This document provides an overview of Hortonworks and Hadoop. It discusses Hortonworks' customer momentum, the Hortonworks Data Platform (HDP) which provides a multi-tenant platform for any application and data, and Hortonworks' focus on customer success through its open source community leadership and support. It also discusses how Hadoop has emerged as the foundation for a modern data architecture to unify data processing and analytics for both traditional and new data sources in order to drive business value.
Hortonworks DataFlow & Apache Nifi @Oslo Hadoop Big DataMats Johansson
This document provides an overview of Hortonworks DataFlow, which is powered by Apache NiFi. It discusses how the growth of IoT data is outpacing our ability to consume it and how NiFi addresses the new requirements around collecting, securing and analyzing data in motion. Key features of NiFi are highlighted such as guaranteed delivery, data provenance, and its ability to securely manage bidirectional data flows in real-time. Common use cases like predictive analytics, compliance and IoT optimization are also summarized.
Hortonworks Data In Motion Series Part 4Hortonworks
How real-world enterprises leverage Hortonworks DataFlow/Apache NiFi to to create real-time data flows in record time to enable new business opportunities, improve customer retention, accelerate big data projects from months to minutes through increased efficiency and reduced costs.
On-Demand webinar: http://hortonworks.com/webinar/paradigm-shift-business-usual-real-time-dataflows-record-time/
Dynamic Column Masking and Row-Level Filtering in HDPHortonworks
As enterprises around the world bring more of their sensitive data into Hadoop data lakes, balancing the need for democratization of access to data without sacrificing strong security principles becomes paramount. In this webinar, Srikanth Venkat, director of product management for security & governance will demonstrate two new data protection capabilities in Apache Ranger – dynamic column masking and row level filtering of data stored in Apache Hive. These features have been introduced as part of HDP 2.5 platform release.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Running Enterprise Workloads with an open source Hybrid Cloud Data Architectu...DataWorks Summit
Cloud accelerates corporate IT landscapes with agility and flexibility. Today, discussion of cloud architecture dominates corporate IT. The cloud enables a number of temporary on-demand use cases that revolutionize analytical workload opportunities. But all of this involves the task of running corporate workloads safely and easily in the cloud.
With the convergence of cloud, IoT, and big data technology, enterprises are increasingly using multiple on-premises Data Lake and multiple Public on different geographies, for example due to regulations and compliance requirements restricting cross- It now distributes data to the cloud Data Lake store of the cloud vendor platform. Diffusion of data types and sources in this complex landscape makes the discovery process, provisioning, and getting insight by performing the appropriate workload on this data more complicated. In addition, to obtain business context, usage, and visibility of data trustworthiness worldwide, it is necessary to display all data and metadata, security management, data access, and monitoring in a centralized way .
All these problems create cracks during the creation of data insights to promote initial data capture and subsequent value creation. As a result, companies now look for compromises between appropriate rules and data control policies while providing a trusted environment that allows them to share data and partner with users responsibly to create value We need "Global Insight Fabric".
In this talk, how the Hortonworks DataPlane Service (DPS) analyzes the data in the data center to expand the storage, implement the open source hybrid architecture utilizing cloud flexibility and new use cases, global in Describes how site fabrics can help customers create. Securely migrate data from on-premises data centers to multiple public clouds, protect the data with replication, then apply consistent safety and governance policies to a wide variety of environments to ensure trustworthy data and inn We provide personal views on the challenges we face in providing the site to the business. I will explain how the DetaPlane service can be useful for traveling to this hybrid architecture and how the open source architecture enables the transformation of the entire enterprise.
Apache NiFi - Flow Based Programming MeetupJoseph Witt
These are the slides from the July 11th Meetup in Toronto for the Flow Based Programming meetup group at Lighthouse covering Enterprise Dataflow with Apache NiFi.
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 & Apache Nifi @Oslo Hadoop Big DataMats Johansson
This document provides an overview of Hortonworks DataFlow, which is powered by Apache NiFi. It discusses how the growth of IoT data is outpacing our ability to consume it and how NiFi addresses the new requirements around collecting, securing and analyzing data in motion. Key features of NiFi are highlighted such as guaranteed delivery, data provenance, and its ability to securely manage bidirectional data flows in real-time. Common use cases like predictive analytics, compliance and IoT optimization are also summarized.
Hortonworks Data In Motion Series Part 4Hortonworks
How real-world enterprises leverage Hortonworks DataFlow/Apache NiFi to to create real-time data flows in record time to enable new business opportunities, improve customer retention, accelerate big data projects from months to minutes through increased efficiency and reduced costs.
On-Demand webinar: http://hortonworks.com/webinar/paradigm-shift-business-usual-real-time-dataflows-record-time/
Dynamic Column Masking and Row-Level Filtering in HDPHortonworks
As enterprises around the world bring more of their sensitive data into Hadoop data lakes, balancing the need for democratization of access to data without sacrificing strong security principles becomes paramount. In this webinar, Srikanth Venkat, director of product management for security & governance will demonstrate two new data protection capabilities in Apache Ranger – dynamic column masking and row level filtering of data stored in Apache Hive. These features have been introduced as part of HDP 2.5 platform release.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Running Enterprise Workloads with an open source Hybrid Cloud Data Architectu...DataWorks Summit
Cloud accelerates corporate IT landscapes with agility and flexibility. Today, discussion of cloud architecture dominates corporate IT. The cloud enables a number of temporary on-demand use cases that revolutionize analytical workload opportunities. But all of this involves the task of running corporate workloads safely and easily in the cloud.
With the convergence of cloud, IoT, and big data technology, enterprises are increasingly using multiple on-premises Data Lake and multiple Public on different geographies, for example due to regulations and compliance requirements restricting cross- It now distributes data to the cloud Data Lake store of the cloud vendor platform. Diffusion of data types and sources in this complex landscape makes the discovery process, provisioning, and getting insight by performing the appropriate workload on this data more complicated. In addition, to obtain business context, usage, and visibility of data trustworthiness worldwide, it is necessary to display all data and metadata, security management, data access, and monitoring in a centralized way .
All these problems create cracks during the creation of data insights to promote initial data capture and subsequent value creation. As a result, companies now look for compromises between appropriate rules and data control policies while providing a trusted environment that allows them to share data and partner with users responsibly to create value We need "Global Insight Fabric".
In this talk, how the Hortonworks DataPlane Service (DPS) analyzes the data in the data center to expand the storage, implement the open source hybrid architecture utilizing cloud flexibility and new use cases, global in Describes how site fabrics can help customers create. Securely migrate data from on-premises data centers to multiple public clouds, protect the data with replication, then apply consistent safety and governance policies to a wide variety of environments to ensure trustworthy data and inn We provide personal views on the challenges we face in providing the site to the business. I will explain how the DetaPlane service can be useful for traveling to this hybrid architecture and how the open source architecture enables the transformation of the entire enterprise.
Apache NiFi - Flow Based Programming MeetupJoseph Witt
These are the slides from the July 11th Meetup in Toronto for the Flow Based Programming meetup group at Lighthouse covering Enterprise Dataflow with Apache NiFi.
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/
The document discusses how big data and analytics can help manufacturers address challenges in quality, maintenance and optimization. It provides examples of companies in various industries like pharmaceuticals, oil and gas, and automotive that have implemented data lakes and analytics platforms using Hortonworks to improve yield, reduce downtime and costs, and gain insights across production and operations. The document outlines the road to maturity with big data and how open source platforms can help manufacturers address data challenges for Industry 4.0 initiatives.
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
The document discusses Apache NiFi and streaming change data capture (CDC) with Attunity Replicate. It provides an overview of NiFi's capabilities for dataflow management and visualization. It then demonstrates how Attunity Replicate can be used for real-time CDC to capture changes from source databases and deliver them to NiFi for further processing, enabling use cases across multiple industries. Examples of source systems include SAP, Oracle, SQL Server, and file data, with targets including Hadoop, data warehouses, and cloud data stores.
Running Enterprise Workloads with an open source Hybrid Cloud Data ArchitectureDataWorks Summit
The document discusses Hortonworks DataPlane Service (DPS), a platform that provides consistent security, governance, and management of data across hybrid cloud environments. Key capabilities of DPS include data lifecycle management using Data Lifecycle Manager (DLM), data discovery and profiling through Data Steward Studio (DSS), and self-service analytics with Data Analytics Studio (DAS). DPS provides a global data fabric to address challenges of securing, governing, and delivering data across multiple data sources and locations.
Pivotal - Advanced Analytics for Telecommunications Hortonworks
Innovative mobile operators need to mine the vast troves of unstructured data now available to them to help develop compelling customer experiences and uncover new revenue opportunities. In this webinar, you’ll learn how HDB’s in-database analytics enable advanced use cases in network operations, customer care, and marketing for better customer experience. Join us, and get started on your advanced analytics journey today!
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
4 Essential Steps for Managing Sensitive DataHortonworks
Data is growing in data lakes, so are security and compliance risks. These risks stem from storing and processing sensitive data. In this webinar, we will go through a 4 step process to proactively discover and manage sensitive data within big data environments.
https://hortonworks.com/webinar/4-essential-steps-managing-sensitive-data-data-lake/
Simplify and Secure your Hadoop Environment with Hortonworks and CentrifyHortonworks
Join this webinar to explore Hadoop security challenges and trends, learn how to simply the connection of your Hortonworks Data Platform to your existing Active Directory infrastructure and hear about real world examples of organizations that are achieving the following benefits:
- Secured Hortonworks environments thanks to Active Directory infrastructure for identity and authentication.
- Increased productivity and security via single sign-on for IT admins and Hadoop users.
- Least privilege and session monitoring for privileged access to Hortonworks clusters.
Webinar URL: http://hortonworks.com/webinar/simplify-and-secure-your-hadoop-environment-with-hortonworks-and-centrify/
Overcoming the AI hype — and what enterprises should really focus onDataWorks Summit
Deep learning for all its hype is brittle, non-generalizeable, and its learnings are not readily transferable from one application to another. Since we are unlikely to see anything close to artificial general intelligence in the next few decades., we should instead focus on how enterprises can capitalize on the state of the art in machine learning and re-implement successful algorithms and follow the data science lifecycles that generate highest ROI.
This talk will cover the current state of the art in AI, its limits vs. hype, and discuss concrete steps that enterprises can take to achieve desired ROI by re-implementing production-grade-ready machine learning algorithms, that have been hardened and demonstrated to work very well in specific, constrained domains.
By the end of this talk, attendees should have a better grasp on how to avoid costly and unnecessary investments into yet unproven technologies, be better equipped to navigate the complex space of AI, and understand where to best focus their resources to maximize ROI. ROBERT HRYNIEWICZ, Technical Evangelist, Hortonworks
Hortonworks Data in Motion Webinar Series - Part 1Hortonworks
VIEW THE ON-DEMAND WEBINAR: http://hortonworks.com/webinar/introduction-hortonworks-dataflow/
Learn about Hortonworks DataFlow (HDFTM) and how you can easily augment your existing data systems – Hadoop and otherwise. Learn what Dataflow is all about and how Apache NiFi, MiNiFi, Kafka and Storm work together for streaming analytics.
Eliminating the Challenges of Big Data Management Inside HadoopHortonworks
Your Big Data strategy is only as good as the quality of your data. Today, deriving business value from data depends on how well your company can capture, cleanse, integrate and manage data. During this webinar, we discussed how to eliminate the challenges to Big Data management inside Hadoop.
Go over these slides to learn:
· How to use the scalability and flexibility of Hadoop to drive faster access to usable information across the enterprise.
· Why a pure-YARN implementation for data integration, quality and management delivers competitive advantage.
· How to use the flexibility of RedPoint and Hortonworks to create an enterprise data lake where data is captured, cleansed, linked and structured in a consistent way.
10 Lessons Learned from Meeting with 150 Banks Across the GlobeDataWorks Summit
This document summarizes 10 practical lessons learned from companies about their big data and analytics journeys:
1. There are clear leaders in each market who are gaining substantial benefits from using big data and machine learning, widening the gap with other companies.
2. Real transformation requires buy-in from top executives, as reflected by new innovation centers, roles, and organizations.
3. Projects should have clear revenue impact objectives and be selected based on estimated return, with pre- and post-implementation measurements.
4. While cost reduction brings the fastest ROI, new revenue opportunities can transform a business more lastingly if the projects address real customer and business needs.
Spark and Hadoop are perfectly together. Spark is a key tool in Hadoop's toolbox that provides elegant developer APIs and accelerates data science and machine learning. It can process streaming data in real-time for applications like web analytics and insurance claims processing. The future of Spark and Hadoop includes innovating the core technologies, providing seamless data access across data platforms, and further accelerating data science tools and libraries.
This document provides an overview of Apache NiFi and data flow fundamentals. It begins with an introduction to Apache NiFi and outlines the agenda. It then discusses data flow and streaming fundamentals, including challenges in moving data effectively. The document introduces Apache NiFi's architecture and capabilities for addressing these challenges. It also previews a live demo of NiFi and discusses the NiFi community.
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next LevelHortonworks
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
How is it that one system can query terabytes of data, yet still provide interactive query support? This talk will discuss two of the underlying technologies that allow Apache Hive to support fast query response, both on-premise in HDFS and in cloud object stores such as S3 and WASB.
LLAP was introduced in Hive 2.6. It provides standing processes that securely cache Hive’s columnar data and can do query processing without ever needing to start tasks in Hadoop. We will cover LLAP’s architecture, intended uses cases, and performance numbers for both on-premise and in the cloud.
The second technology is the integration of Hive with Apache Druid. Druid excels at low-latency, interactive queries over streaming data. Its method of storing data makes it very well suited for OLAP style queries. We will cover how Hive can be integrated with Druid to support real-time streaming of data from Kafka and OLAP queries.
With the rise of IoT and complexity of applications, clouds, networks and infrastructure, it is becoming more difficult to protect data and infrastructure from attackers. When groups of bad actors collaborate, share information, provide unauthorized access, and do botnet as a service, attacks in terabit units also start easily. On the other hand, it is also difficult to find enough security analysts to deal with and defend against such attacks.
Here is the emergence of community cooperation like Apache Metron and efforts to open source. Metron provides a comprehensive framework for applications, networks and security built on Apache Hadoop and open source streaming analysis (eg Apache Nifi, Apache Kafka) tools in scalable data management and processing stacks. Extensions such as profiling, machine learning, and visualization work and real-time streaming detection make SOC analysts more efficient, while intrinsic scalability of open source gives data scientists security insight from data laboratories So that it can be quickly incorporated into production.
This section explains how real-world businesses and managed service providers use Apache Metron, identify and resolve security threats on a large scale, and explain methods and ideas for adapting the platform to your security architecture · I will demonstrate.
Introduction to Apache NiFi - Seattle Scalability MeetupSaptak Sen
The document introduces Apache NiFi, an open source tool for data flow. It discusses how data from the Internet of Things is growing faster than can be consumed and highlights Apache NiFi's ability to securely collect, process and distribute this data in motion. The key concepts of Apache NiFi are described as managing the flow of information, ensuring data provenance, and securing the control and data planes. Example use cases are provided and the document demonstrates Apache NiFi's visual interface for creating data flows between processors to ingest, transform and output data in real-time.
Hortonworks Data In Motion Webinar Series Pt. 2Hortonworks
This document discusses Hortonworks' HDF 2.0 platform for managing data in motion and at rest. The platform includes tools for data ingestion, streaming, and storage. It also allows partners to integrate their solutions and get certified. Use cases highlighted include log analytics, IoT, and connected vehicles. The ecosystem supports ingesting data from various sources and processing it using tools like NiFi, Kafka, and Storm.
HDF 3.1 : An Introduction to New FeaturesTimothy Spann
Hortonworks Data Flow 3.1 introduces new features to improve ease of use, stream processing, cross-product integration, and flow management. Key enhancements include NiFi registry for version control of flows, improved Kafka 1.0 support, and new processors for deeper ecosystem integration. HDF 3.1 provides tools for engineers to aggregate, mediate, and gain insights from data across multiple sources when deployed with Hortonworks Data Platform.
This document discusses a hybrid solution for analyzing streaming sensor data with Spark Streaming and Kafka. It provides an overview of the key technology components used in the solution, including Spark Streaming, Apache Kafka, IBM Bluemix, Node-RED and Secure Gateway. It then outlines a demo scenario where sensor data is streamed to Kafka from devices, processed with Spark Streaming to calculate averages, and visualized in Node-RED. The full presentation includes a live demo of this scenario.
The document discusses how big data and analytics can help manufacturers address challenges in quality, maintenance and optimization. It provides examples of companies in various industries like pharmaceuticals, oil and gas, and automotive that have implemented data lakes and analytics platforms using Hortonworks to improve yield, reduce downtime and costs, and gain insights across production and operations. The document outlines the road to maturity with big data and how open source platforms can help manufacturers address data challenges for Industry 4.0 initiatives.
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
The document discusses Apache NiFi and streaming change data capture (CDC) with Attunity Replicate. It provides an overview of NiFi's capabilities for dataflow management and visualization. It then demonstrates how Attunity Replicate can be used for real-time CDC to capture changes from source databases and deliver them to NiFi for further processing, enabling use cases across multiple industries. Examples of source systems include SAP, Oracle, SQL Server, and file data, with targets including Hadoop, data warehouses, and cloud data stores.
Running Enterprise Workloads with an open source Hybrid Cloud Data ArchitectureDataWorks Summit
The document discusses Hortonworks DataPlane Service (DPS), a platform that provides consistent security, governance, and management of data across hybrid cloud environments. Key capabilities of DPS include data lifecycle management using Data Lifecycle Manager (DLM), data discovery and profiling through Data Steward Studio (DSS), and self-service analytics with Data Analytics Studio (DAS). DPS provides a global data fabric to address challenges of securing, governing, and delivering data across multiple data sources and locations.
Pivotal - Advanced Analytics for Telecommunications Hortonworks
Innovative mobile operators need to mine the vast troves of unstructured data now available to them to help develop compelling customer experiences and uncover new revenue opportunities. In this webinar, you’ll learn how HDB’s in-database analytics enable advanced use cases in network operations, customer care, and marketing for better customer experience. Join us, and get started on your advanced analytics journey today!
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
4 Essential Steps for Managing Sensitive DataHortonworks
Data is growing in data lakes, so are security and compliance risks. These risks stem from storing and processing sensitive data. In this webinar, we will go through a 4 step process to proactively discover and manage sensitive data within big data environments.
https://hortonworks.com/webinar/4-essential-steps-managing-sensitive-data-data-lake/
Simplify and Secure your Hadoop Environment with Hortonworks and CentrifyHortonworks
Join this webinar to explore Hadoop security challenges and trends, learn how to simply the connection of your Hortonworks Data Platform to your existing Active Directory infrastructure and hear about real world examples of organizations that are achieving the following benefits:
- Secured Hortonworks environments thanks to Active Directory infrastructure for identity and authentication.
- Increased productivity and security via single sign-on for IT admins and Hadoop users.
- Least privilege and session monitoring for privileged access to Hortonworks clusters.
Webinar URL: http://hortonworks.com/webinar/simplify-and-secure-your-hadoop-environment-with-hortonworks-and-centrify/
Overcoming the AI hype — and what enterprises should really focus onDataWorks Summit
Deep learning for all its hype is brittle, non-generalizeable, and its learnings are not readily transferable from one application to another. Since we are unlikely to see anything close to artificial general intelligence in the next few decades., we should instead focus on how enterprises can capitalize on the state of the art in machine learning and re-implement successful algorithms and follow the data science lifecycles that generate highest ROI.
This talk will cover the current state of the art in AI, its limits vs. hype, and discuss concrete steps that enterprises can take to achieve desired ROI by re-implementing production-grade-ready machine learning algorithms, that have been hardened and demonstrated to work very well in specific, constrained domains.
By the end of this talk, attendees should have a better grasp on how to avoid costly and unnecessary investments into yet unproven technologies, be better equipped to navigate the complex space of AI, and understand where to best focus their resources to maximize ROI. ROBERT HRYNIEWICZ, Technical Evangelist, Hortonworks
Hortonworks Data in Motion Webinar Series - Part 1Hortonworks
VIEW THE ON-DEMAND WEBINAR: http://hortonworks.com/webinar/introduction-hortonworks-dataflow/
Learn about Hortonworks DataFlow (HDFTM) and how you can easily augment your existing data systems – Hadoop and otherwise. Learn what Dataflow is all about and how Apache NiFi, MiNiFi, Kafka and Storm work together for streaming analytics.
Eliminating the Challenges of Big Data Management Inside HadoopHortonworks
Your Big Data strategy is only as good as the quality of your data. Today, deriving business value from data depends on how well your company can capture, cleanse, integrate and manage data. During this webinar, we discussed how to eliminate the challenges to Big Data management inside Hadoop.
Go over these slides to learn:
· How to use the scalability and flexibility of Hadoop to drive faster access to usable information across the enterprise.
· Why a pure-YARN implementation for data integration, quality and management delivers competitive advantage.
· How to use the flexibility of RedPoint and Hortonworks to create an enterprise data lake where data is captured, cleansed, linked and structured in a consistent way.
10 Lessons Learned from Meeting with 150 Banks Across the GlobeDataWorks Summit
This document summarizes 10 practical lessons learned from companies about their big data and analytics journeys:
1. There are clear leaders in each market who are gaining substantial benefits from using big data and machine learning, widening the gap with other companies.
2. Real transformation requires buy-in from top executives, as reflected by new innovation centers, roles, and organizations.
3. Projects should have clear revenue impact objectives and be selected based on estimated return, with pre- and post-implementation measurements.
4. While cost reduction brings the fastest ROI, new revenue opportunities can transform a business more lastingly if the projects address real customer and business needs.
Spark and Hadoop are perfectly together. Spark is a key tool in Hadoop's toolbox that provides elegant developer APIs and accelerates data science and machine learning. It can process streaming data in real-time for applications like web analytics and insurance claims processing. The future of Spark and Hadoop includes innovating the core technologies, providing seamless data access across data platforms, and further accelerating data science tools and libraries.
This document provides an overview of Apache NiFi and data flow fundamentals. It begins with an introduction to Apache NiFi and outlines the agenda. It then discusses data flow and streaming fundamentals, including challenges in moving data effectively. The document introduces Apache NiFi's architecture and capabilities for addressing these challenges. It also previews a live demo of NiFi and discusses the NiFi community.
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next LevelHortonworks
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
How is it that one system can query terabytes of data, yet still provide interactive query support? This talk will discuss two of the underlying technologies that allow Apache Hive to support fast query response, both on-premise in HDFS and in cloud object stores such as S3 and WASB.
LLAP was introduced in Hive 2.6. It provides standing processes that securely cache Hive’s columnar data and can do query processing without ever needing to start tasks in Hadoop. We will cover LLAP’s architecture, intended uses cases, and performance numbers for both on-premise and in the cloud.
The second technology is the integration of Hive with Apache Druid. Druid excels at low-latency, interactive queries over streaming data. Its method of storing data makes it very well suited for OLAP style queries. We will cover how Hive can be integrated with Druid to support real-time streaming of data from Kafka and OLAP queries.
With the rise of IoT and complexity of applications, clouds, networks and infrastructure, it is becoming more difficult to protect data and infrastructure from attackers. When groups of bad actors collaborate, share information, provide unauthorized access, and do botnet as a service, attacks in terabit units also start easily. On the other hand, it is also difficult to find enough security analysts to deal with and defend against such attacks.
Here is the emergence of community cooperation like Apache Metron and efforts to open source. Metron provides a comprehensive framework for applications, networks and security built on Apache Hadoop and open source streaming analysis (eg Apache Nifi, Apache Kafka) tools in scalable data management and processing stacks. Extensions such as profiling, machine learning, and visualization work and real-time streaming detection make SOC analysts more efficient, while intrinsic scalability of open source gives data scientists security insight from data laboratories So that it can be quickly incorporated into production.
This section explains how real-world businesses and managed service providers use Apache Metron, identify and resolve security threats on a large scale, and explain methods and ideas for adapting the platform to your security architecture · I will demonstrate.
Introduction to Apache NiFi - Seattle Scalability MeetupSaptak Sen
The document introduces Apache NiFi, an open source tool for data flow. It discusses how data from the Internet of Things is growing faster than can be consumed and highlights Apache NiFi's ability to securely collect, process and distribute this data in motion. The key concepts of Apache NiFi are described as managing the flow of information, ensuring data provenance, and securing the control and data planes. Example use cases are provided and the document demonstrates Apache NiFi's visual interface for creating data flows between processors to ingest, transform and output data in real-time.
Hortonworks Data In Motion Webinar Series Pt. 2Hortonworks
This document discusses Hortonworks' HDF 2.0 platform for managing data in motion and at rest. The platform includes tools for data ingestion, streaming, and storage. It also allows partners to integrate their solutions and get certified. Use cases highlighted include log analytics, IoT, and connected vehicles. The ecosystem supports ingesting data from various sources and processing it using tools like NiFi, Kafka, and Storm.
HDF 3.1 : An Introduction to New FeaturesTimothy Spann
Hortonworks Data Flow 3.1 introduces new features to improve ease of use, stream processing, cross-product integration, and flow management. Key enhancements include NiFi registry for version control of flows, improved Kafka 1.0 support, and new processors for deeper ecosystem integration. HDF 3.1 provides tools for engineers to aggregate, mediate, and gain insights from data across multiple sources when deployed with Hortonworks Data Platform.
This document discusses a hybrid solution for analyzing streaming sensor data with Spark Streaming and Kafka. It provides an overview of the key technology components used in the solution, including Spark Streaming, Apache Kafka, IBM Bluemix, Node-RED and Secure Gateway. It then outlines a demo scenario where sensor data is streamed to Kafka from devices, processed with Spark Streaming to calculate averages, and visualized in Node-RED. The full presentation includes a live demo of this scenario.
The document discusses HDFS architecture and components. It describes how HDFS uses NameNodes and DataNodes to store and retrieve file data in a distributed manner across clusters. The NameNode manages the file system namespace and regulates access to files by clients. DataNodes store file data in blocks and replicate them for fault tolerance. The document outlines the write and read workflows in HDFS and how NameNodes and DataNodes work together to manage data storage and access.
The 1 Week Minimum Viable Product (MVP)Alexis Roqué
The document discusses different types of minimum viable products (MVPs) that can be used to validate ideas with users without extensive coding, design, or financial risk. It provides examples of low-fidelity MVPs like interviews, paper sketches, mockups, landing pages, and concierge MVPs. It also discusses higher-fidelity options like video and crash test MVPs, noting you can get user feedback without fully building the product. The overall process of creating a vision, running experiments, creating MVPs, and incorporating feedback is summarized.
Spark-Streaming-as-a-Service with Kafka and YARN: Spark Summit East talk by J...Spark Summit
Since April 2016, Spark-as-a-service has been available to researchers in Sweden from the Swedish ICT SICS Data Center at www.hops.site. Researchers work in an entirely UI-driven environment on a platform built with only open-source software.
Spark applications can be either deployed as jobs (batch or streaming) or written and run directly from Apache Zeppelin. Spark applications are run within a project on a YARN cluster with the novel property that Spark applications are metered and charged to projects. Projects are also securely isolated from each other and include support for project-specific Kafka topics. That is, Kafka topics are protected from access by users that are not members of the project. In this talk we will discuss the challenges in building multi-tenant Spark streaming applications on YARN that are metered and easy-to-debug. We show how we use the ELK stack (Elasticsearch, Logstash, and Kibana) for logging and debugging running Spark streaming applications, how we use Graphana and Graphite for monitoring Spark streaming applications, and how users can debug and optimize terminated Spark Streaming jobs using Dr Elephant. We will also discuss the experiences of our users (over 120 users as of Sept 2016): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and our novel solutions for helping researchers debug and optimize Spark applications.
To conclude, we will also give an overview on our course ID2223 on Large Scale Learning and Deep Learning, in which 60 students designed and ran SparkML applications on the platform.
Real time Analytics with Apache Kafka and Apache SparkRahul Jain
A presentation cum workshop on Real time Analytics with Apache Kafka and Apache Spark. Apache Kafka is a distributed publish-subscribe messaging while other side Spark Streaming brings Spark's language-integrated API to stream processing, allows to write streaming applications very quickly and easily. It supports both Java and Scala. In this workshop we are going to explore Apache Kafka, Zookeeper and Spark with a Web click streaming example using Spark Streaming. A clickstream is the recording of the parts of the screen a computer user clicks on while web browsing.
This document discusses two methods for integrating HDFS with other systems: NFS and WebHDFS. NFS allows browsing, downloading, and uploading files in HDFS by mounting HDFS as an NFS share. WebHDFS provides a REST API for HDFS operations over HTTP such as file metadata retrieval, reading/writing files, and file appends. The document provides examples of mounting HDFS using NFS and making HTTP requests to the WebHDFS API.
A Comprehensive Approach to Building your Big Data - with Cisco, Hortonworks ...Hortonworks
Companies in every industry look for ways to explore new data types and large data sets that were previously too big to capture, store and process. They need to unlock insights from data such as clickstream, geo-location, sensor, server log, social, text and video data. However, becoming a data-first enterprise comes with many challenges.
Join this webinar organized by three leaders in their respective fields and learn from our experts how you can accelerate the implementation of a scalable, cost-efficient and robust Big Data solution. Cisco, Hortonworks and Red Hat will explore how new data sets can enrich existing analytic applications with new perspectives and insights and how they can help you drive the creation of innovative new apps that provide new value to your business.
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.
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
Hortonworks & Bilot Data Driven Transformations with HadoopMats Johansson
- Traditional systems are under pressure due to their inability to manage new data sources and costly scaling. A modern data architecture using Apache Hadoop emerges to provide a centralized platform for all enterprise data and applications.
- Hortonworks Data Platform is powered by Apache Hadoop and provides a flexible, scalable platform for storing and processing all data types from any source and supports a variety of applications. It offers governance, security, and operations controls for enterprise data management.
This document provides an introduction to Hadoop and big data concepts. It discusses what big data is and how companies like Amazon and Netflix have seen returns on investment from applying data science to large amounts of data. It then covers Hadoop and HDFS, explaining what they are, their architecture, and common commands used to work with HDFS like put, get, ls, and cat. The document is an introductory presentation on big data and Hadoop.
Enterprise Apache Hadoop: State of the UnionHortonworks
So what's in store for 2014? This deck was from Shaun Connolly's (VP of Strategy, Hortonworks) State of the Union webinar.
In this deck, you'll find:
- Reflection on Enterprise Hadoop Market in 2013
- The latest releases and innovations within the open source community
- Highlights of what's in store for Apache Hadoop and Big Data in 2014
Eliminating the Challenges of Big Data Management Inside HadoopHortonworks
Your Big Data strategy is only as good as the quality of your data. Today, deriving business value from data depends on how well your company can capture, cleanse, integrate and manage data. During this webinar, we discuss how to eliminate the challenges to Big Data management inside Hadoop.
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
Hortonworks provides an overview of their Tez framework for improving Hadoop query processing. Tez aims to accelerate queries by expressing them as dataflow graphs that can be optimized, rather than relying solely on MapReduce. It also aims to empower users by allowing flexible definition of data pipelines and composition of inputs, processors, and outputs. Early results show a 100x speedup on benchmark queries compared to traditional MapReduce.
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...Hortonworks
The document discusses a Big Data Meetup organized by C-BAG (Chennai Big Data Analytic Group) on October 29, 2014 in Chennai. It provides details about two speakers, Dhruv Kumar from Concurrent Inc. and Vinay Shukla from Hortonworks, who will discuss reducing development time for production-grade Hadoop applications and Hortonworks' Hadoop platform respectively. The remainder of the document consists of presentation slides that cover topics including the modern data architecture with Hadoop, enterprise goals for data architecture, unlocking applications from new data types, and case studies.
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.
This document discusses how Hortonworks Data Platform (HDP) can enable enterprises to build a modern data architecture centered around Hadoop. It describes how HDP provides a centralized platform for managing all types of data at scale using technologies like YARN. Case studies are presented showing how companies have used HDP to optimize costs, develop new analytics applications, and work towards creating a unified "data lake". The document outlines the key components of HDP including its support for any application, any data, and deployment anywhere. It also highlights how partners extend HDP's capabilities and how Hortonworks provides enterprise-grade support.
Slides from the joint webinar. Learn how Pivotal HAWQ, one of the world’s most advanced enterprise SQL on Hadoop technology, coupled with the Hortonworks Data Platform, the only 100% open source Apache Hadoop data platform, can turbocharge your Data Science efforts.
Together, Pivotal HAWQ and the Hortonworks Data Platform provide businesses with a Modern Data Architecture for IT transformation.
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.
Learn how when an organizations combine HP and Vertica Analytics Platform and Hortonworks, they can quickly explore and analyze broad variety of data types to transform to actionable information that allows them to better understand how their customers and site visitors interact with their business, offline and online.
Real-Time Processing in Hadoop for IoT Use Cases - Phoenix HUGskumpf
The document discusses real-time processing in Hadoop using the Hortonworks Data Platform (HDP). It provides an overview of using HDP for real-time streaming analytics in a logistics scenario. Example applications and architectures are presented, including using Kafka for ingesting sensor data, Storm for stream processing, and HBase for real-time querying. Demos will also illustrate integrating predictive analytics into streaming scenarios.
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.
Bridging the Big Data Gap in the Software-Driven WorldCA Technologies
Implementing and managing a Big Data environment effectively requires essential efficiencies such as automation, performance monitoring and flexible infrastructure management. Discover new innovations that enable you to manage entire Big Data environments with unparalleled ease of use and clear enterprise visibility across a variety of data repositories.
To learn more about Mainframe solutions from CA Technologies, visit: http://bit.ly/1wbiPkl
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According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Codeless Generative AI Pipelines
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https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627