Presentation: Overview of Kognitio, Kognitio Cloud and the Kognitio Analytical Platform
Kognitio is driving the convergence of Big Data, in-memory analytics and cloud computing. Having delivered the first in-memory analytical platform in 1989, it was designed from the ground up to provide the highest amount of scalable compute power to allow rapid execution of complex analytical queries without the administrative overhead of manipulating data. Kognitio software runs on industry-standard x86 servers, or as an appliance, or in Kognitio Cloud, a ready-to-use analytical platform. Kognitio Cloud is a secure, private or public cloud Platform-as-a-Service (PaaS), leveraging the cloud computing model to make the Kognitio Analytical Platform available on a subscription basis. Clients span industries, including market research, consumer packaged goods, retail, telecommunications, financial services, insurance, gaming, media and utilities.
To learn more, visit www.kognitio.com and follow us on Facebook, LinkedIn and Twitter.
Using Distributed In-Memory Computing for Fast Data AnalysisScaleOut Software
This is an overview of how distributed data grids can enable sharing across web servers and virtual cloud environments to enable scalability and high availability. It also covers how distributed data grids are highly useful for running MapReduce analysis across large data sets.
This document discusses Replication Server - Real Time Loading (RTL) for replicating data from a source database in real-time to Sybase IQ for analytics purposes. It provides dial-in numbers and passcode for a presentation on the topic. The presentation will cover limitations of pre-RS 15.5 replication solutions to IQ, an overview of RTL, and the new RTL update capabilities in RS.
The document discusses Greenplum Database, an open source massively parallel processing (MPP) relational database system for big data. It provides an overview of Greenplum's architecture, including its master-segment structure and distributed transaction management. It also covers topics like defining data storage, distributions, partitioning, and analytics capabilities. Examples of Greenplum deployments are listed across various industries. Recent accomplishments and roadmap items are also summarized.
Why Every NoSQL Deployment Should Be Paired with Hadoop WebinarCloudera, Inc.
This document discusses how NoSQL databases are well-suited for interactive web applications with large audiences due to their ability to scale out horizontally, while Hadoop is well-suited for analyzing large volumes of data. It provides examples of how NoSQL and Hadoop can work together, with NoSQL serving as a low-latency data store and Hadoop performing batch analysis on the large volumes of data generated by web applications and their users. The document argues that NoSQL and Hadoop address different but complementary challenges and are highly synergistic when used together.
- Greenplum Database is an open source relational database system designed for big data analytics. It uses a massively parallel processing (MPP) architecture that distributes data and processing across multiple servers or "segments" to achieve high performance.
- The master node coordinates the segments and handles connections from client applications. It parses queries, generates execution plans, and manages query dispatch, execution and results retrieval.
- Segments store and process data in parallel. They each have their own storage, memory and CPU resources in a "shared nothing" architecture to ensure scalability.
Performance Management in ‘Big Data’ ApplicationsMichael Kopp
Do applications using NoSQL still require performance management? Is it always the best option to throw more hardware at a MapReduce job? In both cases, performance management is still about the application, but "Big Data" technologies have added a new wrinkle.
Using Distributed In-Memory Computing for Fast Data AnalysisScaleOut Software
This is an overview of how distributed data grids can enable sharing across web servers and virtual cloud environments to enable scalability and high availability. It also covers how distributed data grids are highly useful for running MapReduce analysis across large data sets.
This document discusses Replication Server - Real Time Loading (RTL) for replicating data from a source database in real-time to Sybase IQ for analytics purposes. It provides dial-in numbers and passcode for a presentation on the topic. The presentation will cover limitations of pre-RS 15.5 replication solutions to IQ, an overview of RTL, and the new RTL update capabilities in RS.
The document discusses Greenplum Database, an open source massively parallel processing (MPP) relational database system for big data. It provides an overview of Greenplum's architecture, including its master-segment structure and distributed transaction management. It also covers topics like defining data storage, distributions, partitioning, and analytics capabilities. Examples of Greenplum deployments are listed across various industries. Recent accomplishments and roadmap items are also summarized.
Why Every NoSQL Deployment Should Be Paired with Hadoop WebinarCloudera, Inc.
This document discusses how NoSQL databases are well-suited for interactive web applications with large audiences due to their ability to scale out horizontally, while Hadoop is well-suited for analyzing large volumes of data. It provides examples of how NoSQL and Hadoop can work together, with NoSQL serving as a low-latency data store and Hadoop performing batch analysis on the large volumes of data generated by web applications and their users. The document argues that NoSQL and Hadoop address different but complementary challenges and are highly synergistic when used together.
- Greenplum Database is an open source relational database system designed for big data analytics. It uses a massively parallel processing (MPP) architecture that distributes data and processing across multiple servers or "segments" to achieve high performance.
- The master node coordinates the segments and handles connections from client applications. It parses queries, generates execution plans, and manages query dispatch, execution and results retrieval.
- Segments store and process data in parallel. They each have their own storage, memory and CPU resources in a "shared nothing" architecture to ensure scalability.
Performance Management in ‘Big Data’ ApplicationsMichael Kopp
Do applications using NoSQL still require performance management? Is it always the best option to throw more hardware at a MapReduce job? In both cases, performance management is still about the application, but "Big Data" technologies have added a new wrinkle.
1) HBase satisfied Facebook's requirements for a real-time data store by providing excellent write performance, horizontal scalability, and features like atomic operations.
2) At Facebook, HBase is used for messaging and user activity tracking applications that involve massive write-throughput and petabytes of data.
3) HBase's integration with HDFS provides fault tolerance and scalability, while its column orientation enables complex queries on user activity data.
Timely access to relevant information has always been critical to business success. Thousands and thousands of companies and institutions use SAP NetWeaver Business Warehouse (SAP NetWeaver BW) as the cornerstone for business intelligence in their SAP application landscapes. However, query performance has often been a challenge...
Strata + Hadoop World 2012: Data Science on Hadoop: How Cloudera Impala Unloc...Cloudera, Inc.
This talk will cover what tools and techniques work and don’t work well for data scientists working on Hadoop today and how to leverage the lessons learned by the experts to increase your productivity as well as what to expect for the future of data science on Hadoop. We will leverage insights derived from the top data scientists working on big data systems at Cloudera as well as experiences from running big data systems at Facebook, Google, and Yahoo.
HBase is a distributed, scalable, big data store that provides fast lookup capabilities like Google BigTable. It uses a table-like data structure with rows indexed by a key and stores data in columns grouped by families. HBase is designed to operate on top of Hadoop HDFS for scalability and high availability. It allows for fast lookups, full table scans, and range scans across large datasets distributed across clusters of commodity servers.
Hadoop World 2011: Building Scalable Data Platforms ; Hadoop & Netezza Deploy...Krishnan Parasuraman
Hadoop has rapidly emerged as a viable platform for Big Data analytics. Many experts believe Hadoop will subsume many of the data warehousing tasks presently done by traditional relational systems. In this presentation, you will learn about the similarities and differences of Hadoop and parallel data warehouses, and typical best practices. Edmunds will discuss how they increased delivery speed, reduced risk, and achieved faster reporting by combining ELT and ETL. For example, Edmunds ingests raw data into Hadoop and HBase then reprocesses the raw data in Netezza. You will also learn how Edmunds uses prototyping to work on nearly raw data with the company’s Analytics Team using Netezza.
This document provides an overview of an advanced Big Data hands-on course covering Hadoop, Sqoop, Pig, Hive and enterprise applications. It introduces key concepts like Hadoop and large data processing, demonstrates tools like Sqoop, Pig and Hive for data integration, querying and analysis on Hadoop. It also discusses challenges for enterprises adopting Hadoop technologies and bridging the skills gap.
This document discusses leveraging major market opportunities with Microsoft Azure. It notes that worldwide cloud software revenue is expected to grow significantly between 2010-2017. By 2017, nearly $1 of every $5 spent on applications will be consumed via the cloud. It also notes that hybrid cloud deployments will be common for large enterprises by the end of 2017. The document then outlines several major enterprise workloads that can be moved to Azure, including test/development, SharePoint, SQL/business intelligence, application migration, SAP, and identity/Office 365. It provides examples of how partners can help customers with these types of migrations.
Cloud computing, big data, and mobile technologies are driving major changes in the IT world. Cloud computing provides scalable computing resources over the internet. Big data involves extremely large data sets that are analyzed to reveal business insights. Hadoop is an open-source software framework that allows distributed processing of big data across commodity hardware. It includes tools like HDFS for storage and MapReduce for distributed computing. The Hadoop ecosystem also includes additional tools for tasks like data integration, analytics, workflow management, and more. These emerging technologies are changing how businesses use and analyze data.
Facing enterprise specific challenges – utility programming in hadoopfann wu
This document discusses managing large Hadoop clusters through various automation tools like SaltStack, Puppet, and Chef. It describes how to use SaltStack to remotely control and manage a Hadoop cluster. Puppet can be used to easily deploy Hadoop on hundreds of servers within an hour through Hadooppet. The document also covers Hadoop security concepts like Kerberos and folder permissions. It provides examples of monitoring tools like Ganglia, Nagios, and Splunk that can be used to track cluster metrics and debug issues. Common processes like datanode decommissioning and tools like the HBase Canary tool are also summarized. Lastly, it discusses testing Hadoop on AWS using EMR and techniques to reduce EMR costs
1) The document discusses big data analytics and introduces Greenplum, a massively parallel processing (MPP) database for big data analytics.
2) Greenplum allows for integrated analysis of structured and unstructured data at scale through its SQL database and Hadoop integration.
3) The architecture provides linear scalability, flexibility to handle various data types and schemas, and rich language support for analytics.
This document compares Netezza, Teradata, and Exadata databases across several criteria such as architecture, scalability, reliability, performance, compatibility, affordability, and manageability. Some key highlights are that Netezza uses an asymmetric massively parallel processing architecture while Teradata uses a true MPP architecture. Teradata and Exadata can scale storage and memory linearly while Netezza has fixed hardware. All three databases provide high availability but Exadata has redundancy at every layer.
Offline processing with Hadoop allows for scalable, simplified batch processing of large datasets across distributed systems. It enables increased innovation by supporting complex analytics over large data sets without strict schemas. Hadoop adoption is moving beyond legacy roles to focus on data processing and value creation through scalable and customizable systems like Cascading.
MapReduce is a framework for processing large datasets in a distributed manner. It involves two functions: map and reduce. The map function processes individual elements to generate intermediate key-value pairs, and the reduce function merges all intermediate values with the same key. Hadoop is an open-source implementation of MapReduce that uses HDFS for storage. A typical MapReduce job in Hadoop involves defining map and reduce functions, configuring the job, and submitting it to the JobTracker which schedules tasks across nodes and monitors execution.
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
The document discusses topics related to designing and implementing an SAP HANA infrastructure, including the hardware and software components required for the SAP HANA server, storage, network, backup, and disaster recovery systems. It provides information on sizing SAP HANA systems, certified hardware partners, storage options like TDI, network requirements, security best practices, backup methods, and high availability and disaster recovery strategies. The presentation aims to help with planning and designing the various elements of an SAP HANA infrastructure.
The document discusses a TechTalk webinar on hyperconverged infrastructure from Cisco Thailand that includes a live demo. It provides definitions and explanations of key concepts like hyperconvergence, software defined storage, and hyperconverged architectures. The webinar highlights benefits like agility, efficiency, simplicity and scalability and discusses how hyperconvergence is shifting the market towards server-based ecosystems.
Ibm pure data system for analytics n200xIBM Sverige
The document describes IBM's PureData System for Analytics N200x, a new model that provides faster performance for big data analytics through improvements like more CPU cores, faster FPGAs, and more disk drives per server blade. The N200x offers 3x faster query performance than previous models, improved data center efficiency through 50% greater data capacity per rack, and fewer service calls expected through more frequent drive regeneration.
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Precisely
This document discusses engineering machine learning data pipelines and addresses five big challenges: 1) scattered and difficult to access data, 2) data cleansing at scale, 3) entity resolution, 4) tracking data lineage, and 5) ongoing real-time changed data capture and streaming. It presents DMX Change Data Capture as a solution to capture changes from various data sources and replicate them in real-time to targets like Kafka, HDFS, databases and data lakes to feed machine learning models. Case studies demonstrate how DMX-h has helped customers like a global hotel chain and insurance and healthcare companies build scalable data pipelines.
How to Choose a Host for a Big Data ProjectPeak Hosting
John Johnson will present on Big Data and hosting options for Big Data applications. The presentation will cover what Big Data means, use cases, considerations for building projects, hosting challenges, and a customer example. Options that will be discussed include in-house hosting, cloud, colocation, and dedicated managed hosting (Operations as a Service). The challenges of each option for Big Data applications will also be reviewed.
HP Enterprises in Hana Pankaj Jain May 2016INDUSCommunity
HPE offers solutions for hybrid clouds and SAP HANA based on composable infrastructure. Composable infrastructure allows resources to be composed on demand in seconds and infrastructure to be programmed through a single line of code. This approach dramatically reduces overprovisioning and speeds application and service delivery. HPE's composable infrastructure solution is called Synergy, which provides fluid resource pools, software-defined intelligence, and a unified API. HPE also offers converged systems optimized for SAP HANA that are pre-configured to deliver maximum performance.
1) HBase satisfied Facebook's requirements for a real-time data store by providing excellent write performance, horizontal scalability, and features like atomic operations.
2) At Facebook, HBase is used for messaging and user activity tracking applications that involve massive write-throughput and petabytes of data.
3) HBase's integration with HDFS provides fault tolerance and scalability, while its column orientation enables complex queries on user activity data.
Timely access to relevant information has always been critical to business success. Thousands and thousands of companies and institutions use SAP NetWeaver Business Warehouse (SAP NetWeaver BW) as the cornerstone for business intelligence in their SAP application landscapes. However, query performance has often been a challenge...
Strata + Hadoop World 2012: Data Science on Hadoop: How Cloudera Impala Unloc...Cloudera, Inc.
This talk will cover what tools and techniques work and don’t work well for data scientists working on Hadoop today and how to leverage the lessons learned by the experts to increase your productivity as well as what to expect for the future of data science on Hadoop. We will leverage insights derived from the top data scientists working on big data systems at Cloudera as well as experiences from running big data systems at Facebook, Google, and Yahoo.
HBase is a distributed, scalable, big data store that provides fast lookup capabilities like Google BigTable. It uses a table-like data structure with rows indexed by a key and stores data in columns grouped by families. HBase is designed to operate on top of Hadoop HDFS for scalability and high availability. It allows for fast lookups, full table scans, and range scans across large datasets distributed across clusters of commodity servers.
Hadoop World 2011: Building Scalable Data Platforms ; Hadoop & Netezza Deploy...Krishnan Parasuraman
Hadoop has rapidly emerged as a viable platform for Big Data analytics. Many experts believe Hadoop will subsume many of the data warehousing tasks presently done by traditional relational systems. In this presentation, you will learn about the similarities and differences of Hadoop and parallel data warehouses, and typical best practices. Edmunds will discuss how they increased delivery speed, reduced risk, and achieved faster reporting by combining ELT and ETL. For example, Edmunds ingests raw data into Hadoop and HBase then reprocesses the raw data in Netezza. You will also learn how Edmunds uses prototyping to work on nearly raw data with the company’s Analytics Team using Netezza.
This document provides an overview of an advanced Big Data hands-on course covering Hadoop, Sqoop, Pig, Hive and enterprise applications. It introduces key concepts like Hadoop and large data processing, demonstrates tools like Sqoop, Pig and Hive for data integration, querying and analysis on Hadoop. It also discusses challenges for enterprises adopting Hadoop technologies and bridging the skills gap.
This document discusses leveraging major market opportunities with Microsoft Azure. It notes that worldwide cloud software revenue is expected to grow significantly between 2010-2017. By 2017, nearly $1 of every $5 spent on applications will be consumed via the cloud. It also notes that hybrid cloud deployments will be common for large enterprises by the end of 2017. The document then outlines several major enterprise workloads that can be moved to Azure, including test/development, SharePoint, SQL/business intelligence, application migration, SAP, and identity/Office 365. It provides examples of how partners can help customers with these types of migrations.
Cloud computing, big data, and mobile technologies are driving major changes in the IT world. Cloud computing provides scalable computing resources over the internet. Big data involves extremely large data sets that are analyzed to reveal business insights. Hadoop is an open-source software framework that allows distributed processing of big data across commodity hardware. It includes tools like HDFS for storage and MapReduce for distributed computing. The Hadoop ecosystem also includes additional tools for tasks like data integration, analytics, workflow management, and more. These emerging technologies are changing how businesses use and analyze data.
Facing enterprise specific challenges – utility programming in hadoopfann wu
This document discusses managing large Hadoop clusters through various automation tools like SaltStack, Puppet, and Chef. It describes how to use SaltStack to remotely control and manage a Hadoop cluster. Puppet can be used to easily deploy Hadoop on hundreds of servers within an hour through Hadooppet. The document also covers Hadoop security concepts like Kerberos and folder permissions. It provides examples of monitoring tools like Ganglia, Nagios, and Splunk that can be used to track cluster metrics and debug issues. Common processes like datanode decommissioning and tools like the HBase Canary tool are also summarized. Lastly, it discusses testing Hadoop on AWS using EMR and techniques to reduce EMR costs
1) The document discusses big data analytics and introduces Greenplum, a massively parallel processing (MPP) database for big data analytics.
2) Greenplum allows for integrated analysis of structured and unstructured data at scale through its SQL database and Hadoop integration.
3) The architecture provides linear scalability, flexibility to handle various data types and schemas, and rich language support for analytics.
This document compares Netezza, Teradata, and Exadata databases across several criteria such as architecture, scalability, reliability, performance, compatibility, affordability, and manageability. Some key highlights are that Netezza uses an asymmetric massively parallel processing architecture while Teradata uses a true MPP architecture. Teradata and Exadata can scale storage and memory linearly while Netezza has fixed hardware. All three databases provide high availability but Exadata has redundancy at every layer.
Offline processing with Hadoop allows for scalable, simplified batch processing of large datasets across distributed systems. It enables increased innovation by supporting complex analytics over large data sets without strict schemas. Hadoop adoption is moving beyond legacy roles to focus on data processing and value creation through scalable and customizable systems like Cascading.
MapReduce is a framework for processing large datasets in a distributed manner. It involves two functions: map and reduce. The map function processes individual elements to generate intermediate key-value pairs, and the reduce function merges all intermediate values with the same key. Hadoop is an open-source implementation of MapReduce that uses HDFS for storage. A typical MapReduce job in Hadoop involves defining map and reduce functions, configuring the job, and submitting it to the JobTracker which schedules tasks across nodes and monitors execution.
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
The document discusses topics related to designing and implementing an SAP HANA infrastructure, including the hardware and software components required for the SAP HANA server, storage, network, backup, and disaster recovery systems. It provides information on sizing SAP HANA systems, certified hardware partners, storage options like TDI, network requirements, security best practices, backup methods, and high availability and disaster recovery strategies. The presentation aims to help with planning and designing the various elements of an SAP HANA infrastructure.
The document discusses a TechTalk webinar on hyperconverged infrastructure from Cisco Thailand that includes a live demo. It provides definitions and explanations of key concepts like hyperconvergence, software defined storage, and hyperconverged architectures. The webinar highlights benefits like agility, efficiency, simplicity and scalability and discusses how hyperconvergence is shifting the market towards server-based ecosystems.
Ibm pure data system for analytics n200xIBM Sverige
The document describes IBM's PureData System for Analytics N200x, a new model that provides faster performance for big data analytics through improvements like more CPU cores, faster FPGAs, and more disk drives per server blade. The N200x offers 3x faster query performance than previous models, improved data center efficiency through 50% greater data capacity per rack, and fewer service calls expected through more frequent drive regeneration.
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Precisely
This document discusses engineering machine learning data pipelines and addresses five big challenges: 1) scattered and difficult to access data, 2) data cleansing at scale, 3) entity resolution, 4) tracking data lineage, and 5) ongoing real-time changed data capture and streaming. It presents DMX Change Data Capture as a solution to capture changes from various data sources and replicate them in real-time to targets like Kafka, HDFS, databases and data lakes to feed machine learning models. Case studies demonstrate how DMX-h has helped customers like a global hotel chain and insurance and healthcare companies build scalable data pipelines.
How to Choose a Host for a Big Data ProjectPeak Hosting
John Johnson will present on Big Data and hosting options for Big Data applications. The presentation will cover what Big Data means, use cases, considerations for building projects, hosting challenges, and a customer example. Options that will be discussed include in-house hosting, cloud, colocation, and dedicated managed hosting (Operations as a Service). The challenges of each option for Big Data applications will also be reviewed.
HP Enterprises in Hana Pankaj Jain May 2016INDUSCommunity
HPE offers solutions for hybrid clouds and SAP HANA based on composable infrastructure. Composable infrastructure allows resources to be composed on demand in seconds and infrastructure to be programmed through a single line of code. This approach dramatically reduces overprovisioning and speeds application and service delivery. HPE's composable infrastructure solution is called Synergy, which provides fluid resource pools, software-defined intelligence, and a unified API. HPE also offers converged systems optimized for SAP HANA that are pre-configured to deliver maximum performance.
Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...MSAdvAnalytics
Lance Olson. Cortana Analytics is a fully managed big data and advanced analytics suite that helps you transform your data into intelligent action. Come to this two-part session to learn how you can do "big data" processing and storage in Cortana Analytics. In the first part, we will provide an overview of the processing and storage services. We will then talk about the patterns and use cases which make up most big data solutions. In the second part, we will go hands-on, showing you how to get started today with writing batch/interactive queries, real-time stream processing, or NoSQL transactions all over the same repository of data. Crunch petabytes of data by scaling out your computation power to any sized cluster. Store any amount of unstructured data in its native format with no limits to file or account size. All of this can be done with no hardware to acquire or maintain and minimal time to setup giving you the value of "big data" within minutes. Go to https://channel9.msdn.com/ to find the recording of this session.
Making Hadoop Realtime by Dr. William Bain of Scaleout SoftwareData Con LA
Hadoop has been widely embraced for its ability to economically store and analyze large data sets. Using parallel computing techniques like MapReduce, Hadoop can reduce long computation times to hours or minutes. This works well for mining large volumes of historical data stored on disk, but it is not suitable for gaining real-time insights from live operational data. Still, the idea of using Hadoop for real-time data analytics on live data is appealing because it leverages existing programming skills and infrastructure – and the parallel architecture of Hadoop itself. This presentation will describe how real-time analytics using Hadoop can be performed by combining an in-memory data grid (IMDG) with an integrated, stand-alone Hadoop MapReduce execution engine. This new technology delivers fast results for live data and also accelerates the analysis of large, static data sets.
Journey to the Data Lake: How Progressive Paved a Faster, Smoother Path to In...DataWorks Summit
Progressive Insurance is well known for its innovative use of data to better serve its customers, and the important role that Hortonworks Data Platform has played in that transformation. However, as with most things worth doing, the path to the Data Lake was not without its challenges. In this session, I’ll share our top use cases for Hadoop – including telematics and display ads, how a skills shortage turned supporting these applications into a nightmare, and how – and why – we now use Syncsort DMX-h to accelerate enterprise adoption by making it quick and easy (or faster and easier) to populate the data lake – and keep it up to date – with data from across the enterprise. I’ll discuss the different approaches we tried, the benefits of using a tool vs. open source, and how we created our Hadoop Ingestor app using Syncsort DMX-h.
Customer value analysis of big data productsVikas Sardana
Business value analysis through Customer Value Model for software technology choices with a case study from Mobile Advertising industry for Big Data use case.
This document introduces DayF core, an easy-to-use machine learning platform. Some key points:
- It is designed to be simple for domain experts to use with minimal machine learning knowledge required. Models and algorithms are automatically selected.
- It aims to address challenges like the difficulty for non-experts to exploit data and modern techniques, and the expense of traditional machine learning projects.
- The platform uses Python, H2O.ai and Apache Spark frameworks. It allows users to perform analyses, select recommended models, make predictions, and view results through a web interface or APIs.
The webinar discusses how organizations can make big data easy to use with the right tools and talent. It presents on MetaScale's expertise in helping Sears Holdings implement Hadoop and how Kognitio's in-memory analytics platform can accelerate Hadoop for organizations. The webinar agenda includes an introduction, a case study on Sears Holdings' Hadoop implementation, an explanation of how Kognitio's platform accelerates Hadoop, and a Q&A session.
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
An overview of modern scalable web developmentTung Nguyen
The document provides an overview of modern scalable web development trends. It discusses the motivation to build systems that can handle large amounts of data quickly and reliably. It then summarizes the evolution of software architectures from monolithic to microservices. Specific techniques covered include reactive system design, big data analytics using Hadoop and MapReduce, machine learning workflows, and cloud computing services. The document concludes with an overview of the technologies used in the Septeni tech stack.
SplunkLive! Nutanix Session - Turnkey and scalable infrastructure for Splunk ...Splunk
Nutanix provides a turnkey and scalable infrastructure for Splunk in 3 sentences:
1) The Nutanix solution uses SSD and a scale-out datacenter appliance to address Splunk's IO intensity and provide faster time to value.
2) It employs a scale-out cluster to eliminate server sprawl and simplify adding more data sources.
3) The converged and software-defined Nutanix platform virtualizes Splunk for enterprise features while improving performance, capacity, and manageability over direct deployment.
The document discusses Kognitio, an in-memory analytical platform for big data. It is built from the ground up to perform large, complex analytics on big data sets using a massively parallel architecture. Kognitio offers its platform both on-premises and in the cloud to provide high-performance analytics capabilities to power business insights. It aims to complement existing data infrastructures like Hadoop and data warehouses through its scalable in-memory approach and tight integration capabilities.
Big data analytics and machine intelligence v5.0Amr Kamel Deklel
Why big data
What is big data
When big data is big data
Big data information system layers
Hadoop echo system
What is machine learning
Why machine learning with big data
Using the Power of Big SQL 3.0 to Build a Big Data-Ready Hybrid WarehouseRizaldy Ignacio
Big SQL 3.0 provides a powerful way to run SQL queries on Hadoop data without compromises. It uses a modern MPP architecture instead of MapReduce for high performance. Federation allows Big SQL to access external data sources within a single SQL statement, enabling hybrid data warehouse scenarios.
This document discusses strategies for modernizing data centers through increased abstraction from hardware infrastructure. It advocates for a multi-year strategic planning approach to balance both incremental and transformational changes. Key elements of data center modernization discussed include adopting software-defined, programmable infrastructure through converged solutions, automation, and virtualization. Planning considerations cover people, processes, and technologies to support a transition to software-defined, utility-like operations over time.
How to scale your PaaS with OVH infrastructure?OVHcloud
ForePaaS provides a platform for data infrastructure automation that allows customers to collect, store, transform and analyze data across multiple cloud providers or on-premise in a unified manner. Key features of the ForePaaS platform include being end-to-end, multi-cloud, providing a marketplace for sharing elements of work, and offering automated infrastructure that scales based on customer needs. ForePaaS has partnered with OVH to leverage their public cloud, private cloud, and bare metal server offerings to power ForePaaS infrastructure globally.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
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2. Kognitio
Kognitio is focused on providing the premier high-
performance analytical platform to power business
insight around the world.
•Privately held
•Dev Labs in the UK
•Leadership in US
•~100 employees
Core product:
•MPP in‐memory
analytical platform
•Built from the
ground‐up to satisfy
large and complex
analytics on big data
sets
4. Brand Streamlining – New Kognitio Product Hierarchy
Console Kognitio v8 Software MDX Connector
Admin Tools Analytical Processing Cube Designer
Accelerator for Hadoop Excel Add-in
Accelerator for …
Analytical Appliance
5. Flexibility Engrained to our Business Model
Public Cloud (SaaS) Private Cloud Software Appliance Partnerships
Low costs, no CapEx Pre-built and configured Industry-standard x86 Commodity hardware ISVs:
requirement, immediate Highest security for Linux servers per client preference specialized/industry
provisioning sensititive data sets Rapid deployment and Bespoke formula of solutions
Incur charged per hour Hosted in Tier-3 data implementation RAM data memory, Services:
on demand (CPU core/ centers via specialized Typical license- server cores and disk implementation and
hour) hosting providers maintenance contract specifications delivery
Provided by Kognitio, ~ 48 hour provisioning for customers Profit = software Distributors: expanded
hosted by Amazon Web Massively scalable license + hardware market coverage
Services (AWS) margins
- Flexible delivery model to meet client requirements
- Partnership channel builds ecosystem and expands reach
- Revenue model:
- One-time charge: volume-based software licenses
- Recurring revenue: maintenance and support
7. In-memory Analytical Platform
“pull very large amounts of data from existing data storage
(persistence) systems into high speed computer memory”
– can be existing traditional disk based data warehouse
products, operational systems, Kognitio’s own disk
subsystem or increasingly distributed parallel file systems
such as Hadoop or cloud storage
• Scale the power as required
• Adaptable capacity
– Scale up / down as when needed within server farm
• Utilize local disk for near-line store of regularly used
reference data or result sets
8. What is an “In-memory” Analytical Platform?
• A database where all of the data of interest or specific portions of the
data have been permanently pre-loaded into a computers random
access memory (RAM).
• Not a large cache
– Data is held in structures that take advantage of the properties of
RAM – NOT copies of frequently used disk blocks
– The databases query optimiser knows at all times exactly which
data is in memory and which is not
9. Speed & Scale from “True MPP”
• Memory & CPU on an individual server = NOWHERE near enough for big data
– Moore’s Law – The power of a processor doubles every two years
– Data volumes – Double every year!!
• The only way to keep up is to parallelise or scale-out
• Combine the RAM of many individual servers
Many •
•
many CPU cores spread across
many CPUs, housed in
• many individual computers (1 to 1000+)
– Data is split across all the CPU cores
– All database operations are parallelised with no points of serialisation –
This is true MPP
• Every CPU core in
Every • Every server needs to efficiently involved in
• Every query
10. V8 Enables the Analytical Platform Reference
Architecture
External Functions
Not Only SQL
External Tables
Kognitio Storage
as an External table
Hadoop Connector Other Connectors
11. Not Only SQL: any language in-line
Kognitio External Scripts
– Run third party binaries or scripts embedded within SQL
• Perl, Python, Java, R, SAS, etc.
• One-to-many rows in, zero-to-many rows out, one to one
create interpreter perlinterp
command '/usr/bin/perl' sends 'csv' receives 'csv' ;
select top 1000 words, count(*) This reads long comments
from (external script using environment perlinterp text from customer enquiry
receives (txt varchar(32000))
sends (words varchar(100)) table, in line perl converts
script S'endofperl( long text into output
while(<>)
{ stream of words (one word
chomp(); per row), query selects top
s/[,.!_]//g;
foreach $c (split(/ /)) 1000 words by frequency
{ if($c =~ /^[a-zA-Z]+$/) { print "$cn”} } using standard SQL
}
)endofperl' aggregation
from (select comments from customer_enquiry))dt
group by 1
order by 2 desc;
12. Kognitio Hadoop Connectors
HDFS Connector
• Connector defines access to hdfs file system
• External table accesses row-based data
in hdfs
• Dynamic access or “pin” data into memory
• Complete hdfs file is loaded into memory
Filter Agent Connector
• Connector uploads agent to Hadoop nodes
• Query passes selections and relevant
predicates to agent
• Data filtering and projection takes place
locally on each Hadoop node
• Only data of interest in loaded into memory
via parallel load streams
13. Innovative client solutions
TiVo Research & Analytics 40 TBs of RAM that perform complex media analytics,
cross‐correlating data from over 22 sources with set‐top box data to allow
Software advertisers, networks and agencies to analyze the ROI of creative campaigns
while they are still in flight, enabling self‐service reporting for business users
The VivaKi Nerve Center provides social media and other analytics for campaign
Public
monitoring and near real‐time advertising effectiveness. This enables agencies in the
Cloud Publicis Global Network to provide deep‐dive analytics into TBs of data in seconds
AIMIA provides self‐service customer loyalty analysis on over 24 billion transactions
that are live in‐memory full volumes of POS data. Retailers, Customer Packaged Goods
Appliance companies and other service providers, provide merchandise managers with “train‐of‐
thought” analysis to better target customers.
Orbitz leverages Kognitio Cloud to take large volumes of complex data, ingested in real
Private time from web channels, demographic and psychographic data, customer segmentation
Cloud and modeling scores and turn it into actionable intelligence, allowing them to think of new
ways of offering the right products and services to its current and prospective client base.
PlaceIQ provdes actionable hyper‐local Mobile BI location intelligence. They
leverage Kognitio to extracts intelligence from large amounts of place, social and
Public
mobile location‐based data to create hyper‐local, targetable audience profiles,
Cloud giving advertisers the power to connect with consumers at the right place, at the
right time, with the right message.
14. Analytics on tens of billions of events in
tens of seconds with NO DBA
Context for media analytics:
• In‐memory analytical database for Big Data
• Correlate everything to everything
• MPP + Linear Scalability
• Predictable and ultra‐fast performance
Challenges • > 22 data sources
– Expanding volumes of data
• Commodity servers/equipment
– Few opportunities for
summarization (demographics, • Market‐available IT skills
purchaser targets, etc.)
• No solution re‐engineering
– Data too large/complex for
traditional database systems
– Need for simple administration
Solution Benefits Mars, Inc.:
– Reports allow advertisers, networks and agencies to analyze the “By using TRA to improve media plans, creative and
relative strengths and weaknesses of different creative flighting, Mars has achieved a portfolio increase in ROI
executions, and how such variables as program environment, versus a year ago of 25% in one category and 35% in a
time slots, and pod position impact their ROI second category.”
– Enables self‐service reporting for business users
15. Thank You!
connect contact
Michael Hiskey
kognitio.com
Vice President,
Marketing & Business Development
kognitio.tel michael.hiskey@kognitio.com
+1.917.375.8196
kognitio.com/blog Paul Groom
VP, Business Intelligence
Paul.groom@kognitio.com
twitter.com/kognitio
John Coppins
linkedin.com/companies/kognitio SVP, Kognitio Cloud
john/.coppins@kognitio.com
tinyurl.com/kognitio Steve Friedberg
MMI Communications
youtube.com/kognitio steve@mmicommunications.com