While working with Hadoop, you'll eventually encounter the need to schedule and run workflows to perform various operations like ingesting data or performing ETL. There are a number of tools available to assist you with this type of requirement and one such tool that we at Clairvoyant have been looking to use is Apache Airflow. Apache Airflow is an Apache Incubator project that allows you to programmatically create workflows through a python script. This provides a flexible and effective way to design your workflows with little code and setup. In this talk, we will discuss Apache Airflow and how we at Clairvoyant have utilized it for ETL pipelines on Hadoop.
In the session, we discussed the End-to-end working of Apache Airflow that mainly focused on "Why What and How" factors. It includes the DAG creation/implementation, Architecture, pros & cons. It also includes how the DAG is created for scheduling the Job and what all steps are required to create the DAG using python script & finally with the working demo.
Introduction to Apache Airflow, it's main concepts and features and an example of a DAG. Afterwards some lessons and best practices learned by from the 3 years I have been using Airflow to power workflows in production.
In the session, we discussed the End-to-end working of Apache Airflow that mainly focused on "Why What and How" factors. It includes the DAG creation/implementation, Architecture, pros & cons. It also includes how the DAG is created for scheduling the Job and what all steps are required to create the DAG using python script & finally with the working demo.
Introduction to Apache Airflow, it's main concepts and features and an example of a DAG. Afterwards some lessons and best practices learned by from the 3 years I have been using Airflow to power workflows in production.
Presentation given at Coolblue B.V. demonstrating Apache Airflow (incubating), what we learned from the underlying design principles and how an implementation of these principles reduce the amount of ETL effort. Why choose Airflow? Because it makes your engineering life easier, more people can contribute to how data flows through the organization, so that you can spend more time applying your brain to more difficult problems like Machine Learning, Deep Learning and higher level analysis.
A 20 minute talk about how WePay runs airflow. Discusses usage and operations. Also covers running Airflow in Google cloud.
Video of the talk is available here:
https://wepayinc.box.com/s/hf1chwmthuet29ux2a83f5quc8o5q18k
We will introduce Airflow, an Apache Project for scheduling and workflow orchestration. We will discuss use cases, applicability and how best to use Airflow, mainly in the context of building data engineering pipelines. We have been running Airflow in production for about 2 years, we will also go over some learnings, best practices and some tools we have built around it.
Speakers: Robert Sanders, Shekhar Vemuri
Orchestrating workflows Apache Airflow on GCP & AWSDerrick Qin
Working in a cloud or on-premises environment, we all somehow move data from A to B on-demand or on schedule. It is essential to have a tool that can automate recurring workflows. This can be anything from an ETL(Extract, Transform, and Load) job for a regular analytics report all the way to automatically re-training a machine learning model.
In this talk, we will introduce Apache Airflow and how it can help orchestrate your workflows. We will cover key concepts, features, and use cases of Apache Airflow, as well as how you can enjoy Apache Airflow on GCP and AWS by demo-ing a few practical workflows.
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud (Hadoop / Spark Conference Japan 2019)
# English version #
http://hadoop.apache.jp/hcj2019-program/
Introducing the Apache Flink Kubernetes OperatorFlink Forward
Flink Forward San Francisco 2022.
The Apache Flink Kubernetes Operator provides a consistent approach to manage Flink applications automatically, without any human interaction, by extending the Kubernetes API. Given the increasing adoption of Kubernetes based Flink deployments the community has been working on a Kubernetes native solution as part of Flink that can benefit from the rich experience of community members and ultimately make Flink easier to adopt. In this talk we give a technical introduction to the Flink Kubernetes Operator and demonstrate the core features and use-cases through in-depth examples."
by
Thomas Weise
Slide deck for the fourth data engineering lunch, presented by guest speaker Will Angel. It covered the topic of using Airflow for data engineering. Airflow is a scheduling tool for managing data pipelines.
Simplify CDC Pipeline with Spark Streaming SQL and Delta LakeDatabricks
Change Data Capture CDC is a typical use case in Real-Time Data Warehousing. It tracks the data change log -binlog- of a relational database [OLTP], and replay these change log timely to an external storage to do Real-Time OLAP, such as delta/kudu. To implement a robust CDC streaming pipeline, lots of factors should be concerned, such as how to ensure data accuracy , how to process OLTP source schema changed, whether it is easy to build for variety databases with less code.
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.
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
Flink Forward San Francisco 2022.
Resource Elasticity is a frequently requested feature in Apache Flink: Users want to be able to easily adjust their clusters to changing workloads for resource efficiency and cost saving reasons. In Flink 1.13, the initial implementation of Reactive Mode was introduced, later releases added more improvements to make the feature production ready. In this talk, we’ll explain scenarios to deploy Reactive Mode to various environments to achieve autoscaling and resource elasticity. We’ll discuss the constraints to consider when planning to use this feature, and also potential improvements from the Flink roadmap. For those interested in the internals of Flink, we’ll also briefly explain how the feature is implemented, and if time permits, conclude with a short demo.
by
Robert Metzger
Building a Data Pipeline using Apache Airflow (on AWS / GCP)Yohei Onishi
This is the slide I presented at PyCon SG 2019. I talked about overview of Airflow and how we can use Airflow and the other data engineering services on AWS and GCP to build data pipelines.
Apache Druid Auto Scale-out/in for Streaming Data Ingestion on KubernetesDataWorks Summit
The importance of ingestion and processing streaming data in telecommunication industry is ever increasing. We, SK Telecom which is Korea's number-one telecommunications provider, encounter how to use infra resources more efficiently. Apache Druid supports auto scaling feature for data ingestion, but it is only available on AWS EC2. We cannot rely on the feature on our private cloud.
In this talk, we are going to introduce auto scale-out/in on Kubernetes. This approach is more outstanding than Druid's scaling implementation. Here are the benefits. The first is our approach can be used anywhere on private cloud or (managed) Kubernetes in Azure, AWS and GKE. The second is AWS EC2's startup and termination requires a few minutes, but our approach requires a few seconds. The last is the scaling mechanism is decoupled from Druid's source code. We will also share development of Druid Helm chart, rolling update, custom metric usage for horizontal auto scaling.
The below is about detailed benefit compared with Druid's auto scaling approach:
1. Druid's auto scaling is only available in AWS, but our approach does not have the obstacle. It can be used in Private cloud(on-premise) are (managed) Kubernetes in Azure, AWS and GKE.
2. AWS EC2 is an instance of virtual machine, so the startup is slower than docker container. A few minutes are required for startup or termination of EC2. Docker container is very lightweight, so it requires a few seconds.
3. Druid's auto scaling is tightly coupled with AWS API because Druid engine code uses AWS API. Our scale-out/in algorithm is conceptually equal to Druid's auto scaling approach, but we decoupled the dependency because Kubernetes communicate with one of dispatcher nodes(i.e. Overlord node) using REST API.
Presentation given at Coolblue B.V. demonstrating Apache Airflow (incubating), what we learned from the underlying design principles and how an implementation of these principles reduce the amount of ETL effort. Why choose Airflow? Because it makes your engineering life easier, more people can contribute to how data flows through the organization, so that you can spend more time applying your brain to more difficult problems like Machine Learning, Deep Learning and higher level analysis.
A 20 minute talk about how WePay runs airflow. Discusses usage and operations. Also covers running Airflow in Google cloud.
Video of the talk is available here:
https://wepayinc.box.com/s/hf1chwmthuet29ux2a83f5quc8o5q18k
We will introduce Airflow, an Apache Project for scheduling and workflow orchestration. We will discuss use cases, applicability and how best to use Airflow, mainly in the context of building data engineering pipelines. We have been running Airflow in production for about 2 years, we will also go over some learnings, best practices and some tools we have built around it.
Speakers: Robert Sanders, Shekhar Vemuri
Orchestrating workflows Apache Airflow on GCP & AWSDerrick Qin
Working in a cloud or on-premises environment, we all somehow move data from A to B on-demand or on schedule. It is essential to have a tool that can automate recurring workflows. This can be anything from an ETL(Extract, Transform, and Load) job for a regular analytics report all the way to automatically re-training a machine learning model.
In this talk, we will introduce Apache Airflow and how it can help orchestrate your workflows. We will cover key concepts, features, and use cases of Apache Airflow, as well as how you can enjoy Apache Airflow on GCP and AWS by demo-ing a few practical workflows.
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud (Hadoop / Spark Conference Japan 2019)
# English version #
http://hadoop.apache.jp/hcj2019-program/
Introducing the Apache Flink Kubernetes OperatorFlink Forward
Flink Forward San Francisco 2022.
The Apache Flink Kubernetes Operator provides a consistent approach to manage Flink applications automatically, without any human interaction, by extending the Kubernetes API. Given the increasing adoption of Kubernetes based Flink deployments the community has been working on a Kubernetes native solution as part of Flink that can benefit from the rich experience of community members and ultimately make Flink easier to adopt. In this talk we give a technical introduction to the Flink Kubernetes Operator and demonstrate the core features and use-cases through in-depth examples."
by
Thomas Weise
Slide deck for the fourth data engineering lunch, presented by guest speaker Will Angel. It covered the topic of using Airflow for data engineering. Airflow is a scheduling tool for managing data pipelines.
Simplify CDC Pipeline with Spark Streaming SQL and Delta LakeDatabricks
Change Data Capture CDC is a typical use case in Real-Time Data Warehousing. It tracks the data change log -binlog- of a relational database [OLTP], and replay these change log timely to an external storage to do Real-Time OLAP, such as delta/kudu. To implement a robust CDC streaming pipeline, lots of factors should be concerned, such as how to ensure data accuracy , how to process OLTP source schema changed, whether it is easy to build for variety databases with less code.
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.
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
Flink Forward San Francisco 2022.
Resource Elasticity is a frequently requested feature in Apache Flink: Users want to be able to easily adjust their clusters to changing workloads for resource efficiency and cost saving reasons. In Flink 1.13, the initial implementation of Reactive Mode was introduced, later releases added more improvements to make the feature production ready. In this talk, we’ll explain scenarios to deploy Reactive Mode to various environments to achieve autoscaling and resource elasticity. We’ll discuss the constraints to consider when planning to use this feature, and also potential improvements from the Flink roadmap. For those interested in the internals of Flink, we’ll also briefly explain how the feature is implemented, and if time permits, conclude with a short demo.
by
Robert Metzger
Building a Data Pipeline using Apache Airflow (on AWS / GCP)Yohei Onishi
This is the slide I presented at PyCon SG 2019. I talked about overview of Airflow and how we can use Airflow and the other data engineering services on AWS and GCP to build data pipelines.
Apache Druid Auto Scale-out/in for Streaming Data Ingestion on KubernetesDataWorks Summit
The importance of ingestion and processing streaming data in telecommunication industry is ever increasing. We, SK Telecom which is Korea's number-one telecommunications provider, encounter how to use infra resources more efficiently. Apache Druid supports auto scaling feature for data ingestion, but it is only available on AWS EC2. We cannot rely on the feature on our private cloud.
In this talk, we are going to introduce auto scale-out/in on Kubernetes. This approach is more outstanding than Druid's scaling implementation. Here are the benefits. The first is our approach can be used anywhere on private cloud or (managed) Kubernetes in Azure, AWS and GKE. The second is AWS EC2's startup and termination requires a few minutes, but our approach requires a few seconds. The last is the scaling mechanism is decoupled from Druid's source code. We will also share development of Druid Helm chart, rolling update, custom metric usage for horizontal auto scaling.
The below is about detailed benefit compared with Druid's auto scaling approach:
1. Druid's auto scaling is only available in AWS, but our approach does not have the obstacle. It can be used in Private cloud(on-premise) are (managed) Kubernetes in Azure, AWS and GKE.
2. AWS EC2 is an instance of virtual machine, so the startup is slower than docker container. A few minutes are required for startup or termination of EC2. Docker container is very lightweight, so it requires a few seconds.
3. Druid's auto scaling is tightly coupled with AWS API because Druid engine code uses AWS API. Our scale-out/in algorithm is conceptually equal to Druid's auto scaling approach, but we decoupled the dependency because Kubernetes communicate with one of dispatcher nodes(i.e. Overlord node) using REST API.
Tokyo Azure Meetup #7 - Introduction to Serverless Architectures with Azure F...Tokyo Azure Meetup
Serverless architecture is the next big shift in computing - completely abstracting the underlying infrastructure and focusing 100% on the business logic.
Today we can create applications directly in our browser and leave the decision how they are hosted and scaled to the cloud provider. Moreover, this approach give us incredible control over the granularity of our applications since most of the time we are dealing with single function at a time.
In this presentation we will cover:
• Introduce Serverless Architectures
• Talk about the advantages of Serverless Architectures
• Discuss in details in event-driven computing
• Cover common Serverless approaches
• See practical applications with Azure Functions
• Compare AWS Lambda and Azure Functions
• Talk about open source alternatives
• Explore the relation between Microservices and Serverless Architectures
Apache Airflow (incubating) NL HUG Meetup 2016-07-19Bolke de Bruin
Introduction to Apache Airflow (Incubating), best practices and roadmap. Airflow is a platform to programmatically author, schedule and monitor workflows.
Going Serverless - an Introduction to AWS GlueMichael Rainey
Going "serverless" is the latest technology trend for enterprises moving their processing to the cloud, including data integration and ETL tools. But what does that mean and when should I use serverless ETL? In this session, we'll dive into the world of Amazon's fully managed data processing service called AWS Glue. With no server to provision or resources to allocate, and an easy to populate metadata catalog, AWS Glue allows the data engineer to focus on his or her craft; building data transformations and pipelines. Gaining an understanding of the similarities and differences between traditional ETL tools, such as Oracle Data Integrator, and Glue will prepare attendees for the new world of data integration. Presented at Collaborate 18.
Cask Webinar
Date: 08/10/2016
Link to video recording: https://www.youtube.com/watch?v=XUkANr9iag0
In this webinar, Nitin Motgi, CTO of Cask, walks through the new capabilities of CDAP 3.5 and explains how your organization can benefit.
Some of the highlights include:
- Enterprise-grade security - Authentication, authorization, secure keystore for storing configurations. Plus integration with Apache Sentry and Apache Ranger.
- Preview mode - Ability to preview and debug data pipelines before deploying them.
- Joins in Cask Hydrator - Capabilities to join multiple data sources in data pipelines
- Real-time pipelines with Spark Streaming - Drag & drop real-time pipelines using Spark Streaming.
- Data usage analytics - Ability to report application usage of data sets.
- And much more!
Adding Support for Networking and Web Technologies to an Embedded SystemJohn Efstathiades
These are the slides for a presentation we gave at Device Developer Conference 2014 in the UK. The presentation discusses the work done, experiences, and lessons learnt from adding an open source TCP/IP network stack and web server to an existing industrial control system running on an ARM Cortex M3-based processor from TI.
The presentation covers the following:
· Integrating the network stack into the existing software base
· Configuring and using the network stack and web server
· Adding support for HTTP basic authentication to restrict user access
· Using HTTP to remotely access the target system and retrieve operational data
· Debugging hints and tips
· Pitfalls to avoid and other lessons learnt
Orchestrating Linux Containers while tolerating failuresDocker, Inc.
lthough containers are bringing a refreshing flexibility when deploying services in production, the management of those containers in such an environment still requires special care in order to keep the application up and running. In this regard, orchestration platforms like Docker, Kubernetes and Nomad have been trying to alleviate this responsibility, facilitating the task of deploying and maintaining the entire application stack in its desired state. This ensures that a service will be always running, tolerating machine failures, network erratic behavior or software updates and downtime. The purpose of this talk is to explain the mechanisms and architecture of the Docker Engine orchestration platform (using a framework called swarmkit) to tolerate failures of services and machines, from cluster state replication and leader-election to container re-scheduling logic when a host goes down.
Apache Solr on Hadoop is enabling organizations to collect, process and search larger, more varied data. Apache Spark is is making a large impact across the industry, changing the way we think about batch processing and replacing MapReduce in many cases. But how can production users easily migrate ingestion of HDFS data into Solr from MapReduce to Spark? How can they update and delete existing documents in Solr at scale? And how can they easily build flexible data ingestion pipelines? Cloudera Search Software Engineer Wolfgang Hoschek will present an architecture and solution to this problem. How was Apache Solr, Spark, Crunch, and Morphlines integrated to allow for scalable and flexible ingestion of HDFS data into Solr? What are the solved problems and what's still to come? Join us for an exciting discussion on this new technology.
Simplifying Hadoop with RecordService, A Secure and Unified Data Access Path ...Cloudera, Inc.
SFHUG presentation from February 2, 2016. One of the key values of the Hadoop ecosystem is its flexibility. There is a myriad of components that make up this ecosystem, allowing Hadoop to tackle otherwise intractable problems. However, having so many components provides a significant integration, implementation, and usability burden. Features that ought to work in all the components often require sizable per-component effort to ensure correctness across the stack.
Lenni Kuff explores RecordService, a new solution to this problem that provides an API to read data from Hadoop storage managers and return them as canonical records. This eliminates the need for components to support individual file formats, handle security, perform auditing, and implement sophisticated IO scheduling and other common processing that is at the bottom of any computation.
Lenni discusses the architecture of the service and the integration work done for MapReduce and Spark. Many existing applications on those frameworks can take advantage of the service with little to no modification. Lenni demonstrates how this provides fine grain (column level and row level) security, through Sentry integration, and improves performance for existing MapReduce and Spark applications by up to 5×. Lenni concludes by discussing how this architecture can enable significant future improvements to the Hadoop ecosystem.
About the speaker: Lenni Kuff is an engineering manager at Cloudera. Before joining Cloudera, he worked at Microsoft on a number of projects including SQL Server storage engine, SQL Azure, and Hadoop on Azure. Lenni graduated from the University of Wisconsin-Madison with degrees in computer science and computer engineering.
Learn what you need to consider when moving from the world of relational databases to a NoSQL document store.
Hear from Developer Advocate Glynn Bird as he explains the key differences between relational databases and JSON document stores like Cloudant, as well as how to dodge the pitfalls of migrating from a relational database to NoSQL.
There is increased interest in using Kubernetes, the open-source container orchestration system for modern, stateful Big Data analytics workloads. The promised land is a unified platform that can handle cloud native stateless and stateful Big Data applications. However, stateful, multi-service Big Data cluster orchestration brings unique challenges. This session will delve into the technical gaps and considerations for Big Data on Kubernetes.
Containers offer significant value to businesses; including increased developer agility, and the ability to move applications between on-premises servers, cloud instances, and across data centers. Organizations have embarked on this journey to containerization with an emphasis on stateless workloads. Stateless applications are usually microservices or containerized applications that don’t “store” data. Web services (such as front end UIs and simple, content-centric experiences) are often great candidates as stateless applications since HTTP is stateless by nature. There is no dependency on the local container storage for the stateless workload.
Stateful applications, on the other hand, are services that require backing storage and keeping state is critical to running the service. Hadoop, Spark and to lesser extent, noSQL platforms such as Cassandra, MongoDB, Postgres, and mySQL are great examples. They require some form of persistent storage that will survive service restarts...
Speakers
Anant Chintamaneni, VP Products, BlueData
Nanda Vijaydev, Director Solutions, BlueData
Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Apex
Stream processing applications built on Apache Apex run on Hadoop clusters and typically power analytics use cases where availability, flexible scaling, high throughput, low latency and correctness are essential. These applications consume data from a variety of sources, including streaming sources like Apache Kafka, Kinesis or JMS, file based sources or databases. Processing results often need to be stored in external systems (sinks) for downstream consumers (pub-sub messaging, real-time visualization, Hive and other SQL databases etc.). Apex has the Malhar library with a wide range of connectors and other operators that are readily available to build applications. We will cover key characteristics like partitioning and processing guarantees, generic building blocks for new operators (write-ahead-log, incremental state saving, windowing etc.) and APIs for application specification.
Similar to Running Airflow Workflows as ETL Processes on Hadoop (20)
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
An overview on the Databricks Community Cloud platform offered by Databricks at: https://community.cloud.databricks.com/
Provides step by step instructions on how to create a Spark Standalone Cluster and how to use notebooks.
Strata+Hadoop World NY 2016 - Avinash Ramineniclairvoyantllc
Narasimhan Sampath and Avinash Ramineni share how Choice Hotels International used Spark Streaming, Kafka, Spark, and Spark SQL to create an advanced analytics platform that enables business users to be self-reliant by accessing the data they need from a variety of sources to generate customer insights and property dashboards and enable data-driven decisions with minimal IT engagement. Narasimhan and Avinash highlight the architecture, lessons learned, and the challenges that were overcome on both the business and technology fronts.
The analytics platform is designed as a framework to enable self-service data intake, data processing, and report/model generation by the business users. The data-driven framework consists of a distributed hybrid-cloud data ingestor for data intake and a Cloudera CDH cluster with Spark as the distributed compute engine. The solution is built in such a way that storage and compute have been decoupled and encourages the concept of BYOC (bring your own compute). The platform uses EC2 instances to run CDH and leverages Amazon S3 as a data warehouse storage layer (data lake), Spark as an ETL engine, and Spark SQL as a distributed query engine. Results (computations/derived tables) are exposed to the end users via Spark SQL and are discovered via Tableau. The platform supports both batch and streaming use cases and is built on the following technology stack: AWS (S3, EC2, SQS, SNS), Cloudera CDH (YARN, Navigator, Sentry), Spark, Kafka, Spark SQL, and Spark Streaming.
HBase from the Trenches - Phoenix Data Conference 2015clairvoyantllc
Apache HBase has been widely adopted at many enterprises. In this talk we will cover a few war stories with troubleshooting, tuning and fixing problems with HBase Cluster. We will be covering some of the best practices, tools , utilities and lessons learnt from evaluating deployments at different organizations
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
2. 2Page:
Agenda
• What is Apache Airflow?
• Features
• Architecture
• Terminology
• Operator Types
• ETL Best Practices
• How they’re supported in Apache Airflow
• Executing Airflow Workflows on Hadoop
• Use Cases
• Q&A
3. 3Page:
Robert Sanders
• Big Data Manager, Engineer, Architect, etc.
• Work for Clairvoyant LLC
• 5+ Years of Big Data Experience
• Email: robert.sanders@clairvoyantsoft.com
• LinkedIn: https://www.linkedin.com/in/robert-sanders-
61446732
• Slide Share: http://www.slideshare.net/RobertSanders49
4. 4Page:
What’s the problem?
• As a Big Data Engineer you work to create jobs that will
perform various operations
• Ingest data from external data sources
• Transformation of Data
• Run Predictions
• Export data
• Etc.
• You need to have some mechanism to schedule and run
these jobs
• Cron
• Oozie
• Existing Scheduling Services have a number of limitations
that make them difficult to work with and not usable in all
instances
5. 5Page:
What is Apache Airflow?
• Airflow is an Open Source platform to programmatically
author, schedule and monitor workflows
• Workflows as Code
• Schedules Jobs through Cron Expressions
• Provides monitoring tools like alerts and a web interface
• Written in Python
• As well as user defined Workflows and Plugins
• Was started in the fall of 2014 by Maxime Beauchemin at
Airbnb
• Apache Incubator Project
• Joined Apache Foundation in early 2016
• https://github.com/apache/incubator-airflow/
6. 6Page:
Why use Apache Airflow?
• Define Workflows as Code
• Makes workflows more maintainable, versionable, and
testable
• More flexible execution and workflow generation
• Lots of Features
• Feature Rich Web Interface
• Worker Processes Scale Horizontally and Vertically
• Can be a cluster or single node setup
• Lightweight Workflow Platform
7. 7Page:
Apache Airflow Features (Some of them)
• Automatic Retries
• SLA monitoring/alerting
• Complex dependency rules: branching, joining, sub-
workflows
• Defining ownership and versioning
• Resource Pools: limit concurrency + prioritization
• Plugins
• Operators
• Executors
• New Views
• Built-in integration with other services
• Many more…
10. 10Page:
What is a DAG?
• Directed Acyclic Graph
• A finite directed graph that doesn’t have any cycles
• A collection of tasks to run, organized in a way that reflects
their relationships and dependencies
• Defines your Workflow
11. 11Page:
What is an Operator?
• An operator describes a single task in a workflow
• Operators allow for generation of certain types of tasks that
become nodes in the DAG when instantiated
• All operators derive from BaseOperator and inherit many
attributes and methods that way
12. 12Page:
Workflow Operators (Sensors)
• A type of operator that keeps running until a certain criteria
is met
• Periodically pokes
• Parameterized poke interval and timeout
• Example
• HdfsSensor
• HivePartitionSensor
• NamedHivePartitionSensor
• S3KeyPartition
• WebHdfsSensor
• Many More…
13. 13Page:
Workflow Operators (Transfer)
• Operator that moves data from one system to another
• Data will be pulled from the source system, staged on the
machine where the executor is running and then transferred
to the target system
• Example:
• HiveToMySqlTransfer
• MySqlToHiveTransfer
• HiveToMsSqlTransfer
• MsSqlToHiveTransfer
• S3ToHiveTransfer
• Many More…
14. 14Page:
Defining a DAG
from airflow.models import DAG
from airflow.operators import …
from datetime import datetime, timedelta
default_args = dict(
'owner'='Airflow’,
'retries': 1,
'retry_delay': timedelta(minutes=5),
)
# Define the DAG
dag = DAG('dag_id', default_args=default_args, schedule_interval='0 0 * * *')
# Define the Tasks
task1 = BashOperator(task_id='task1', bash_command="echo 'Task 1'", dag=dag)
task2 = BashOperator(task_id='task2', bash_command="echo 'Task 2'", dag=dag)
task3 = BashOperator(task_id='task3', bash_command="echo 'Task 3'", dag=dag)
# Define the task relationships
task1.set_downstream(task2)
task2.set_downstream(task3)
task1 task2 task3
15. 15Page:
Defining a DAG (Dynamically)
dag = DAG('dag_id', default_args=default_args, schedule_interval='0 0 * * *')
last_task = None
for i in range(1, 3):
task = BashOperator(
task_id='task' + str(i),
bash_command="echo 'Task" + str(i) + "'",
dag=dag)
if last_task is None:
last_task = task
else:
last_task.set_downstream(task)
last_task = task
task1 task2 task3
16. 16Page:
ETL Best Practices (Some of Them)
• Load Data Incrementally
• Operators will receive an execution_date entry which you can
use to pull in data since that date
• Process historic Data
• Backfill operations are supported
• Enforce Idempotency (retry safe)
• Execute Conditionally
• Branching, Joining
• Understand SLA’s and Alerts
• Alert if Failures
• Sense when to start a task
• Sensor Operators
• Build Validation into your Workflows
17. 17Page:
Executing Airflow Workflows on Hadoop
• Airflow Workers should be installed on a edge/gateway
nodes
• Allows Airflow to interact with Hadoop related commands
• Utilize the BashOperator to run command line functions
and interact with Hadoop services
• Put all necessary scripts and Jars in HDFS and pull the files
down from HDFS during the execution of the script
• Avoids requiring you to keep copies of the scripts on
every machine where the executors are running
• Support for Kerborized Clusters
• Airflow can renew Kerberos tickets for itself and store it
in the ticket cache
18. 18Page:
Use Case (BPV)
• Daily ETL Batch Process to Ingest data into Hadoop
• Extract
• 23 databases total
• 1226 tables total
• Transform
• Impala scripts to join and transform data
• Load
• Impala scripts to load data into common final tables
• Other requirements
• Make it extensible to allow the client to import more databases and
tables in the future
• Status emails to be sent out after daily job to report on success and
failures
• Solution
• Create a DAG that dynamically generates the workflow based off data
in a Metastore
21. 21Page:
Use Case (Kogni)
• New Product being built by Clairvoyant to facilitate:
• kogni-inspector – Sensitive Data Analyzer
• kogni-ingestor – Ingests Data
• kogni-guardian – Sensitive Data Masking (Encrypt and
Tokenize)
• Others components coming soon
• Utilizes Airflow for Data Ingestion and Masking
• Dynamically creates a workflow based off what is in the
Metastore
• Learn More: http://kogni.io/
