola.com)
1. You know you have accessible Resources
(Brain, WWW, Pluralsight free course, Videos, #kusto, Microsoft)
1. You saw how easy it is to run Kusto queries
1. You saw that there are Dashboards & Alerts
1. You are aware of the existence of built-in Data Science power
60. FAQ
60
1. Does it cost money? It is prepaid
2. What about Kibana, Grafana, BQ? Here to stay for now
3. What about applicative logs / my data? Currently Fastly logs
4. Will my elastic-fastly alerts be converted to Kusto for me? No
5. When will the other fastly logs be available? Updates in slack #kusto
6. Can we have more Kusto trainings? Dashboard? Workshops? Yes
7. Does Kusto support distinct count? Yes
8. Does Kusto have materialized views? Yes
9. Can we add to the schema our common recommendation fields? Yes
10. What about API 2.0 HTTP POST payload? It is in discussions
11. Can I look in all fields like in Kibana? Yes
12. Do all have access? Many have, or else ticket to prod-it
13. Can I use the alerts? Work in progress
14. Can I automatically derive smaller tables? Yes
Myself: 2 year in Taboola, Crazy about Taboola.
User Data R&D
Been developing software since I was 13
Maher from production id is the go-to person who set all this magic
Recently added this in my Linkedin
Evident you really care
We are in it together
Will help anyone
that have something big to show
EXPECTATIONS
Who is the audience? DIVERSE AUDIENCE BIG
This is the PART OF IT WILL BE TOO FAST AND PART TOO SLOW
This is the VALUE
You know other tools
Simple Hebrew
Insights were never so accessible - you will find
stuff, you never had the ability to find. it is very simple.
This tool enables you to investigate
Taboola’s crazy traffic with ease.
Killer combination
Who is using Kusto?
Who plans to use it?
Questions in the end, this is a big crowd, use #Slack
This is המחשה - סבלנות
To make sure we are on the same page
Request URL very useful
This is production IT’s great work
This technical gap that we need to close
Thanks production IT!
Use error for fast insight
1,000,000$ Question
Are you happy about it?
Take your time
Fluent (like Java streams)
For the SQL lovers
Another language, a good one
Learning by Example
Ask people what is wrong here?
Use hot
New stuff in yellow
Now ago(5m)
SQL Group by == summarize
Aggregation functions - avg(num-field)
Powerful graphs
Best is HORIZONTAL - BELLA GUTIE
Most useful - timechart
Is this clear?
Black theme for cool guys
Adi Dan / Michael Dragetski
THEY KNEW NOW I KNOW TOO
SQL Having
Walkthrough
Stay tuned to see this root cause drill down was done
by Kusto Data Science Capabilities
Prod-IT can see who to wake up even easier
ALL is in BRAIN
ASK - was it too fast? Too complex? OK? Ask me...
Don’t sleep just yet!
BRAIN
Copy it to your own use
Meetings (like this one), Meetups, Guild, and… #Slack
Make its UX cool, use nice formatting
Kibana Based
Super rich, however Alerts come first
Only when there is an alert - you can open dashboard - to see the history
Tips & Best Practices
We use sensu for alerting
Over 50 errors in Yahoo Japan - Call Media PD On Call
Advanced topics
Jupyter
Of course it works. This is our beloved visualisation tool.
Grafana 5
The first point is the most important one
Even if you didn’t catch much, you can always ask
Let’s works hard, play hard, and drink up
Tips & Best Practices
This explains what is Kusto
Who believes that this is a true chat?
