The document discusses Apache HCatalog, which provides metadata services and a unified view of data across Hadoop tools like Hive, Pig, and MapReduce. It allows sharing of data and metadata between tools and external systems through a consistent schema. HCatalog simplifies data management by allowing tools to access metadata like the schema, location, and format of data from a shared metastore instead of encoding that information within each application.
Sept 17 2013 - THUG - HBase a Technical IntroductionAdam Muise
HBase Technical Introduction. This deck includes a description of memory design, write path, read path, some operational tidbits, SQL on HBase (Phoenix and Hive), as well as HOYA (HBase on YARN).
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While developed by Facebook.
This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.
Sept 17 2013 - THUG - HBase a Technical IntroductionAdam Muise
HBase Technical Introduction. This deck includes a description of memory design, write path, read path, some operational tidbits, SQL on HBase (Phoenix and Hive), as well as HOYA (HBase on YARN).
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While developed by Facebook.
This Hadoop will help you understand the different tools present in the Hadoop ecosystem. This Hadoop video will take you through an overview of the important tools of Hadoop ecosystem which include Hadoop HDFS, Hadoop Pig, Hadoop Yarn, Hadoop Hive, Apache Spark, Mahout, Apache Kafka, Storm, Sqoop, Apache Ranger, Oozie and also discuss the architecture of these tools. It will cover the different tasks of Hadoop such as data storage, data processing, cluster resource management, data ingestion, machine learning, streaming and more. Now, let us get started and understand each of these tools in detail.
Below topics are explained in this Hadoop ecosystem presentation:
1. What is Hadoop ecosystem?
1. Pig (Scripting)
2. Hive (SQL queries)
3. Apache Spark (Real-time data analysis)
4. Mahout (Machine learning)
5. Apache Ambari (Management and monitoring)
6. Kafka & Storm
7. Apache Ranger & Apache Knox (Security)
8. Oozie (Workflow system)
9. Hadoop MapReduce (Data processing)
10. Hadoop Yarn (Cluster resource management)
11. Hadoop HDFS (Data storage)
12. Sqoop & Flume (Data collection and ingestion)
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Learn Spark SQL, creating, transforming, and querying Data frames
14. Understand the common use-cases of Spark and the various interactive algorithms
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training.
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Keynote: Getting Serious about MySQL and Hadoop at ContinuentContinuent
Lean, mean MySQL and hulking Hadoop clusters may seem like an odd couple, but tying them together is now priority #1 for many MySQL users. This keynote talk on 1st day of this year's Percona Live MySQL Conference & Expo 2014 explores the data management trends spurring integration, how the MySQL community is stepping up, and where the integration may go in the future. Robert Hodges, CEO at Continuent, outlines how work at Continuent fits into this picture and how we are contributing to the MySQL community response to Hadoop.
Scale 12 x Efficient Multi-tenant Hadoop 2 Workloads with YarnDavid Kaiser
Hadoop is about so much more than batch processing. With the recent release of Hadoop 2, there have been significant changes to how a Hadoop cluster uses resources. YARN, the new resource management component, allows for a more efficient mix of workloads across hardware resources, and enables new applications and new processing paradigms such as stream-processing. This talk will discuss the new design and components of Hadoop 2, and examples of Modern Data Architectures that leverage Hadoop for maximum business efficiency.
These slides cover the very basics of Hadoop architecture, in particular HDFS. This was my presentation in the first Delhi Hadoop User Group (DHUG) meetup held at Gurgaon on 10th September 2011. Loved the positive feedback. I'll also upload a more elaborate version covering Hadoop mapreduce architecture as well soon. Most of the stuff covered in these slides can be found in Tom White's book as well (See the last slide)
In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the first half of the tutorial.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
Mining big data streams with APACHE SAMOA by Albert BifetJ On The Beach
In this talk, we present Apache SAMOA, an open-source platform for mining big data streams with Apache Flink, Storm and Samza. Real time analytics is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when problems appear or to detect new trends helping to improve their performance. Apache SAMOA includes algorithms for the most common machine learning tasks such as classification and clustering. It provides a pluggable architecture that allows it to run on Apache Flink, but also with other several distributed stream processing engines such as Storm and Samza.
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don't overlook the charming yellow elephant you see, which is basically named after Doug's son's toy elephant!
The topics covered in presentation are:
1. Big Data Learning Path
2.Big Data Introduction
3. Hadoop and its Eco-system
4.Hadoop Architecture
5.Next Step on how to setup Hadoop
The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Keynote: Getting Serious about MySQL and Hadoop at ContinuentContinuent
Lean, mean MySQL and hulking Hadoop clusters may seem like an odd couple, but tying them together is now priority #1 for many MySQL users. This keynote talk on 1st day of this year's Percona Live MySQL Conference & Expo 2014 explores the data management trends spurring integration, how the MySQL community is stepping up, and where the integration may go in the future. Robert Hodges, CEO at Continuent, outlines how work at Continuent fits into this picture and how we are contributing to the MySQL community response to Hadoop.
Scale 12 x Efficient Multi-tenant Hadoop 2 Workloads with YarnDavid Kaiser
Hadoop is about so much more than batch processing. With the recent release of Hadoop 2, there have been significant changes to how a Hadoop cluster uses resources. YARN, the new resource management component, allows for a more efficient mix of workloads across hardware resources, and enables new applications and new processing paradigms such as stream-processing. This talk will discuss the new design and components of Hadoop 2, and examples of Modern Data Architectures that leverage Hadoop for maximum business efficiency.
These slides cover the very basics of Hadoop architecture, in particular HDFS. This was my presentation in the first Delhi Hadoop User Group (DHUG) meetup held at Gurgaon on 10th September 2011. Loved the positive feedback. I'll also upload a more elaborate version covering Hadoop mapreduce architecture as well soon. Most of the stuff covered in these slides can be found in Tom White's book as well (See the last slide)
In KDD2011, Vijay Narayanan (Yahoo!) and Milind Bhandarkar (Greenplum Labs, EMC) conducted a tutorial on "Modeling with Hadoop". This is the first half of the tutorial.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
Mining big data streams with APACHE SAMOA by Albert BifetJ On The Beach
In this talk, we present Apache SAMOA, an open-source platform for mining big data streams with Apache Flink, Storm and Samza. Real time analytics is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when problems appear or to detect new trends helping to improve their performance. Apache SAMOA includes algorithms for the most common machine learning tasks such as classification and clustering. It provides a pluggable architecture that allows it to run on Apache Flink, but also with other several distributed stream processing engines such as Storm and Samza.
Creating a Data Science Team from an Architect's perspective. This is about team building on how to support a data science team with the right staff, including data engineers and devops.
Moving to a data-centric architecture: Toronto Data Unconference 2015Adam Muise
Why use a datalake? Why use lambda? A conversation starter for Toronto Data Unconference 2015. We will discuss technologies such as Hadoop, Kafka, Spark Streaming, and Cassandra.
2015 nov 27_thug_paytm_rt_ingest_brief_finalAdam Muise
Paytm Labs provides a quick overview of their Hadoop data ingest platform. We cover our journey from a batch focused ingest system with SQOOP to a streaming ingest supported by Kafka, Confluent.io, Hadoop, Cassandra, and Spark Streaming. This presentation also provides an overview of our complete data platform including our feature creation template
Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...Big Data Spain
Session presented at Big Data Spain 2012 Conference
16th Nov 2012
ETSI Telecomunicacion UPM Madrid
www.bigdataspain.org
More info: http://www.bigdataspain.org/es-2012/conference/coordinating-many-tools-of-big-data/alan-gates
Organizations are now increasingly interested in finding more efficient ways to tackle deeply hierarchical data including XML and JSON as wellas other complex data formats like Web logs, binaries, and machine generated data in Hadoop.
How are you currently developing setting up data parsing tasks insideMapReduce? Are you interested in native streaming and splitting capabilities allow effective handling of files in any size regardless of format. In this session, we will share with you about HParseroptimized for parallel parsing in Hadoop including technical demonstration of HParser.
Hadoop makes data storage and processing at scale available as a lower cost and open solution. If you ever wanted to get your feet wet but found the elephant intimidating fear no more.
We will explore several integration considerations from a Windows application prospective like accessing HDFS content, writing streaming jobs, using .NET SDK, as well as HDInsight on premise or on Azure.
Hadoop World 2011: Big Data Architecture: Integrating Hadoop with Other Enter...Cloudera, Inc.
Recent research has pointed out the complementary nature of Hadoop and other data management solutions and the importance of leveraging existing systems, SQL, engineering, and operational skills, as well as incorporating novel uses of MapReduce to improve analytic processing. Come to this session to learn how companies optimize the use of Hadoop with other enterprise systems to improve overall analytical throughput and build new data-driven products. This session covers: ways to achieve high-performance integration between Hadoop and relational-based systems; Hadoop+NoSQL vs Hadoop+SQL architectures; high-speed, massively parallel data transfer to analytical platforms that can aggregate web log data with granular fact data; and strategies for freeing up capacity for more explorative, iterative analytics and ad hoc queries.
HCatalog is a table abstraction and a storage abstraction system that makes it easy for multiple tools to interact with the same underlying data. A common buzzword in the NoSQL world today is that of polyglot persistence. Basically, what that comes down to is that you pick the right tool for the job. In the hadoop ecosystem, you have many tools that might be used for data processing - you might use pig or hive, or your own custom mapreduce program, or that shiny new GUI-based tool that's just come out. And which one to use might depend on the user, or on the type of query you're interested in, or the type of job we want to run. From another perspective, you might want to store your data in columnar storage for efficient storage and retrieval for particular query types, or in text so that users can write data producers in scripting languages like perl or python, or you may want to hook up that hbase table as a data source. As a end-user, I want to use whatever data processing tool is available to me. As a data designer, I want to optimize how data is stored. As a cluster manager / data architect, I want the ability to share pieces of information across the board, and move data back and forth fluidly. HCatalog's hopes and promises are the realization of all of the above.
The venerable MapReduce framework has allowed Hadoop to prove its worth in the big data space, and to store and analyze much larger data sets than was possible before. But there is a lot of activity in the big data ecosystem currently surrounding other major categories of workflows beyond batch.
These emerging tools include low latency i/o (HBase), interactive queries (Drill), stream processing (Storm), and text processing / indexing (Solr). This talk discusses some of the more interesting developments in Drill and Storm, their capabilities, and how they are being put to use in real world situations.
The initial work in HCatalog has allowed users to share their data in Hadoop regardless of the tools they use and relieved them of needing to know where and how their data is stored. But there is much more to be done to deliver on the full promise of providing metadata and table management for Hadoop clusters. It should be easy to store and process semi-structured and unstructured data via HCatalog. We need interfaces and simple implementations of data life cycle management tools. We need to deepen the integration with NoSQL and MPP data stores. And we need to be able to store larger metadata such as partition level statistics and user generated metadata. This talk will cover these areas of growth and give an overview of how they might be approached.
An overview of securing Hadoop. Content primarily by Balaji Ganesan, one of the leaders of the Apache Argus project. Presented on Sept 4, 2014 at the Toronto Hadoop User Group by Adam Muise.
2014 feb 24_big_datacongress_hadoopsession2_moderndataarchitectureAdam Muise
An introduction to Hadoop's core components as well as the core Hadoop use case: the Data Lake. This deck was delivered at Big Data Congress 2014 in Saint John, NB on Feb 24.
What is Hadoop brief intro for Georgian Partners CTO Conference. This outlines the origins of Open Source Apache Hadoop and how Hortonworks fits into this picture. There is also a brief introduction to YARN, the new resource negotiation layer.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
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:
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
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.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
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.