This is the June 2018 variant of the "Put is the new Rename Talk", looking at Hadoop stack integration with object stores, including S3, Azure storage and GCS.
August 2018 version of my "What does rename() do", includes the full details on what the Hadoop MapReduce and Spark commit protocols are, so the audience will really understand why rename really, really matters
InfluxDB IOx Tech Talks: Query Processing in InfluxDB IOxInfluxData
Query Processing in InfluxDB IOx
InfluxDB IOx Query Processing: In this talk we will provide an overview of Query Execution in IOx describing how once data is ingested that it is queryable, both via SQL and Flux and InfluxQL (via storage gRPC APIs).
HDF5 is a powerful and feature-rich creature, and getting the most out of it requires powerful tools. The MathWorks provides a "low-level" interface to the HDF5 library that closely corresponds to the C API and exposes much of its richness. This short tutorial will present ways to use the low-level MATLAB interface to build those tools and tackle such topics as subsetting, chunking, and compression.
August 2018 version of my "What does rename() do", includes the full details on what the Hadoop MapReduce and Spark commit protocols are, so the audience will really understand why rename really, really matters
InfluxDB IOx Tech Talks: Query Processing in InfluxDB IOxInfluxData
Query Processing in InfluxDB IOx
InfluxDB IOx Query Processing: In this talk we will provide an overview of Query Execution in IOx describing how once data is ingested that it is queryable, both via SQL and Flux and InfluxQL (via storage gRPC APIs).
HDF5 is a powerful and feature-rich creature, and getting the most out of it requires powerful tools. The MathWorks provides a "low-level" interface to the HDF5 library that closely corresponds to the C API and exposes much of its richness. This short tutorial will present ways to use the low-level MATLAB interface to build those tools and tackle such topics as subsetting, chunking, and compression.
How to use Parquet as a basis for ETL and analyticsJulien Le Dem
Parquet is a columnar format designed to be extremely efficient and interoperable across the hadoop ecosystem. Its integration in most of the Hadoop processing frameworks (Impala, Hive, Pig, Cascading, Crunch, Scalding, Spark, …) and serialization models (Thrift, Avro, Protocol Buffers, …) makes it easy to use in existing ETL and processing pipelines, while giving flexibility of choice on the query engine (whether in Java or C++). In this talk, we will describe how one can us Parquet with a wide variety of data analysis tools like Spark, Impala, Pig, Hive, and Cascading to create powerful, efficient data analysis pipelines. Data management is simplified as the format is self describing and handles schema evolution. Support for nested structures enables more natural modeling of data for Hadoop compared to flat representations that create the need for often costly joins.
Efficient processing of large and complex XML documents in HadoopDataWorks Summit
Many systems capture XML data in Hadoop for analytical processing. When XML documents are large and have complex nested structures, processing such data repeatedly would be inefficient as parsing XML becomes CPU intensive, not to mention the inefficiency of storing XML in its native form. The problem is compounded in the Big Data space, when millions of such documents have to be processed and analyzed within a reasonable time. In this talk an efficient method is proposed by leveraging the Avro storage and communication format, which is flexible, compact and specifically built for Hadoop environments to model complex data structures. XML documents may be parsed and converted into Avro format on load, which can then be accessed via Hive using a SQL-like interface, Java MapReduce or Pig. A concrete use-case is provided that validates this approach along with variations of the same and their relative trade-offs.
Frustration-Reduced Spark: DataFrames and the Spark Time-Series LibraryIlya Ganelin
In this talk I talk about my recent experience working with Spark Data Frames and the Spark TimeSeries library. For data frames, the focus will be on usability. Specifically, a lot of the documentation does not cover common use cases like intricacies of creating data frames, adding or manipulating individual columns, and doing quick and dirty analytics. For the time series library, I dive into the kind of use cases it supports and why it’s actually super useful.
Introduction to Pig & Pig Latin | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2LF3pBA
This CloudxLab Introduction to Pig & Pig Latin tutorial helps you to understand Pig and Pig Latin in detail. Below are the topics covered in this tutorial:
1) Introduction to Pig
2) Why Do We Need Pig?
3) Pig - Usecases
4) Pig - Philosophy
5) Pig Latin - Data Flow Language
6) Pig - Local and MapReduce Mode
7) Pig Data Types
8) Load, Store, and Dump in Pig
9) Lazy Evaluation in Pig
10) Pig - Relational Operators - FOREACH, GROUP and FILTER
11) Hands-on on Pig - Calculate Average Dividend of NYSE
So you want to get started with Hadoop, but how. This session will show you how to get started with Hadoop development using Pig. Prior Hadoop experience is not needed.
Thursday, May 8th, 02:00pm-02:50pm
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.
Introduction to SparkR | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2LCTufA
This CloudxLab Introduction to SparkR tutorial helps you to understand SparkR in detail. Below are the topics covered in this tutorial:
1) SparkR (R on Spark)
2) SparkR DataFrames
3) Launch SparkR
4) Creating DataFrames from Local DataFrames
5) DataFrame Operation
6) Creating DataFrames - From JSON
7) Running SQL Queries from SparkR
Apache Spark - Dataframes & Spark SQL - Part 2 | Big Data Hadoop Spark Tutori...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sm9c61
This CloudxLab Introduction to Spark SQL & DataFrames tutorial helps you to understand Spark SQL & DataFrames in detail. Below are the topics covered in this slide:
1) Loading XML
2) What is RPC - Remote Process Call
3) Loading AVRO
4) Data Sources - Parquet
5) Creating DataFrames From Hive Table
6) Setting up Distributed SQL Engine
OSDC 2014: Sebastian Harl - SysDB the system management and inventory collect...NETWAYS
System DataBase” (SysDB) is a multi-backend system management and inventory collection service. It may be used to (continuously) collect information about your systems from various backends (inventory services, monitoring services, etc. like Nagios, Puppet, collectd) and provides a unique interface to access the information independent of the active backends. This is done by storing and mapping the backend objects to generic objects and correlating the attributes to create a single hierarchical view of your infrastructure. This way, all important information about your systems is accessible from a central location allowing for use-cases such as central dashboards, cross-link monitoring or inventory information, identify missing pieces in your system configuration, and much more.
This talk provides an overview over SysDB and its features as well as sample use-cases. The project is still in an early development stage but already usable. The talk also covers future directions and further integration with existing services.
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...Databricks
Watch video at: http://youtu.be/Wg2boMqLjCg
Want to learn how to write faster and more efficient programs for Apache Spark? Two Spark experts from Databricks, Vida Ha and Holden Karau, provide some performance tuning and testing tips for your Spark applications
A review of the state of cloud store integration with the Hadoop stack in 2018; including S3Guard, the new S3A committers and S3 Select.
Presented at Dataworks Summit Berlin 2018, where the demos were live.
Apache Spark and Object Stores —for London Spark User GroupSteve Loughran
The March 2017 version of the "Apache Spark and Object Stores", includes coverage of the Staging Committer. If you'd been at the talk you'd have seen the projector fail just before the demo. It worked earlier! Honest!
How to use Parquet as a basis for ETL and analyticsJulien Le Dem
Parquet is a columnar format designed to be extremely efficient and interoperable across the hadoop ecosystem. Its integration in most of the Hadoop processing frameworks (Impala, Hive, Pig, Cascading, Crunch, Scalding, Spark, …) and serialization models (Thrift, Avro, Protocol Buffers, …) makes it easy to use in existing ETL and processing pipelines, while giving flexibility of choice on the query engine (whether in Java or C++). In this talk, we will describe how one can us Parquet with a wide variety of data analysis tools like Spark, Impala, Pig, Hive, and Cascading to create powerful, efficient data analysis pipelines. Data management is simplified as the format is self describing and handles schema evolution. Support for nested structures enables more natural modeling of data for Hadoop compared to flat representations that create the need for often costly joins.
Efficient processing of large and complex XML documents in HadoopDataWorks Summit
Many systems capture XML data in Hadoop for analytical processing. When XML documents are large and have complex nested structures, processing such data repeatedly would be inefficient as parsing XML becomes CPU intensive, not to mention the inefficiency of storing XML in its native form. The problem is compounded in the Big Data space, when millions of such documents have to be processed and analyzed within a reasonable time. In this talk an efficient method is proposed by leveraging the Avro storage and communication format, which is flexible, compact and specifically built for Hadoop environments to model complex data structures. XML documents may be parsed and converted into Avro format on load, which can then be accessed via Hive using a SQL-like interface, Java MapReduce or Pig. A concrete use-case is provided that validates this approach along with variations of the same and their relative trade-offs.
Frustration-Reduced Spark: DataFrames and the Spark Time-Series LibraryIlya Ganelin
In this talk I talk about my recent experience working with Spark Data Frames and the Spark TimeSeries library. For data frames, the focus will be on usability. Specifically, a lot of the documentation does not cover common use cases like intricacies of creating data frames, adding or manipulating individual columns, and doing quick and dirty analytics. For the time series library, I dive into the kind of use cases it supports and why it’s actually super useful.
Introduction to Pig & Pig Latin | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2LF3pBA
This CloudxLab Introduction to Pig & Pig Latin tutorial helps you to understand Pig and Pig Latin in detail. Below are the topics covered in this tutorial:
1) Introduction to Pig
2) Why Do We Need Pig?
3) Pig - Usecases
4) Pig - Philosophy
5) Pig Latin - Data Flow Language
6) Pig - Local and MapReduce Mode
7) Pig Data Types
8) Load, Store, and Dump in Pig
9) Lazy Evaluation in Pig
10) Pig - Relational Operators - FOREACH, GROUP and FILTER
11) Hands-on on Pig - Calculate Average Dividend of NYSE
So you want to get started with Hadoop, but how. This session will show you how to get started with Hadoop development using Pig. Prior Hadoop experience is not needed.
Thursday, May 8th, 02:00pm-02:50pm
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.
Introduction to SparkR | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2LCTufA
This CloudxLab Introduction to SparkR tutorial helps you to understand SparkR in detail. Below are the topics covered in this tutorial:
1) SparkR (R on Spark)
2) SparkR DataFrames
3) Launch SparkR
4) Creating DataFrames from Local DataFrames
5) DataFrame Operation
6) Creating DataFrames - From JSON
7) Running SQL Queries from SparkR
Apache Spark - Dataframes & Spark SQL - Part 2 | Big Data Hadoop Spark Tutori...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sm9c61
This CloudxLab Introduction to Spark SQL & DataFrames tutorial helps you to understand Spark SQL & DataFrames in detail. Below are the topics covered in this slide:
1) Loading XML
2) What is RPC - Remote Process Call
3) Loading AVRO
4) Data Sources - Parquet
5) Creating DataFrames From Hive Table
6) Setting up Distributed SQL Engine
OSDC 2014: Sebastian Harl - SysDB the system management and inventory collect...NETWAYS
System DataBase” (SysDB) is a multi-backend system management and inventory collection service. It may be used to (continuously) collect information about your systems from various backends (inventory services, monitoring services, etc. like Nagios, Puppet, collectd) and provides a unique interface to access the information independent of the active backends. This is done by storing and mapping the backend objects to generic objects and correlating the attributes to create a single hierarchical view of your infrastructure. This way, all important information about your systems is accessible from a central location allowing for use-cases such as central dashboards, cross-link monitoring or inventory information, identify missing pieces in your system configuration, and much more.
This talk provides an overview over SysDB and its features as well as sample use-cases. The project is still in an early development stage but already usable. The talk also covers future directions and further integration with existing services.
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...Databricks
Watch video at: http://youtu.be/Wg2boMqLjCg
Want to learn how to write faster and more efficient programs for Apache Spark? Two Spark experts from Databricks, Vida Ha and Holden Karau, provide some performance tuning and testing tips for your Spark applications
A review of the state of cloud store integration with the Hadoop stack in 2018; including S3Guard, the new S3A committers and S3 Select.
Presented at Dataworks Summit Berlin 2018, where the demos were live.
Apache Spark and Object Stores —for London Spark User GroupSteve Loughran
The March 2017 version of the "Apache Spark and Object Stores", includes coverage of the Staging Committer. If you'd been at the talk you'd have seen the projector fail just before the demo. It worked earlier! Honest!
Cloud deployments of Apache Hadoop are becoming more commonplace. Yet Hadoop and it's applications don't integrate that well —something which starts right down at the file IO operations. This talk looks at how to make use of cloud object stores in Hadoop applications, including Hive and Spark. It will go from the foundational "what's an object store?" to the practical "what should I avoid" and the timely "what's new in Hadoop?" — the latter covering the improved S3 support in Hadoop 2.8+. I'll explore the details of benchmarking and improving object store IO in Hive and Spark, showing what developers can do in order to gain performance improvements in their own code —and equally, what they must avoid. Finally, I'll look at ongoing work, especially "S3Guard" and what its fast and consistent file metadata operations promise.
Cloudy with a chance of Hadoop - DataWorks Summit 2017 San JoseMingliang Liu
This talks about use cases and scenarios of running Hadoop applications in the cloud. It covers the problems encountered and lessons learned at Hortonworks. In the talk, we will see a couple deep dives, including Hadoop cluster/service auto-scaling, fault tolerance, and object storage consistency problems. This appears at DataWorks Summit 2017 San Jose, a co-joint talk by Ram Venkatesh and Mingliang Liu.
Dancing Elephants - Efficiently Working with Object Stores from Apache Spark ...DataWorks Summit
As Hadoop applications move into cloud deployments, object stores become more and more the source and destination of data. But object stores are not filesystems: sometimes they are slower; security is different,
What are the secret settings to get maximum performance from queries against data living in cloud object stores? That's at the filesystem client, the file format and the query engine layers? It's even how you lay out the files —the directory structure and the names you give them.
We know these things, from our work in all these layers, from the benchmarking we've done —and the support calls we get when people have problems. And now: we'll show you.
This talk will start from the ground up "why isn't an object store a filesystem?" issue, showing how that breaks fundamental assumptions in code, and so causes performance issues which you don't get when working with HDFS. We'll look at the ways to get Apache Hive and Spark to work better, looking at optimizations which have been done to enable this —and what work is ongoing. Finally, we'll consider what your own code needs to do in order to adapt to cloud execution.
Speaker:
Sanjay Radia, Founder and Chief Architect, Hortonworks
Cloudy with a chance of Hadoop - real world considerationsDataWorks Summit
Over the last eighteen months, we have seen significant adoption of Hadoop eco-system centric big data processing in Microsoft Azure and Amazon AWS. In this talk we present some of the lessons learned and architectural considerations for cloud-based deployments including security, fault tolerance and auto-scaling.
We look at how Hortonworks Data Cloud and Cloudbreak can automate that scaling of Hadoop clusters, showing how it can react dynamically to workloads, and what that can deliver in cost-effective Hadoop-in-cloud deployments.
Spark and Object Stores —What You Need to Know: Spark Summit East talk by Ste...Spark Summit
If you are running Apache Spark in cloud environments, Object Stores —such as Amazon S3 or Azure WASB— are a core part of your system. What you can’t do is treat them like “just another filesystem” —do that and things will, eventually, go horribly wrong.
This talk looks at the object stores in the cloud infrastructures, including underlying architectures., compares them to what a “real filesystem” is expected to do and shows how to use object stores efficiently and safely as sources of and destinations of data.
It goes into depth on recent “S3a” work, showing how including improvements in performance, security, functionality and measurement —and demonstrating how to use make best use of it from a spark application.
If you are planning to deploy Spark in cloud, or doing so today: this is information you need to understand. The performance of you code and integrity of your data depends on it.
Dancing elephants - efficiently working with object stores from Apache Spark ...DataWorks Summit
As Hadoop applications move into cloud deployments, object stores become more and more the source and destination of data. But object stores are not filesystems: sometimes they are slower; security is different,
What are the secret settings to get maximum performance from queries against data living in cloud object stores? That's at the filesystem client, the file format and the query engine layers? It's even how you lay out the files —the directory structure and the names you give them.
We know these things, from our work in all these layers, from the benchmarking we've done —and the support calls we get when people have problems. And now: we'll show you.
This talk will start from the ground up "why isn't an object store a filesystem?" issue, showing how that breaks fundamental assumptions in code, and so causes performance issues which you don't get when working with HDFS. We'll look at the ways to get Apache Hive and Spark to work better, looking at optimizations which have been done to enable this —and what work is ongoing. Finally, we'll consider what your own code needs to do in order to adapt to cloud execution.
It’s no longer a world of just relational databases. Companies are increasingly adopting specialized datastores such as Hadoop, HBase, MongoDB, Elasticsearch, Solr and S3. Apache Drill, an open source, in-memory, columnar SQL execution engine, enables interactive SQL queries against more datastores.
Introduction
This workshop is a hands-on session to quickly deploy Hadoop and Streaming on AWS / Azure / Google Cloud.
Cloudbreak simplifies the deployment of Hadoop in cloud environments. It enables the enterprise to quickly run big data workloads in the cloud while optimizing the use of cloud resources.
Format
A short introductory lecture about Cloudbreak. This is followed by a walk through and lab leveraging Hadoop and Streaming in the Cloud with Cloudbreak.
Objective
To provide a quick and short hands-on introduction to Hadoop on the cloud. Review key benefits of cluster deployment automation.
This lab will use Cloudbreak to quickly and effortlessly stand up Hadoop and Streaming clusters in a cloud provider of your choice. The lab shows the use of Ambari blueprints that are your declarative definitions of your Hadoop or Streaming clusters. Steps to dynamically change these blueprints and use external databases and external authentication sources and in essence showing a way to provide Shared Authentication, Authorization and Audit across ephemeral and long-lasting clusters. However it is not limited to only custom blueprints, the lab also shows how Cloudbreak provides easy to use custom scripts called recipes that can be executed before or after Ambari start or after cluster installation.
This workshop is a hands-on session to quickly deploy Hadoop and Streaming on AWS / Azure / Google Cloud.
Cloudbreak simplifies the deployment of Hadoop in cloud environments. It enables the enterprise to quickly run big data workloads in the cloud while optimizing the use of cloud resources
Objective
To provide a quick and short hands-on introduction to Hadoop on the cloud. Review key benefits of cluster deployment automation.
This lab will use Cloudbreak to quickly and effortlessly stand up Hadoop and Streaming clusters in a cloud provider of your choice. The lab shows the use of Ambari blueprints that are your declarative definitions of your Hadoop or Streaming clusters. Steps to dynamically change these blueprints and use external databases and external authentication sources and in essence showing a way to provide Shared Authentication, Authorization and Audit across ephemeral and long-lasting clusters. However it is not limited to only custom blueprints, the lab also shows how Cloudbreak provides easy to use custom scripts called recipes that can be executed before or after Ambari start or after cluster installation.
Pre-requisites
Registrants must bring a laptop for the lab. These labs will be done in the Cloud. Please follow below steps to setup an AWS or Azure account prior to this session starting.
a. Before launching Cloudbreak on AWS, you must meet the AWS prerequisites.
b. Before launching Cloudbreak on Azure, you must meet the Azure prerequisites.
Speaker: Santosh Gowda
Hortonworks Technical Workshop: What's New in HDP 2.3Hortonworks
The recently launched HDP 2.3 is a major advancement of Open Enterprise Hadoop. It represents the best of community led development with innovations spanning Apache Hadoop, Apache Ambari, Ranger, HBase, Spark and Storm. In this session we will provide an in-depth overview of new functionality and discuss it's impact on new and ongoing big data initiatives.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
The lessons from implementing a twitter bot designed to live on a raspberry pi and heckle politicians —and deployed into production in the 2017 UK General Election
Berlin Buzzwords 2017 talk: A look at what our storage models, metaphors and APIs are, showing how we need to rethink the Posix APIs to work with object stores, while looking at different alternatives for local NVM.
This is the unabridged talk; the BBuzz talk was 20 minutes including demo and questions, so had ~half as many slides
Dancing Elephants: Working with Object Storage in Apache Spark and HiveSteve Loughran
A talk looking at the intricate details of working with an object store from Hadoop, Hive, Spark, etc, why the "filesystem" metaphor falls down, and what work myself and others have been up to to try and fix things
My talk from Berlin Buzzwords 2016, looking at whether it is actually possible to lock down a household to the extent you could call it "secure". I also try to highlight that we need to consider "privacy" alongside security.
Hadoop and Kerberos: the Madness Beyond the Gate: January 2016 editionSteve Loughran
An update of the "Hadoop and Kerberos: the Madness Beyond the Gate" talk, covering recent work "the Fix Kerberos" JIRA and its first deliverable: KDiag
An iteration of the Hoya slides put up as part of a review of the code with others writing YARN services; looks at what Hoya offers -what we need from Apps to be able to deploy them this way, and what we need from YARN
Overview of the above and beyond MapReduce, for the HPC/science community. Key point: move up the stack, reuse what is there. But: some of these people are capable of writing their own YARN apps, so they should be encouraged to do so if they see a need.
Presentation on 2013-06-27, Workshop on the future of Big Data management, discussing hadoop for a science audience that are either HPC/grid users or people suddenly discovering that their data is accruing towards PB.
The other talks were on GPFS, LustreFS and Ceph, so rather than just do beauty-contest slides, I decided to raise the question of "what is a filesystem?", whether the constraints imposed by the Unix metaphor and API are becoming limits on scale and parallelism (both technically and, for GPFS and Lustre Enterprise in cost).
Then: HDFS as the foundation for the Hadoop stack.
All the other FS talks did emphasise their Hadoop integration, with the Intel talk doing the most to assert performance improvements of LustreFS over HDFSv1 in dfsIO and Terasort (no gridmix?), which showed something important: Hadoop is the application that add DFS developers have to have a story for
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
TALK TRACK
Hortonworks Powers the Future of Data: data-in-motion, data-at-rest, and Modern Data Applications.
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I'm going to warn there's a bias in this talk to S3. Not out of any personal preference, just because I've been busiest there
If there's a fault with the adl:// connector, it's usually "error(null"). This can mean anything, including "you don't have your proxy settings and an HTML page is coming back, which confuses our JSON parser"…the kind or problem which surface in the field.
Need also to highlight possibility that it's caching things client side for perf. If true, risk of consistency.
A key thing to note here is that the Hadoop connector dev has been going on in sync with thee store service, with full stack regression testing going on. This increases its
Details about out-of-band, and source of truth etc.
Details about out-of-band, and source of truth etc.
I seem to be getting bug reports related to distCP and clouds assigned to me. This is probably a metric of (a) rising popularity and (b) performance/functionality issues
I've got a quick PoC of actually using Spark to schedule the work: you can do a basic distcp successor in 200 LoC by ignoring all the existing options and throttling. Tempting to use as a basis for a CloudCP, but as distcp is often used as a library for other apps, it fails to meet some core deployment needs.
More likely: extend distcp with ability to save & restore (src, dest) checksums and then compare these on the next incremental update.
Choosing which cloud infrastructure to use is something which tends to choose what your storage option is going to be: AWS and GCS dictate the options. Azure is interesting in that there are two: WASB vs ADL, and a successor coming