Sahara is an OpenStack project that provides an abstraction layer for provisioning and managing Apache Hadoop clusters and jobs in OpenStack clouds. It allows users to easily deploy and scale Hadoop clusters on demand without having to manage the underlying infrastructure. Sahara uses plugins to integrate various Hadoop distributions like Hortonworks Data Platform (HDP) and Cloudera Distribution including Apache Hadoop (CDH). It leverages other OpenStack services like Nova, Neutron, Swift, Cinder, Heat etc. to provision, configure and manage the Hadoop clusters and jobs.
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks
Apache Spark is an excellent tool to accelerate your analytics, whether you’re doing ETL, Machine Learning, or Data Warehousing. However, to really make the most of Spark it pays to understand best practices for data storage, file formats, and query optimization. This talk will cover best practices I’ve applied over years in the field helping customers write Spark applications as well as identifying what patterns make sense for your use case.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
This Hadoop Hive Tutorial will unravel the complete Introduction to Hive, Hive Architecture, Hive Commands, Hive Fundamentals & HiveQL. In addition to this, even fundamental concepts of BIG Data & Hadoop are extensively covered.
At the end, you'll have a strong knowledge regarding Hadoop Hive Basics.
PPT Agenda
✓ Introduction to BIG Data & Hadoop
✓ What is Hive?
✓ Hive Data Flows
✓ Hive Programming
----------
What is Apache Hive?
Apache Hive is a data warehousing infrastructure built over Hadoop which is targeted towards SQL programmers. Hive permits SQL programmers to directly enter the Hadoop ecosystem without any pre-requisites in Java or other programming languages. HiveQL is similar to SQL, it is utilized to process Hadoop & MapReduce operations by managing & querying data.
----------
Hive has the following 5 Components:
1. Driver
2. Compiler
3. Shell
4. Metastore
5. Execution Engine
----------
Applications of Hive
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks
Apache Spark is an excellent tool to accelerate your analytics, whether you’re doing ETL, Machine Learning, or Data Warehousing. However, to really make the most of Spark it pays to understand best practices for data storage, file formats, and query optimization. This talk will cover best practices I’ve applied over years in the field helping customers write Spark applications as well as identifying what patterns make sense for your use case.
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
This Hadoop Hive Tutorial will unravel the complete Introduction to Hive, Hive Architecture, Hive Commands, Hive Fundamentals & HiveQL. In addition to this, even fundamental concepts of BIG Data & Hadoop are extensively covered.
At the end, you'll have a strong knowledge regarding Hadoop Hive Basics.
PPT Agenda
✓ Introduction to BIG Data & Hadoop
✓ What is Hive?
✓ Hive Data Flows
✓ Hive Programming
----------
What is Apache Hive?
Apache Hive is a data warehousing infrastructure built over Hadoop which is targeted towards SQL programmers. Hive permits SQL programmers to directly enter the Hadoop ecosystem without any pre-requisites in Java or other programming languages. HiveQL is similar to SQL, it is utilized to process Hadoop & MapReduce operations by managing & querying data.
----------
Hive has the following 5 Components:
1. Driver
2. Compiler
3. Shell
4. Metastore
5. Execution Engine
----------
Applications of Hive
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
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.
End-to-End Spark/TensorFlow/PyTorch Pipelines with Databricks DeltaDatabricks
Hopsworks is an open-source data platform that can be used to both develop and operate horizontally scalable machine learning pipelines. A key part of our pipelines is the world’s first open-source Feature Store, based on Apache Hive, that acts as a data warehouse for features, providing a natural API between data engineers – who write feature engineering code in Spark (in Scala or Python) – and Data Scientists, who select features from the feature store to generate training/test data for models. In this talk, we will discuss how Databricks Delta solves several of the key challenges in building both feature engineering pipelines that feed our Feature Store and in managing the feature data itself.
Firstly, we will show how expectations and schema enforcement in Databricks Delta can be used to provide data validation, ensuring that feature data does not have missing or invalid values that could negatively affect model training. Secondly, time-travel in Databricks Delta can be used to provide version management and experiment reproducability for training/test datasets. That is, given a model, you can re-run the training experiment for that model using the same version of the data that was used to train the model.
We will also discuss the next steps needed to take this work to the next level. Finally, we will perform a live demo, showing how Delta can be used in end-to-end ML pipelines using Spark on Hopsworks.
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/
Data Discovery at Databricks with AmundsenDatabricks
Databricks used to use a static manually maintained wiki page for internal data exploration. We will discuss how we leverage Amundsen, an open source data discovery tool from Linux Foundation AI & Data, to improve productivity with trust by surfacing the most relevant dataset and SQL analytics dashboard with its important information programmatically at Databricks internally.
We will also talk about how we integrate Amundsen with Databricks world class infrastructure to surface metadata including:
Surface the most popular tables used within Databricks
Support fuzzy search and facet search for dataset- Surface rich metadata on datasets:
Lineage information (downstream table, upstream table, downstream jobs, downstream users)
Dataset owner
Dataset frequent users
Delta extend metadata (e.g change history)
ETL job that generates the dataset
Column stats on numeric type columns
Dashboards that use the given dataset
Use Databricks data tab to show the sample data
Surface metadata on dashboards including: create time, last update time, tables used, etc
Last but not least, we will discuss how we incorporate internal user feedback and provide the same discovery productivity improvements for Databricks customers in the future.
Presented the "A Cloud Journey - Move to the Oracle Cloud" on behalf of Ricardo Gonzalez during Bulgarian Oracle User Group Spring Conference 2019. This presentation discusses various methods on how to migrate to the Oracle Cloud and provides recommendations as to which tool to use (and where to find it) especially assuming that Zero Downtime Migration is desired, for which the new Zero Downtime Migration tool is described and discussed in detail. More information: http://www.oracle.com/goto/move
Data Analytics Meetup: Introduction to Azure Data Lake Storage CCG
Microsoft Azure Data Lake Storage is designed to enable operational and exploratory analytics through a hyper-scale repository. Journey through Azure Data Lake Storage Gen1 with Microsoft Data Platform Specialist, Audrey Hammonds. In this video she explains the fundamentals to Gen 1 and Gen 2, walks us through how to provision a Data Lake, and gives tips to avoid turning your Data Lake into a swamp.
Learn more about Data Lakes with our blog - Data Lakes: Data Agility is Here Now https://bit.ly/2NUX1H6
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceCloudera, Inc.
Most developers are familiar with the topic of “database design”. In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
Troubleshooting Kerberos in Hadoop: Taming the BeastDataWorks Summit
Kerberos is the ubiquitous authentication mechanism when it comes to secure any Hadoop Services. With recent updates in Hadoop core and various Apache Hadoop components, inherent Kerberos support has matured and has come a long way.
Understanding & configuring Kerberos is still a challenge but even more painful & frustrating is troubleshooting a Kerberos issue. There are lot of things (small & big) that can go wrong (and will go wrong!). This talk covers the Kerberos debugging part in detail and discusses the tools & tricks that can be used to narrow down any Kerberos issue.
Rather than discussing the issues and their resolution, we will focus on how to approach a Kerberos problem and do's / dont's in Kerberos scene. This talk will provide a step by step guide that will equip the audience for troubleshooting future Kerberos problems.
Agenda is to discuss:
- Systematic approach to Kerberos troubleshooting
- Kerberos Tools available in Hadoop arsenal
- Tips & Tricks to narrow down Kerberos issues quickly
- Some nasty Kerberos issues from Support trenches
Some prior knowledge on Kerberos basics will be appreciated but is not a prerequisite.
Speaker:
Vipin Rathor, Sr. Product Specialist (HDP Security), Hortonworks
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.
Alfresco DevCon 2019 (Edinburgh)
"Transforming the Transformers" for Alfresco Content Services (ACS) 6.1 & beyond
https://community.alfresco.com/community/ecm/blog/2019/02/07/alfresco-transform-service-new-with-acs-61
Alfresco provides various content transformation options across the Digital Business Platform (DBP). In this talk, we will explore the new independently-scalable Alfresco Transform Service. This enables a new option for transforms to be asynchronously off-loaded by Alfresco Content Services (ACS).
https://devcon.alfresco.com/speaker/jan-vonka/
Hadoop is often viewed as needing racks of dedicated boxes -despite the fact that in sheer number terms, the majority of Hadoop clusters ever created have been brought up on public cloud infrastructures -particularly Amazon`s. Yet the rest of datacenter computing is moving towards virtualization -be it in-cloud startups or in-enterprise IT departments. Some organizations are standing up private clouds: a rack or two of servers with an API for VM creation. Hadoop can live there -it just needs to integrate better. At the same time, OpenStack is emerging as the de-facto standard open source cloud platform for private use, and is available publicly from a number of cloud infrastructure service providers. This talk looks at what we`ve done -and are doing- to integrate Hadoop with OpenStack. This is taking it beyond Hadoop`s current support for Amazon`s infrastructure, making a combined Hadoop + OpenStack cluster something to consider in-house -and in-cloud. This work is being done in collaboration with members of the OpenStack community, showing how cloud and big data projects can not only co-exist, we can co-develop our platforms.
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.
End-to-End Spark/TensorFlow/PyTorch Pipelines with Databricks DeltaDatabricks
Hopsworks is an open-source data platform that can be used to both develop and operate horizontally scalable machine learning pipelines. A key part of our pipelines is the world’s first open-source Feature Store, based on Apache Hive, that acts as a data warehouse for features, providing a natural API between data engineers – who write feature engineering code in Spark (in Scala or Python) – and Data Scientists, who select features from the feature store to generate training/test data for models. In this talk, we will discuss how Databricks Delta solves several of the key challenges in building both feature engineering pipelines that feed our Feature Store and in managing the feature data itself.
Firstly, we will show how expectations and schema enforcement in Databricks Delta can be used to provide data validation, ensuring that feature data does not have missing or invalid values that could negatively affect model training. Secondly, time-travel in Databricks Delta can be used to provide version management and experiment reproducability for training/test datasets. That is, given a model, you can re-run the training experiment for that model using the same version of the data that was used to train the model.
We will also discuss the next steps needed to take this work to the next level. Finally, we will perform a live demo, showing how Delta can be used in end-to-end ML pipelines using Spark on Hopsworks.
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/
Data Discovery at Databricks with AmundsenDatabricks
Databricks used to use a static manually maintained wiki page for internal data exploration. We will discuss how we leverage Amundsen, an open source data discovery tool from Linux Foundation AI & Data, to improve productivity with trust by surfacing the most relevant dataset and SQL analytics dashboard with its important information programmatically at Databricks internally.
We will also talk about how we integrate Amundsen with Databricks world class infrastructure to surface metadata including:
Surface the most popular tables used within Databricks
Support fuzzy search and facet search for dataset- Surface rich metadata on datasets:
Lineage information (downstream table, upstream table, downstream jobs, downstream users)
Dataset owner
Dataset frequent users
Delta extend metadata (e.g change history)
ETL job that generates the dataset
Column stats on numeric type columns
Dashboards that use the given dataset
Use Databricks data tab to show the sample data
Surface metadata on dashboards including: create time, last update time, tables used, etc
Last but not least, we will discuss how we incorporate internal user feedback and provide the same discovery productivity improvements for Databricks customers in the future.
Presented the "A Cloud Journey - Move to the Oracle Cloud" on behalf of Ricardo Gonzalez during Bulgarian Oracle User Group Spring Conference 2019. This presentation discusses various methods on how to migrate to the Oracle Cloud and provides recommendations as to which tool to use (and where to find it) especially assuming that Zero Downtime Migration is desired, for which the new Zero Downtime Migration tool is described and discussed in detail. More information: http://www.oracle.com/goto/move
Data Analytics Meetup: Introduction to Azure Data Lake Storage CCG
Microsoft Azure Data Lake Storage is designed to enable operational and exploratory analytics through a hyper-scale repository. Journey through Azure Data Lake Storage Gen1 with Microsoft Data Platform Specialist, Audrey Hammonds. In this video she explains the fundamentals to Gen 1 and Gen 2, walks us through how to provision a Data Lake, and gives tips to avoid turning your Data Lake into a swamp.
Learn more about Data Lakes with our blog - Data Lakes: Data Agility is Here Now https://bit.ly/2NUX1H6
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceCloudera, Inc.
Most developers are familiar with the topic of “database design”. In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
Troubleshooting Kerberos in Hadoop: Taming the BeastDataWorks Summit
Kerberos is the ubiquitous authentication mechanism when it comes to secure any Hadoop Services. With recent updates in Hadoop core and various Apache Hadoop components, inherent Kerberos support has matured and has come a long way.
Understanding & configuring Kerberos is still a challenge but even more painful & frustrating is troubleshooting a Kerberos issue. There are lot of things (small & big) that can go wrong (and will go wrong!). This talk covers the Kerberos debugging part in detail and discusses the tools & tricks that can be used to narrow down any Kerberos issue.
Rather than discussing the issues and their resolution, we will focus on how to approach a Kerberos problem and do's / dont's in Kerberos scene. This talk will provide a step by step guide that will equip the audience for troubleshooting future Kerberos problems.
Agenda is to discuss:
- Systematic approach to Kerberos troubleshooting
- Kerberos Tools available in Hadoop arsenal
- Tips & Tricks to narrow down Kerberos issues quickly
- Some nasty Kerberos issues from Support trenches
Some prior knowledge on Kerberos basics will be appreciated but is not a prerequisite.
Speaker:
Vipin Rathor, Sr. Product Specialist (HDP Security), Hortonworks
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.
Alfresco DevCon 2019 (Edinburgh)
"Transforming the Transformers" for Alfresco Content Services (ACS) 6.1 & beyond
https://community.alfresco.com/community/ecm/blog/2019/02/07/alfresco-transform-service-new-with-acs-61
Alfresco provides various content transformation options across the Digital Business Platform (DBP). In this talk, we will explore the new independently-scalable Alfresco Transform Service. This enables a new option for transforms to be asynchronously off-loaded by Alfresco Content Services (ACS).
https://devcon.alfresco.com/speaker/jan-vonka/
Hadoop is often viewed as needing racks of dedicated boxes -despite the fact that in sheer number terms, the majority of Hadoop clusters ever created have been brought up on public cloud infrastructures -particularly Amazon`s. Yet the rest of datacenter computing is moving towards virtualization -be it in-cloud startups or in-enterprise IT departments. Some organizations are standing up private clouds: a rack or two of servers with an API for VM creation. Hadoop can live there -it just needs to integrate better. At the same time, OpenStack is emerging as the de-facto standard open source cloud platform for private use, and is available publicly from a number of cloud infrastructure service providers. This talk looks at what we`ve done -and are doing- to integrate Hadoop with OpenStack. This is taking it beyond Hadoop`s current support for Amazon`s infrastructure, making a combined Hadoop + OpenStack cluster something to consider in-house -and in-cloud. This work is being done in collaboration with members of the OpenStack community, showing how cloud and big data projects can not only co-exist, we can co-develop our platforms.
IBM Endpoint Manager for Mobile Devices (Overview)Kimber Spradlin
Manage all devices - smartphones, tablets, laptops, desktops, and servers - from a single console. IBM Endpoint Manager also integrates Enterproid Divide secure container and NitroDesk TouchDown secure email technologies for separation of organizational content on BYOD and contractor devices.
Mandibular nerve block and mental nerve / oral surgery courses Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
00919248678078
Telecom Fraud Detection - Naive Bayes ClassificationMaruthi Nataraj K
To create a fraud management classification model that is powerful enough to handle the subscription fraud that the company has encountered and flexible enough to potentially apply to things that had not been witnessed yet
Designed a fully customized 128x10b SRAM by constructing schematic & virtuoso layout of memory cell array (6T cell), row & column decoder, pre-charge circuit, write circuit and sense amplifier using Cadence. Manually placed and routed all components, performed DRC & LVS debugging of constructed schematic and layout and ran PEX to generate the final Netlist, Hspice Spectre simulation of final design for verification of the correct functionality and analysis of best read, best write cycles & the worst case timing for read and write. Timing and power consumed is analyzed through STA-Primetime (Static timing Analysis)
We present a software model built on the Apache software stack (ABDS) that is well used in modern cloud computing, which we enhance with HPC concepts to derive HPC-ABDS.
We discuss layers in this stack
We give examples of integrating ABDS with HPC
We discuss how to implement this in a world of multiple infrastructures and evolving software environments for users, developers and administrators
We present Cloudmesh as supporting Software-Defined Distributed System as a Service or SDDSaaS with multiple services on multiple clouds/HPC systems.
We explain the functionality of Cloudmesh as well as the 3 administrator and 3 user modes supported
We present a software model built on the Apache software stack (ABDS) that is well used in modern cloud computing, which we enhance with HPC concepts to derive HPC-ABDS.
We discuss layers in this stack
We give examples of integrating ABDS with HPC
We discuss how to implement this in a world of multiple infrastructures and evolving software environments for users, developers and administrators
We present Cloudmesh as supporting Software-Defined Distributed System as a Service or SDDSaaS with multiple services on multiple clouds/HPC systems.
We explain the functionality of Cloudmesh as well as the 3 administrator and 3 user modes supported
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Build Your Own PaaS, Just like Red Hat's OpenShift from LinuxCon 2013 New Orl...OpenShift Origin
Learn how to build your platform as a service just like RedHat's OpenShift PaaS - covers all the architecture & internals of OpenShift Origin OpenSource project, how to deploy it & configure it for bare metal, AWS, OpenStack, CloudStack or any IaaS, and the community that's collaborating on the project to deliver the next-generation of secure, scale-able PaaS visit: openshift.com for more information
presented at LinuxCon by Diane Mueller in the CloudOpen track
These slides provide highlights of my book HDInsight Essentials. Book link is here: http://www.packtpub.com/establish-a-big-data-solution-using-hdinsight/book
HdInsight essentials Hadoop on Microsoft Platformnvvrajesh
This book gives a quick introduction to Hadoop-like problems, and gives a primer on the real value of HDInsight. Next, it will show how to set up your HDInsight cluster.
Then, it will take you through the four stages: collect, process, analyze, and report.
For each of these stages you will see a practical example with the working code.
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
A session in the DevNet Zone at Cisco Live, Berlin. There are several new OpenStack projects/services that are built on core OpenStack infrastructure services. This session will first briefly discuss the changes introduced for the project governance structure in OpenStack. Subsequently, the focus of the presentation will be to provide feature and architecture details on few of the new projects and services in OpenStack. These will include Trove-Database Service, Sahara-Dataprocessing Service, Congress - Policy Service and Magnum -- Container Service. A summary of other OpenStack related services will also be provided.
High Performance Processing of Streaming DataGeoffrey Fox
Describes two parallel robot planning algorithms implemented with Apache Storm on OpenStack -- SLAM (Simultaneous Localization & Mapping) and collision avoidance. Performance (response time) studied and improved as example of HPC-ABDS (High Performance Computing enhanced Apache Big Data Software Stack) concept.
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache RatisDataWorks Summit
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...DataWorks Summit
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at UberDataWorks Summit
Data serves as the platform for decision-making at Uber. To facilitate data driven decisions, many datasets at Uber are ingested in a Hadoop Data Lake and exposed to querying via Hive. Analytical queries joining various datasets are run to better understand business data at Uber.
Data ingestion, at its most basic form, is about organizing data to balance efficient reading and writing of newer data. Data organization for efficient reading involves factoring in query patterns to partition data to ensure read amplification is low. Data organization for efficient writing involves factoring the nature of input data - whether it is append only or updatable.
At Uber we ingest terabytes of many critical tables such as trips that are updatable. These tables are fundamental part of Uber's data-driven solutions, and act as the source-of-truth for all the analytical use-cases across the entire company. Datasets such as trips constantly receive updates to the data apart from inserts. To ingest such datasets we need a critical component that is responsible for bookkeeping information of the data layout, and annotates each incoming change with the location in HDFS where this data should be written. This component is called as Global Indexing. Without this component, all records get treated as inserts and get re-written to HDFS instead of being updated. This leads to duplication of data, breaking data correctness and user queries. This component is key to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. This component will need to have strong consistency and provide large throughputs for index writes and reads.
At Uber, we have chosen HBase to be the backing store for the Global Indexing component and is a critical component in allowing us to scaling our jobs where we are now handling greater than 500 billion writes a day in our current ingestion systems. In this talk, we will discuss data@Uber and expound more on why we built the global index using Apache Hbase and how this helps to scale out our cluster usage. We’ll give details on why we chose HBase over other storage systems, how and why we came up with a creative solution to automatically load Hfiles directly to the backend circumventing the normal write path when bootstrapping our ingestion tables to avoid QPS constraints, as well as other learnings we had bringing this system up in production at the scale of data that Uber encounters daily.
Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google CloudDataWorks Summit
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFiDataWorks Summit
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...DataWorks Summit
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkDataWorks Summit
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
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.
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.
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.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
3. OpenStack is
A cloud operating system that controls large
pools of compute, storage, and networking
resources throughout a datacenter, all
managed through a dashboard that gives
administrators control while empowering their
users to provision resources through a web
interface.
4. An ecosystem of projects
● Compute - Nova
● Networking - Neutron
● Object Storage - Swift
● Block Storage - Cinder
● Identity - Keystone
● Image Service - Glance
● Dashboard - Horizon
● Telemetry - Ceilometer
● Orchestration - Heat
● Data Processing - Sahara
8. Data analysis is hard...
● Come up w/ a relevant question
○ The question you answer won’t be the question you
set out to ask
○ Mine: Can I predict doctor specialty from what
procedures they perform?
● Find the data
○ Tons, little consistency, unknown origin, hidden in
silos, horded
○ Data w/o a dictionary is worse than code w/o
9. Data analysis is hard...
● Data usability
○ Acceptable license? (Even for Gov’t sets)
■ Mine: Metadata copyrighted by AMA!
○ Private is often highly protected, no/narrow DMZ
● Explore and clean
○ Two of the oldest people in the medical profession
working with medicare
○ Stephen Glasser graduated in 1773
○ Cheryl Palma graduated in 1776
10. Data analysis is hard...
● You got some answer to a question you
approximately asked
● You must refine the question and process
● Repeat
This is hard enough without having to manage
tools and infrastructure!
12. Sahara provides
● Apache Hadoop cluster and workload
management
○ Cluster - construct and manage the lifecycle of a
Hadoop cluster
○ Workload - workflow for big data processing with
Hadoop (AWS EMR-like)
● Through a Python library, REST API, Web
UI, command line interface
14. Sahara’s features
● Plugin mechanism - distro choice
● Cluster scaling - elasticity
● Swift integration - data storage
● Cinder integration - persistent HDFS
● Network management with Nova and Neutron
● Anti-affinity, separate services on physical hardware
● Data locality with Swift
● Repeatable cluster creation w/ template mechanism
● http://docs.openstack.
org/developer/sahara/userdoc/features.html
15. Storage considerations
● Swift
○ Input/output through Swift HCFS plugin
○ Intermediate data stored in HDFS on cluster
○ Locality when co-locating swift & nova-compute
● HDFS
○ Local (long lived cluster) and remote (copy in)
● HDFS backed by ephemeral disk or Cinder
○ Ephemeral - /var/lib/nova/instances on compute host
○ Cinder - persistent block devices attached to instances
16. Sahara’s plugin architecture
● This is important!
● It’s where Hadoop distribution vendors
integrate their management software
● It’s how users pick different software
versions
● Currently: Vanilla (reference impl. w/ Apache
versions), HDP (via Ambari), IDH (via Intel
Manager), and Spark (w/ minimal CDH)
17. HDP Plugin Overview
● Full support for all Sahara Functionality
● Nova and Neutron network
● Cluster Scaling
● Scale Up
● Swift Integration
● Cinder Support
● Data Locality
● EDP
● Apache Ambari REST API’s used for cluster
provisioning
● Monitoring/Management of clusters via Ambari
● Full support for multiple HDP stacks
● HDP pre-installed or generic VM images
18. HDP 1.3
● NameNode
● Secondary NameNode
● DataNode
● HDFS
● ZooKeeper
● Ambari Server/Agent
● HCatalog
● Sqoop
● Job Tracker
● Task Tracker
● MapReduce
● Hive
● MySQL
● Pig
● WebHCat Server
● Oozie
● Ganglia
● Nagios
● HBase
HDP Plugin Stack Support
HDP 2.0
● History Server
● MapReduce 2 / YARN
● Resource Manager
● YARN Client
HDP 2.1
● Storm
● Falcon
Com
ing Soon!
Available
Available
HDP 2.1 +
● SOLR
● Cascading
Roadm
ap
19. Ambari Blueprints
● Two primary goals of Ambari Blueprints
○ Ability to export a complete description of a running
cluster
○ Provide API based cluster installations based on a self-
contained cluster description
● Blueprints contain cluster topology and configuration
information
● Enables Interesting use cases between physical and virtual,
including OpenStack/Sahara
22. Demo - youtu.be/vmry_kXqn4c
● http://jayunit100.github.io/bigpetstore/slides
● Bigpetstore
o A full stack hadoop application
o Uses the main players in the hadoop ecosystem
o To demonstrate a single domain
o Just accepted into the Bigtop project!
● Come by the Red Hat booth - G18