Intuit Analytics Cloud 101
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The document discusses Intuit's vision to transform customers' lives by unleashing the power of data. It describes Intuit's Analytics Cloud (IAC), which provides a data platform and foundational services to derive value from data. The IAC allows for real-time and batch data ingestion from various sources and provides services like business lookups, unified customer profiles, and personalization. An example use case of using tax data to personalize the tax preparation experience is also mentioned. The document outlines Intuit's journey to building the IAC, including initially lifting existing systems to the cloud and now focusing on real-time streaming capabilities. Key practices for planning, deploying and managing the IAC are also listed.
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Finding the needle in the haystack: how Nestle is leveraging big data to defe...
Finding the needle in the haystack: how Nestle is leveraging big data to defe...
Leveraging Spark to Democratize Data for Omni-Commerce with Shafaq Abdullah
Leveraging Spark to Democratize Data for Omni-Commerce with Shafaq Abdullah
Unifying Streaming and Historical Telemetry Data For Real-time Performance Re...
Unifying Streaming and Historical Telemetry Data For Real-time Performance Re...
The key to unlocking the Value in the IoT? Managing the Data!
The key to unlocking the Value in the IoT? Managing the Data!
Migrate and Modernize Hadoop-Based Security Policies for Databricks
Migrate and Modernize Hadoop-Based Security Policies for Databricks
Power Your Delta Lake with Streaming Transactional Changes
Power Your Delta Lake with Streaming Transactional Changes
Advanced data science algorithms applied to scalable stream processing by Dav...
Advanced data science algorithms applied to scalable stream processing by Dav...
Calum McCrea, Software Engineer at Kx Systems, "Kx: How Wall Street Tech can ...
Calum McCrea, Software Engineer at Kx Systems, "Kx: How Wall Street Tech can ...
Delivering digital transformation and business impact with io t, machine lear...
Delivering digital transformation and business impact with io t, machine lear...
2016 Spark Summit East Keynote: Ali Ghodsi and Databricks Community Edition demo
2016 Spark Summit East Keynote: Ali Ghodsi and Databricks Community Edition demo
Migration and Coexistence between Relational and NoSQL Databases by Manuel H...
Migration and Coexistence between Relational and NoSQL Databases by Manuel H...
Webinar: The Modern Streaming Data Stack with Kinetica & StreamSets
Webinar: The Modern Streaming Data Stack with Kinetica & StreamSets
Enabling Next Gen Analytics with Azure Data Lake and StreamSets
Enabling Next Gen Analytics with Azure Data Lake and StreamSets
The State of the Data Warehouse in 2017 and Beyond
The State of the Data Warehouse in 2017 and Beyond
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Data Regions: Modernizing your company's data ecosystem
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Cloud Computing and Data Center Futures
A copy of the presentation given to the Innovation Group Italy's Cloud Computing Summit March 19th 2014
The Big Data Journey – How Companies Adopt Hadoop - StampedeCon 2016
Hadoop adoption is a journey. Depending on the business the process can take weeks, months, or even years. Hadoop is a transformative technology so the challenges have less to do with the technology and more to do with how a company adapts itself to a new way of thinking about data. There are challenges for companies who have lived with an application driven business for the last two decades to suddenly become data driven. Companies need to begin thinking less in terms of single, silo’d servers and more about “the cluster”.
The concept of the cluster becomes the center of data gravity drawing all the applications to it. Companies, especially the IT organizations, embark on a process of understanding how to maintain and operationalize this environment and provide the data lake as a service to the businesses. They must empower the business by providing the resources for the use cases which drive both renovation and innovation. IT needs to adopt new technologies and new methodologies which enable the solutions. This is not technology for technology sake. Hadoop is a data platform servicing and enabling all facets of an organization. Building out and expanding this platform is the ongoing journey as word gets out to businesses that they can have any data they want and any time. Success is what drives the journey.
The length of the journey varies from company to company. Sometimes the challenges are based on the size of the company but many times the challenges are based on the difficulty of unseating established IT processes companies have adopted without forethought for the past two decades. Companies must navigate through the noise. Sifting through the noise to find those solutions which bring real value takes time. As the platform matures and becomes mainstream, more and more companies are finding it easier to adopt Hadoop. Hundreds of companies have already taken many steps; hundreds more have already taken the first step. As the wave of successful Hadoop adoption continues, more and more companies will see the value in starting the journey and paving the way for others.
Innovation in the Data Warehouse - StampedeCon 2016
Enterprise Holding’s first started with Hadoop as a POC in 2013. Today, we have clusters on premises and in the cloud. This talk will explore our experience with Big Data and outline three common big data architectures (batch, lambda, and kappa). Then, we’ll dive into the decision points to necessary for your own cluster, for example: cloud vs on premises, physical vs virtual, workload, and security. These decisions will help you understand what direction to take. Finally, we’ll share some lessons learned with the pieces of our architecture worked well and rant about those which didn’t. No deep Hadoop knowledge is necessary, architect or executive level.
Provisioning Big Data Platform using Cloudbreak & Ambari
Provisioning Big Data Platform using Cloudbreak & Ambari
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Data Regions: Modernizing your company's data ecosystem
Data Regions: Modernizing your company's data ecosystem
Hadoop Summit 2016 - Evolution of Big Data Pipelines At Intuit
Hadoop Summit 2016 - Evolution of Big Data Pipelines At Intuit
The Big Data Journey – How Companies Adopt Hadoop - StampedeCon 2016
The Big Data Journey – How Companies Adopt Hadoop - StampedeCon 2016
Innovation in the Data Warehouse - StampedeCon 2016
Innovation in the Data Warehouse - StampedeCon 2016
Provisioning Big Data Platform using Cloudbreak & Ambari
Provisioning Big Data Platform using Cloudbreak & Ambari
Similar to Intuit Analytics Cloud 101
A Connected Data Landscape: Virtualization and the Internet of Things
The Briefing Room with Dr. Robin Bloor and Cisco
Live Webcast March 3, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=a75f0f379405de155800a37b2bf104db
Data at rest, data in motion - regardless of its trajectory, data remains the lifeblood of today's information economy. But finding a way to bridge old systems with new opportunities requires an innovative data strategy, one that takes advantage of multiple processing technologies. With the optimal architecture in place, companies can harness years of work in traditional information systems, while opening the door to the flood of new data sources available.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor, as he explains how data virtualization and other data technologies fundamentally change what's possible with data access, movement and analysis. He'll be briefed by David Besemer of Cisco, who will discuss how this new kind of data strategy can enable the integration of legacy systems, Cloud computing and the Internet of Things. He'll also answer questions about how Big Data and the IoT are helping to redefine the practice of data management.
Visis InsideAnalysis.com for more information.
Building Confidence in Big Data - IBM Smarter Business 2013
Success with big data comes down to confidence. Without confidence in the underlying data, decision makers may not trust and act on analytic insight. You need confidence in your data – that it’s correct, trusted, and protected through automated integration, visual context, and agile governance. You need confidence in your ability to accelerate time to value, with fast deployments of big data appliances. Learn how clients have succeeded with big data by building confidence in their data, ability to deploy, and skills. Presenter: David Corrigan, Big Data specialist, IBM. Mer från dagen på http://bit.ly/sb13se
There are 250 Database products, are you running the right one?
Slide deck from a webinar from Aerospike featuring Forrester Research
Streaming Analytics Comparison of Open Source Frameworks, Products, Cloud Ser...
Streaming Analytics Comparison of Open Source Frameworks, Products and Cloud Services. Includes Apache Storm, Flink, Spark, TIBCO, IBM, AWS Kinesis, Striim, Zoomdata, ...
This session discusses the technical concepts of stream processing / streaming analytics and how it is related to big data, mobile, cloud and internet of things. Different use cases such as predictive fault management or fraud detection are used to show and compare alternative frameworks and products for stream processing and streaming analytics.
The focus of the session lies on comparing
- different open source frameworks such as Apache Apex, Apache Flink or Apache Spark Streaming
- engines from software vendors such as IBM InfoSphere Streams, TIBCO StreamBase
- cloud offerings such as AWS Kinesis.
- real time streaming UIs such as Striim, Zoomdata or TIBCO Live Datamart.
Live demos will give the audience a good feeling about how to use these frameworks and tools.
The session will also discuss how stream processing is related to Apache Hadoop frameworks (such as MapReduce, Hive, Pig or Impala) and machine learning (such as R, Spark ML or H2O.ai).
Cisco Connect Toronto 2018 an introduction to Cisco kinetic
Cisco Connect Toronto 2018 an introduction to Cisco kinetic
Cisco Connect Toronto 2018 an introduction to Cisco kinetic
Cisco Connect Toronto 2018 an introduction to Cisco kinetic
Bridging the Gap: Analyzing Data in and Below the Cloud
The Briefing Room with Dean Abbott and Tableau Software
Live Webcast July 23, 2013
http://www.insideanalysis.com
Today’s desire for analytics extends well beyond the traditional domain of Business Intelligence. That’s partly because business users are realizing the value of mixing and matching all kinds of data, from all kinds of sources. One emerging market driver is Cloud-based data, and the desire companies have to analyze this data cohesively with their on-premise data sets.
Register for this episode of The Briefing Room to learn from Analyst Dean Abbott, who will explain how the ability to access data in the cloud can play a critical role for generating business value from analytics. He’ll be briefed by Ellie Fields of Tableau Software who will tout Tableau’s latest release, which includes native connectors to cloud-based applications like Salesforce.com, Amazon Redshift, Google Analytics and BigQuery. She’ll also demonstrate how Tableau can combine cloud data with other data sources, including spreadsheets, databases, cubes and even Big Data.
Open Blueprint for Real-Time Analytics in Retail: Strata Hadoop World 2017 S...
This presentation outlines key business drivers for real-time analytics applications in retail and describes the emerging architectures based on In-Stream Processing (ISP) technologies. The slides present a complete open blueprint for an ISP platform - including a demo application for real-time Twitter Sentiment Analytics - designed with 100% open source components and deployable to any cloud.
To learn more, read an adjoining blog series on this topic here : https://blog.griddynamics.com/in-stream-processing-service-blueprint
Time Difference: How Tomorrow's Companies Will Outpace Today's
The Briefing Room with Mark Madsen and WebAction
Live Webcast Feb. 10, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=fa83c6283de99dfb6f38b9d7199cb452
In our increasingly interconnected world, the windows of opportunity for meaningful action are shrinking. Where hours once sufficed, minutes are now the norm. For some transactions, seconds make all the difference, even sub-seconds. Meeting these demands requires a new approach to information architecture, one that embraces the many innovations that are fundamentally changing the data-driven economy.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature as he explains how a confluence of advances are changing the nature of data management. He'll be briefed by Sami Akbay of WebAction, who will showcase his company's real-time data platform, designed from the ground up to meet the challenges of leveraging Big Data in concert with all manner of operational enterprise systems.
Visit InsideAnalysis.com for more information.
A Key to Real-time Insights in a Post-COVID World (ASEAN)
Watch full webinar here: https://bit.ly/2EpHGyd
Presented at Data Champions, Online Asia 2020
Businesses and individuals around the world are experiencing the impact of a global pandemic. With many workers and potential shoppers still sequestered, COVID-19 is proving to have a momentous impact on the global economy. Regardless of the current situation and post-pandemic era, real-time data becomes even more critical to healthcare practitioners, business owners, government officials, and the public at large where holistic and timely information are important to make quick decisions. It enables doctors to make quick decisions about where to focus the care, business owners to alter production schedules to meet the demand, government agencies to contain the epidemic, and the public to be informed about prevention.
In this on-demand session, you will learn about the capabilities of data virtualization as a modern data integration technique and how can organisations:
- Rapidly unify information from disparate data sources to make accurate decisions and analyse data in real-time
- Build a single engine for security that provides audit and control by geographies
- Accelerate delivery of insights from your advanced analytics project
Moving Targets: Harnessing Real-time Value from Data in Motion
The Briefing Room with David Loshin and Datawatch
Live Webcast Feb. 17, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=4a053043c45cf0c2f6453dfb8577c72a
Patience may be a virtue, but when it comes to streaming analytics, waiting is no option. Between Big Data and the Internet of Things, businesses are faced with more data and greater complexity than ever before. Traditional information architectures simply cannot support the kind of processing necessary to make use of this fast-moving resource. The modern context requires a shorter path to analytics, one that narrows the gap between governance and discovery
Register for this episode of The Briefing Room to hear veteran Analyst David Loshin as he explains how the prevalence of streaming data is changing business pace and processes. He’ll be briefed by Dan Potter of Datawatch, who will tout his company’s real-time data discovery platform for data in motion. He will show how self-service data preparation can lead to faster insights, ultimately fostering the ability to make precise decisions at the right time.
Visit InsideAnalysis.com for more information.
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)
Watch full webinar here: https://bit.ly/3idAnbf
Heute werden hochwertige Daten schnell und integriert benötigt, mittlerweile häufig auch über unterschiedliche Clouds hinweg.
Datenvirtualisierung kann hier als logische Datenschicht wahre Wunder wirken und die Modernisierung der Datenarchitektur drastisch beschleunigen.
In unserem kostenlosen Webinar interviewen wir den Experten Otto Neuer von Denodo, der die hier nur angerissenen Gedanken weiter ausführt. Er wird uns Einblicke in den Wandel von Datenarchitekturen geben und wie aus seinem Blickwinkel die nächste Phase der Business Intelligence aussieht.
Was Sie mitnehmen:
- Was sind die Herausforderungen und Limitierungen traditioneller Datenarchitekturen
- Wie können mit modernen Architekturen diese Limitierungen aufgehoben werden
- Welche Rolle spielt Datenvirtualisierung bei modernen Datenarchitekturen
- Was ist die nächste Phase der Business Intelligence
Erfahren Sie am 23. September 2020, den Experten Otto Neuer von Denodo zusammen mit unserem Partner QuinScape GmbH wird uns Einblicke in den Wandel von Datenarchitekturen geben und wie aus seinem Blickwinkel die nächste Phase der Business Intelligence aussieht.
Sie haben Interesse? Dann melden Sie sich am besten direkt an - die Plätze der Veranstaltung sind begrenzt.
CL2015 - Datacenter and Cloud Strategy and Planning
Keynote session by Shashi Kiran, VP, DC and Cloud, Cisco PSM
Rethinking Data Availability and Governance in a Mobile World
The Briefing Room with Malcolm Chisholm and Druva
Live Webcast on June 9, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=baf82d3835c5dfa63202dcbe322a3ad7
The emergence of the mobile workforce has left an indelible mark on the enterprise; every employee is now mobile, and business data continues to be dispatched to the far reaches of the enterprise. While this has added enormous opportunity for increased productivity, it has also muddied the waters when it comes to controlling and protecting valuable data assets. As companies quickly evolve to address the new set of challenges posed by this shift in data usage, IT must ensure that all data, no matter where it’s generated or stored, is available and governed just as if it were still safely behind the corporate firewall.
Register for this episode of The Briefing Room to hear veteran Analyst Malcolm Chisholm as he explains the myriad challenges that mobile data introduces when addressing regulations and compliance needs, requiring new approaches to data governance. He’ll be briefed by Dave Packer of Druva, who will outline his company’s converged data protection strategy, which brings data center class capabilities to backup, availability and governance for the mobile workforce. He will share strategies to meet regional data residency, data recovery, legal hold and eDiscovery requirements and more.
Visit InsideAnalysis.com for more information.
Rethinking Data Availability and Governance in a Mobile World
The Briefing Room with Malcolm Chisholm and Druva
Live
The emergence of the mobile workforce has left an indelible mark on the enterprise; every employee is now mobile, and business data continues to be dispatched to the far reaches of the enterprise. While this has added enormous opportunity for increased productivity, it has also muddied the waters when it comes to controlling and protecting valuable data assets. As companies quickly evolve to address the new set of challenges posed by this shift in data usage, IT must ensure that all data, no matter where it’s generated or stored, is available and governed just as if it were still safely behind the corporate firewall.
Register for this episode of The Briefing Room to hear veteran Analyst Malcolm Chisholm as he explains the myriad challenges that mobile data introduces when addressing regulations and compliance needs, requiring new approaches to data governance. He’ll be briefed by Dave Packer of Druva, who will outline his company’s converged data protection strategy, which brings data center class capabilities to backup, availability and governance for the mobile workforce. He will share strategies to meet regional data residency, data recovery, legal hold and eDiscovery requirements and more.
Streaming Cyber Security into Graph: Accelerating Data into DataStax Graph an...
Traditional security tools like security information and event managers (SIEMs) are struggling to keep up with the terabytes of event data (250M to 2B events) being generated each day from an ever-growing number of devices. Cybersecurity has become a data problem, and enterprises need to reply with scalable solutions to enable effective hunting and combat evolving attacks. Rethinking the cybersecurity problem as a data-centric problem led Accenture Labs’s Cybersecurity team to use emerging big data tools along with new approaches such as graph databases and analysis to exploit the connected nature of the data to its advantage. Joshua Patterson, Michael Wendt, and Keith Kraus explain how Accenture Labs’s Cybersecurity team is using Apache Kafka, Spark, and Flink to stream data into Blazegraph and Datastax Graph to accelerate cyber defense.
Leveraging Datastax Graph and Blazegraph allows Accenture Labs to greatly accelerate query and analysis performance compared to traditional security tools like SIEM. Josh, Michael, and Keith share the challenges of fitting cybersecurity data into each of the graph structures, as well as the ways they exploited the connectedness of events to discover new threats that would have been missed in traditional SIEM tools. In addition, they explain how they use GPUs to accelerate graph analysis by using Blazegraph DASL. Josh, Michael, and Keith end by demonstrating how to efficiently and effectively stream data into these graph databases using best-in-breed technologies such as Apache Kafka, Spark, and Flink and touch on why Kudu is becoming an integral part of Accenture’s technology stack. Utilizing these technologies, clients have supercharged their security analysts’ cyber-hunting abilities and are uncovering threats faster.
Keynote Address at 2013 CloudCon: Future of Big Data by Richard McDougall (In...
Over the last few years we’ve seen a frenzy of interest and buzz around the area of Big Data. Beyond the hype, there is a solid base of growing use cases, which are becoming center stage to most businesses. 2012 was the year of awareness. There was a great amount of sharing from the early core developers of the analytic platforms – showing the rest of the world the capabilities of the tools and platforms that had been developed for special purpose high scale analytics. The big names at the core of open source analytics development include Facebook, eBay, Linkedin, Twitter – all blazing the trail with new approaches. These companies brought along with them a new and expanding interest in leveraging the same technologies for commercial interest.
This talk is focused at how a growing number of enterprises that are already heavily invested in the use cases – but by volume, most customers now have some form of big data proof-of-concept underway. These proof of concepts typically start with a thesis of how competitive advantage can be gained through insight from the data. A proof of concept can quickly validate the theory, and helps sell further investment in the analytics platform, and it snowballs from there.
Barcelona Digital Festival 28th Nov 2019 - Data Analytics in eSports. UbeatCa...
Taken from our BCN Digital Festival last week, for info on attending, speaking or sponsoring our next event on the 29/30th April 2020 email enquiry@digitalenterprisefest.com
Data Analytics in eSports. UbeatCase Study
Building AI & Automate services need a solid base on Data Management, but the current environment is volatile, uncertain, complex and ambiguous so you never know what data will be important in the following months.
The Data Management Platform in an extremely dynamic market like eSports where everything is currently being created, in reinvention and is to be validated is even more challenging.
UBEAT is the leading streaming platform of eSports related content. Created in November 2018, it still hasn’t 12 months of existence but a lot of learnings in its rear mirror and a lot of future to come in its high beam. Especially regarding Data Management.
To apply to speak or sponsor our 2020 events goto www.digitalenterprisefest.com
Keeping Up with the ELK Stack: Elasticsearch, Kibana, Beats, and Logstash
Version 7 of the Elastic Stack adds powerful new features to the popular open source platform for search, logging, and analytics. Come hear directly from Elastic engineers and architecture team members on powerful new additions like GIS functionality and frozen-tier search. Plus, hear about the full range of orchestration options for getting the most out of your deployments, however and wherever you choose to run them. This session is sponsored by Elastic.
Similar to Intuit Analytics Cloud 101 (20)
A Connected Data Landscape: Virtualization and the Internet of Things
A Connected Data Landscape: Virtualization and the Internet of Things
Building Confidence in Big Data - IBM Smarter Business 2013
Building Confidence in Big Data - IBM Smarter Business 2013
There are 250 Database products, are you running the right one?
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Streaming Analytics Comparison of Open Source Frameworks, Products, Cloud Ser...
Streaming Analytics Comparison of Open Source Frameworks, Products, Cloud Ser...
Cisco Connect Toronto 2018 an introduction to Cisco kinetic
Cisco Connect Toronto 2018 an introduction to Cisco kinetic
Cisco Connect Toronto 2018 an introduction to Cisco kinetic
Cisco Connect Toronto 2018 an introduction to Cisco kinetic
Bridging the Gap: Analyzing Data in and Below the Cloud
Bridging the Gap: Analyzing Data in and Below the Cloud
Open Blueprint for Real-Time Analytics in Retail: Strata Hadoop World 2017 S...
Open Blueprint for Real-Time Analytics in Retail: Strata Hadoop World 2017 S...
Time Difference: How Tomorrow's Companies Will Outpace Today's
Time Difference: How Tomorrow's Companies Will Outpace Today's
A Key to Real-time Insights in a Post-COVID World (ASEAN)
A Key to Real-time Insights in a Post-COVID World (ASEAN)
Moving Targets: Harnessing Real-time Value from Data in Motion
Moving Targets: Harnessing Real-time Value from Data in Motion
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)
CL2015 - Datacenter and Cloud Strategy and Planning
CL2015 - Datacenter and Cloud Strategy and Planning
Rethinking Data Availability and Governance in a Mobile World
Rethinking Data Availability and Governance in a Mobile World
Rethinking Data Availability and Governance in a Mobile World
Rethinking Data Availability and Governance in a Mobile World
Streaming Cyber Security into Graph: Accelerating Data into DataStax Graph an...
Streaming Cyber Security into Graph: Accelerating Data into DataStax Graph an...
Data Virtualization: revolutionizing database cloning
Data Virtualization: revolutionizing database cloning
Keynote Address at 2013 CloudCon: Future of Big Data by Richard McDougall (In...
Keynote Address at 2013 CloudCon: Future of Big Data by Richard McDougall (In...
Barcelona Digital Festival 28th Nov 2019 - Data Analytics in eSports. UbeatCa...
Barcelona Digital Festival 28th Nov 2019 - Data Analytics in eSports. UbeatCa...
Keeping Up with the ELK Stack: Elasticsearch, Kibana, Beats, and Logstash
Keeping Up with the ELK Stack: Elasticsearch, Kibana, Beats, and Logstash
More from DataWorks Summit/Hadoop Summit
Running Apache Spark & Apache Zeppelin in Production
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Revolutionize Text Mining with Spark and Zeppelin
Revolutionize Text Mining with Spark and Zeppelin Slides
Double Your Hadoop Performance with Hortonworks SmartSense
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Schema Registry - Set you Data Free
Many Organizations are currently processing various types of data and in different formats. Most often this data will be in free form, As the consumers of this data growing it’s imperative that this free-flowing data needs to adhere to a schema. It will help data consumers to have an expectation of about the type of data they are getting and also they will be able to avoid immediate impact if the upstream source changes its format. Having a uniform schema representation also gives the Data Pipeline a really easy way to integrate and support various systems that use different data formats.
SchemaRegistry is a central repository for storing, evolving schemas. It provides an API & tooling to help developers and users to register a schema and consume that schema without having any impact if the schema changed. Users can tag different schemas and versions, register for notifications of schema changes with versions etc.
In this talk, we will go through the need for a schema registry and schema evolution and showcase the integration with Apache NiFi, Apache Kafka, Apache Storm.
Building a Large-Scale, Adaptive Recommendation Engine with Apache Flink and ...
Building a Large-Scale, Adaptive Recommendation Engine with Apache Flink and ...DataWorks Summit/Hadoop Summit
There is increasing need for large-scale recommendation systems. Typical solutions rely on periodically retrained batch algorithms, but for massive amounts of data, training a new model could take hours. This is a problem when the model needs to be more up-to-date. For example, when recommending TV programs while they are being transmitted the model should take into consideration users who watch a program at that time.
The promise of online recommendation systems is fast adaptation to changes, but methods of online machine learning from streams is commonly believed to be more restricted and hence less accurate than batch trained models. Combining batch and online learning could lead to a quickly adapting recommendation system with increased accuracy. However, designing a scalable data system for uniting batch and online recommendation algorithms is a challenging task. In this talk we present our experiences in creating such a recommendation engine with Apache Flink and Apache Spark.Real-Time Anomaly Detection using LSTM Auto-Encoders with Deep Learning4J on ...
Real-Time Anomaly Detection using LSTM Auto-Encoders with Deep Learning4J on ...DataWorks Summit/Hadoop Summit
DeepLearning is not just a hype - it outperforms state-of-the-art ML algorithms. One by one. In this talk we will show how DeepLearning can be used for detecting anomalies on IoT sensor data streams at high speed using DeepLearning4J on top of different BigData engines like ApacheSpark and ApacheFlink. Key in this talk is the absence of any large training corpus since we are using unsupervised machine learning - a domain current DL research threats step-motherly. As we can see in this demo LSTM networks can learn very complex system behavior - in this case data coming from a physical model simulating bearing vibration data. Once draw back of DeepLearning is that normally a very large labaled training data set is required. This is particularly interesting since we can show how unsupervised machine learning can be used in conjunction with DeepLearning - no labeled data set is necessary. We are able to detect anomalies and predict braking bearings with 10 fold confidence. All examples and all code will be made publicly available and open sources. Only open source components are used.Mool - Automated Log Analysis using Data Science and ML
QE automation for large systems is a great step forward in increasing system reliability. In the big-data world, multiple components have to come together to provide end-users with business outcomes. This means, that QE Automations scenarios need to be detailed around actual use cases, cross-cutting components. The system tests potentially generate large amounts of data on a recurring basis, verifying which is a tedious job. Given the multiple levels of indirection, the false positives of actual defects are higher, and are generally wasteful.
At Hortonworks, we’ve designed and implemented Automated Log Analysis System - Mool, using Statistical Data Science and ML. Currently the work in progress has a batch data pipeline with a following ensemble ML pipeline which feeds into the recommendation engine. The system identifies the root cause of test failures, by correlating the failing test cases, with current and historical error records, to identify root cause of errors across multiple components. The system works in unsupervised mode with no perfect model/stable builds/source-code version to refer to. In addition the system provides limited recommendations to file/open past tickets and compares run-profiles with past runs.
How Hadoop Makes the Natixis Pack More Efficient
Improving business performance is never easy! The Natixis Pack is like Rugby. Working together is key to scrum success. Our data journey would undoubtedly have been so much more difficult if we had not made the move together.
This session is the story of how ‘The Natixis Pack’ has driven change in its current IT architecture so that legacy systems can leverage some of the many components in Hortonworks Data Platform in order to improve the performance of business applications. During this session, you will hear:
• How and why the business and IT requirements originated
• How we leverage the platform to fulfill security and production requirements
• How we organize a community to:
o Guard all the players, no one gets left on the ground!
o Us the platform appropriately (Not every problem is eligible for Big Data and standard databases are not dead)
• What are the most usable, the most interesting and the most promising technologies in the Apache Hadoop community
We will finish the story of a successful rugby team with insight into the special skills needed from each player to win the match!
DETAILS
This session is part business, part technical. We will talk about infrastructure, security and project management as well as the industrial usage of Hive, HBase, Kafka, and Spark within an industrial Corporate and Investment Bank environment, framed by regulatory constraints.
HBase in Practice
HBase hast established itself as the backend for many operational and interactive use-cases, powering well-known services that support millions of users and thousands of concurrent requests. In terms of features HBase has come a long way, overing advanced options such as multi-level caching on- and off-heap, pluggable request handling, fast recovery options such as region replicas, table snapshots for data governance, tuneable write-ahead logging and so on. This talk is based on the research for the an upcoming second release of the speakers HBase book, correlated with the practical experience in medium to large HBase projects around the world. You will learn how to plan for HBase, starting with the selection of the matching use-cases, to determining the number of servers needed, leading into performance tuning options. There is no reason to be afraid of using HBase, but knowing its basic premises and technical choices will make using it much more successful. You will also learn about many of the new features of HBase up to version 1.3, and where they are applicable.
The Challenge of Driving Business Value from the Analytics of Things (AOT)
The Challenge of Driving Business Value from the Analytics of Things (AOT)DataWorks Summit/Hadoop Summit
There has been an explosion of data digitising our physical world – from cameras, environmental sensors and embedded devices, right down to the phones in our pockets. Which means that, now, companies have new ways to transform their businesses – both operationally, and through their products and services – by leveraging this data and applying fresh analytical techniques to make sense of it. But are they ready? The answer is “no” in most cases.
In this session, we’ll be discussing the challenges facing companies trying to embrace the Analytics of Things, and how Teradata has helped customers work through and turn those challenges to their advantage.Breaking the 1 Million OPS/SEC Barrier in HOPS Hadoop
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From Regulatory Process Verification to Predictive Maintenance and Beyond wit...
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