This webinar series covers Apache Kafka and Apache Storm for streaming data processing. Also, it discusses new streaming innovations for Kafka and Storm included in HDP 2.2
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...Hortonworks
Many enterprises are turning to Apache Hadoop to enable Big Data Analytics and reduce the costs of traditional data warehousing. Yet, it is hard to succeed when 80% of the time is spent on moving data and only 20% on using it. It’s time to swap the 80/20! The Big Data experts at Attunity and Hortonworks have a solution for accelerating data movement into and out of Hadoop that enables faster time-to-value for Big Data projects and a more complete and trusted view of your business. Join us to learn how this solution can work for you.
Discover HDP 2.1: Interactive SQL Query in Hadoop with Apache HiveHortonworks
In February 2013, the open source community launched the Stinger Initiative to improve speed, scale and SQL semantics in Apache Hive. After thirteen months of constant, concerted collaboration (and more than 390,000 new lines of Java code) Stinger is complete with Hive 0.13.
In this presentation, Carter Shanklin, Hortonworks director of product management, and Owen O'Malley, Hortonworks co-founder and committer to Apache Hive, discuss how Hive enables interactive query using familiar SQL semantics.
Discover HDP 2.2: Apache Falcon for Hadoop Data GovernanceHortonworks
Hortonworks Data Platform 2.2 includes Apache Falcon for Hadoop data governance. In this 30-minute webinar, we discussed why the enterprise needs Falcon for governance, and demonstrated data pipeline construction, policies for data retention and management with Ambari. We also discussed new innovations including: integration of user authentication, data lineage, an improved interface for pipeline management, and the new Falcon capability to establish an automated policy for cloud backup to Microsoft Azure or Amazon S3.
Discover HDP 2.1: Apache Falcon for Data Governance in HadoopHortonworks
Beginning with HDP 2.1, Hortonworks Data Platform ships with Apache Falcon for Hadoop data governance. Himanshu Bari, Hortonworks senior product manager, and Venkatesh Seetharam, Hortonworks co-founder and committer to Apache Falcon, lead this 30-minute webinar, including:
+ Why you need Apache Falcon
+ Key new Falcon features
+ Demo: Defining data pipelines with replication; policies for retention and late data arrival; managing Falcon server with Ambari
Implementing a Data Lake with Enterprise Grade Data GovernanceHortonworks
Hadoop provides a powerful platform for data science and analytics, where data engineers and data scientists can leverage myriad data from external and internal data sources to uncover new insight. Such power is also presenting a few new challenges. On the one hand, the business wants more and more self-service, and on the other hand IT is trying to keep up with the demand for data, while maintaining architecture and data governance standards.
In this webinar, Andrew Ahn, Data Governance Initiative Product Manager at Hortonworks, will address the gaps and offer best practices in providing end-to-end data governance in HDP. Andrew Ahn will be followed by Oliver Claude of Waterline Data, who will share a case study of how Waterline Data Inventory works with HDP in the Modern Data Architecture to automate the discovery of business and compliance metadata, data lineage, as well as data quality metrics.
Hortonworks and Platfora in Financial Services - WebinarHortonworks
Big Data Analytics is transforming how banks and financial institutions unlock insights, make more meaningful decisions, and manage risk. Join this webinar to see how you can gain a clear understanding of the customer journey by leveraging Platfora to interactively analyze the mass of raw data that is stored in your Hortonworks Data Platform. Our experts will highlight use cases, including customer analytics and security analytics.
Speakers: Mark Lochbihler, Partner Solutions Engineer at Hortonworks, and Bob Welshmer, Technical Director at Platfora
Discover hdp 2.2: Data storage innovations in Hadoop Distributed Filesystem (...Hortonworks
Hortonworks Data Platform 2.2 include HDFS for data storage . In this 30-minute webinar, we discussed data storage innovations, including Heterogeneous storage, encryption, and operational security enhancements.
Discover HDP 2.2: Even Faster SQL Queries with Apache Hive and Stinger.nextHortonworks
The document discusses new features in Apache Hive 0.14 that improve SQL query performance. It introduces a cost-based optimizer that can optimize join orders, enabling faster query times. An example TPC-DS query is shown to demonstrate how the optimizer selects an efficient join order based on statistics about table and column sizes. Faster SQL queries are now possible in Hive through this query optimization capability.
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...Hortonworks
Many enterprises are turning to Apache Hadoop to enable Big Data Analytics and reduce the costs of traditional data warehousing. Yet, it is hard to succeed when 80% of the time is spent on moving data and only 20% on using it. It’s time to swap the 80/20! The Big Data experts at Attunity and Hortonworks have a solution for accelerating data movement into and out of Hadoop that enables faster time-to-value for Big Data projects and a more complete and trusted view of your business. Join us to learn how this solution can work for you.
Discover HDP 2.1: Interactive SQL Query in Hadoop with Apache HiveHortonworks
In February 2013, the open source community launched the Stinger Initiative to improve speed, scale and SQL semantics in Apache Hive. After thirteen months of constant, concerted collaboration (and more than 390,000 new lines of Java code) Stinger is complete with Hive 0.13.
In this presentation, Carter Shanklin, Hortonworks director of product management, and Owen O'Malley, Hortonworks co-founder and committer to Apache Hive, discuss how Hive enables interactive query using familiar SQL semantics.
Discover HDP 2.2: Apache Falcon for Hadoop Data GovernanceHortonworks
Hortonworks Data Platform 2.2 includes Apache Falcon for Hadoop data governance. In this 30-minute webinar, we discussed why the enterprise needs Falcon for governance, and demonstrated data pipeline construction, policies for data retention and management with Ambari. We also discussed new innovations including: integration of user authentication, data lineage, an improved interface for pipeline management, and the new Falcon capability to establish an automated policy for cloud backup to Microsoft Azure or Amazon S3.
Discover HDP 2.1: Apache Falcon for Data Governance in HadoopHortonworks
Beginning with HDP 2.1, Hortonworks Data Platform ships with Apache Falcon for Hadoop data governance. Himanshu Bari, Hortonworks senior product manager, and Venkatesh Seetharam, Hortonworks co-founder and committer to Apache Falcon, lead this 30-minute webinar, including:
+ Why you need Apache Falcon
+ Key new Falcon features
+ Demo: Defining data pipelines with replication; policies for retention and late data arrival; managing Falcon server with Ambari
Implementing a Data Lake with Enterprise Grade Data GovernanceHortonworks
Hadoop provides a powerful platform for data science and analytics, where data engineers and data scientists can leverage myriad data from external and internal data sources to uncover new insight. Such power is also presenting a few new challenges. On the one hand, the business wants more and more self-service, and on the other hand IT is trying to keep up with the demand for data, while maintaining architecture and data governance standards.
In this webinar, Andrew Ahn, Data Governance Initiative Product Manager at Hortonworks, will address the gaps and offer best practices in providing end-to-end data governance in HDP. Andrew Ahn will be followed by Oliver Claude of Waterline Data, who will share a case study of how Waterline Data Inventory works with HDP in the Modern Data Architecture to automate the discovery of business and compliance metadata, data lineage, as well as data quality metrics.
Hortonworks and Platfora in Financial Services - WebinarHortonworks
Big Data Analytics is transforming how banks and financial institutions unlock insights, make more meaningful decisions, and manage risk. Join this webinar to see how you can gain a clear understanding of the customer journey by leveraging Platfora to interactively analyze the mass of raw data that is stored in your Hortonworks Data Platform. Our experts will highlight use cases, including customer analytics and security analytics.
Speakers: Mark Lochbihler, Partner Solutions Engineer at Hortonworks, and Bob Welshmer, Technical Director at Platfora
Discover hdp 2.2: Data storage innovations in Hadoop Distributed Filesystem (...Hortonworks
Hortonworks Data Platform 2.2 include HDFS for data storage . In this 30-minute webinar, we discussed data storage innovations, including Heterogeneous storage, encryption, and operational security enhancements.
Discover HDP 2.2: Even Faster SQL Queries with Apache Hive and Stinger.nextHortonworks
The document discusses new features in Apache Hive 0.14 that improve SQL query performance. It introduces a cost-based optimizer that can optimize join orders, enabling faster query times. An example TPC-DS query is shown to demonstrate how the optimizer selects an efficient join order based on statistics about table and column sizes. Faster SQL queries are now possible in Hive through this query optimization capability.
YARN Ready: Integrating to YARN with Tez Hortonworks
YARN Ready webinar series helps developers integrate their applications to YARN. Tez is one vehicle to do that. We take a deep dive including code review to help you get started.
This is the presentation from the "Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS" webinar on May 28, 2014. Rohit Bahkshi, a senior product manager at Hortonworks, and Vinod Vavilapalli, PMC for Apache Hadoop, discuss an overview of YARN in HDFS and new features in HDP 2.1. Those new features include: HDFS extended ACLs, HTTPs wire encryption, HDFS DataNode caching, resource manager high availability, application timeline server, and capacity scheduler pre-emption.
Supporting Financial Services with a More Flexible Approach to Big DataHortonworks
The document discusses how Hortonworks Data Platform (HDP) enables a modern data architecture with Apache Hadoop. HDP provides a common data set stored in HDFS that can be accessed through various applications for batch, interactive, and real-time processing. This allows organizations to store all their data in one place and access it simultaneously through multiple means. YARN is the architectural center of HDP and enables this modern data architecture. HDP also provides enterprise capabilities like security, governance, and operations to make Hadoop suitable for business use.
Hortonworks Technical Workshop: Real Time Monitoring with Apache HadoopHortonworks
Real Time Monitoring requires a high scalable infrastructure of message bus, database, distributed event processing and scalable analytics engine. By bringing together leading open source projects of Apache Kafka, Apache HBase, Apache Storm and Apache Hive, the Hortonworks Data Platform offers a comprehensive Real Time Analysis platform. In this session, we will provide an in-depth overview all the key technology components and demonstrate a working solution for monitoring a fleet of trucks.
Audience: Developers, Architects and System Engineers from the Hortonworks Technology Partner community.
Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=0278dc8aa49a9991e1ce436c71f53d30
Don't Let Security Be The 'Elephant in the Room'Hortonworks
Don't let security be the "elephant in the room" for enterprise big data. As big data now includes sensitive data from various sources, there are hidden risks to simply adopting big data technologies without also implementing proper data protection. While traditional IT security approaches provide some coverage, they also have gaps and do not fully address protecting data across its lifecycle and wherever it may travel. A data-centric security approach that encrypts data at capture can lock down data and keep it protected as it is stored, processed, and shared across systems.
Discover HDP 2.1: Apache Solr for Hadoop SearchHortonworks
This document appears to be a presentation about Apache Solr for Hadoop search using the Hortonworks Data Platform (HDP). The agenda includes an overview of Apache Solr and Hadoop search, a demo of Hadoop search, and a question and answer section. The presentation discusses how Solr provides scalable indexing of data stored in HDFS and powerful search capabilities. It also includes a reference architecture showing how Solr integrates with Hadoop for search and indexing.
HDP Advanced Security: Comprehensive Security for Enterprise HadoopHortonworks
With the introduction of YARN, Hadoop has emerged as a first class citizen in the data center as a single Hadoop cluster can now be used to power multiple applications and hold more data. This advance has also put a spotlight on a need for more comprehensive approach to Hadoop security.
Hortonworks recently acquired Hadoop security company XA Secure to provide a common interface for central administration of security policy and coordinated enforcement across authentication, authorization, audit and data protection for the entire Hadoop stack.
In this presentation, Balaji Ganesan and Bosco Durai (previously with XA Secure, now with Hortonworks) introduce HDP Advanced Security, review a comprehensive set of Hadoop security requirements and demonstrate how HDP Advanced Security addresses them.
Join Cloudian, Hortonworks and 451 Research for a panel-style Q&A discussion about the latest trends and technology innovations in Big Data and Analytics. Matt Aslett, Data Platforms and Analytics Research Director at 451 Research, John Kreisa, Vice President of Strategic Marketing at Hortonworks, and Paul Turner, Chief Marketing Officer at Cloudian, will answer your toughest questions about data storage, data analytics, log data, sensor data and the Internet of Things. Bring your questions or just come and listen!
Rescue your Big Data from Downtime with HP Operations Bridge and Apache HadoopHortonworks
How can you simplify the management and monitoring of your Hadoop environment? Ensure IT can focus on the right business priorities supported by Hadoop? Take a look at this presentation and learn how you can simplify the management and monitoring of your Hadoop environment, and ensure IT can focus on the right business priorities supported by Hadoop.
Enterprise Hadoop with Hortonworks and Nimble StorageHortonworks
Join us to learn how Hortonworks Data Platform and Nimble Storage provide an enterprise-ready data platform for multi-workload data processing. HDP supports an array of processing methods — from batch through interactive to real-time, with key capabilities required of an enterprise data platform — spanning Governance, Security and Operations. Nimble Storage provides the performance, capacity, and availability for HDP and allows you to take advantage of Hadoop with minimal changes to existing data architectures and skillsets.
Starting Small and Scaling Big with Hadoop (Talend and Hortonworks webinar)) ...Hortonworks
This document discusses using Hadoop and the Hortonworks Data Platform (HDP) for big data applications. It outlines how HDP can help organizations optimize their existing data warehouse, lower storage costs, unlock new applications from new data sources, and achieve an enterprise data lake architecture. The document also discusses how Talend's data integration platform can be used with HDP to easily develop batch, real-time, and interactive data integration jobs on Hadoop. Case studies show how companies have used Talend and HDP together to modernize their data architecture and product inventory and pricing forecasting.
Hortonworks - What's Possible with a Modern Data Architecture?Hortonworks
This is Mark Ledbetter's presentation from the September 22, 2014 Hortonworks webinar “What’s Possible with a Modern Data Architecture?” Mark is vice president for industry solutions at Hortonworks. He has more than twenty-five years experience in the software industry with a focus on Retail and supply chain.
Hortonworks and Red Hat Webinar_Sept.3rd_Part 1Hortonworks
As the enterprise's big data program matures and Apache Hadoop becomes more deeply embedded in critical operations, the ability to support and operate it efficiently and reliably becomes increasingly important. To aid enterprise in operating modern data architecture at scale, Red hat and Hortonworks have collaborated to integrate Hortonworks Data Platform with Red Hat's proven platform technologies. Join us in this interactive 3-part webinar series, as we'll demonstrate how Red Hat JBoss Data Virtualization can integrate with Hadoop through Hive and provide users easy access to data.
In 2012, we released Hortonworks Data Platform powered by Apache Hadoop and established partnerships with major enterprise software vendors including Microsoft and Teradata that are making enterprise ready Hadoop easier and faster to consume. As we start 2013, we invite you to join us for this live webinar where Shaun Connolly, VP of Strategy at Hortonworks, will cover the highlights of 2012 and the road ahead in 2013 for Hortonworks and Apache Hadoop.
This document summarizes a webinar presented by Hortonworks and Sqrrl on using big data analytics for cybersecurity. It discusses how the growth of data sources and targeted attacks require new security approaches. A modern data architecture with Hadoop can provide a common platform to analyze all security-related data and gain new insights. Sqrrl's linked data model and analytics run on Hortonworks to help investigate security incidents like a network breach, mapping different data sources and identifying abnormal activity patterns.
Data Lake for the Cloud: Extending your Hadoop ImplementationHortonworks
As more applications are created using Apache Hadoop that derive value from the new types of data from sensors/machines, server logs, click-streams, and other sources, the enterprise "Data Lake" forms with Hadoop acting as a shared service. While these Data Lakes are important, a broader life-cycle needs to be considered that spans development, test, production, and archival and that is deployed across a hybrid cloud architecture.
If you have already deployed Hadoop on-premise, this session will also provide an overview of the key scenarios and benefits of joining your on-premise Hadoop implementation with the cloud, by doing backup/archive, dev/test or bursting. Learn how you can get the benefits of an on-premise Hadoop that can seamlessly scale with the power of the cloud.
Combine Apache Hadoop and Elasticsearch to Get the Most of Your Big DataHortonworks
Hadoop is a great platform for storing and processing massive amounts of data. Elasticsearch is the ideal solution for Searching and Visualizing the same data. Join us to learn how you can leverage the full power of both platforms to maximize the value of your Big Data.
In this webinar we'll walk you through:
How Elasticsearch fits in the Modern Data Architecture.
A demo of Elasticsearch and Hortonworks Data Platform.
Best practices for combining Elasticsearch and Hortonworks Data Platform to extract maximum insights from your data.
Discover Red Hat and Apache Hadoop for the Modern Data Architecture - Part 3Hortonworks
The document discusses using Hortonworks Data Platform (HDP) and Red Hat JBoss Data Virtualization to create a data lake solution and virtual data marts. It describes how a data lake enables storing all types of data in a single repository and accessing it through tools. Virtual data marts allow lines of business to access relevant data through self-service interfaces while maintaining governance and security over the central data lake. The presentation includes demonstrations of virtual data marts integrating data from Hadoop and other sources.
1) The webinar covered Apache Hadoop on the open cloud, focusing on key drivers for Hadoop adoption like new types of data and business applications.
2) Requirements for enterprise Hadoop include core services, interoperability, enterprise readiness, and leveraging existing skills in development, operations, and analytics.
3) The webinar demonstrated Hortonworks Apache Hadoop running on Rackspace's Cloud Big Data Platform, which is built on OpenStack for security, optimization, and an open platform.
Stinger.Next by Alan Gates of HortonworksData Con LA
The document discusses Hortonworks' Stinger initiative to deliver interactive SQL query capabilities in Hadoop. Stinger aims to improve Hive query performance by 100x to enable interactive query times through optimizations like SQL types, analytic functions, and the ORC file format (Phase 1). Future phases will integrate Hive with Apache Tez and introduce a new low-latency execution engine called LLAP to enable sub-second queries (Phase 2-3). The document provides details on various Stinger phases, optimizations, and capabilities to support a wider range of SQL semantics and use cases.
Discover HDP2.1: Apache Storm for Stream Data Processing in HadoopHortonworks
For the first time, Hortonworks Data Platform ships with Apache Storm for processing stream data in Hadoop.
In this presentation, Himanshu Bari, Hortonworks senior product manager, and Taylor Goetz, Hortonworks engineer and committer to Apache Storm, cover Storm and stream processing in HDP 2.1:
+ Key requirements of a streaming solution and common use cases
+ An overview of Apache Storm
+ Q & A
Hortonworks Data in Motion Webinar Series Part 7 Apache Kafka Nifi Better Tog...Hortonworks
Apache NiFi, Storm and Kafka augment each other in modern enterprise architectures. NiFi provides a coding free solution to get many different formats and protocols in and out of Kafka and compliments Kafka with full audit trails and interactive command and control. Storm compliments NiFi with the capability to handle complex event processing.
Join us to learn how Apache NiFi, Storm and Kafka can augment each other for creating a new dataplane connecting multiple systems within your enterprise with ease, speed and increased productivity.
https://www.brighttalk.com/webcast/9573/224063
YARN Ready: Integrating to YARN with Tez Hortonworks
YARN Ready webinar series helps developers integrate their applications to YARN. Tez is one vehicle to do that. We take a deep dive including code review to help you get started.
This is the presentation from the "Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS" webinar on May 28, 2014. Rohit Bahkshi, a senior product manager at Hortonworks, and Vinod Vavilapalli, PMC for Apache Hadoop, discuss an overview of YARN in HDFS and new features in HDP 2.1. Those new features include: HDFS extended ACLs, HTTPs wire encryption, HDFS DataNode caching, resource manager high availability, application timeline server, and capacity scheduler pre-emption.
Supporting Financial Services with a More Flexible Approach to Big DataHortonworks
The document discusses how Hortonworks Data Platform (HDP) enables a modern data architecture with Apache Hadoop. HDP provides a common data set stored in HDFS that can be accessed through various applications for batch, interactive, and real-time processing. This allows organizations to store all their data in one place and access it simultaneously through multiple means. YARN is the architectural center of HDP and enables this modern data architecture. HDP also provides enterprise capabilities like security, governance, and operations to make Hadoop suitable for business use.
Hortonworks Technical Workshop: Real Time Monitoring with Apache HadoopHortonworks
Real Time Monitoring requires a high scalable infrastructure of message bus, database, distributed event processing and scalable analytics engine. By bringing together leading open source projects of Apache Kafka, Apache HBase, Apache Storm and Apache Hive, the Hortonworks Data Platform offers a comprehensive Real Time Analysis platform. In this session, we will provide an in-depth overview all the key technology components and demonstrate a working solution for monitoring a fleet of trucks.
Audience: Developers, Architects and System Engineers from the Hortonworks Technology Partner community.
Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=0278dc8aa49a9991e1ce436c71f53d30
Don't Let Security Be The 'Elephant in the Room'Hortonworks
Don't let security be the "elephant in the room" for enterprise big data. As big data now includes sensitive data from various sources, there are hidden risks to simply adopting big data technologies without also implementing proper data protection. While traditional IT security approaches provide some coverage, they also have gaps and do not fully address protecting data across its lifecycle and wherever it may travel. A data-centric security approach that encrypts data at capture can lock down data and keep it protected as it is stored, processed, and shared across systems.
Discover HDP 2.1: Apache Solr for Hadoop SearchHortonworks
This document appears to be a presentation about Apache Solr for Hadoop search using the Hortonworks Data Platform (HDP). The agenda includes an overview of Apache Solr and Hadoop search, a demo of Hadoop search, and a question and answer section. The presentation discusses how Solr provides scalable indexing of data stored in HDFS and powerful search capabilities. It also includes a reference architecture showing how Solr integrates with Hadoop for search and indexing.
HDP Advanced Security: Comprehensive Security for Enterprise HadoopHortonworks
With the introduction of YARN, Hadoop has emerged as a first class citizen in the data center as a single Hadoop cluster can now be used to power multiple applications and hold more data. This advance has also put a spotlight on a need for more comprehensive approach to Hadoop security.
Hortonworks recently acquired Hadoop security company XA Secure to provide a common interface for central administration of security policy and coordinated enforcement across authentication, authorization, audit and data protection for the entire Hadoop stack.
In this presentation, Balaji Ganesan and Bosco Durai (previously with XA Secure, now with Hortonworks) introduce HDP Advanced Security, review a comprehensive set of Hadoop security requirements and demonstrate how HDP Advanced Security addresses them.
Join Cloudian, Hortonworks and 451 Research for a panel-style Q&A discussion about the latest trends and technology innovations in Big Data and Analytics. Matt Aslett, Data Platforms and Analytics Research Director at 451 Research, John Kreisa, Vice President of Strategic Marketing at Hortonworks, and Paul Turner, Chief Marketing Officer at Cloudian, will answer your toughest questions about data storage, data analytics, log data, sensor data and the Internet of Things. Bring your questions or just come and listen!
Rescue your Big Data from Downtime with HP Operations Bridge and Apache HadoopHortonworks
How can you simplify the management and monitoring of your Hadoop environment? Ensure IT can focus on the right business priorities supported by Hadoop? Take a look at this presentation and learn how you can simplify the management and monitoring of your Hadoop environment, and ensure IT can focus on the right business priorities supported by Hadoop.
Enterprise Hadoop with Hortonworks and Nimble StorageHortonworks
Join us to learn how Hortonworks Data Platform and Nimble Storage provide an enterprise-ready data platform for multi-workload data processing. HDP supports an array of processing methods — from batch through interactive to real-time, with key capabilities required of an enterprise data platform — spanning Governance, Security and Operations. Nimble Storage provides the performance, capacity, and availability for HDP and allows you to take advantage of Hadoop with minimal changes to existing data architectures and skillsets.
Starting Small and Scaling Big with Hadoop (Talend and Hortonworks webinar)) ...Hortonworks
This document discusses using Hadoop and the Hortonworks Data Platform (HDP) for big data applications. It outlines how HDP can help organizations optimize their existing data warehouse, lower storage costs, unlock new applications from new data sources, and achieve an enterprise data lake architecture. The document also discusses how Talend's data integration platform can be used with HDP to easily develop batch, real-time, and interactive data integration jobs on Hadoop. Case studies show how companies have used Talend and HDP together to modernize their data architecture and product inventory and pricing forecasting.
Hortonworks - What's Possible with a Modern Data Architecture?Hortonworks
This is Mark Ledbetter's presentation from the September 22, 2014 Hortonworks webinar “What’s Possible with a Modern Data Architecture?” Mark is vice president for industry solutions at Hortonworks. He has more than twenty-five years experience in the software industry with a focus on Retail and supply chain.
Hortonworks and Red Hat Webinar_Sept.3rd_Part 1Hortonworks
As the enterprise's big data program matures and Apache Hadoop becomes more deeply embedded in critical operations, the ability to support and operate it efficiently and reliably becomes increasingly important. To aid enterprise in operating modern data architecture at scale, Red hat and Hortonworks have collaborated to integrate Hortonworks Data Platform with Red Hat's proven platform technologies. Join us in this interactive 3-part webinar series, as we'll demonstrate how Red Hat JBoss Data Virtualization can integrate with Hadoop through Hive and provide users easy access to data.
In 2012, we released Hortonworks Data Platform powered by Apache Hadoop and established partnerships with major enterprise software vendors including Microsoft and Teradata that are making enterprise ready Hadoop easier and faster to consume. As we start 2013, we invite you to join us for this live webinar where Shaun Connolly, VP of Strategy at Hortonworks, will cover the highlights of 2012 and the road ahead in 2013 for Hortonworks and Apache Hadoop.
This document summarizes a webinar presented by Hortonworks and Sqrrl on using big data analytics for cybersecurity. It discusses how the growth of data sources and targeted attacks require new security approaches. A modern data architecture with Hadoop can provide a common platform to analyze all security-related data and gain new insights. Sqrrl's linked data model and analytics run on Hortonworks to help investigate security incidents like a network breach, mapping different data sources and identifying abnormal activity patterns.
Data Lake for the Cloud: Extending your Hadoop ImplementationHortonworks
As more applications are created using Apache Hadoop that derive value from the new types of data from sensors/machines, server logs, click-streams, and other sources, the enterprise "Data Lake" forms with Hadoop acting as a shared service. While these Data Lakes are important, a broader life-cycle needs to be considered that spans development, test, production, and archival and that is deployed across a hybrid cloud architecture.
If you have already deployed Hadoop on-premise, this session will also provide an overview of the key scenarios and benefits of joining your on-premise Hadoop implementation with the cloud, by doing backup/archive, dev/test or bursting. Learn how you can get the benefits of an on-premise Hadoop that can seamlessly scale with the power of the cloud.
Combine Apache Hadoop and Elasticsearch to Get the Most of Your Big DataHortonworks
Hadoop is a great platform for storing and processing massive amounts of data. Elasticsearch is the ideal solution for Searching and Visualizing the same data. Join us to learn how you can leverage the full power of both platforms to maximize the value of your Big Data.
In this webinar we'll walk you through:
How Elasticsearch fits in the Modern Data Architecture.
A demo of Elasticsearch and Hortonworks Data Platform.
Best practices for combining Elasticsearch and Hortonworks Data Platform to extract maximum insights from your data.
Discover Red Hat and Apache Hadoop for the Modern Data Architecture - Part 3Hortonworks
The document discusses using Hortonworks Data Platform (HDP) and Red Hat JBoss Data Virtualization to create a data lake solution and virtual data marts. It describes how a data lake enables storing all types of data in a single repository and accessing it through tools. Virtual data marts allow lines of business to access relevant data through self-service interfaces while maintaining governance and security over the central data lake. The presentation includes demonstrations of virtual data marts integrating data from Hadoop and other sources.
1) The webinar covered Apache Hadoop on the open cloud, focusing on key drivers for Hadoop adoption like new types of data and business applications.
2) Requirements for enterprise Hadoop include core services, interoperability, enterprise readiness, and leveraging existing skills in development, operations, and analytics.
3) The webinar demonstrated Hortonworks Apache Hadoop running on Rackspace's Cloud Big Data Platform, which is built on OpenStack for security, optimization, and an open platform.
Stinger.Next by Alan Gates of HortonworksData Con LA
The document discusses Hortonworks' Stinger initiative to deliver interactive SQL query capabilities in Hadoop. Stinger aims to improve Hive query performance by 100x to enable interactive query times through optimizations like SQL types, analytic functions, and the ORC file format (Phase 1). Future phases will integrate Hive with Apache Tez and introduce a new low-latency execution engine called LLAP to enable sub-second queries (Phase 2-3). The document provides details on various Stinger phases, optimizations, and capabilities to support a wider range of SQL semantics and use cases.
Discover HDP2.1: Apache Storm for Stream Data Processing in HadoopHortonworks
For the first time, Hortonworks Data Platform ships with Apache Storm for processing stream data in Hadoop.
In this presentation, Himanshu Bari, Hortonworks senior product manager, and Taylor Goetz, Hortonworks engineer and committer to Apache Storm, cover Storm and stream processing in HDP 2.1:
+ Key requirements of a streaming solution and common use cases
+ An overview of Apache Storm
+ Q & A
Hortonworks Data in Motion Webinar Series Part 7 Apache Kafka Nifi Better Tog...Hortonworks
Apache NiFi, Storm and Kafka augment each other in modern enterprise architectures. NiFi provides a coding free solution to get many different formats and protocols in and out of Kafka and compliments Kafka with full audit trails and interactive command and control. Storm compliments NiFi with the capability to handle complex event processing.
Join us to learn how Apache NiFi, Storm and Kafka can augment each other for creating a new dataplane connecting multiple systems within your enterprise with ease, speed and increased productivity.
https://www.brighttalk.com/webcast/9573/224063
Kafka and Storm - event processing in realtimeGuido Schmutz
Apache Kafka is publish-subscribe messaging rethought as a distributed commit log. It is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Storm is a distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. This session presents the main concepts of Kafka and Storm and then shows how a simple stream processing application is implemented using these two technologies.
MiNiFi is a recently started sub-project of Apache NiFi that is a complementary data collection approach which supplements the core tenets of NiFi in dataflow management, focusing on the collection of data at the source of its creation. Simply, MiNiFi agents take the guiding principles of NiFi and pushes them to the edge in a purpose built design and deploy manner. This talk will focus on MiNiFi's features, go over recent developments and prospective plans, and give a live demo of MiNiFi.
The config.yml is available here: https://gist.github.com/JPercivall/f337b8abdc9019cab5ff06cb7f6ff09a
This document provides an overview of resource aware scheduling in Apache Storm. It discusses the challenges of scheduling Storm topologies at Yahoo scale, including increasing heterogeneous clusters, low cluster utilization, and unbalanced resource usage. It then introduces the Resource Aware Scheduler (RAS) built for Storm, which allows fine-grained resource control and isolation for topologies through APIs and cgroups. Key features of RAS include pluggable scheduling strategies, per user resource guarantees, and topology priorities. Experimental results from Yahoo Storm clusters show significant improvements to throughput and resource utilization with RAS. The talk concludes with future work on improved scheduling strategies and real-time resource monitoring.
Storm: distributed and fault-tolerant realtime computationnathanmarz
Storm is a distributed real-time computation system that provides guaranteed message processing, horizontal scalability, and fault tolerance. It allows users to define data processing topologies and submit them to a Storm cluster for distributed execution. Spouts emit streams of tuples that are processed by bolts. Storm tracks processing to ensure reliability and replays failed tasks. It provides tools for deployment, monitoring, and optimization of real-time data processing.
Apache Storm 0.9 basic training - VerisignMichael Noll
Apache Storm 0.9 basic training (130 slides) covering:
1. Introducing Storm: history, Storm adoption in the industry, why Storm
2. Storm core concepts: topology, data model, spouts and bolts, groupings, parallelism
3. Operating Storm: architecture, hardware specs, deploying, monitoring
4. Developing Storm apps: Hello World, creating a bolt, creating a topology, running a topology, integrating Storm and Kafka, testing, data serialization in Storm, example apps, performance and scalability tuning
5. Playing with Storm using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/09/15/apache-storm-training-deck-and-tutorial/
Many thanks to the Twitter Engineering team (the creators of Storm) and the Apache Storm open source community!
Hadoop Summit Europe 2014: Apache Storm ArchitectureP. Taylor Goetz
Storm is an open-source distributed real-time computation system. It uses a distributed messaging system to reliably process streams of data. The core abstractions in Storm are spouts, which are sources of streams, and bolts, which are basic processing elements. Spouts and bolts are organized into topologies which represent the flow of data. Storm provides fault tolerance through message acknowledgments and guarantees exactly-once processing semantics. Trident is a high-level abstraction built on Storm that supports operations like aggregations, joins, and state management through its micro-batch oriented and stream-based API.
Apache Ambari is a single framework for IT administrators to provision, manage and monitor a Hadoop cluster. Apache Ambari 1.7.0 is included with Hortonworks Data Platform 2.2.
In this 30-minute webinar, Hortonworks Product Manager Jeff Sposetti and Apache Ambari committer Mahadev Konar discussed new capabilities including:
Improvements to Ambari core - such as support for ResourceManager HA
Extensions to Ambari platform - introducing Ambari Administration and Ambari Views
Enhancements to Ambari Stacks - dynamic configuration recommendations and validations via a "Stack Advisor"
This document discusses anomaly detection in streaming data using Hierarchical Temporal Memory (HTM). It describes how HTM can be used to build a real-time anomaly detection system that continuously learns and predicts patterns in streaming data. It also introduces the Numenta Anomaly Benchmark (NAB), an open benchmark for evaluating streaming anomaly detection algorithms that contains labeled real-world data streams and a scoring system that rewards early detection of anomalies. Several state-of-the-art algorithms are shown to perform well on NAB, including HTM, and a competition is announced to expand NAB with more data and algorithms.
A data stream is a process that reads or sends data from/to one resource or location. There are two types of streams: byte streams for binary data and character streams for Unicode text data. Java's IO package provides classes for input and output streams to read from and write to various sources like files, networks, and memory. Reader and Writer classes handle text data while InputStream and OutputStream classes handle bytes. Buffered streams improve performance by using buffering. To read/write files, FileReader/FileWriter or FileInputStream/FileOutputStream are used.
Big Data and Stream Data Analysis at Politecnico di MilanoMarco Brambilla
This document discusses the work of Marco Brambilla and Emanuele Della Valle in analyzing big data. They are professors who specialize in data science, social media analysis, and stream computing. Their work involves collecting and fusing data from various sources, analyzing it using stream reasoning to gain real-time insights, and applying these insights in domains like smart cities, contact centers, and oil operations.
1) The document discusses algorithms for computing statistics like minimum, maximum, average over data streams using limited memory in a single pass. It covers algorithms for computing cardinality, heavy hitters, order statistics and histograms.
2) Cardinality can be estimated using the Flajolet-Martin algorithm which tracks the position of the rightmost zero bit in a bitmap. Heavy hitters can be found using the Count-Min sketch. Order statistics like the median can be approximated using the Frugal and T-Digest algorithms. Wavelet-based approaches can be used to compute histograms over data streams.
3) The document provides high-level explanations of these streaming algorithms along with references for further reading, but does not
An overview of streaming algorithms: what they are, what the general principles regarding them are, and how they fit into a big data architecture. Also four specific examples of streaming algorithms and use-cases.
Data Stream Outlier Detection Algorithm Hamza Aslam
This document presents a new data stream outlier detection algorithm called SODRNN, which is based on reverse k nearest neighbors. It uses a sliding window model to detect anomalies in the current window by performing outlier queries. The algorithm consists of a Stream Manager procedure that efficiently updates the window with insertions and deletions by scanning the window only once. It also includes a Query Manager procedure that can detect concept drift. Experimental results on both synthetic and real datasets show that SODRNN is effective and efficient at detecting outliers in data streams.
Advanced Threat Detection on Streaming DataCarol McDonald
The document discusses using a stream processing architecture to enable real-time detection of advanced threats from large volumes of streaming data. The solution ingests data using fast distributed messaging like Kafka or MapR Streams. Complex event processing with Storm and Esper is used to detect patterns. Data is stored in scalable NoSQL databases like HBase and analyzed using machine learning. The parallelized, partitioned architecture allows for high performance and scalability.
These slides to the Discover HDP 2.2 Webinar Series: Data Storage Innovations in HDFS explore Heterogeneous storage, Data Encryption and Operational security.
Discover HDP 2.2: Comprehensive Hadoop Security with Apache Ranger and Apache...Hortonworks
The document discusses security enhancements in Hortonworks Data Platform (HDP) 2.2, including centralized security with Apache Ranger and API security with Apache Knox. It provides an overview of Ranger's ability to centrally administer, authorize, and audit access across Hadoop. Knox is described as securing access to Hadoop APIs from various devices and applications by acting as a gateway. The document highlights new features for Ranger and Knox in HDP 2.2 such as deeper integration with Hadoop components and Ambari management capabilities.
Hortonworks Data Platform 2.2 includes Apache HBase for fast NoSQL data access. In this 30-minute webinar, we discussed HBase innovations that are included in HDP 2.2, including: support for Apache Slider; Apache HBase high availability (HA); block ache compression; and wire-level encryption.
This document summarizes Hortonworks' Hadoop distribution called Hortonworks Data Platform (HDP). It discusses how HDP provides a comprehensive data management platform built around Apache Hadoop and YARN. HDP includes tools for storage, processing, security, operations and accessing data through batch, interactive and real-time methods. The document also outlines new capabilities in HDP 2.2 like improved engines for SQL, Spark and streaming and expanded deployment options.
This document discusses real-time analytics using Hadoop. It provides an overview of Hadoop and its components HDFS and YARN. It then describes how Hadoop can be used for real-time analytics through an example of analyzing truck driving data with Kafka, Storm and other tools. The document concludes with a demonstration of the real-time truck driving analytics architecture and application.
The document discusses Hadoop and its capabilities for real-time analytics. It describes how Hadoop provides massive storage and parallel processing. It also explains the key components of Hadoop including HDFS for storage and YARN as the data operating system. The document outlines use cases for real-time analytics in various industries and demonstrates a real-time analytics application for monitoring truck driver behavior.
The document discusses how Hadoop can be used for interactive and real-time data analysis. It notes that the amount of digital data is growing exponentially and will reach 40 zettabytes by 2020. Traditional data systems are struggling to manage this new data. Hadoop provides a solution by tying together inexpensive servers to act as one large computer for processing big data using various Apache projects for data access, governance, security and operations. Examples show how Hadoop can be used to analyze real-time streaming data from sensors on trucks to monitor routes, vehicles and drivers.
Azure Cafe Marketplace with Hortonworks March 31 2016Joan Novino
Azure Big Data: “Got Data? Go Modern and Monetize”.
In this session you will learn how to architected, developed, and build completely in the open, Hortonworks Data Platform (HDP) that provides an enterprise ready data platform to adopt a Modern Data Architecture.
Mr. Slim Baltagi is a Systems Architect at Hortonworks, with over 4 years of Hadoop experience working on 9 Big Data projects: Advanced Customer Analytics, Supply Chain Analytics, Medical Coverage Discovery, Payment Plan Recommender, Research Driven Call List for Sales, Prime Reporting Platform, Customer Hub, Telematics, Historical Data Platform; with Fortune 100 clients and global companies from Financial Services, Insurance, Healthcare and Retail.
Mr. Slim Baltagi has worked in various architecture, design, development and consulting roles at.
Accenture, CME Group, TransUnion, Syntel, Allstate, TransAmerica, Credit Suisse, Chicago Board Options Exchange, Federal Reserve Bank of Chicago, CNA, Sears, USG, ACNielsen, Deutshe Bahn.
Mr. Baltagi has also over 14 years of IT experience with an emphasis on full life cycle development of Enterprise Web applications using Java and Open-Source software. He holds a master’s degree in mathematics and is an ABD in computer science from Université Laval, Québec, Canada.
Languages: Java, Python, JRuby, JEE , PHP, SQL, HTML, XML, XSLT, XQuery, JavaScript, UML, JSON
Databases: Oracle, MS SQL Server, MYSQL, PostreSQL
Software: Eclipse, IBM RAD, JUnit, JMeter, YourKit, PVCS, CVS, UltraEdit, Toad, ClearCase, Maven, iText, Visio, Japser Reports, Alfresco, Yslow, Terracotta, Toad, SoapUI, Dozer, Sonar, Git
Frameworks: Spring, Struts, AppFuse, SiteMesh, Tiles, Hibernate, Axis, Selenium RC, DWR Ajax , Xstream
Distributed Computing/Big Data: Hadoop, MapReduce, HDFS, Hive, Pig, Sqoop, HBase, R, RHadoop, Cloudera CDH4, MapR M7, Hortonworks HDP 2.1
Part of the core Hadoop project, YARN is the architectural center of Hadoop that allows multiple data processing engines such as interactive SQL, real-time streaming, data science and batch processing to handle data stored in a single platform, unlocking an entirely new approach to analytics. It is the foundation of the new generation of Hadoop and is enabling organizations everywhere to realize a Modern Data Architecture.
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.
Supporting Financial Services with a More Flexible Approach to Big DataWANdisco Plc
In this webinar, WANdisco and Hortonworks look at three examples of using 'Big Data' to get a more comprehensive view of customer behavior and activity in the banking and insurance industries. Then we'll pull out the common threads from these examples, and see how a flexible next-generation Hadoop architecture lets you get a step up on improving your business performance. Join us to learn:
- How to leverage data from across an entire global enterprise
- How to analyze a wide variety of structured and unstructured data to get quick, meaningful answers to critical questions
- What industry leaders have put in place
Hadoop - Looking to the Future By Arun Murthyhuguk
Hadoop - Looking to the Future
By Arun Murthy (Founder of Hortonworks, Creator of YARN)
The Apache Hadoop ecosystem began as just HDFS & MapReduce nearly 10 years ago in 2006.
Very much like the Ship of Theseus (http://en.wikipedia.org/wiki/Ship_of_Theseus), Hadoop has undergone incredible amount of transformation from multi-purpose YARN to interactive SQL with Hive/Tez to machine learning with Spark.
Much more lies ahead: whether you want sub-second SQL with Hive or use SSDs/Memory effectively in HDFS or manage Metadata-driven security policies in Ranger, the Hadoop ecosystem in the Apache Software Foundation continues to evolve to meet new challenges and use-cases.
Arun C Murthy has been involved with Apache Hadoop since the beginning of the project - nearly 10 years now. In the beginning he led MapReduce, went on to create YARN and then drove Tez & the Stinger effort to get to interactive & sub-second Hive. Recently he has been very involved in the Metadata and Governance efforts. In between he founded Hortonworks, the first public Hadoop distribution company.
The Future of Hadoop by Arun Murthy, PMC Apache Hadoop & Cofounder HortonworksData Con LA
Arun Murthy will be discussing the future of Hadoop and the next steps in what the big data world would start to look like in the future. With the advent of tools like Spark and Flink and containerization of apps using Docker, there is a lot of momentum currently in this space. Arun will share his thoughts and ideas on what the future holds for us.
Bio:-
Arun C. Murthy
Arun is a Apache Hadoop PMC member and has been a full time contributor to the project since the inception in 2006. He is also the lead of the MapReduce project and has focused on building NextGen MapReduce (YARN). Prior to co-founding Hortonworks, Arun was responsible for all MapReduce code and configuration deployed across the 42,000+ servers at Yahoo!. In essence, he was responsible for running Apache Hadoop’s MapReduce as a service for Yahoo!. Also, he jointly holds the current world sorting record using Apache Hadoop. Follow Arun on Twitter: @acmurthy.
Hortonworks provides an open source Apache Hadoop distribution called Hortonworks Data Platform (HDP). Their mission is to enable modern data architectures through delivering enterprise Apache Hadoop. They have over 300 employees and are headquartered in Palo Alto, CA. Hortonworks focuses on driving innovation through the open source Apache community process, integrating Hadoop with existing technologies, and engineering Hadoop for enterprise reliability and support.
Hortonworks and Red Hat Webinar - Part 2Hortonworks
Learn more about creating reference architectures that optimize the delivery the Hortonworks Data Platform. You will hear more about Hive, JBoss Data Virtualization Security, and you will also see in action how to combine sentiment data from Hadoop with data from traditional relational sources.
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https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
IoT Predictions for 2019 and Beyond: Data at the Heart of Your IoT StrategyHortonworks
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But successful IoT implementations require far more than simply connecting sensors to a network. The data generated by these devices must be collected, aggregated, cleaned, processed, interpreted, understood, and used. Data-driven decisions and actions must be taken, without which an IoT implementation is bound to fail.
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https://hortonworks.com/webinar/johns-hopkins-using-hadoop-securely-access-log-events/
Catch a Hacker in Real-Time: Live Visuals of Bots and Bad GuysHortonworks
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https://hortonworks.com/webinar/catch-hacker-real-time-live-visuals-bots-bad-guys/
We have introduced several new features as well as delivered some significant updates to keep the platform tightly integrated and compatible with HDP 3.0.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-2-release-raises-bar-operational-efficiency/
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With the growth of Apache Kafka adoption in all major streaming initiatives across large organizations, the operational and visibility challenges associated with Kafka are on the rise as well. Kafka users want better visibility in understanding what is going on in the clusters as well as within the stream flows across producers, topics, brokers, and consumers.
With no tools in the market that readily address the challenges of the Kafka Ops teams, the development teams, and the security/governance teams, Hortonworks Streams Messaging Manager is a game-changer.
https://hortonworks.com/webinar/curing-kafka-blindness-hortonworks-streams-messaging-manager/
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Watch the webinar: https://hortonworks.com/webinar/interpretation-tool-genomic-sequencing-data-clinical-environments/
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View the webinar here: https://hortonworks.com/webinar/ibmhortonworks-transformation-big-data-landscape/
The document provides an overview of Apache Druid, an open-source distributed real-time analytics database. It discusses Druid's architecture including segments, indexing, and nodes like brokers, historians and coordinators. It also covers integrating Druid with Hortonworks Data Platform for unified querying and visualization of streaming and historical data.
Accelerating Data Science and Real Time Analytics at ScaleHortonworks
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Blockchain with Machine Learning Powered by Big Data: Trimble Transportation ...Hortonworks
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Making Enterprise Big Data Small with EaseHortonworks
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Presented at Gartner Data and Analytics Summit
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HDF 3.1 pt. 2: A Technical Deep-Dive on New Streaming FeaturesHortonworks
Hortonworks DataFlow (HDF) is the complete solution that addresses the most complex streaming architectures of today’s enterprises. More than 20 billion IoT devices are active on the planet today and thousands of use cases across IIOT, Healthcare and Manufacturing warrant capturing data-in-motion and delivering actionable intelligence right NOW. “Data decay” happens in a matter of seconds in today’s digital enterprises.
To meet all the needs of such fast-moving businesses, we have made significant enhancements and new streaming features in HDF 3.1.
https://hortonworks.com/webinar/series-hdf-3-1-technical-deep-dive-new-streaming-features/
Hortonworks DataFlow (HDF) 3.1 - Redefining Data-In-Motion with Modern Data A...Hortonworks
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You will learn about the three main themes that HDF addresses:
Developer productivity
Operational efficiency
Platform interoperability
https://hortonworks.com/webinar/series-hdf-3-1-redefining-data-motion-modern-data-architectures/
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A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.
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For more info visit us https://valintry360.com/solutions/health-life-sciences
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Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
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