The document provides an overview of big data and Hadoop for database administrators. It discusses the history of data management systems and how the role of data is changing to focus on collecting as much data as possible. The key characteristics of big data, including volume, velocity, and variety, are outlined. An overview of the Hadoop ecosystem is provided, including components like HDFS, MapReduce, Hive, Pig, and HBase. Sample use cases and architectures for big data are also summarized.
The document provides an overview of performance tuning for Oracle databases. It discusses tuning goals such as accessing the least number of blocks and caching blocks in memory. It outlines the tuning process which includes tuning the design, application, memory, I/O, contention and operating system. Common performance issues for OLTP systems like I/O bottlenecks are also covered. Various tools for identifying performance problems are presented.
Apache Kudu is a storage layer for Apache Hadoop that provides low-latency queries and high throughput for fast data access use cases like real-time analytics. It was designed to address gaps in HDFS and HBase by providing both efficient scanning of large amounts of data as well as efficient lookups of individual rows. Kudu tables store data in a columnar format and use a distributed architecture with tablets and masters to enable high performance and scalability for workloads involving both sequential and random access of data.
This document provides an overview of Automatic Workload Repository (AWR) and Active Session History (ASH) reports in Oracle Database. It discusses the various reports available in AWR and ASH, how to generate and interpret them. Key sections include explanations of the AWR reports, using ASH reports to identify specific database issues, and techniques for querying ASH data directly for detailed analysis. The document concludes with examples of using SQL to generate graphs of ASH data from the command line.
The AWR Warehouse provides a centralized location for retaining Automatic Workload Repository (AWR) data from multiple databases for long periods of time. It addresses issues like limited AWR retention periods and resource overhead on source databases. An ETL process moves AWR snapshots from source databases to the warehouse. The Enterprise Manager interface provides unified access to current and historical AWR data across databases for troubleshooting performance issues.
Apache Hadoop YARN is the modern Distributed Operating System. It enables the Hadoop compute layer to be a common resource-management platform that can host a wide variety of applications. Multiple organizations are able to leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues etc.
In this talk, we’ll first hit the ground with the current status of Apache Hadoop YARN – how it is faring today in deployments large and small. We will cover different types of YARN deployments, in different environments and scale.
We'll then move on to the exciting present & future of YARN – features that are further strengthening YARN as the first-class resource-management platform for datacenters running enterprise Hadoop. We’ll discuss the current status as well as the future promise of features and initiatives like – 10x scheduler throughput improvements, docker containers support on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, fine-grained isolation for multi-tenancy using CGroups on disk & network resources, powerful scheduling features like application priorities, intra-queue preemption across applications and operational enhancements including insights through Timeline Service V2, a new web UI and better queue management.
The document provides details about an SQL expert's background and certifications. It summarizes the expert's career starting in 1982 working with computers and 1988 starting in the computer industry. In 1996, they started working with SQL Server 6.0 and have since earned multiple Microsoft certifications. The expert now provides training and consultation services, and created an online school called SQL School Greece to teach SQL Server.
Cloudera Impala - Las Vegas Big Data Meetup Nov 5th 2014cdmaxime
Maxime Dumas gives a presentation on Cloudera Impala, which provides fast SQL query capability for Apache Hadoop. Impala allows for interactive queries on Hadoop data in seconds rather than minutes by using a native MPP query engine instead of MapReduce. It offers benefits like SQL support, improved performance of 3-4x up to 90x faster than MapReduce, and flexibility to query existing Hadoop data without needing to migrate or duplicate it. The latest release of Impala 2.0 includes new features like window functions, subqueries, and spilling joins and aggregations to disk when memory is exhausted.
Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard
This document discusses building near-real-time analytics pipelines using Apache Spark Streaming and Apache Kudu on the Cloudera platform. It defines near-real-time analytics, describes the relevant components of the Cloudera stack (Kafka, Spark, Kudu, Impala), and how they can work together. The document then outlines the typical stages involved in implementing a Spark Streaming to Kudu pipeline, including sourcing from a queue, translating data, deriving storage records, planning mutations, and storing the data. It provides performance considerations and introduces Envelope, a Spark Streaming application on Cloudera Labs that implements these stages through configurable pipelines.
The document provides an overview of performance tuning for Oracle databases. It discusses tuning goals such as accessing the least number of blocks and caching blocks in memory. It outlines the tuning process which includes tuning the design, application, memory, I/O, contention and operating system. Common performance issues for OLTP systems like I/O bottlenecks are also covered. Various tools for identifying performance problems are presented.
Apache Kudu is a storage layer for Apache Hadoop that provides low-latency queries and high throughput for fast data access use cases like real-time analytics. It was designed to address gaps in HDFS and HBase by providing both efficient scanning of large amounts of data as well as efficient lookups of individual rows. Kudu tables store data in a columnar format and use a distributed architecture with tablets and masters to enable high performance and scalability for workloads involving both sequential and random access of data.
This document provides an overview of Automatic Workload Repository (AWR) and Active Session History (ASH) reports in Oracle Database. It discusses the various reports available in AWR and ASH, how to generate and interpret them. Key sections include explanations of the AWR reports, using ASH reports to identify specific database issues, and techniques for querying ASH data directly for detailed analysis. The document concludes with examples of using SQL to generate graphs of ASH data from the command line.
The AWR Warehouse provides a centralized location for retaining Automatic Workload Repository (AWR) data from multiple databases for long periods of time. It addresses issues like limited AWR retention periods and resource overhead on source databases. An ETL process moves AWR snapshots from source databases to the warehouse. The Enterprise Manager interface provides unified access to current and historical AWR data across databases for troubleshooting performance issues.
Apache Hadoop YARN is the modern Distributed Operating System. It enables the Hadoop compute layer to be a common resource-management platform that can host a wide variety of applications. Multiple organizations are able to leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues etc.
In this talk, we’ll first hit the ground with the current status of Apache Hadoop YARN – how it is faring today in deployments large and small. We will cover different types of YARN deployments, in different environments and scale.
We'll then move on to the exciting present & future of YARN – features that are further strengthening YARN as the first-class resource-management platform for datacenters running enterprise Hadoop. We’ll discuss the current status as well as the future promise of features and initiatives like – 10x scheduler throughput improvements, docker containers support on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, fine-grained isolation for multi-tenancy using CGroups on disk & network resources, powerful scheduling features like application priorities, intra-queue preemption across applications and operational enhancements including insights through Timeline Service V2, a new web UI and better queue management.
The document provides details about an SQL expert's background and certifications. It summarizes the expert's career starting in 1982 working with computers and 1988 starting in the computer industry. In 1996, they started working with SQL Server 6.0 and have since earned multiple Microsoft certifications. The expert now provides training and consultation services, and created an online school called SQL School Greece to teach SQL Server.
Cloudera Impala - Las Vegas Big Data Meetup Nov 5th 2014cdmaxime
Maxime Dumas gives a presentation on Cloudera Impala, which provides fast SQL query capability for Apache Hadoop. Impala allows for interactive queries on Hadoop data in seconds rather than minutes by using a native MPP query engine instead of MapReduce. It offers benefits like SQL support, improved performance of 3-4x up to 90x faster than MapReduce, and flexibility to query existing Hadoop data without needing to migrate or duplicate it. The latest release of Impala 2.0 includes new features like window functions, subqueries, and spilling joins and aggregations to disk when memory is exhausted.
Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard
This document discusses building near-real-time analytics pipelines using Apache Spark Streaming and Apache Kudu on the Cloudera platform. It defines near-real-time analytics, describes the relevant components of the Cloudera stack (Kafka, Spark, Kudu, Impala), and how they can work together. The document then outlines the typical stages involved in implementing a Spark Streaming to Kudu pipeline, including sourcing from a queue, translating data, deriving storage records, planning mutations, and storing the data. It provides performance considerations and introduces Envelope, a Spark Streaming application on Cloudera Labs that implements these stages through configurable pipelines.
This document discusses application architectures using Hadoop. It provides an example case study of clickstream analysis. It covers challenges of Hadoop implementation and various architectural considerations for data storage and modeling, data ingestion, and data processing. For data processing, it discusses different processing engines like MapReduce, Pig, Hive, Spark and Impala. It also discusses what specific processing needs to be done for the clickstream data like sessionization and filtering.
The document discusses performance monitoring tools Automatic Workload Repository (AWR) and Active Session History (ASH) in Oracle Database 12c. It provides a brief history of AWR and ASH and describes how they are used to capture database performance metrics. The document also summarizes various reports available through AWR and ASH and how they can be accessed through Oracle Enterprise Manager and command line interfaces. Examples of queries are provided to analyze wait events, time spent in SQL, I/O and other activities from the data collected in AWR and ASH.
High concurrency, Low latency analytics using Spark/KuduChris George
With the right combination of open source projects, you can have a high concurrency and low latency spark jobs for doing data analysis. We'll show both REST and JDBC access to access data from a persistent spark context and then show how the combination of Spark Job Server, Spark Thrift Server and Apache Kudu can create a scalable backend for low latency analytics.
Using Kafka and Kudu for fast, low-latency SQL analytics on streaming dataMike Percy
The document discusses using Kafka and Kudu for low-latency SQL analytics on streaming data. It describes the challenges of supporting both streaming and batch workloads simultaneously using traditional solutions. The authors propose using Kafka to ingest data and Kudu for structured storage and querying. They demonstrate how this allows for stream processing, batch processing, and querying of up-to-second data with low complexity. Case studies from Xiaomi and TPC-H benchmarks show the advantages of this approach over alternatives.
Intel and Cloudera: Accelerating Enterprise Big Data SuccessCloudera, Inc.
The data center has gone through several inflection points in the past decades: adoption of Linux, migration from physical infrastructure to virtualization and Cloud, and now large-scale data analytics with Big Data and Hadoop.
Please join us to learn about how Cloudera and Intel are jointly innovating through open source software to enable Hadoop to run best on IA (Intel Architecture) and to foster the evolution of a vibrant Big Data ecosystem.
Cloudera’s performance engineering team recently completed a new round of benchmark testing based on Impala 2.5 and the most recent stable releases of the major SQL engine options for the Apache Hadoop platform, including Apache Hive-on-Tez and Apache Spark/Spark SQL. This presentation explains the methodology and results.
Big Data Day LA 2016/ Big Data Track - How To Use Impala and Kudu To Optimize...Data Con LA
This session describes how Impala integrates with Kudu for analytic SQL queries on Hadoop and how this integration, taking full advantage of the distinct properties of Kudu, has significant performance benefits.
This document proposes a container-based sizing framework for Apache Hadoop/Spark clusters that uses a multi-objective genetic algorithm approach. It emulates container execution on different cloud platforms to optimize configuration parameters for minimizing execution time and deployment cost. The framework uses Docker containers with resource constraints to model cluster performance on various public clouds and instance types. Optimization finds Pareto-optimal configurations balancing time and cost across objectives.
This document discusses strategies for migrating workloads to the cloud. It begins by providing an overview of cloud trends, such as the rise of hybrid cloud environments. It then discusses common approaches to cloud migrations, such as backing up on-premises data and restoring it in the cloud, which can be inefficient. The document emphasizes the need to optimize environments for the cloud before migrating in order to reduce costs associated with storage usage and data transfers. It also stresses the importance of masking confidential data rather than just encrypting it when used in non-production environments. The document provides recommendations around monitoring performance in the cloud and choosing cloud monitoring tools to aid in migrations.
First part of the talk will describe the anatomy of a typical data pipeline and how Apache Oozie meets the demands of large-scale data pipelines. In particular, we will focus on recent advancements in Oozie for dependency management among pipeline stages, incremental and partial processing, combinatorial, conditional and optional processing, priority processing, late processing and BCP management. Second part of the talk will focus on out of box support for spark jobs.
Speakers:
Purshotam Shah is a senior software engineer with the Hadoop team at Yahoo, and an Apache Oozie PMC member and committer.
Satish Saley is a software engineer at Yahoo!. He contributes to Apache Oozie.
Getting Started with Azure SQL Database (Presented at Pittsburgh TechFest 2018)Chad Green
Are you still hosting your databases on your own SQL Server? Would you like to consider putting those up in the cloud? Then come and learn what exactly Azure SQL can do for you and how to go about moving your databases to the cloud.
Concur, the leading provider of spend management solutions and services, will be joining us to discuss how they implemented Cloudera for data discovery and analytics. Using an enterprise data hub, Concur was able to provide their data scientists a centralized environment that allowed for faster and smarter analytic development.
During this session you will learn about:
The end user process of building smarter analytics and how Cloudera can help
Concurs pre-Hadoop and post-Hadoop environment
Summary of key lessons and end benefits of Concur’s modern architecture
Denny Lee: Sr. Director, Data Sciences Engineering
Denny is a hands on data architect and developer / hacker with more than 15 years of experience developing internet-scale infrastructure, data platforms, and distributed systems for both On-Premises and Cloud. His key focus surround solving complex large scale data problems - providing not only architectural direction but hands-on implementation of these systems to facilitate a successful data discovery and analytic environment.
Azure SQL Database for the SQL Server DBA - Azure Bootcamp Athens 2018 Antonios Chatzipavlis
Azure SQL Database is a managed database service hosted in Microsoft's Azure cloud. Some key differences from SQL Server include: the service is paid by the hour based on the selected service tier; users can dynamically scale resources up or down; backups and high availability are managed by the service provider; and common administration tasks are handled by the provider rather than the user. The service offers automatic backups, point-in-time restore, and geo-restore capabilities along with built-in high availability through replication across three copies in the primary region.
Accelerating Apache Spark-based Analytics on Intel Architecture-(Michael Gree...Spark Summit
This document discusses Intel's efforts to accelerate Apache Spark-based analytics on Intel architecture. It highlights performance improvements achieved by Intel optimizations for Spark and its components like Spark Streaming and SQL. Case studies show customers achieving up to 10x larger models and 4x faster training times for machine learning workloads on Spark using Intel technology. The document promotes Intel's involvement in the open source Spark community and its goal of helping customers deliver on big data's promise through partnerships.
Azure SQL Database Introduction by Tim RadneyHasan Savran
Have you been hearing about Azure Managed Instances and want to know what all the fuss is about? Come see how Managed Instances is changing how we think about cloud databases. Managed Instances can be considered a hybrid of Azure SQL Database and on-premises SQL Server with all the awesome benefits of Platform as a Service. You’ll get to see first-hand how easy it is to migrate databases from on-premises to a Managed Instance. We’ll explore the differences between Azure SQL Database, Managed Instances, and SQL Server on an Azure VM to help you determine what is the best fit for your organization. If you’ve been considering Azure for your organization, this session is for you!
This document discusses the performance metrics and capabilities of an enterprise grade streaming platform called Onyx. It can process streaming data with latencies under 2ms on Hadoop clusters. The key metrics it aims for are latencies under 16ms, throughput of 2000 events/second, 99.5% uptime, and the ability to scale resources while maintaining latency. It also aims to have open source components, extensible rules, and transparent integration with existing systems. Testing showed it can process over 70,000 records/second with average latency of 0.19ms and meet stringent reliability targets.
Explore big data at speed of thought with Spark 2.0 and SnappydataData Con LA
Abstract:
Data exploration often requires running aggregation/slice-dice queries on data sourced from disparate sources. You may want to identify distribution patterns, outliers, etc and aid the feature selection process as you train your predictive models. As you begin to understand your data, you want to ask ad-hoc questions expressed through your visualization tool (which typically translates to SQL queries), study the results and iteratively explore the data set through more queries. Unfortunately, even when data sets can be in-memory, large data set computations take time breaking the train of thought and increasing time to insight . We know Spark can be fast through its in-memory parallel processing. But, Spark 1.x isn’t quite there. Spark 2.0 promises to offer 10X better speed than its predecessor. Spark 2.0 ushers some impressive improvements to interactive query performance. We first explore these advances - compiling the query plan eliminating virtual function calls, and other improvements in the Catalyst engine. We compare the performance to other popular popular query processing engines by studying the spark query plans. We then go through SnappyData (an open source project that integrates Spark with a database that offers OLTP, OLAP and stream processing in a single cluster) where we use smarter data colocation and Synopses data (.e.g. Stratified sampling) to dramatically cut down on the memory requirements as well as the query latency. We explain the key concepts in summarizing data using structures like stratified sampling by walking through some examples in Apache Zeppelin notebooks (a open source visualization tool for spark) and demonstrate how we can explore massive data sets with just your laptop resources while achieving remarkable speeds.
Bio:
Jags is a founder and the CTO of SnappyData. Previously, Jags was the Chief Architect for “fast data” products at Pivotal and served in the extended leadership team of the company. At Pivotal and previously at VMWare, he led the technology direction for GemFire and other distributed in-memory Bio:
Jags Ramnarayan is a founder and the CTO of SnappyData. Previously, Jags was the Chief Architect for “fast data” products at Pivotal and served in the extended leadership team of the company. At Pivotal and previously at VMWare, he led the technology direction for GemFire and other distributed in-memory products.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
Impala 2.0 - The Best Analytic Database for HadoopCloudera, Inc.
A look at why SQL access in Hadoop is critical and the benefits of a native Hadoop analytic database, what’s new with Impala 2.0 and some of the recent performance benchmarks, some common Impala use cases and production customer stories, and insight into what’s next for Impala.
This very short document does not contain enough content to summarize meaningfully in 3 sentences or less. The document consists of a single word "Playing" followed by repetitive symbols and the word "Einde", providing no context or meaningful information that could be condensed into a high-level summary.
La educación virtual en el periodismo - Tarea foro 6Electrosur
El documento describe un caso de estudio sobre la implementación de la educación virtual en la enseñanza del periodismo en la Universidad de Málaga. El Departamento de Periodismo modernizó el curso "Periodismo Interactivo y Creación de Medios Digitales" en 2006, pasando de 17 estudiantes presenciales a 75 estudiantes virtuales. El proyecto tuvo éxito gracias a su enfoque en tres fases y al uso de una plataforma virtual bajo Moodle, mejorando la formación, comunicación y eficiencia del tiempo.
This document discusses application architectures using Hadoop. It provides an example case study of clickstream analysis. It covers challenges of Hadoop implementation and various architectural considerations for data storage and modeling, data ingestion, and data processing. For data processing, it discusses different processing engines like MapReduce, Pig, Hive, Spark and Impala. It also discusses what specific processing needs to be done for the clickstream data like sessionization and filtering.
The document discusses performance monitoring tools Automatic Workload Repository (AWR) and Active Session History (ASH) in Oracle Database 12c. It provides a brief history of AWR and ASH and describes how they are used to capture database performance metrics. The document also summarizes various reports available through AWR and ASH and how they can be accessed through Oracle Enterprise Manager and command line interfaces. Examples of queries are provided to analyze wait events, time spent in SQL, I/O and other activities from the data collected in AWR and ASH.
High concurrency, Low latency analytics using Spark/KuduChris George
With the right combination of open source projects, you can have a high concurrency and low latency spark jobs for doing data analysis. We'll show both REST and JDBC access to access data from a persistent spark context and then show how the combination of Spark Job Server, Spark Thrift Server and Apache Kudu can create a scalable backend for low latency analytics.
Using Kafka and Kudu for fast, low-latency SQL analytics on streaming dataMike Percy
The document discusses using Kafka and Kudu for low-latency SQL analytics on streaming data. It describes the challenges of supporting both streaming and batch workloads simultaneously using traditional solutions. The authors propose using Kafka to ingest data and Kudu for structured storage and querying. They demonstrate how this allows for stream processing, batch processing, and querying of up-to-second data with low complexity. Case studies from Xiaomi and TPC-H benchmarks show the advantages of this approach over alternatives.
Intel and Cloudera: Accelerating Enterprise Big Data SuccessCloudera, Inc.
The data center has gone through several inflection points in the past decades: adoption of Linux, migration from physical infrastructure to virtualization and Cloud, and now large-scale data analytics with Big Data and Hadoop.
Please join us to learn about how Cloudera and Intel are jointly innovating through open source software to enable Hadoop to run best on IA (Intel Architecture) and to foster the evolution of a vibrant Big Data ecosystem.
Cloudera’s performance engineering team recently completed a new round of benchmark testing based on Impala 2.5 and the most recent stable releases of the major SQL engine options for the Apache Hadoop platform, including Apache Hive-on-Tez and Apache Spark/Spark SQL. This presentation explains the methodology and results.
Big Data Day LA 2016/ Big Data Track - How To Use Impala and Kudu To Optimize...Data Con LA
This session describes how Impala integrates with Kudu for analytic SQL queries on Hadoop and how this integration, taking full advantage of the distinct properties of Kudu, has significant performance benefits.
This document proposes a container-based sizing framework for Apache Hadoop/Spark clusters that uses a multi-objective genetic algorithm approach. It emulates container execution on different cloud platforms to optimize configuration parameters for minimizing execution time and deployment cost. The framework uses Docker containers with resource constraints to model cluster performance on various public clouds and instance types. Optimization finds Pareto-optimal configurations balancing time and cost across objectives.
This document discusses strategies for migrating workloads to the cloud. It begins by providing an overview of cloud trends, such as the rise of hybrid cloud environments. It then discusses common approaches to cloud migrations, such as backing up on-premises data and restoring it in the cloud, which can be inefficient. The document emphasizes the need to optimize environments for the cloud before migrating in order to reduce costs associated with storage usage and data transfers. It also stresses the importance of masking confidential data rather than just encrypting it when used in non-production environments. The document provides recommendations around monitoring performance in the cloud and choosing cloud monitoring tools to aid in migrations.
First part of the talk will describe the anatomy of a typical data pipeline and how Apache Oozie meets the demands of large-scale data pipelines. In particular, we will focus on recent advancements in Oozie for dependency management among pipeline stages, incremental and partial processing, combinatorial, conditional and optional processing, priority processing, late processing and BCP management. Second part of the talk will focus on out of box support for spark jobs.
Speakers:
Purshotam Shah is a senior software engineer with the Hadoop team at Yahoo, and an Apache Oozie PMC member and committer.
Satish Saley is a software engineer at Yahoo!. He contributes to Apache Oozie.
Getting Started with Azure SQL Database (Presented at Pittsburgh TechFest 2018)Chad Green
Are you still hosting your databases on your own SQL Server? Would you like to consider putting those up in the cloud? Then come and learn what exactly Azure SQL can do for you and how to go about moving your databases to the cloud.
Concur, the leading provider of spend management solutions and services, will be joining us to discuss how they implemented Cloudera for data discovery and analytics. Using an enterprise data hub, Concur was able to provide their data scientists a centralized environment that allowed for faster and smarter analytic development.
During this session you will learn about:
The end user process of building smarter analytics and how Cloudera can help
Concurs pre-Hadoop and post-Hadoop environment
Summary of key lessons and end benefits of Concur’s modern architecture
Denny Lee: Sr. Director, Data Sciences Engineering
Denny is a hands on data architect and developer / hacker with more than 15 years of experience developing internet-scale infrastructure, data platforms, and distributed systems for both On-Premises and Cloud. His key focus surround solving complex large scale data problems - providing not only architectural direction but hands-on implementation of these systems to facilitate a successful data discovery and analytic environment.
Azure SQL Database for the SQL Server DBA - Azure Bootcamp Athens 2018 Antonios Chatzipavlis
Azure SQL Database is a managed database service hosted in Microsoft's Azure cloud. Some key differences from SQL Server include: the service is paid by the hour based on the selected service tier; users can dynamically scale resources up or down; backups and high availability are managed by the service provider; and common administration tasks are handled by the provider rather than the user. The service offers automatic backups, point-in-time restore, and geo-restore capabilities along with built-in high availability through replication across three copies in the primary region.
Accelerating Apache Spark-based Analytics on Intel Architecture-(Michael Gree...Spark Summit
This document discusses Intel's efforts to accelerate Apache Spark-based analytics on Intel architecture. It highlights performance improvements achieved by Intel optimizations for Spark and its components like Spark Streaming and SQL. Case studies show customers achieving up to 10x larger models and 4x faster training times for machine learning workloads on Spark using Intel technology. The document promotes Intel's involvement in the open source Spark community and its goal of helping customers deliver on big data's promise through partnerships.
Azure SQL Database Introduction by Tim RadneyHasan Savran
Have you been hearing about Azure Managed Instances and want to know what all the fuss is about? Come see how Managed Instances is changing how we think about cloud databases. Managed Instances can be considered a hybrid of Azure SQL Database and on-premises SQL Server with all the awesome benefits of Platform as a Service. You’ll get to see first-hand how easy it is to migrate databases from on-premises to a Managed Instance. We’ll explore the differences between Azure SQL Database, Managed Instances, and SQL Server on an Azure VM to help you determine what is the best fit for your organization. If you’ve been considering Azure for your organization, this session is for you!
This document discusses the performance metrics and capabilities of an enterprise grade streaming platform called Onyx. It can process streaming data with latencies under 2ms on Hadoop clusters. The key metrics it aims for are latencies under 16ms, throughput of 2000 events/second, 99.5% uptime, and the ability to scale resources while maintaining latency. It also aims to have open source components, extensible rules, and transparent integration with existing systems. Testing showed it can process over 70,000 records/second with average latency of 0.19ms and meet stringent reliability targets.
Explore big data at speed of thought with Spark 2.0 and SnappydataData Con LA
Abstract:
Data exploration often requires running aggregation/slice-dice queries on data sourced from disparate sources. You may want to identify distribution patterns, outliers, etc and aid the feature selection process as you train your predictive models. As you begin to understand your data, you want to ask ad-hoc questions expressed through your visualization tool (which typically translates to SQL queries), study the results and iteratively explore the data set through more queries. Unfortunately, even when data sets can be in-memory, large data set computations take time breaking the train of thought and increasing time to insight . We know Spark can be fast through its in-memory parallel processing. But, Spark 1.x isn’t quite there. Spark 2.0 promises to offer 10X better speed than its predecessor. Spark 2.0 ushers some impressive improvements to interactive query performance. We first explore these advances - compiling the query plan eliminating virtual function calls, and other improvements in the Catalyst engine. We compare the performance to other popular popular query processing engines by studying the spark query plans. We then go through SnappyData (an open source project that integrates Spark with a database that offers OLTP, OLAP and stream processing in a single cluster) where we use smarter data colocation and Synopses data (.e.g. Stratified sampling) to dramatically cut down on the memory requirements as well as the query latency. We explain the key concepts in summarizing data using structures like stratified sampling by walking through some examples in Apache Zeppelin notebooks (a open source visualization tool for spark) and demonstrate how we can explore massive data sets with just your laptop resources while achieving remarkable speeds.
Bio:
Jags is a founder and the CTO of SnappyData. Previously, Jags was the Chief Architect for “fast data” products at Pivotal and served in the extended leadership team of the company. At Pivotal and previously at VMWare, he led the technology direction for GemFire and other distributed in-memory Bio:
Jags Ramnarayan is a founder and the CTO of SnappyData. Previously, Jags was the Chief Architect for “fast data” products at Pivotal and served in the extended leadership team of the company. At Pivotal and previously at VMWare, he led the technology direction for GemFire and other distributed in-memory products.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
Impala 2.0 - The Best Analytic Database for HadoopCloudera, Inc.
A look at why SQL access in Hadoop is critical and the benefits of a native Hadoop analytic database, what’s new with Impala 2.0 and some of the recent performance benchmarks, some common Impala use cases and production customer stories, and insight into what’s next for Impala.
This very short document does not contain enough content to summarize meaningfully in 3 sentences or less. The document consists of a single word "Playing" followed by repetitive symbols and the word "Einde", providing no context or meaningful information that could be condensed into a high-level summary.
La educación virtual en el periodismo - Tarea foro 6Electrosur
El documento describe un caso de estudio sobre la implementación de la educación virtual en la enseñanza del periodismo en la Universidad de Málaga. El Departamento de Periodismo modernizó el curso "Periodismo Interactivo y Creación de Medios Digitales" en 2006, pasando de 17 estudiantes presenciales a 75 estudiantes virtuales. El proyecto tuvo éxito gracias a su enfoque en tres fases y al uso de una plataforma virtual bajo Moodle, mejorando la formación, comunicación y eficiencia del tiempo.
La primera revolución industrial (1780-1860) transformó la fabricación de bienes a través de la mecanización y el uso de maquinaria impulsada por vapor en las fábricas, lo que aumentó enormemente la productividad y redujo los costos de producción. Condiciones como mano de obra disponible, yacimientos de recursos como el hierro y el carbón, y una mentalidad liberal crearon un ambiente propicio para la industrialización en Europa entre 1820-1840.
Hadoop World 2011: HDFS Name Node High Availablity - Aaron Myers, Cloudera & ...Cloudera, Inc.
HDFS HA has been a highly sought after feature for years. Through collaboration between Cloudera, Facebook, Yahoo!, and others, a high availability system for the HDFS Name Node is actively being worked on, and will likely be complete by Hadoop World. This talk will discuss the architecture and setup of this system.
Kongregate - Maximizing Player Retention and Monetization in Free-to-Play Gam...David Piao Chiu
Kongregate - Maximizing Player Retention and Monetization in Free-to-Play Games: Comparative Stats for 2D & 3D Games and Asian & Western Games (MIGS 2013 Presentation)
The age of orchestration: from Docker basics to cluster managementNicola Paolucci
The container abstraction hit the collective developer mind with great force and created a space of innovation for the distribution, configuration and deployment of cloud based applications. Now that this new model has established itself work is moving towards orchestration and coordination of loosely coupled network services. There is an explosion of tools in this arena at different degrees of stability but the momentum is huge.
On the above premise this session we'll delve into a selection of the following topics:
- Two minute Docker intro refresher
- Overview of the orchestration landscape (Kubernetes, Mesos, Helios and Docker tools)
- Introduction to Docker own ecosystem orchestration tools (machine, swarm and compose)
- Live demo of cluster management using a sample application.
A basic understanding of Docker is suggested to fully enjoy the talk.
We are focused on providing accurate and high-quality candidates for our clients. Both the candidates we provide and the clients we serve emphasize the importance of accuracy and quality in filling roles.
Outware Mobile is dedicated to being a great place to work. Here are some photos of the team in and around office.
We are a diverse and dynamic team of individuals that have experience across many disciplines, including Software Development, UX Design, Visual Design, Business Analysis, Quality Assurance, Project Management, and Business Operations.
We specialise in mobile strategy, software design and development. We work collaboratively with our clients to create intuitive, effective and engaging mobile experiences that make a difference by helping people get things done, be more productive, learn, grow and be entertained.
As a team, we’ve produced some of Australia’s most popular apps including Grow by ANZ, AFL Live, nib Health Insurance, Coles Mobile Wallet and many more.
Los videojuegos pueden dividirse en tres categorías y a pesar de que algunos parecen aburridos, son uno de los tipos más vendidos a nivel mundial. Existen debates sobre los efectos de los videojuegos, con algunos argumentando que son una forma positiva de entretenimiento y otros creyendo que son dañinos. Se cree que los videojuegos pueden causar problemas como posturas violentas, sexismo, racismo, sedentarismo y aislamiento social, pero también pueden mejorar habilidades cognitivas si se controla el tiempo de juego. Al
This document introduces Docker Swarm for clustering Docker hosts into a single virtual host. It discusses using Swarm with Consul and an overlay network. Key points:
- Docker Swarm turns a pool of Docker hosts into a single virtual host with a standard API.
- Consul provides service discovery, key-value storage, and health checking.
- An overlay network allows containers on different hosts to communicate, with networking defined by Docker but implemented by the hosts' kernels.
VAST/HQ in Minneapolis was created in a historic Valspar building that had previously served as the company's corporate headquarters, symbolizing its 200-year history of technology leadership. The Minneapolis campus consists of four buildings housing several hundred scientists and technologists developing coatings. The new VAST showroom and product exhibit features a spectacular modern space blended with historic elements of the 100-year-old building.
Linkedin us regional page intro slides edited_160902_finalPaul Hussey
Samsung has several subsidiaries across the US focused on different aspects of its business including sales, marketing, research and development, and production. This includes Samsung Electronics America in Ridgefield Park, NJ; Samsung Research America in Mountain View, CA; Samsung Semiconductor Inc. in San Jose, CA; Samsung Austin Semiconductor in Austin, TX; and Samsung Austin R&D Center also in Austin, TX. These subsidiaries develop consumer electronics, mobile devices, semiconductors, and other technologies.
Este documento describe diferentes periféricos de salida como el monitor, la impresora, las placas de sonido y los parlantes, y los proyectores de video. El monitor muestra la información de la computadora a través de pixeles, la impresora imprime resultados en papel, las placas de sonido y parlantes reproducen audio, y los proyectores de video proyectan imágenes usando lentes.
Dattatray Pramod Bhat is seeking a career opportunity with honesty, loyalty, and dedication. He has over 5 years of experience in hospitality and tourism, having worked at Alagoa Resorts, Taj Exotica, and currently at Travel Systems as an In-bound operations Executive. Bhat holds a B.SC in hotel Management and Tourism Technology as well as a Diploma in Hospitality & Tourism Management. He is reliable, responsible, and committed to assigned tasks.
Este documento describe los procesos de enseñanza-aprendizaje y la relación maestro-alumno. Explica que el maestro es el líder de clase y coordinador de actividades, mientras que el alumno depende de la relación con el maestro para sentir confianza y lograr sus metas. También describe estrategias motivacionales como conocer a los alumnos y fomentar el trabajo en equipo. El vínculo maestro-alumno se basa en la interacción y orientación del maestro para guiar a los
The Value of the Modern Data Architecture with Apache Hadoop and Teradata Hortonworks
This webinar discusses why Apache Hadoop most typically the technology underpinning "Big Data". How it fits in a modern data architecture and the current landscape of databases and data warehouses that are already in use.
This document provides an introduction to a course on big data and analytics. It outlines the instructor and teaching assistant contact information. It then lists the main topics to be covered, including data analytics and mining techniques, Hadoop/MapReduce programming, graph databases and analytics. It defines big data and discusses the 3Vs of big data - volume, variety and velocity. It also covers big data technologies like cloud computing, Hadoop, and graph databases. Course requirements and the grading scheme are outlined.
R is an open source programming language and software environment for statistical analysis and graphics. It is widely used among data scientists for tasks like data manipulation, calculation, and graphical data analysis. Some key advantages of R include that it is open source and free, has a large collection of statistical tools and packages, is flexible, and has strong capabilities for data visualization. It also has an active user community and can integrate with other software like SAS, Python, and Tableau. R is a popular and powerful tool for data scientists.
This document discusses cloud computing, big data, Hadoop, and data analytics. It begins with an introduction to cloud computing, explaining its benefits like scalability, reliability, and low costs. It then covers big data concepts like the 3 Vs (volume, variety, velocity), Hadoop for processing large datasets, and MapReduce as a programming model. The document also discusses data analytics, describing different types like descriptive, diagnostic, predictive, and prescriptive analytics. It emphasizes that insights from analyzing big data are more valuable than raw data. Finally, it concludes that cloud computing can enhance business efficiency by enabling flexible access to computing resources for tasks like big data analytics.
Developed by Google’s Artificial Intelligence division, the Sycamore quantum processor boasts 53 qubits1.
In 2019, it achieved a feat that would take a state-of-the-art supercomputer 10,000 years to accomplish: completing a specific task in just 200 seconds1
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large datasets. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. HDFS stores data reliably across machines in a Hadoop cluster and MapReduce processes data in parallel by breaking the job into smaller fragments of work executed across cluster nodes.
Big data refers to large datasets that cannot be processed using traditional computing techniques. Hadoop is an open-source framework that allows processing of big data across clustered, commodity hardware. It uses MapReduce as a programming model to parallelize processing and HDFS for reliable, distributed file storage. Hadoop distributes data across clusters, parallelizes processing, and can dynamically add or remove nodes, providing scalability, fault tolerance and high availability for large-scale data processing.
This document discusses how a DBA can transition to becoming a data scientist using Oracle's big data tools. It provides an overview of big data concepts like Hadoop, NoSQL databases, and the Hadoop ecosystem. It also describes Oracle's Big Data Appliance and how it integrates with tools like Oracle NoSQL Database, Cloudera Hadoop, and the R programming environment. The document argues that with skills in Hadoop, MapReduce, NoSQL, and Hive/Pig, along with tools in Oracle's Big Data Appliance, a DBA can become a data scientist.
This document provides an overview of big data and Hadoop. It discusses what big data is, its types including structured, semi-structured and unstructured data. Some key sources of big data are also outlined. Hadoop is presented as a solution for managing big data through its core components like HDFS for storage and MapReduce for processing. The Hadoop ecosystem including other related tools like Hive, Pig, Spark and YARN is also summarized. Career opportunities in working with big data are listed in the end.
The document discusses cloud computing, big data, and big data analytics. It defines cloud computing as an internet-based technology that provides on-demand access to computing resources and data storage. Big data is described as large and complex datasets that are difficult to process using traditional databases due to their size, variety, and speed of growth. Hadoop is presented as an open-source framework for distributed storage and processing of big data using MapReduce. The document outlines the importance of analyzing big data using descriptive, diagnostic, predictive, and prescriptive analytics to gain insights.
An Analytical Study on Research Challenges and Issues in Big Data Analysis.pdfApril Knyff
This document discusses research challenges and issues in big data analysis. It begins with an introduction to big data and its key characteristics of volume, velocity, variety, and veracity. It then discusses challenges related to big data storage, privacy and security of data, and data processing. Specifically, it explores issues around data accessibility, application domains, privacy and security, and analytics such as heterogeneity, incompleteness, and real-time analysis of streaming data.
This document provides an overview of big data and how to start a career working with big data. It discusses the growth of data from various sources and challenges of dealing with large, unstructured data. Common data types and measurement units are defined. Hadoop is introduced as an open-source framework for storing and processing big data across clusters of computers. Key components of Hadoop's ecosystem are explained, including HDFS for storage, MapReduce/Spark for processing, and Hive/Impala for querying. Examples are given of how companies like Walmart and UPS use big data analytics to improve business decisions. Career opportunities and typical salaries in big data are also mentioned.
Apache Hadoop and its role in Big Data architecture - Himanshu Barijaxconf
In today’s world of exponentially growing big data, enterprises are becoming increasingly more aware of the business utility and necessity of harnessing, storing and analyzing this information. Apache Hadoop has rapidly evolved to become a leading platform for managing and processing big data, with the vital management, monitoring, metadata and integration services required by organizations to glean maximum business value and intelligence from their burgeoning amounts of information on customers, web trends, products and competitive markets. In this session, Hortonworks' Himanshu Bari will discuss the opportunities for deriving business value from big data by looking at how organizations utilize Hadoop to store, transform and refine large volumes of this multi-structured information. Connolly will also discuss the evolution of Apache Hadoop and where it is headed, the component requirements of a Hadoop-powered platform, as well as solution architectures that allow for Hadoop integration with existing data discovery and data warehouse platforms. In addition, he will look at real-world use cases where Hadoop has helped to produce more business value, augment productivity or identify new and potentially lucrative opportunities.
This document provides an overview and agenda for a presentation on big data landscape and implementation strategies. It defines big data, describes its key characteristics of volume, velocity and variety. It outlines the big data technology landscape including data acquisition, storage, organization and analysis tools. Finally it discusses an integrated big data architecture and considerations for implementation.
Apache Hadoop es un framework de software que soporta aplicaciones distribuidas bajo una licencia libre.1 Permite a las aplicaciones trabajar con miles de nodos y petabytes de datos. Hadoop se inspiró en los documentos Google para MapReduce y Google File System (GFS).
Hadoop es un proyecto de alto nivel Apache que está siendo construido y usado por una comunidad global de contribuyentes,2 mediante el lenguaje de programación Java. Yahoo! ha sido el mayor contribuyente al proyecto,3 y usa Hadoop extensivamente en su negocio
This document discusses big data and Hadoop. It defines big data as high volume data that cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework that can store and process large data sets across clusters of commodity hardware. It has two main components - HDFS for storage and MapReduce for distributed processing. HDFS stores data across clusters and replicates it for fault tolerance, while MapReduce allows data to be mapped and reduced for analysis.
Big Data with Hadoop – For Data Management, Processing and StoringIRJET Journal
This document discusses big data and Hadoop. It begins with defining big data and explaining its characteristics of volume, variety, velocity, and veracity. It then provides an overview of Hadoop, describing its core components of HDFS for storage and MapReduce for processing. Key technologies in Hadoop's ecosystem are also summarized like Hive, Pig, and HBase. The document concludes by outlining some challenges of big data like issues of heterogeneity and incompleteness of data.
This document discusses how Apache Hadoop provides a solution for enterprises facing challenges from the massive growth of data. It describes how Hadoop can integrate with existing enterprise data systems like data warehouses to form a modern data architecture. Specifically, Hadoop provides lower costs for data storage, optimization of data warehouse workloads by offloading ETL tasks, and new opportunities for analytics through schema-on-read and multi-use data processing. The document outlines the core capabilities of Hadoop and how it has expanded to meet enterprise requirements for data management, access, governance, integration and security.
The document discusses Oracle Golden Gate, a data replication tool. It provides an overview of Golden Gate's components including the manager, extract, trails, data pump, collector, and replicat. It also covers the logical architecture and supported topologies. Golden Gate enables real-time data replication across heterogeneous databases and platforms.
The outer query and inner query will not share cursors because they are in different contexts - the outer query is a SQL statement while the inner query is inside a PL/SQL function. Each will be parsed separately.
To enable cursor sharing between the outer and inner queries, you can:
1. Pass the deptno value directly to the function instead of a bind variable
2. Define the function as pipelined and return ref cursor from it so the inner query becomes a subquery of the outer query.
3. Use inline views instead of a function.
So in summary, different contexts prevent cursor sharing. You need to modify the code to bring the queries in the same context.
Oracle database in cloud, dr in cloud and overview of oracle database 18cAiougVizagChapter
This document provides a profile summary of Malay Kumar Khawas, a Principal Consultant at Oracle India. It outlines his professional experience including over 12 years working with Oracle technologies. It also lists his areas of expertise, which include Oracle Database, Cloud implementations, identity management, disaster recovery, and various Oracle products. The document then provides an agenda for a presentation on Oracle Database Cloud Services, disaster recovery in Oracle Public Cloud, and new features in Oracle Database 18c.
This document summarizes an Oracle Automatic Data Optimization (ADO) presentation about using heat maps to determine how often data is accessed and optimize storage based on access patterns. It discusses space-based versus time-based ADO policies, using heat maps to track data access patterns, sample ADO policies that move or compress data based on time since access, testing ADO policies, and shows the results of space-based ADO moving data between storage tiers.
This document provides an overview of Oracle Automatic Workload Repository (AWR) and Active Session History (ASH) analytics. It discusses the key components and architecture of AWR and ASH, how they collect and store database performance data, and how that data can be analyzed using tools like the Automatic Database Diagnostic Monitor (ADDM) and ASH Analytics. It also highlights new capabilities in Oracle 12c like Real-Time ADDM, AWR Compare Periods reporting, and enhanced dimensions and filters for the Top Activity page in ASH Analytics.
The document summarizes new features in Oracle Database 12c from Oracle 11g that would help a DBA currently using 11g. It lists and briefly describes features such as the READ privilege, temporary undo, online data file move, DDL logging, and many others. The objectives are to make the DBA aware of useful 12c features when working with a 12c database and to discuss each feature at a high level within 90 seconds.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
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.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
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.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
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.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
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
36. Hadoop 1 – Job & Task Trackers
Master Node - The majority of hadoop deployments consist of sevaral master node
instances. Having more than one master node helps eliminate the risk of single
point of failure.
NameNode - These processes are charged with storing a directory tree of all files
in the Hadoop Distributed File SYstem (HDFS). They also keep track of where the
file data is kept within in the cluster. Client Applications contact Name Nodes when
they need to locate a file, or add, or copy or delete a file.
DataNodes - The datanode stores data in the HDFS and is responsible for
replicating data across clusters. Data Nodes interact with client applications when
the NameNopde has supplied the Datanode's address.
WorkerNode: Unlike a master node, whose numbers we can count on one hand, a
representative Hadoop Deployment consists of dozens or hundreds of worker
nodes, which provides enough processing power to analyze a
few hundreds terabytes all the way upto one petabyte. Each worker node includes
a DataNode as well as Task Tracker.
37. Map Reduce
Job Tracker /MapReduce Workload Management Layer - This
process is assigned to interact with client applications. It is
responsible for distributing MapReduce tasks to particular nodes
within in a cluster. This engine coordinates all aspects of hadoop
such as scheduling and launching jobs.
Task Tracker - This is a process in the cluster that is capable of
receiving tasks( inlcuding Map, Reduce, and Shuffle) from a Job
Tracker
48. Coordination in a distributed system
• Coordination: An act that multiple nodes must perform together.
• Examples:
– Group membership
– Locking
– Publisher/Subscriber
– Leader Election
– Synchronization
• Getting node coordination correct is very hard!
49.
50. ZooKeeper allows distributed processes to
coordinate with each other through a shared
hierarchical name space of data registers.
Introducing ZooKeeper
- ZooKeeper Wiki
ZooKeeper is much more than a
distributed lock server!
51. What is ZooKeeper?
• An open source, high-performance coordination service for
distributed applications.
• Exposes common services in simple interface:
– naming
– configuration management
– locks & synchronization
– group services
… developers don't have to write them from scratch
• Build your own on it for specific needs.
69. Name Site Counter
Dick Ebay 507,018
Dick Google 690,414
Jane Google 716,426
Dick Facebook 723,649
Jane Facebook 643,261
Jane ILoveLarry.com 856,767
Dick MadBillFans.com 675,230
NameId Name
1 Dick
2 Jane
SiteId SiteName
1 Ebay
2 Google
3 Facebook
4 ILoveLarry.com
5 MadBillFans.com
NameId SiteId Counter
1 1 507,018
1 3 690,414
2 3 716,426
1 3 723,649
2 3 643,261
2 4 856,767
1 5 675,230
Id Name Ebay Google Facebook (other columns) MadBillFans.com
1 Dick 507,018 690,414 723,649 . . . . . . . . . . . . . . 675,230
Id Name Google Facebook (other columns) ILoveLarry.com
2 Jane 716,426 643,261 . . . . . . . . . . . . . . 856,767
BigTable Data Model
70. Document databases
• Structured documents – XML and JSON
(JavaScript Object Notation) become more
prevalent within applications
• Web programmers start storing these in BLOBS in
MySQL
• Emergence of XML and JSON databases