InfoSphere Streams toolkits :Real-Time Analytics on Data in MotionAvadhoot Patwardhan
InfoSphere Streams comes standard with several real-time analytic toolkits to help provide quicker time to value. These include telecommunications event data, time series, text, messaging, database, geospatial, and more. Many of these toolkits are part of the InfoSphere Streams Open Source Project.
Presentation explaining how all companies can benefit from moving their applications onto the Windows Azure platform. Freeing up their IT function to truly add value rather than merely keeping the lights on.
Making the Most of Data in Multiple Data Sources (with Virtual Data Lakes)DataWorks Summit
Most organizations today implement different data stores to support business operations. As a result, data ends up stored across a multitude of often heterogenous systems, like RDBMS, NoSQL, data warehouses, data marts, Hadoop, etc., with limited interaction and/or interoperability between them. The end result is often a vast eco-system of data stores with different "temperature" data, some level of duplication and, no effective way of bringing it all together for business analytics. With such disparate data, how can an organization exploit the wealth of information? This opens up the need for proven techniques to quickly and easily deliver the data to the people who need it. In this session, you'll see how to modernize your enterprise by making data accessible with enterprise capabilities like querying using SQL, granular security for data access, and maintaining high query performance and high concurrency.
IBM Storage for Analytics, Cognitive and CloudTony Pearson
Presentation on Software-Defined Storage, Spectrum Scale for Analytics with Hadoop and Hortonworks, and IBM Cloud Object Storage, presented March 15 in San Juan, Puerto Rico
Introduction: This workshop will provide a hands-on introduction to Machine & Deep Learning.
Format: An introductory lecture on several supervised and unsupervised Machine Learning techniques followed by light introduction to Deep Learning. Both Apache Spark as well as TensorFlow will be introduced with relevant code samples that users can run in the cloud and explore.
Objective: To provide a quick and short hands-on introduction to Machine Learning with Spark Machine Learning library (MLlib) and Deep Learning with TensorFlow. In the lab, you will use the following components: Apache Zeppelin and Jupyter notebooks with Apache Spark and TensorFlow processing engines (respectively). You will learn how to analyze and structure data, train Machine Learning models and apply them to answer real-world questions. You will also learn how to select, train, and test Deep Learning models.
Prerequisites: Registrants must bring a laptop with a Chrome or Firefox web browser installed (with proxies disabled, i.e. must show venue IP to access cloud resources). These labs will be done in the cloud. At this Crash Course everyone will be assigned a cluster to try several workloads using Apache Spark and TensorFlow in Zeppelin and Jupyter notebooks (respectively) hosted in the cloud.
Hybrid Cloud Journey - Maximizing Private and Public CloudRyan Lynn
This presentation walks through the elements of private and public cloud and how to start looking at use cases for hybrid cloud architectures. It covers benefits, statistics, trends and practical next steps for your hybrid cloud journey.
Live presentation of some of this content: https://www.youtube.com/watch?v=9_5yJr0HKw4&t=13s
InfoSphere Streams toolkits :Real-Time Analytics on Data in MotionAvadhoot Patwardhan
InfoSphere Streams comes standard with several real-time analytic toolkits to help provide quicker time to value. These include telecommunications event data, time series, text, messaging, database, geospatial, and more. Many of these toolkits are part of the InfoSphere Streams Open Source Project.
Presentation explaining how all companies can benefit from moving their applications onto the Windows Azure platform. Freeing up their IT function to truly add value rather than merely keeping the lights on.
Making the Most of Data in Multiple Data Sources (with Virtual Data Lakes)DataWorks Summit
Most organizations today implement different data stores to support business operations. As a result, data ends up stored across a multitude of often heterogenous systems, like RDBMS, NoSQL, data warehouses, data marts, Hadoop, etc., with limited interaction and/or interoperability between them. The end result is often a vast eco-system of data stores with different "temperature" data, some level of duplication and, no effective way of bringing it all together for business analytics. With such disparate data, how can an organization exploit the wealth of information? This opens up the need for proven techniques to quickly and easily deliver the data to the people who need it. In this session, you'll see how to modernize your enterprise by making data accessible with enterprise capabilities like querying using SQL, granular security for data access, and maintaining high query performance and high concurrency.
IBM Storage for Analytics, Cognitive and CloudTony Pearson
Presentation on Software-Defined Storage, Spectrum Scale for Analytics with Hadoop and Hortonworks, and IBM Cloud Object Storage, presented March 15 in San Juan, Puerto Rico
Introduction: This workshop will provide a hands-on introduction to Machine & Deep Learning.
Format: An introductory lecture on several supervised and unsupervised Machine Learning techniques followed by light introduction to Deep Learning. Both Apache Spark as well as TensorFlow will be introduced with relevant code samples that users can run in the cloud and explore.
Objective: To provide a quick and short hands-on introduction to Machine Learning with Spark Machine Learning library (MLlib) and Deep Learning with TensorFlow. In the lab, you will use the following components: Apache Zeppelin and Jupyter notebooks with Apache Spark and TensorFlow processing engines (respectively). You will learn how to analyze and structure data, train Machine Learning models and apply them to answer real-world questions. You will also learn how to select, train, and test Deep Learning models.
Prerequisites: Registrants must bring a laptop with a Chrome or Firefox web browser installed (with proxies disabled, i.e. must show venue IP to access cloud resources). These labs will be done in the cloud. At this Crash Course everyone will be assigned a cluster to try several workloads using Apache Spark and TensorFlow in Zeppelin and Jupyter notebooks (respectively) hosted in the cloud.
Hybrid Cloud Journey - Maximizing Private and Public CloudRyan Lynn
This presentation walks through the elements of private and public cloud and how to start looking at use cases for hybrid cloud architectures. It covers benefits, statistics, trends and practical next steps for your hybrid cloud journey.
Live presentation of some of this content: https://www.youtube.com/watch?v=9_5yJr0HKw4&t=13s
Explore IoT in Big Data while brewing beer. All verticals are instrumenting devices to learn more about their process to help cut costs or improve efficiency.
Presented at the New Zealand Computer Society 50th Anniversary Conference. The conference theme was about ICT Innovation.
This presentation was delivered during the conference by Phil Patton, IBM NZ will focus on answering in simple terms the key questions many are asking in their quest to understand why there is so much hype around Cloud – what are the key ingredients of Cloud Computing? And what’s different about it, what are the deployment types, and what workloads are suitable for Cloud deployment?
Phil will also cover the Enterprise Roadmap for Cloud adoption, the integration and connectivity between Cloud and legacy applications and address the significant security concerns related to the uptake of Cloud.
Azure architecture design patterns - proven solutions to common challengesIvo Andreev
Building a reliable, scalable, secure applications could happen either following verified design patterns or the hard way - following the trial and error approach. Azure architecture patterns are a tested and accepted solutions of common challenges thus reducing the technical risk to the project by not having to employ a new and untested design. However, most of the patterns are relevant to any distributed system, whether hosted on Azure or on other cloud platforms.
Data Bases, Data Warehousing, Data Mining, Decision Support System (DSS), OLAP, OLTP, MOLAP, ROLAP, Data Mart, Meta Data, ETL Process, Drill Up, Roll Down, Slicing, Dicing, Star Schema, SnowFlake Scheme, Dimentional Modelling
Introducing Amazon Kinesis: Real-time Processing of Streaming Big Data (BDT10...Amazon Web Services
"This presentation will introduce Kinesis, the new AWS service for real-time streaming big data ingestion and processing.
We’ll provide an overview of the key scenarios and business use cases suitable for real-time processing, and discuss how AWS designed Amazon Kinesis to help customers shift from a traditional batch-oriented processing of data to a continual real-time processing model. We’ll provide an overview of the key concepts, attributes, APIs and features of the service, and discuss building a Kinesis-enabled application for real-time processing. We’ll also contrast with other approaches for streaming data ingestion and processing. Finally, we’ll also discuss how Kinesis fits as part of a larger big data infrastructure on AWS, including S3, DynamoDB, EMR, and Redshift."
AWS re:Invent 2016: How News UK Centralized Cloud Governance Through Policy M...Amazon Web Services
When you run a complex AWS environment with thousands of Amazon EC2 instances, more than half a petabyte of object storage, and support the largest daily newspapers in the UK, you need a world-class cloud management strategy. For companies like News Corp, implementing policies that automate infrastructure schedules, right-size workloads, and manage and modify reservations is critical. As you scale your cloud infrastructure, defining centralized governance rules while enabling decentralized management is key to running an optimized cloud.
This session is designed for advanced operations, infrastructure, and engineering teams to improve/deploy optimization strategies. It covers the five best cloud management practices, including automating Reserved Instance modifications, setting policies to ensure proper tagging, and scheduling lights-on/lights-off policies. Session sponsored by CloudHealth Technologies.
Data science is the critical element in exploiting data, but several problems prevent organisations from maximising its value. Data scientists often find it hard to work efficiently, with delays in getting access to needed data and resources. Enterprise developers find it hard to incorporate machine learning models into their applications, and IT spends too much time supporting complex environments. Business users rarely are directly involved in the process and don’t have the means to build and consume their own predictive models. All of this means that business executives are not seeing the full ROI they expect from their data science and analytics investments. In this session, we will introduce some cloud based solutions designed to address these challenges.
Speaker: Stephen Weingartner, Solution Engineer, Oracle
Harness the Power of the Cloud for Grid Computing and Batch Processing Applic...RightScale
RightScale Webinar: June 29, 2010 – In this webinar you'll learn how you can save by only paying for what you use and no more. See first-hand how scheduling issues can be a thing of the past. And for projects that require massive resources, you'll see how you can complete your projects in less time for the same cost.
Data Preparation vs. Inline Data Wrangling in Data Science and Machine LearningKai Wähner
Comparison of Data Preparation vs. Data Wrangling Programming Languages, Frameworks and Tools in Machine Learning / Deep Learning Projects.
A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.
This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session also discusses how this is related to visual analytics tools (like TIBCO Spotfire), and best practices for how the data scientist and business user should work together to build good analytic models.
Key takeaways for the audience:
- Learn various options for preparing data sets to build analytic models
- Understand the pros and cons and the targeted persona for each option
- See different technologies and open source frameworks for data preparation
- Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation
Video Recording / Screencast of this Slide Deck: https://youtu.be/2MR5UynQocs
Lambda Architecture 2.0 Convergence between Real-Time Analytics, Context-awar...Sabri Skhiri
At Huawei, we have developed a scalable Complex Event Processing with a significant improvement of the expressiveness. In the scope of the "context-aware" distributed systems, we need to define new architecture patterns. In this way we open new doors to new features and capabilities.
The 2014 AWS Enterprise Summit - TCO and Cost Optimization Amazon Web Services
Cost is often the conversation starter when customers think about moving to the cloud. AWS helps lower costs for customers through its “pay only for what you use” pricing model, frequent price drops, and pricing model choice to support variable & stable workloads. In this session, you will learn about the financial considerations of owning and operating a traditional data center or managed hosting provider versus utilizing AWS. We will detail our TCO methodology and showcase cost comparisons for some common customer use-cases. We’ll also cover a few AWS cost optimization areas, including Spot and Reserved Instances, EC2 Auto Scaling, and consolidated billing. He can only do so from his corporate desktop on the corporate network, from Monday-Friday 9-5 and when he uses MFA?" That's the level of granularity you can choose to implement if you wish. In this session, we'll cover these topics to provide a practical understanding of the security programs, procedures, and best practices you can use to enhance your current security posture.
IBM Cloud Pak for Data is a single unified platform which helps to unify and simplify the collection, organization and analysis of data. Enterprises can turn data into insights through an integrated cloud-native architecture. IBM Cloud Pak for Data is extensible, easily customized to unique client data and AI landscapes through an integrated catalog of IBM, open source and third-party microservices add-ons
Operational systems manage our finances, shopping, devices and much more. Adding real-time analytics to these systems enables them to instantly respond to changing conditions and provide immediate, targeted feedback. This use of analytics is called "operational intelligence," and the need for it is widespread.
This talk will explain how in-memory computing techniques can be used to implement operational intelligence. It will show how an in-memory data grid integrated with a data-parallel compute engine can track events generated by a live system, analyze them in real time, and create alerts that help steer the system’s behavior. Code samples will demonstrate how an in-memory data grid employs object-oriented techniques to simplify the correlation and analysis of incoming events by maintaining an in-memory model of a live system.
The talk also will examine simplifications offered by this approach over directly analyzing incoming event streams from a live system using complex event processing or Storm. Lastly, it will explain key requirements of the in-memory computing platform for operational intelligence, in particular real-time updating of individual objects and high availability using data replication, and contrast these requirements to the design goals for stream processing in Spark.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are available for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.
Informix Spark Streaming is an extension of Informix that allows data to be streamed out of the database as soon as it is inserted, updated, or deleted.
The protocol currently used to stream the changes is MQTT v3.1.1 (older versions not supported!). This extension is able to stream data to any MQTT broker where it can be processed or passed on to subscribing clients for processing.
Explore IoT in Big Data while brewing beer. All verticals are instrumenting devices to learn more about their process to help cut costs or improve efficiency.
Presented at the New Zealand Computer Society 50th Anniversary Conference. The conference theme was about ICT Innovation.
This presentation was delivered during the conference by Phil Patton, IBM NZ will focus on answering in simple terms the key questions many are asking in their quest to understand why there is so much hype around Cloud – what are the key ingredients of Cloud Computing? And what’s different about it, what are the deployment types, and what workloads are suitable for Cloud deployment?
Phil will also cover the Enterprise Roadmap for Cloud adoption, the integration and connectivity between Cloud and legacy applications and address the significant security concerns related to the uptake of Cloud.
Azure architecture design patterns - proven solutions to common challengesIvo Andreev
Building a reliable, scalable, secure applications could happen either following verified design patterns or the hard way - following the trial and error approach. Azure architecture patterns are a tested and accepted solutions of common challenges thus reducing the technical risk to the project by not having to employ a new and untested design. However, most of the patterns are relevant to any distributed system, whether hosted on Azure or on other cloud platforms.
Data Bases, Data Warehousing, Data Mining, Decision Support System (DSS), OLAP, OLTP, MOLAP, ROLAP, Data Mart, Meta Data, ETL Process, Drill Up, Roll Down, Slicing, Dicing, Star Schema, SnowFlake Scheme, Dimentional Modelling
Introducing Amazon Kinesis: Real-time Processing of Streaming Big Data (BDT10...Amazon Web Services
"This presentation will introduce Kinesis, the new AWS service for real-time streaming big data ingestion and processing.
We’ll provide an overview of the key scenarios and business use cases suitable for real-time processing, and discuss how AWS designed Amazon Kinesis to help customers shift from a traditional batch-oriented processing of data to a continual real-time processing model. We’ll provide an overview of the key concepts, attributes, APIs and features of the service, and discuss building a Kinesis-enabled application for real-time processing. We’ll also contrast with other approaches for streaming data ingestion and processing. Finally, we’ll also discuss how Kinesis fits as part of a larger big data infrastructure on AWS, including S3, DynamoDB, EMR, and Redshift."
AWS re:Invent 2016: How News UK Centralized Cloud Governance Through Policy M...Amazon Web Services
When you run a complex AWS environment with thousands of Amazon EC2 instances, more than half a petabyte of object storage, and support the largest daily newspapers in the UK, you need a world-class cloud management strategy. For companies like News Corp, implementing policies that automate infrastructure schedules, right-size workloads, and manage and modify reservations is critical. As you scale your cloud infrastructure, defining centralized governance rules while enabling decentralized management is key to running an optimized cloud.
This session is designed for advanced operations, infrastructure, and engineering teams to improve/deploy optimization strategies. It covers the five best cloud management practices, including automating Reserved Instance modifications, setting policies to ensure proper tagging, and scheduling lights-on/lights-off policies. Session sponsored by CloudHealth Technologies.
Data science is the critical element in exploiting data, but several problems prevent organisations from maximising its value. Data scientists often find it hard to work efficiently, with delays in getting access to needed data and resources. Enterprise developers find it hard to incorporate machine learning models into their applications, and IT spends too much time supporting complex environments. Business users rarely are directly involved in the process and don’t have the means to build and consume their own predictive models. All of this means that business executives are not seeing the full ROI they expect from their data science and analytics investments. In this session, we will introduce some cloud based solutions designed to address these challenges.
Speaker: Stephen Weingartner, Solution Engineer, Oracle
Harness the Power of the Cloud for Grid Computing and Batch Processing Applic...RightScale
RightScale Webinar: June 29, 2010 – In this webinar you'll learn how you can save by only paying for what you use and no more. See first-hand how scheduling issues can be a thing of the past. And for projects that require massive resources, you'll see how you can complete your projects in less time for the same cost.
Data Preparation vs. Inline Data Wrangling in Data Science and Machine LearningKai Wähner
Comparison of Data Preparation vs. Data Wrangling Programming Languages, Frameworks and Tools in Machine Learning / Deep Learning Projects.
A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.
This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session also discusses how this is related to visual analytics tools (like TIBCO Spotfire), and best practices for how the data scientist and business user should work together to build good analytic models.
Key takeaways for the audience:
- Learn various options for preparing data sets to build analytic models
- Understand the pros and cons and the targeted persona for each option
- See different technologies and open source frameworks for data preparation
- Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation
Video Recording / Screencast of this Slide Deck: https://youtu.be/2MR5UynQocs
Lambda Architecture 2.0 Convergence between Real-Time Analytics, Context-awar...Sabri Skhiri
At Huawei, we have developed a scalable Complex Event Processing with a significant improvement of the expressiveness. In the scope of the "context-aware" distributed systems, we need to define new architecture patterns. In this way we open new doors to new features and capabilities.
The 2014 AWS Enterprise Summit - TCO and Cost Optimization Amazon Web Services
Cost is often the conversation starter when customers think about moving to the cloud. AWS helps lower costs for customers through its “pay only for what you use” pricing model, frequent price drops, and pricing model choice to support variable & stable workloads. In this session, you will learn about the financial considerations of owning and operating a traditional data center or managed hosting provider versus utilizing AWS. We will detail our TCO methodology and showcase cost comparisons for some common customer use-cases. We’ll also cover a few AWS cost optimization areas, including Spot and Reserved Instances, EC2 Auto Scaling, and consolidated billing. He can only do so from his corporate desktop on the corporate network, from Monday-Friday 9-5 and when he uses MFA?" That's the level of granularity you can choose to implement if you wish. In this session, we'll cover these topics to provide a practical understanding of the security programs, procedures, and best practices you can use to enhance your current security posture.
IBM Cloud Pak for Data is a single unified platform which helps to unify and simplify the collection, organization and analysis of data. Enterprises can turn data into insights through an integrated cloud-native architecture. IBM Cloud Pak for Data is extensible, easily customized to unique client data and AI landscapes through an integrated catalog of IBM, open source and third-party microservices add-ons
Operational systems manage our finances, shopping, devices and much more. Adding real-time analytics to these systems enables them to instantly respond to changing conditions and provide immediate, targeted feedback. This use of analytics is called "operational intelligence," and the need for it is widespread.
This talk will explain how in-memory computing techniques can be used to implement operational intelligence. It will show how an in-memory data grid integrated with a data-parallel compute engine can track events generated by a live system, analyze them in real time, and create alerts that help steer the system’s behavior. Code samples will demonstrate how an in-memory data grid employs object-oriented techniques to simplify the correlation and analysis of incoming events by maintaining an in-memory model of a live system.
The talk also will examine simplifications offered by this approach over directly analyzing incoming event streams from a live system using complex event processing or Storm. Lastly, it will explain key requirements of the in-memory computing platform for operational intelligence, in particular real-time updating of individual objects and high availability using data replication, and contrast these requirements to the design goals for stream processing in Spark.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are available for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.
Informix Spark Streaming is an extension of Informix that allows data to be streamed out of the database as soon as it is inserted, updated, or deleted.
The protocol currently used to stream the changes is MQTT v3.1.1 (older versions not supported!). This extension is able to stream data to any MQTT broker where it can be processed or passed on to subscribing clients for processing.
Gain New Insights by Analyzing Machine Logs using Machine Data Analytics and BigInsights.
Half of Fortune 500 companies experience more than 80 hours of system down time annually. Spread evenly over a year, that amounts to approximately 13 minutes every day. As a consumer, the thought of online bank operations being inaccessible so frequently is disturbing. As a business owner, when systems go down, all processes come to a stop. Work in progress is destroyed and failure to meet SLA’s and contractual obligations can result in expensive fees, adverse publicity, and loss of current and potential future customers. Ultimately the inability to provide a reliable and stable system results in loss of $$$’s. While the failure of these systems is inevitable, the ability to timely predict failures and intercept them before they occur is now a requirement.
A possible solution to the problem can be found is in the huge volumes of diagnostic big data generated at hardware, firmware, middleware, application, storage and management layers indicating failures or errors. Machine analysis and understanding of this data is becoming an important part of debugging, performance analysis, root cause analysis and business analysis. In addition to preventing outages, machine data analysis can also provide insights for fraud detection, customer retention and other important use cases.
Data Center Transformation to Cloud - MindmapWAJAHAT IQBAL
In this Mindmap I have taken the key practices being practiced by Microsoft,Juniper,Dell,Cisco etc to show the key steps and processes involved in successfully migrating from a Legacy Data Center to a Cloud Domain.The mindmap is not exhaustive and reader is advised to do further research on the topic.Please share your Comments at my Email Id:Wajahat_Iqbal@Yahoo.com.Thank You
Note: The Source of Information are the Internet repositories and the Author does not take any responsibility for any Errors
InfoSphere Streams is an advanced computing platform that can quickly ingest, analyze and correlate information as it arrives from thousands of real-time sources.
IT professionals are being asked to do more with less and highly skilled resources are in demand. As streaming applications play a growing role in critical applications so does the need for simplicity. InfoSphere Streams empowers IT users of all types and skill levels to have deeper insights into operations and performance. In today’s engaged world, a five minute delay means business goes elsewhere. A new administration console, a Java Management Extensions (JMX) management and monitoring application programming interface (API), simpler security and adoption of Apache Zookeeper are now available in InfoSphere Streams
In this presentation we review the basic architecture behind SQL Server StreamInsight.
Regards,
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Imagine an entire IT infrastructure controlled not by hands and hardware, but by software. One in which application workloads such as big data, analytics, simulation and design are serviced automatically by the most appropriate resource, whether running locally or in the cloud. A Software Defined Infrastructure enables your organization to deliver IT services in the most efficient way possible, optimizing resource utilization to accelerate time to results and reduce costs. It is the foundation for a fully integrated software defined environment, optimizing your compute, storage and networking infrastructure so you can quickly adapt to changing business requirements. A comprehensive portfolio of management tools dynamically manage workloads and data, transforming a static IT infrastructure into a workload- , resource- and data-aware environment.
Learn more: http://ibm.co/1wkoXtc
Watch the video presentation: http://insidehpc.com/2015/03/slidecast-software-defined-infrastructure/
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
New usage model for real-time analytics by Dr. WILLIAM L. BAIN at Big Data S...Big Data Spain
Operational systems manage our finances, shopping, devices and much more. Adding real-time analytics to these systems enables them to instantly respond to changing conditions and provide immediate, targeted feedback. This use of analytics is called “operational intelligence,” and the need for it is widespread.
Ironstream for IBM i - Enabling Splunk Insight into Key Security and Operatio...Precisely
IBM i servers and workloads can produce large amounts of log data daily, but as it’s written in different formats, to different journals, queues and system logs, it’s difficult to access and make usable for reporting. Join us for a webinar as introduce the Syncsort Ironstream for IBM i: a new product that expands our machine data solutions for Splunk to the IBM i. Learn how Ironstream can help your organization gain insight into operations, security and service delivery for the ultimate success of your business.
View this webinar on-demand to learn:
• How to leverage Splunk Enterprise to gain insight into IBM i log data
• Ways to gain better insight into security threats
• How to discover and act upon operational and performance issues that impact service delivery
Leveraging Governance in the IBM WebSphere Service Registry and Repository fo...Prolifics
Abstract: Governance has not been enforced or followed in enterprises due to lack of skills or no clear understanding on how use cases can be applied across various technology paradigms. Customers want governance to be implemented in their enterprise but lack awareness. In this presentation we will focus on how IIB and Data Power can be aligned with WSRR for various governance use cases. We will primarily focus on IIB/WSRR Integration capabilities and similar approaches will be followed for Data Power/WSRR Integration capabilities. This will give a solid start on how governance capabilities of WSRR can be leveraged for ESBs.
Applying an IBM SOA Approach to Manual Processes AutomationProlifics
Abstract: One of the world's largest financial services company is enabling management of client opportunities in an efficient and error free manner by implementing IBM SOA technologies. Integration of IBM BPM, IIB, and WODM, provides seamless transition of manual processes into a state of the art automation. Data persistence and retrieval is assured via IIB business Web services, orchestrated to provide pertinent information access via integration with multiple data sources utilizing various communication means. IBM SOA Web services architecture ensures self-containment, reusability, and adaptability to change, guaranteeing easiness of future integration of any applications irrespective of their communications means or supported platforms.
How Broadcast Music, Inc. Devised and Enabled Enterprise Architecture from Co...Prolifics
Abstract: Devising flexible, value-based , transformational but cost effective BPM implementation road map from corporate strategy is not only difficult but almost impossible to manage. BMI with the help of Prolifics’ proven business architecture framework, BPM implementation methodology and IBM rational , BPM suite has enabled and achieved it. In this session we will focus and demonstrate how at BMI - Prolifics framework, methodologies & IBM Suites bridges the gap between Corporate Strategy and BPM Implementation roadmap. Presentation will also demonstrate how BMI is creating actionable, model driven enterprise architecture to ensure delivery of large BPM /SOA/ODM implementation in flexible, cost effective manner in diversified team.
Using the Power of IBM Tivoli Common Reporting to Make Smart Decisions: The U...Prolifics
Abstract: What is the secret of making better decisions? The answer is simple. Base the decisions on real data. The objective of this session to showcase the power of the Tivoli Common Reporting tool to provide accurate yet fully customizable and easy to understand reports about the performance, availability, usage etc. of enterprise applications and systems to help customers make informed decisions. By making effective use of the tool, customers can better identify problem areas, improve Quality of Service, reduce costs, optimize return on investment and optimize capacity planning. The attendees will also be provided with a live demo of using the tool to customize and generate reports for performance monitoring, capacity planning and SLA reporting.
Empowering SmartCloud APM - Predictive Insights and Analysis: A Use Case Scen...Prolifics
Abstract: You currently have SmartCloud APM. Now, it is time to empower and enhance APM with the power of SmartCloud Analytics - Predictive Insights and Log Analysis. We will be discussing an actual customer use case scenario involving WebSphere Application Server and MQ, understand how to integrate these components and how to use the integrated solution to make problem determination and root cause analysis significantly easier and faster. We will be doing a real-time demo as well to see the integrated solution in action.
Best Practices for Monitoring Your Cloud Environment and ApplicationsProlifics
Abstract: You have completed the heavy lifting of migrating applications to the cloud. But you are not done yet. What is your monitoring strategy for the cloud? What are the best practices to monitor the cloud infrastructure, deployed applications and end user experience? In this session, we will be answering these questions and explore the various IBM APM and Analytics offerings that will help you in your decision making process. Having a comprehensive monitoring strategy is critical as most customers use a combination of public and private cloud environments and being able to monitor these using a fully integrated and customizable solution is essential to the health, availability and performance of the cloud deployed applications and services.
Smarter Integration Using the IBM SOA Foundation Stack: Best Practices and Le...Prolifics
Abstract: Enterprise integration can be challenging given the number of products, teams and technology frameworks involved. At the same time, most organizations require their applications to seamlessly communicate with each other, internally and externally. Our experience as field practitioners have enabled us to see these challenges up close and after fighting many battles to successfully implement integrated solutions, we want to share the best practices and lessons learned. We will be discussing IBM products such as DataPower, Integration Bus, BPM, WSRR and SmartCloud APM; however, many of these best practices are product and technology agnostic. We will be looking at what it takes from an organizational, personal and architecture perspective.
Abstract: Many organizations are struggling with the speed and diversity of technology change. In this presentation we will discuss how to use concepts of patterns to make configurable and repeatable infrastructure topologies, improve speed and environmental consistency. By leveraging elasticity and auto scaling capacities of IBM PureApplications show how to increase application capacity. Demonstrate how the use of patterns improves testing, deployment and lowers risk. Finally we will show how the use of these concepts can be a catalyst for change, letting us challenge established barriers so we can embrace continuous improvement and DevOps.
Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...Prolifics
Abstract: Recent projects have stressed the "need for speed" while handling large amounts of data, with near zero downtime. An analysis of multiple environments has identified optimizations and architectures that improve both performance and reliability. The session covers data gathering and analysis, discussing everything from the network (multiple NICs, nearby catalogs, high speed Ethernet), to the latest features of extreme scale. Performance analysis helps pinpoint where time is spent (bottlenecks) and we discuss optimization techniques (MQ tuning, IIB performance best practices) as well as helpful IBM support pacs. Log Analysis pinpoints system stress points (e.g. CPU starvation) and steps on the path to near zero downtime.
Integrating IBM PureApplication System and IBM UrbanCode Deploy: A GE Capital...Prolifics
As organizations move into virtualized environments using IBM PureApplication System, the need for concise patterns is becoming a necessity. This is where UrbanCode Deploy comes in to play. uDeploy can pre-package the environment configuration with pattern capabilities, deploy applications via patterns and assure consistency on all levels—ensuring peace of mind during the lifecycle of an application. Developers and testers will be able to deploy applications to consistent environments, eliminating errors and issues. The time of “true DevOps” has arrived and is in place at GE Capital.
Broadcast Music Inc. Release Rockstars: Program-Wide DevOps Success with Urba...Prolifics
In order to keep up with the demand for new functionality caused by the explosion of new music delivery channels, the Broadcast Music Inc. IT team has undergone a revolution in capability over the last 4 years. It began with an initiative that adopted agile software development techniques paired with IBM Rational Team Concert (as well as RQM, RRC and Focal Point). The strategy was to continuously improve - and this has led their DevOps team to add UrbanCode Deploy to the mix. Join us to learn how their DevOps pipeline capability across their broad software stack (includes IBM BPM, Portal, ODM, Integration Bus, Data Power, Enterprise Service Bus & WSRR) has been further optimized with the inclusion of UrbanCode Deploy.
From Print to the Cloud and Beyond: The Story of a Century Old Company and it...Prolifics
How does a century old company, who used to consider data integration placing the binder on a book keep up with younger, nimbler companies? You ever-evolve! You must always be adapting and you MUST change your environment before it changes you!
This is the success story of Chemical Abstracts Services . A 108 year old company who is the world’s authority for curating and classifying chemical information.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).