Introduction to Big Data Analytics using Apache Spark on HDInsights on Azure (SaaS) and/or HDP on Azure(PaaS)
This workshop will provide an introduction to Big Data Analytics using Apache Spark using the HDInsights on Azure (SaaS) and/or HDP deployment on Azure(PaaS) . There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
Workshop - How to Build Recommendation Engine using Spark 1.6 and HDP
Hands-on - Build a Data analytics application using SPARK, Hortonworks, and Zeppelin. The session explains RDD concepts, DataFrames, sqlContext, use SparkSQL for working with DataFrames and explore graphical abilities of Zeppelin.
b) Follow along - Build a Recommendation Engine - This will show how to build a predictive analytics (MLlib) recommendation engine with scoring This will give a better understanding of architecture and coding in Spark for ML.
Transitioning Compute Models: Hadoop MapReduce to SparkSlim Baltagi
This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.
This document discusses Apache Zeppelin, an open-source web-based notebook that allows for interactive data analytics. It can be used for data exploration, visualization, collaboration and publishing. Zeppelin has deep integration with Apache Spark and supports multiple languages including Scala, Python, and SQL. It provides a Spark interpreter that allows users to analyze data using Spark without having to configure Spark themselves. The document demonstrates Zeppelin's functionality through examples and encourages readers to try it out and get involved in the community.
Intro to Big Data Analytics using Apache Spark and Apache ZeppelinAlex Zeltov
This workshop will provide an introduction to Big Data Analytics using Apache Spark and Apache Zeppelin.
https://github.com/zeltovhorton/intro_spark_zeppelin_meetup
There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
This document discusses Apache Zeppelin, an open-source web-based notebook that enables interactive data analytics. It provides an overview of Zeppelin's history and architecture, including how interpreters and notebook storage are pluggable. The document also outlines Zeppelin's roadmap for improving enterprise support through features like multi-tenancy, impersonation, job management and frontend performance.
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)Spark Summit
This document describes BBVA's implementation of a Big Data Lake using Apache Spark for log collection, storage, and analytics. It discusses:
1) Using Syslog-ng for log collection from over 2,000 applications and devices, distributing logs to Kafka.
2) Storing normalized logs in HDFS and performing analytics using Spark, with outputs to analytics, compliance, and indexing systems.
3) Choosing Spark because it allows interactive, batch, and stream processing with one system using RDDs, SQL, streaming, and machine learning.
Spark is an open-source software framework for rapid calculations on in-memory datasets. It uses Resilient Distributed Datasets (RDDs) that can be recreated if lost and supports transformations and actions on RDDs. Spark is useful for batch, interactive, and real-time processing across various problem domains like SQL, streaming, and machine learning via MLlib.
Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan VolzDatabricks
This document discusses using Hadoop for archiving, e-discovery, and supervision. It outlines the key components of each task and highlights traditional shortcomings. Hadoop provides strengths like speed, ease of use, and security. An architectural overview shows how Hadoop can be used for ingestion, processing, analysis, and machine learning. Examples demonstrate surveillance use cases. While some obstacles remain, partners can help address areas like user interfaces and compliance storage.
Workshop - How to Build Recommendation Engine using Spark 1.6 and HDP
Hands-on - Build a Data analytics application using SPARK, Hortonworks, and Zeppelin. The session explains RDD concepts, DataFrames, sqlContext, use SparkSQL for working with DataFrames and explore graphical abilities of Zeppelin.
b) Follow along - Build a Recommendation Engine - This will show how to build a predictive analytics (MLlib) recommendation engine with scoring This will give a better understanding of architecture and coding in Spark for ML.
Transitioning Compute Models: Hadoop MapReduce to SparkSlim Baltagi
This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.
This document discusses Apache Zeppelin, an open-source web-based notebook that allows for interactive data analytics. It can be used for data exploration, visualization, collaboration and publishing. Zeppelin has deep integration with Apache Spark and supports multiple languages including Scala, Python, and SQL. It provides a Spark interpreter that allows users to analyze data using Spark without having to configure Spark themselves. The document demonstrates Zeppelin's functionality through examples and encourages readers to try it out and get involved in the community.
Intro to Big Data Analytics using Apache Spark and Apache ZeppelinAlex Zeltov
This workshop will provide an introduction to Big Data Analytics using Apache Spark and Apache Zeppelin.
https://github.com/zeltovhorton/intro_spark_zeppelin_meetup
There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
This document discusses Apache Zeppelin, an open-source web-based notebook that enables interactive data analytics. It provides an overview of Zeppelin's history and architecture, including how interpreters and notebook storage are pluggable. The document also outlines Zeppelin's roadmap for improving enterprise support through features like multi-tenancy, impersonation, job management and frontend performance.
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)Spark Summit
This document describes BBVA's implementation of a Big Data Lake using Apache Spark for log collection, storage, and analytics. It discusses:
1) Using Syslog-ng for log collection from over 2,000 applications and devices, distributing logs to Kafka.
2) Storing normalized logs in HDFS and performing analytics using Spark, with outputs to analytics, compliance, and indexing systems.
3) Choosing Spark because it allows interactive, batch, and stream processing with one system using RDDs, SQL, streaming, and machine learning.
Spark is an open-source software framework for rapid calculations on in-memory datasets. It uses Resilient Distributed Datasets (RDDs) that can be recreated if lost and supports transformations and actions on RDDs. Spark is useful for batch, interactive, and real-time processing across various problem domains like SQL, streaming, and machine learning via MLlib.
Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan VolzDatabricks
This document discusses using Hadoop for archiving, e-discovery, and supervision. It outlines the key components of each task and highlights traditional shortcomings. Hadoop provides strengths like speed, ease of use, and security. An architectural overview shows how Hadoop can be used for ingestion, processing, analysis, and machine learning. Examples demonstrate surveillance use cases. While some obstacles remain, partners can help address areas like user interfaces and compliance storage.
This document discusses best practices for running Spark in production. It begins with introductions from the presenters and an overview of Spark deployment modes on YARN. The main topics covered are Spark security using Kerberos authentication and authorization, communication channels and encryption in YARN cluster mode, common issues, and performance tuning. For performance, it recommends choosing executor and task sizes to balance efficiency and overhead, and increasing task parallelism to mitigate data skew problems. The goal is to understand workload patterns and monitor behavior to effectively tune Spark for different situations.
Intro to big data analytics using microsoft machine learning server with sparkAlex Zeltov
Alex Zeltov - Intro to Big Data Analytics using Microsoft Machine Learning Server with Spark
By combining enterprise-scale R analytics software with the power of Apache Hadoop and Apache Spark, Microsoft R Server for HDP or HDInsight gives you the scale and performance you need. Multi-threaded math libraries and transparent parallelization in R Server handle up to 1000x more data and up to 50x faster speeds than open-source R, which helps you to train more accurate models for better predictions. R Server works with the open-source R language, so all of your R scripts run without changes.
Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business with full support for Python and R. Machine Learning Server meets the needs of all constituents of the process – from data engineers and data scientists to line-of-business programmers and IT professionals. It offers a choice of languages and features algorithmic innovation that brings the best of open source and proprietary worlds together.
R support is built on a legacy of Microsoft R Server 9.x and Revolution R Enterprise products. Significant machine learning and AI capabilities enhancements have been made in every release. In 9.2.1, Machine Learning Server adds support for the full data science lifecycle of your Python-based analytics.
This meetup will NOT be a data science intro or R intro to programming. It is about working with data and big data on MLS .
- How to Scale R
- Work with R and Hadoop + Spark
-Demo of MLS on HDP/HDInsight server with RStudio
- How to operationalize deploying models using MLS Webservice operationalization features on MLS Server or on the cloud Azure ML (PaaS) offering. Speaker Bio:
Alex Zeltov is Big Data Solutions Architect / Software Engineer / Programmer Analyst / Data Scientist with over 19 years of industry experience in Information Technology and most recently in Big Data and Predictive Analytics. He currently works as Global black belt Technical Specialist in Microsoft where he concentrates on Big Data and Advanced Analytics use cases. Previously to joining Microsoft he worked as a Sr. Solutions Engineer at Hortonworks where he specialized in HDP and HDF platforms.
This document provides an overview of SK Telecom's use of big data analytics and Spark. Some key points:
- SKT collects around 250 TB of data per day which is stored and analyzed using a Hadoop cluster of over 1400 nodes.
- Spark is used for both batch and real-time processing due to its performance benefits over other frameworks. Two main use cases are described: real-time network analytics and a network enterprise data warehouse (DW) built on Spark SQL.
- The network DW consolidates data from over 130 legacy databases to enable thorough analysis of the entire network. Spark SQL, dynamic resource allocation in YARN, and integration with BI tools help meet requirements for timely processing and quick
Introduction to Apache Amaterasu (Incubating): CD Framework For Your Big Data...DataWorks Summit
In the last few years, the DevOps movement has introduced ground breaking approaches to the way we manage the lifecycle of software development and deployment. Today organisations aspire to fully automate the deployment of microservices and web applications with tools such as Chef, Puppet and Ansible. However, the deployment of data-processing pipelines remains a relic from the dark-ages of software development.
Processing large-scale data pipelines is the main engineering task of the Big Data era, and it should be treated with the same respect and craftsmanship as any other piece of software. That is why we created Apache Amaterasu (Incubating) - an open source framework that takes care of the specific needs of Big Data applications in the world of continuous delivery.
In this session, we will take a close look at Apache Amaterasu (Incubating) a simple and powerful framework to build and dispense pipelines. Amaterasu aims to help data engineers and data scientists to compose, configure, test, package, deploy and execute data pipelines written using multiple tools, languages and frameworks.
We will see what Amaterasu provides today, and how it can help existing Big Data application and demo some of the new bits that are coming in the near future.
Speaker:
Yaniv Rodenski, Senior Solutions Architect, Couchbase
The document summarizes the past, present, and future of Hadoop at LinkedIn. It describes how LinkedIn initially implemented PYMK on Oracle in 2006, then moved to Hadoop in 2008 with 20 nodes, scaling up to over 10,000 nodes and 1000 users by 2016 running various big data frameworks. It discusses the challenges of scaling hardware and processes, and how LinkedIn developed tools like HDFS Dynamometer, Dr. Elephant, Byte-Ray and SoakCycle to help with scaling, performance tuning, dependency management and integration testing of Hadoop clusters. The future may include the Dali project to make data more accessible through different views.
Sa introduction to big data pipelining with cassandra & spark west mins...Simon Ambridge
This document provides an overview and outline of a 1-hour introduction to building a big data pipeline using Docker, Cassandra, Spark, Spark-Notebook and Akka. The introduction is presented as a half-day workshop at Devoxx November 2015. It uses a data pipeline environment from Data Fellas and demonstrates how to use scalable distributed technologies like Docker, Spark, Spark-Notebook and Cassandra to build a reactive, repeatable big data pipeline. The key takeaway is understanding how to construct such a pipeline.
Data Science in the Cloud with Spark, Zeppelin, and CloudbreakDataWorks Summit
This document discusses Apache Zeppelin, an open-source web-based notebook that allows for interactive data analytics. It can be used for data exploration, visualization, collaboration and publishing. Zeppelin has deep integration with Apache Spark and supports multiple languages including Scala, Python, and SQL. It provides a modern data science studio environment and allows users to easily share code and results. The document demonstrates Zeppelin's capabilities through examples and encourages readers to join the open source community to help shape its development.
Spark Summit EU talk by Stephan KesslerSpark Summit
This document summarizes a talk given by Stephan Kessler at the Spark Summit Europe 2016 about integrating business functionality and specialized engines into Apache Spark using SAP HANA Vora. Key points discussed include using currency conversion and time series query capabilities directly in Spark by pushing computations to the relevant data sources via Spark extensions. SAP HANA Vora allows moving parts of the Spark logical query plan to various data sources like HANA, graph and document stores to perform analysis close to the data.
The document discusses tools and techniques used by Uber's Hadoop team to make their Spark and Hadoop platforms more user-friendly and efficient. It introduces tools like SCBuilder to simplify Spark context creation, Kafka dispersal to distribute RDD results, and SparkPlug to provide templates for common jobs. It also describes a distributed log debugger called SparkChamber to help debug Spark jobs and techniques like building a spatial index to optimize geo-spatial joins. The goal is to abstract out infrastructure complexities and enforce best practices to make the platforms more self-service for users.
Apache Spark 1.6 with Zeppelin - Transformations and Actions on RDDsTimothy Spann
The document discusses transformations and actions that can be performed on Resilient Distributed Datasets (RDDs) in Apache Spark. It defines RDD transformations as operations that return pointers to new RDDs without losing the lineage, while actions return final values by running computations on the datasets. The document then proceeds to describe various RDD transformations like map, filter, flatMap, sample, union, join, cogroup and their meanings and provides code examples. It also covers RDD actions like collect, count, take, etc.
Zeppelin Interpreters
PSQL (to became JDBC in 0.6.x)
Geode
SpringXD
Apache Ambari
Zeppelin Service
Geode, HAWQ and Spring XD services
Webpage Embedder View
This talk will address new architectures emerging for large scale streaming analytics. Some based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK) and other newer streaming analytics platforms and frameworks using Apache Flink or GearPump. Popular architecture like Lambda separate layers of computation and delivery and require many technologies which have overlapping functionality. Some of this results in duplicated code, untyped processes, or high operational overhead, let alone the cost (e.g. ETL).
I will discuss the problem domain and what is needed in terms of strategies, architecture and application design and code to begin leveraging simpler data flows. We will cover how the particular set of technologies addresses common requirements and how collaboratively they work together to enrich and reinforce each other.
Stream All Things—Patterns of Modern Data Integration with Gwen ShapiraDatabricks
This document discusses patterns for modern data integration using streaming data. It outlines an evolution from data warehouses to data lakes to streaming data. It then describes four key patterns: 1) Stream all things (data) in one place, 2) Keep schemas compatible and process data on, 3) Enable ridiculously parallel single message transformations, and 4) Perform streaming data enrichment to add additional context to events. Examples are provided of using Apache Kafka and Kafka Connect to implement these patterns for a large hotel chain integrating various data sources and performing real-time analytics on customer events.
Spark Summit EU talk by Christos ErotocritouSpark Summit
This document discusses Apache Ignite and how it can be used with Apache Spark for fast data applications. It provides an overview of Ignite's in-memory data fabric capabilities, how it compares to Spark, and how Ignite can be integrated with Spark to provide shared resilient storage and distributed computing. Examples are given of reading and writing data between Ignite and Spark and using Ignite's in-memory file system and SQL support from Spark.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...Databricks
At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure.
GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
MLflow: Infrastructure for a Complete Machine Learning Life CycleDatabricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
This document provides an overview of Apache Spark, including:
- Apache Spark is a next generation data processing engine for Hadoop that allows for fast in-memory processing of huge distributed and heterogeneous datasets.
- Spark offers tools for data science and components for data products and can be used for tasks like machine learning, graph processing, and streaming data analysis.
- Spark improves on MapReduce by being faster, allowing parallel processing, and supporting interactive queries. It works on both standalone clusters and Hadoop clusters.
Apache Spark At Apple with Sam Maclennan and Vishwanath LakkundiDatabricks
At Apple we rely on processing large datasets to power key components of Apple’s largest production services. Spark is continuing to replace and augment traditional MR workloads with its speed and low barrier to entry. Our current analytics infrastructure consists of over an exabyte of storage and close to a million cores. Our footprint is also growing further with the addition of new elastic services for streaming, adhoc and interactive analytics.
In this talk we will cover the challenges of working at scale with tricks and lessons learned managing large multi-tenant clusters. We will also discuss designing and building a self-service elastic analytics platform on Mesos.
Tim Spann will present on learning Apache Spark. He is a senior solutions architect who previously worked as a senior field engineer and startup engineer. airis.DATA, where Spann works, specializes in machine learning and graph solutions using Spark, H20, Mahout, and Flink on petabyte datasets. The agenda includes an overview of Spark, an explanation of MapReduce, and hands-on exercises to install Spark, run a MapReduce job locally, and build a project with IntelliJ and SBT.
This document discusses end-to-end processing of 3.7 million telemetry events per second using a lambda architecture at Symantec. It provides an overview of Symantec's security data lake infrastructure, the telemetry data processing architecture using Kafka, Storm and HBase, tuning targets for the infrastructure components, and performance benchmarks for Kafka, Storm and Hive.
Rob peglar introduction_analytics _big data_hadoopGhassan Al-Yafie
This document provides an introduction to analytics and big data using Hadoop. It discusses the growth of digital data and challenges of big data. Hadoop is presented as a solution for storing and processing large, unstructured datasets across commodity servers. The key components of Hadoop - HDFS for distributed storage and MapReduce for distributed processing - are described at a high level. Examples of industries using big data analytics are also listed.
Git is a version control system that allows developers to track changes in code and collaborate on projects. GitHub is a hosting service for Git repositories that offers collaboration features like code review and branching workflows. The document introduces Git and GitHub basics and outlines the GitHub Flow for collaborating via feature branching, pull requests, and code review before merging changes into the master branch. It concludes with reminders for good version control practices and sources for further information.
This document discusses best practices for running Spark in production. It begins with introductions from the presenters and an overview of Spark deployment modes on YARN. The main topics covered are Spark security using Kerberos authentication and authorization, communication channels and encryption in YARN cluster mode, common issues, and performance tuning. For performance, it recommends choosing executor and task sizes to balance efficiency and overhead, and increasing task parallelism to mitigate data skew problems. The goal is to understand workload patterns and monitor behavior to effectively tune Spark for different situations.
Intro to big data analytics using microsoft machine learning server with sparkAlex Zeltov
Alex Zeltov - Intro to Big Data Analytics using Microsoft Machine Learning Server with Spark
By combining enterprise-scale R analytics software with the power of Apache Hadoop and Apache Spark, Microsoft R Server for HDP or HDInsight gives you the scale and performance you need. Multi-threaded math libraries and transparent parallelization in R Server handle up to 1000x more data and up to 50x faster speeds than open-source R, which helps you to train more accurate models for better predictions. R Server works with the open-source R language, so all of your R scripts run without changes.
Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business with full support for Python and R. Machine Learning Server meets the needs of all constituents of the process – from data engineers and data scientists to line-of-business programmers and IT professionals. It offers a choice of languages and features algorithmic innovation that brings the best of open source and proprietary worlds together.
R support is built on a legacy of Microsoft R Server 9.x and Revolution R Enterprise products. Significant machine learning and AI capabilities enhancements have been made in every release. In 9.2.1, Machine Learning Server adds support for the full data science lifecycle of your Python-based analytics.
This meetup will NOT be a data science intro or R intro to programming. It is about working with data and big data on MLS .
- How to Scale R
- Work with R and Hadoop + Spark
-Demo of MLS on HDP/HDInsight server with RStudio
- How to operationalize deploying models using MLS Webservice operationalization features on MLS Server or on the cloud Azure ML (PaaS) offering. Speaker Bio:
Alex Zeltov is Big Data Solutions Architect / Software Engineer / Programmer Analyst / Data Scientist with over 19 years of industry experience in Information Technology and most recently in Big Data and Predictive Analytics. He currently works as Global black belt Technical Specialist in Microsoft where he concentrates on Big Data and Advanced Analytics use cases. Previously to joining Microsoft he worked as a Sr. Solutions Engineer at Hortonworks where he specialized in HDP and HDF platforms.
This document provides an overview of SK Telecom's use of big data analytics and Spark. Some key points:
- SKT collects around 250 TB of data per day which is stored and analyzed using a Hadoop cluster of over 1400 nodes.
- Spark is used for both batch and real-time processing due to its performance benefits over other frameworks. Two main use cases are described: real-time network analytics and a network enterprise data warehouse (DW) built on Spark SQL.
- The network DW consolidates data from over 130 legacy databases to enable thorough analysis of the entire network. Spark SQL, dynamic resource allocation in YARN, and integration with BI tools help meet requirements for timely processing and quick
Introduction to Apache Amaterasu (Incubating): CD Framework For Your Big Data...DataWorks Summit
In the last few years, the DevOps movement has introduced ground breaking approaches to the way we manage the lifecycle of software development and deployment. Today organisations aspire to fully automate the deployment of microservices and web applications with tools such as Chef, Puppet and Ansible. However, the deployment of data-processing pipelines remains a relic from the dark-ages of software development.
Processing large-scale data pipelines is the main engineering task of the Big Data era, and it should be treated with the same respect and craftsmanship as any other piece of software. That is why we created Apache Amaterasu (Incubating) - an open source framework that takes care of the specific needs of Big Data applications in the world of continuous delivery.
In this session, we will take a close look at Apache Amaterasu (Incubating) a simple and powerful framework to build and dispense pipelines. Amaterasu aims to help data engineers and data scientists to compose, configure, test, package, deploy and execute data pipelines written using multiple tools, languages and frameworks.
We will see what Amaterasu provides today, and how it can help existing Big Data application and demo some of the new bits that are coming in the near future.
Speaker:
Yaniv Rodenski, Senior Solutions Architect, Couchbase
The document summarizes the past, present, and future of Hadoop at LinkedIn. It describes how LinkedIn initially implemented PYMK on Oracle in 2006, then moved to Hadoop in 2008 with 20 nodes, scaling up to over 10,000 nodes and 1000 users by 2016 running various big data frameworks. It discusses the challenges of scaling hardware and processes, and how LinkedIn developed tools like HDFS Dynamometer, Dr. Elephant, Byte-Ray and SoakCycle to help with scaling, performance tuning, dependency management and integration testing of Hadoop clusters. The future may include the Dali project to make data more accessible through different views.
Sa introduction to big data pipelining with cassandra & spark west mins...Simon Ambridge
This document provides an overview and outline of a 1-hour introduction to building a big data pipeline using Docker, Cassandra, Spark, Spark-Notebook and Akka. The introduction is presented as a half-day workshop at Devoxx November 2015. It uses a data pipeline environment from Data Fellas and demonstrates how to use scalable distributed technologies like Docker, Spark, Spark-Notebook and Cassandra to build a reactive, repeatable big data pipeline. The key takeaway is understanding how to construct such a pipeline.
Data Science in the Cloud with Spark, Zeppelin, and CloudbreakDataWorks Summit
This document discusses Apache Zeppelin, an open-source web-based notebook that allows for interactive data analytics. It can be used for data exploration, visualization, collaboration and publishing. Zeppelin has deep integration with Apache Spark and supports multiple languages including Scala, Python, and SQL. It provides a modern data science studio environment and allows users to easily share code and results. The document demonstrates Zeppelin's capabilities through examples and encourages readers to join the open source community to help shape its development.
Spark Summit EU talk by Stephan KesslerSpark Summit
This document summarizes a talk given by Stephan Kessler at the Spark Summit Europe 2016 about integrating business functionality and specialized engines into Apache Spark using SAP HANA Vora. Key points discussed include using currency conversion and time series query capabilities directly in Spark by pushing computations to the relevant data sources via Spark extensions. SAP HANA Vora allows moving parts of the Spark logical query plan to various data sources like HANA, graph and document stores to perform analysis close to the data.
The document discusses tools and techniques used by Uber's Hadoop team to make their Spark and Hadoop platforms more user-friendly and efficient. It introduces tools like SCBuilder to simplify Spark context creation, Kafka dispersal to distribute RDD results, and SparkPlug to provide templates for common jobs. It also describes a distributed log debugger called SparkChamber to help debug Spark jobs and techniques like building a spatial index to optimize geo-spatial joins. The goal is to abstract out infrastructure complexities and enforce best practices to make the platforms more self-service for users.
Apache Spark 1.6 with Zeppelin - Transformations and Actions on RDDsTimothy Spann
The document discusses transformations and actions that can be performed on Resilient Distributed Datasets (RDDs) in Apache Spark. It defines RDD transformations as operations that return pointers to new RDDs without losing the lineage, while actions return final values by running computations on the datasets. The document then proceeds to describe various RDD transformations like map, filter, flatMap, sample, union, join, cogroup and their meanings and provides code examples. It also covers RDD actions like collect, count, take, etc.
Zeppelin Interpreters
PSQL (to became JDBC in 0.6.x)
Geode
SpringXD
Apache Ambari
Zeppelin Service
Geode, HAWQ and Spring XD services
Webpage Embedder View
This talk will address new architectures emerging for large scale streaming analytics. Some based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK) and other newer streaming analytics platforms and frameworks using Apache Flink or GearPump. Popular architecture like Lambda separate layers of computation and delivery and require many technologies which have overlapping functionality. Some of this results in duplicated code, untyped processes, or high operational overhead, let alone the cost (e.g. ETL).
I will discuss the problem domain and what is needed in terms of strategies, architecture and application design and code to begin leveraging simpler data flows. We will cover how the particular set of technologies addresses common requirements and how collaboratively they work together to enrich and reinforce each other.
Stream All Things—Patterns of Modern Data Integration with Gwen ShapiraDatabricks
This document discusses patterns for modern data integration using streaming data. It outlines an evolution from data warehouses to data lakes to streaming data. It then describes four key patterns: 1) Stream all things (data) in one place, 2) Keep schemas compatible and process data on, 3) Enable ridiculously parallel single message transformations, and 4) Perform streaming data enrichment to add additional context to events. Examples are provided of using Apache Kafka and Kafka Connect to implement these patterns for a large hotel chain integrating various data sources and performing real-time analytics on customer events.
Spark Summit EU talk by Christos ErotocritouSpark Summit
This document discusses Apache Ignite and how it can be used with Apache Spark for fast data applications. It provides an overview of Ignite's in-memory data fabric capabilities, how it compares to Spark, and how Ignite can be integrated with Spark to provide shared resilient storage and distributed computing. Examples are given of reading and writing data between Ignite and Spark and using Ignite's in-memory file system and SQL support from Spark.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...Databricks
At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure.
GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
MLflow: Infrastructure for a Complete Machine Learning Life CycleDatabricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
This document provides an overview of Apache Spark, including:
- Apache Spark is a next generation data processing engine for Hadoop that allows for fast in-memory processing of huge distributed and heterogeneous datasets.
- Spark offers tools for data science and components for data products and can be used for tasks like machine learning, graph processing, and streaming data analysis.
- Spark improves on MapReduce by being faster, allowing parallel processing, and supporting interactive queries. It works on both standalone clusters and Hadoop clusters.
Apache Spark At Apple with Sam Maclennan and Vishwanath LakkundiDatabricks
At Apple we rely on processing large datasets to power key components of Apple’s largest production services. Spark is continuing to replace and augment traditional MR workloads with its speed and low barrier to entry. Our current analytics infrastructure consists of over an exabyte of storage and close to a million cores. Our footprint is also growing further with the addition of new elastic services for streaming, adhoc and interactive analytics.
In this talk we will cover the challenges of working at scale with tricks and lessons learned managing large multi-tenant clusters. We will also discuss designing and building a self-service elastic analytics platform on Mesos.
Tim Spann will present on learning Apache Spark. He is a senior solutions architect who previously worked as a senior field engineer and startup engineer. airis.DATA, where Spann works, specializes in machine learning and graph solutions using Spark, H20, Mahout, and Flink on petabyte datasets. The agenda includes an overview of Spark, an explanation of MapReduce, and hands-on exercises to install Spark, run a MapReduce job locally, and build a project with IntelliJ and SBT.
This document discusses end-to-end processing of 3.7 million telemetry events per second using a lambda architecture at Symantec. It provides an overview of Symantec's security data lake infrastructure, the telemetry data processing architecture using Kafka, Storm and HBase, tuning targets for the infrastructure components, and performance benchmarks for Kafka, Storm and Hive.
Rob peglar introduction_analytics _big data_hadoopGhassan Al-Yafie
This document provides an introduction to analytics and big data using Hadoop. It discusses the growth of digital data and challenges of big data. Hadoop is presented as a solution for storing and processing large, unstructured datasets across commodity servers. The key components of Hadoop - HDFS for distributed storage and MapReduce for distributed processing - are described at a high level. Examples of industries using big data analytics are also listed.
Git is a version control system that allows developers to track changes in code and collaborate on projects. GitHub is a hosting service for Git repositories that offers collaboration features like code review and branching workflows. The document introduces Git and GitHub basics and outlines the GitHub Flow for collaborating via feature branching, pull requests, and code review before merging changes into the master branch. It concludes with reminders for good version control practices and sources for further information.
AWS User Group Presentation.
Hosted by PolarSeven - http://polarseven.com
5th October 2016
Session 1: Presentation
Jason Umiker:
Art of PaaS - Lessons learned from running Micros, a platform for hundreds of microservices on AWS
This document provides biographical information about the members of the band Led Zeppelin, including Jimmy Page, Robert Plant, John Bonham, and John Paul Jones. It also summarizes some of their most famous songs like "Stairway to Heaven" and "Moby Dick" and albums. The document concludes that Led Zeppelin is considered the world's best band because of their good music, band history, and members' dedication to the group.
Comment bâtir un cloud hybride en mode IaaS ou SaaS et apporter le meilleur d...Microsoft Technet France
Session Hitachi Data Systems: Aujourd’hui, les Datacenters se transforment pour répondre à de nouveaux besoins avec toujours plus d’agilité et de performance mais en parallèle, les DSI réfléchissent à l’optimisation et la réduction des coûts. Hitachi Data Systems propose de nouvelles solutions en mode Cloud Hybride capable de répondre à ces challenges. A travers les solutions de convergences Hitachi Unified Compute Platform pour Microsoft Private Cloud, vous bâtirez facilement un cloud hybride IaaS en vous appuyant sur la gestion logiciel de votre Datacenter ( SDDC ) et les pack d’intégration pour Microsoft Azure. Dans un second temps grâce à nos solutions, vous pourrez aussi apporter un service de mobilité et de synchronisation aux utilisateurs Windows en mode cloud privé, tout en utilisant Azure pour l’archivage de vos données. Ainsi, vous utilisez le meilleur des deux mondes, cloud privé et cloud public, pour vos utilisateurs tout en réduisant vos coûts opérationnels.
Introduction to Big Data Analytics on Apache HadoopAvkash Chauhan
The document discusses Hadoop and big data. It defines Hadoop as an open source, scalable, and fault tolerant platform for storing and processing large amounts of unstructured data distributed across machines. It describes Hadoop's core components like HDFS for data storage and MapReduce/YARN for data processing. It also discusses how Hadoop fits into big data scenarios and landscapes, applying Hadoop to save money, the concept of data lakes, Hadoop in the cloud, and big data analytics with Hadoop.
4.Building a Data Product using apache Zeppelin - Apache Kylin Meetup @ShanghaiLuke Han
The document discusses building a data product using Apache Zeppelin (incubating) that sends email notifications about new open source projects on GitHub. It outlines the steps to download data from the GitHub archive, explore and filter the data to focus on interesting companies, join additional data from the GitHub API, generate an HTML template to visualize the results, and schedule sending the email notifications.
The document discusses the Windows Azure platform, which provides an internet-scale, highly available cloud fabric hosted in Microsoft's globally distributed data centers. It offers compute, storage, data, integration, access control, and other services to build applications that can automatically scale out and integrate on-premises systems. The document outlines different application models, architectural patterns, and benefits of building on the Windows Azure platform.
This document compares cloud platforms Amazon Web Services (AWS) and Microsoft Azure. It finds that AWS is more oriented toward infrastructure as a service (IaaS) while Azure is more platform as a service (PaaS) oriented, though both platforms offer services across IaaS and PaaS. The document also compares specific cloud storage, databases, networking, deployment, middleware, tools and high availability/disaster recovery features between AWS and Azure.
Introducing Kafka Streams: Large-scale Stream Processing with Kafka, Neha Nar...confluent
The concept of stream processing has been around for a while and most software systems continuously transform streams of inputs into streams of outputs. Yet the idea of directly modeling stream processing in infrastructure systems is just coming into its own after a few decades on the periphery.
At its core, stream processing is simple: read data in, process it, and maybe emit some data out. So why are there so many stream processing frameworks that all define their own terminology? And are the components of each even comparable? Why do I need to know about spouts or DStreams just to process a simple sequence of records? Depending on your application’s requirements, you may not need a framework.
This talk will be delivered by one of the creators of the popular stream data systems Apache Kafka and will abstract away the details of individual frameworks while describing the key features they provide. These core features include scalability and parallelism through data partitioning, fault tolerance and event processing order guarantees, support for stateful stream processing, and handy stream processing primitives such as windowing. Based on our experience building and scaling Kafka to handle streams that captured hundreds of billions of records per day — this presentation will help you understand how to map practical data problems to stream processing and how to write applications that process streams of data at scale.
Introduction to Big Data Analytics: Batch, Real-Time, and the Best of Both Wo...WSO2
In this webinar, Srinath Perera, director of research at WSO2, will discuss
Big data landscape: concepts, use cases, and technologies
Real-time analytics with WSO2 CEP
Batch analytics with WSO2 BAM
Combining batch and real-time analytics
Introducing WSO2 Machine Learner
This document provides an introduction to GitHub. It defines Git as a version control system that records changes to files and allows users to revert files to earlier versions. GitHub is described as a hosting service for Git repositories that provides a graphical interface and collaboration features. The document outlines key GitHub concepts like repositories, branches, commits, forking, pull requests and issues. It also summarizes the typical GitHub workflow and includes a link to download GitHub Desktop for a demo.
View the recording:
http://hortonworks.com/webinar/accelerating-real-time-data-ingest-hadoop/
Hadoop didn’t disrupt the data center. The exploding amounts of data did. But, let’s face it, if you can’t move your data to Hadoop, then you can’t use it in Hadoop. The experts from Hortonworks, the #1 leader in Hadoop development, and Attunity, a leading data management software provider, cover:
- How to ingest your most valuable data into Hadoop using Attunity Replicate
- About how customers are using Hortonworks DataFlow (HDF) powered by Apache NiFi
- How to combine the real-time change data capture (CDC) technology with connected data platforms from Hortonworks
We discuss how Attunity Replicate and Hortonworks Data Flow (HDF) work together to move data into Hadoop.
Introduction to Hortonworks Data Cloud for AWSYifeng Jiang
Hortonworks Data Cloud is a new cloud product from Hortonworks that offers pay-as-you-go pricing for launching and managing Hadoop clusters on AWS. It handles common big data use cases and focuses on ease of use by providing prescriptive cluster types. The product aims to improve enterprise readiness in the cloud by providing scalable storage, security and governance features, and reliability through auto-recovery of unhealthy nodes. It also matches Hadoop with cloud capabilities like scalable storage, customizability, and cost-effective compute.
This document provides an overview of installing and programming with Apache Spark on the Hortonworks Data Platform (HDP). It discusses how Spark fits within HDP and can be used for batch processing, streaming, SQL queries and machine learning. The document outlines how to install Spark on HDP using Ambari and describes Spark programming with Resilient Distributed Datasets (RDDs), transformations, actions and caching/persistence. It provides examples of Spark APIs and programming patterns.
This document provides an overview of installing and programming with Apache Spark on Hortonworks Data Platform (HDP). It introduces Spark and its components, benefits over other frameworks, and Hortonworks' commitment to Spark. The document outlines an example Spark programming workflow using Resilient Distributed Datasets (RDDs) in Scala, and covers common RDD transformations, actions, and persistence methods. It also discusses Spark deployment modes like standalone and on YARN, and reference HDP architectures using Spark.
Hortonworks tech workshop in-memory processing with sparkHortonworks
Apache Spark offers unique in-memory capabilities and is well suited to a wide variety of data processing workloads including machine learning and micro-batch processing. With HDP 2.2, Apache Spark is a fully supported component of the Hortonworks Data Platform. In this session we will cover the key fundamentals of Apache Spark and operational best practices for executing Spark jobs along with the rest of Big Data workloads. We will also provide a working example to showcase micro-batch and machine learning processing using Apache Spark.
Unit II Real Time Data Processing tools.pptxRahul Borate
Apache Spark is a lightning-fast cluster computing framework designed for real-time processing. It overcomes limitations of Hadoop by running 100 times faster in memory and 10 times faster on disk. Spark uses resilient distributed datasets (RDDs) that allow data to be partitioned across clusters and cached in memory for faster processing.
This document provides an overview of the Apache Spark framework. It covers Spark fundamentals including the Spark execution model using Resilient Distributed Datasets (RDDs), basic Spark programming, and common Spark libraries and use cases. Key topics include how Spark improves on MapReduce by operating in-memory and supporting general graphs through its directed acyclic graph execution model. The document also reviews Spark installation and provides examples of basic Spark programs in Scala.
http://hortonworks.com/hadoop/spark/
Recording:
https://hortonworks.webex.com/hortonworks/lsr.php?RCID=03debab5ba04b34a033dc5c2f03c7967
As the ratio of memory to processing power rapidly evolves, many within the Hadoop community are gravitating towards Apache Spark for fast, in-memory data processing. And with YARN, they use Spark for machine learning and data science use cases along side other workloads simultaneously. This is a continuation of our YARN Ready Series, aimed at helping developers learn the different ways to integrate to YARN and Hadoop. Tools and applications that are YARN Ready have been verified to work within YARN.
Spark is a fast, general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R for distributed tasks including SQL, streaming, and machine learning. Spark improves on MapReduce by keeping data in-memory, allowing iterative algorithms to run faster than disk-based approaches. Resilient Distributed Datasets (RDDs) are Spark's fundamental data structure, acting as a fault-tolerant collection of elements that can be operated on in parallel.
Spark is an open-source cluster computing framework that can run analytics applications much faster than Hadoop by keeping data in memory rather than on disk. While Spark can access Hadoop's HDFS storage system and is often used as a replacement for Hadoop's MapReduce, Hadoop remains useful for batch processing and Spark is not expected to fully replace it. Spark provides speed, ease of use, and integration of SQL, streaming, and machine learning through its APIs in multiple languages.
- Apache Spark is an open-source cluster computing framework that is faster than Hadoop for batch processing and also supports real-time stream processing.
- Spark was created to be faster than Hadoop for interactive queries and iterative algorithms by keeping data in-memory when possible.
- Spark consists of Spark Core for the basic RDD API and also includes modules for SQL, streaming, machine learning, and graph processing. It can run on several cluster managers including YARN and Mesos.
http://bit.ly/1BTaXZP – As organizations look for even faster ways to derive value from big data, they are turning to Apache Spark is an in-memory processing framework that offers lightning-fast big data analytics, providing speed, developer productivity, and real-time processing advantages. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Spark Streaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis. This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop. By the end of the session, you’ll come away with a deeper understanding of how you can unlock deeper insights from your data, faster, with Spark.
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.
Big Data Warsaw v 4 I "The Role of Hadoop Ecosystem in Advance Analytics" - R...Dataconomy Media
What is Big Data? What is Hadoop? What is MapReduce? How do the other components such as: Oozie, Hue, Hive, Impala works? Which are the main Hadoop distributions? What is Spark? What are the differences between Batch and Streaming processing? What are some Business Intelligence Solutions by focusing on some business cases?
spark example spark example spark examplespark examplespark examplespark exampleShidrokhGoudarzi1
Spark is a fast general-purpose engine for large-scale data processing. It has advantages over MapReduce like speed, ease of use, and running everywhere. Spark supports SQL querying, streaming, machine learning, and graph processing. It can run on Scala, Java, Python. Spark applications have drivers, executors, tasks and run RDDs and shared variables. The Spark shell provides an interactive way to learn the API and analyze data.
Apache Spark is an open-source distributed processing engine that is up to 100 times faster than Hadoop for processing data stored in memory and 10 times faster for data stored on disk. It provides high-level APIs in Java, Scala, Python and SQL and supports batch processing, streaming, and machine learning. Spark runs on Hadoop, Mesos, Kubernetes or standalone and can access diverse data sources using its core abstraction called resilient distributed datasets (RDDs).
Big Data Hoopla Simplified - TDWI Memphis 2014Rajan Kanitkar
The document provides an overview and quick reference guide to big data concepts including Hadoop, MapReduce, HDFS, YARN, Spark, Storm, Hive, Pig, HBase and NoSQL databases. It discusses the evolution of Hadoop from versions 1 to 2, and new frameworks like Tez and YARN that allow different types of processing beyond MapReduce. The document also summarizes common big data challenges around skills, integration and analytics.
In this one day workshop, we will introduce Spark at a high level context. Spark is fundamentally different than writing MapReduce jobs so no prior Hadoop experience is needed. You will learn how to interact with Spark on the command line and conduct rapid in-memory data analyses. We will then work on writing Spark applications to perform large cluster-based analyses including SQL-like aggregations, machine learning applications, and graph algorithms. The course will be conducted in Python using PySpark.
This is an introductory tutorial to Apache Spark at the Lagos Scala Meetup II. We discussed the basics of processing engine, Spark, how it relates to Hadoop MapReduce. Little handson at the end of the session.
An Engine to process big data in faster(than MR), easy and extremely scalable way. An Open Source, parallel, in-memory processing, cluster computing framework. Solution for loading, processing and end to end analyzing large scale data. Iterative and Interactive : Scala, Java, Python, R and with Command line interface.
This document provides an overview of Apache Spark, including what it is, its evolution and features, components, and the difference between Spark and Hadoop. Spark was originally developed in 2009 as a fast and general engine for large-scale data processing. It has since become a top-level Apache project and is designed to be up to 100 times faster than Hadoop in memory and 10 times faster on disk. Spark supports SQL, streaming, machine learning and graph processing through components built on its core engine.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.