This document discusses building a machine learning model for real-time time series analysis on big data. It describes using Spark and Kafka to ingest streaming sensor data and train a model to identify patterns and predict failures. The training phase identifies concepts in historical data to build a knowledge base. In real-time, incoming data is processed in microbatches to identify patterns and sequences matching the concepts, triggering alerts. Challenges addressed include handling large volumes of small files and sharing data between batches for signals spanning multiple batches.
-Introduction to sample problem statement
-Which Graph database is used and why
-Installing Titan
-Titan with Cassandra
-The Gremlin Cassandra script: A way to store data in cassandra from Titan Gremlin
-Accessing Titan with Spark
Contemporary computing hardware offers massive new performance opportunities. Yet high-performance programming remains a daunting challenge.
We present some of the lessons learned while designing faster indexes, with a particular emphasis on compressed bitmap indexes. Compressed bitmap indexes accelerate queries in popular systems such as Apache Spark, Git, Elastic, Druid and Apache Kylin.
-Introduction to sample problem statement
-Which Graph database is used and why
-Installing Titan
-Titan with Cassandra
-The Gremlin Cassandra script: A way to store data in cassandra from Titan Gremlin
-Accessing Titan with Spark
Contemporary computing hardware offers massive new performance opportunities. Yet high-performance programming remains a daunting challenge.
We present some of the lessons learned while designing faster indexes, with a particular emphasis on compressed bitmap indexes. Compressed bitmap indexes accelerate queries in popular systems such as Apache Spark, Git, Elastic, Druid and Apache Kylin.
Distributed Stream Processing - Spark Summit East 2017Petr Zapletal
The demand for stream processing is increasing a lot these days. Immense amounts of data have to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples.
A number of powerful, easy-to-use open source platforms have emerged to address this. But the same problem can be solved differently, various but sometimes overlapping use-cases can be targeted or different vocabularies for similar concepts can be used. This may lead to confusion, longer development time or costly wrong decisions.
Frustration-Reduced Spark: DataFrames and the Spark Time-Series LibraryIlya Ganelin
In this talk I talk about my recent experience working with Spark Data Frames and the Spark TimeSeries library. For data frames, the focus will be on usability. Specifically, a lot of the documentation does not cover common use cases like intricacies of creating data frames, adding or manipulating individual columns, and doing quick and dirty analytics. For the time series library, I dive into the kind of use cases it supports and why it’s actually super useful.
Large-Scale Text Processing Pipeline with Spark ML and GraphFrames: Spark Sum...Spark Summit
In this talk we evaluate Apache Spark for a data-intensive machine learning problem. Our use case focuses on policy diffusion detection across the state legislatures in the United States over time. Previous work on policy diffusion has been unable to make an all-pairs comparison between bills due to computational intensity. As a substitute, scholars have studied single topic areas.
We provide an implementation of this analysis workflow as a distributed text processing pipeline with Spark ML and GraphFrames.
Histogrammar package—a cross-platform suite of data aggregation primitives for making histograms, calculating descriptive statistics and plotting in Scala—is introduced to enable interactive data analysis in Spark REPL.
We discuss the challenges and strategies of unstructured data processing, data formats for storage and efficient access, and graph processing at scale.
Analyzing Time Series Data with Apache Spark and CassandraPatrick McFadin
You have collected a lot of time series data so now what? It's not going to be useful unless you can analyze what you have. Apache Spark has become the heir apparent to Map Reduce but did you know you don't need Hadoop? Apache Cassandra is a great data source for Spark jobs! Let me show you how it works, how to get useful information and the best part, storing analyzed data back into Cassandra. That's right. Kiss your ETL jobs goodbye and let's get to analyzing. This is going to be an action packed hour of theory, code and examples so caffeine up and let's go.
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
Founding committer of Spark, Patrick Wendell, gave this talk at 2015 Strata London about Apache Spark.
These slides provides an introduction to Spark, and delves into future developments, including DataFrames, Datasource API, Catalyst logical optimizer, and Project Tungsten.
Scalable Data Modeling by Example (Carlos Alonso, Job and Talent) | Cassandra...DataStax
Cassandra is getting more and more buzz and that means two things, more development and more issues. Some issues are unavoidable, but some of them are, just by understanding how our tooling works.
In this talk I'd like to review the core concepts on which Cassandra is built and how they impose the way we should work with it using some examples that will hopefully give you both a 'Quick Reference' and a 'Checklist' to go through every time you want to build scalable data models.
About the Speaker
Carlos Alonso Software Engineer, Job and Talent
Carlos received his Masters CS at Salamanca University, Spain. He worked a few years there in a digital agency, gaining expertise on a very wide range of technologies before moving to London where he narrowed down the focus on to the backend and data engineering disciplines. The latest step in his professional career was to move back to Madrid to work for Job and Talent where he currently helps on building the best candidate-job opening matching technology. Aside from work he likes sharing as much as he can by public speaking, mentoring or getting involved in OSS or OpenData initiatives.
SASI: Cassandra on the Full Text Search Ride (DuyHai DOAN, DataStax) | C* Sum...DataStax
Since the introduction of SASI in Cassandra 3.4, it is way easier than before to query data. Now you can create performant indices on your columns as well as benefit from full text search capabilities with the introduction of the new `LIKE '%term%'` syntax.
This talk will show the architecture on a high level and exposes all the trade-offs so you can choose and use SAS wisely.
We also highlight some use-cases where SASI is not a good fit and should be avoided (there is no magic sorry)
To illustrate the talk, we'll use a sample database of 110 000 albums and artists and create indices on them
About the Speaker
DuyHai DOAN Apache Cassandra Evangelist, Datastax
DuyHai DOAN is an Apache Cassandra Evangelist at DataStax. He spends his time between technical presentations/meetups on Cassandra, coding on open source projects like Achilles or Apache Zeppelin to support the community and helping all companies using Cassandra to make their project successful. Previously he was working as a freelance Java/Cassandra consultant.
SMACK Stack 1.0 has been Spark, Mesos, Akka, Cassandra and Kafka working into different cohesive systems delivering different solutions for different use cases. Haven't heard about it before? Oh man! Where have you been? https://www.google.com/search?q=smack+stack+1.0
SMACK Stack 1.1 we go a step further Streaming, Mesos, Analytics, Cassandra and Kafka and Joe Stein will walk through in detail some of the different viable options for Streaming and Analytics with Mesos, Kafka and Cassandra.
Practical Large Scale Experiences with Spark 2.0 Machine Learning: Spark Summ...Spark Summit
Spark 2.0 provided strong performance enhancements to the Spark core while advancing Spark ML usability to use data frames. But what happens when you run Spark 2.0 machine learning algorithms on a large cluster with a very large data set? Do you even get any benefit from using a very large data set? It depends. How do new hardware advances affect the topology of high performance Spark clusters. In this talk we will explore Spark 2.0 Machine Learning at scale and share our findings with the community.
As our test platform we will be using a new cluster design, different from typical Hadoop clusters, with more cores, more RAM and latest generation NVMe SSD’s and a 100GbE network with a goal of more performance, in a more space and energy efficient footprint.
Scaling Data Analytics Workloads on DatabricksDatabricks
Imagine an organization with thousands of users who want to run data analytics workloads. These users shouldn’t have to worry about provisioning instances from a cloud provider, deploying a runtime processing engine, scaling resources based on utilization, or ensuring their data is secure. Nor should the organization’s system administrators.
In this talk we will highlight some of the exciting problems we’re working on at Databricks in order to meet the demands of organizations that are analyzing data at scale. In particular, data engineers attending this session will walk away with learning how we:
Manage a typical query lifetime through the Databricks software stack
Dynamically allocate resources to satisfy the elastic demands of a single cluster
Isolate the data and the generated state within a large organization with multiple clusters
Structuring Spark: DataFrames, Datasets, and StreamingDatabricks
As Spark becomes more widely adopted, we have focused on creating higher-level APIs that provide increased opportunities for automatic optimization. In this talk I given an overview of some of the exciting new API’s available in Spark 2.0, namely Datasets and Streaming DataFrames/Datasets. Datasets provide an evolution of the RDD API by allowing users to express computation as type-safe lambda functions on domain objects, while still leveraging the powerful optimizations supplied by the Catalyst optimizer and Tungsten execution engine. I will describe the high-level concepts as well as dive into the details of the internal code generation that enable us to provide good performance automatically. Streaming DataFrames/Datasets let developers seamlessly turn their existing structured pipelines into real-time incremental processing engines. I will demonstrate this new API’s capabilities and discuss future directions including easy sessionization and event-time-based windowing.
Distributed Stream Processing - Spark Summit East 2017Petr Zapletal
The demand for stream processing is increasing a lot these days. Immense amounts of data have to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples.
A number of powerful, easy-to-use open source platforms have emerged to address this. But the same problem can be solved differently, various but sometimes overlapping use-cases can be targeted or different vocabularies for similar concepts can be used. This may lead to confusion, longer development time or costly wrong decisions.
Frustration-Reduced Spark: DataFrames and the Spark Time-Series LibraryIlya Ganelin
In this talk I talk about my recent experience working with Spark Data Frames and the Spark TimeSeries library. For data frames, the focus will be on usability. Specifically, a lot of the documentation does not cover common use cases like intricacies of creating data frames, adding or manipulating individual columns, and doing quick and dirty analytics. For the time series library, I dive into the kind of use cases it supports and why it’s actually super useful.
Large-Scale Text Processing Pipeline with Spark ML and GraphFrames: Spark Sum...Spark Summit
In this talk we evaluate Apache Spark for a data-intensive machine learning problem. Our use case focuses on policy diffusion detection across the state legislatures in the United States over time. Previous work on policy diffusion has been unable to make an all-pairs comparison between bills due to computational intensity. As a substitute, scholars have studied single topic areas.
We provide an implementation of this analysis workflow as a distributed text processing pipeline with Spark ML and GraphFrames.
Histogrammar package—a cross-platform suite of data aggregation primitives for making histograms, calculating descriptive statistics and plotting in Scala—is introduced to enable interactive data analysis in Spark REPL.
We discuss the challenges and strategies of unstructured data processing, data formats for storage and efficient access, and graph processing at scale.
Analyzing Time Series Data with Apache Spark and CassandraPatrick McFadin
You have collected a lot of time series data so now what? It's not going to be useful unless you can analyze what you have. Apache Spark has become the heir apparent to Map Reduce but did you know you don't need Hadoop? Apache Cassandra is a great data source for Spark jobs! Let me show you how it works, how to get useful information and the best part, storing analyzed data back into Cassandra. That's right. Kiss your ETL jobs goodbye and let's get to analyzing. This is going to be an action packed hour of theory, code and examples so caffeine up and let's go.
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
Founding committer of Spark, Patrick Wendell, gave this talk at 2015 Strata London about Apache Spark.
These slides provides an introduction to Spark, and delves into future developments, including DataFrames, Datasource API, Catalyst logical optimizer, and Project Tungsten.
Scalable Data Modeling by Example (Carlos Alonso, Job and Talent) | Cassandra...DataStax
Cassandra is getting more and more buzz and that means two things, more development and more issues. Some issues are unavoidable, but some of them are, just by understanding how our tooling works.
In this talk I'd like to review the core concepts on which Cassandra is built and how they impose the way we should work with it using some examples that will hopefully give you both a 'Quick Reference' and a 'Checklist' to go through every time you want to build scalable data models.
About the Speaker
Carlos Alonso Software Engineer, Job and Talent
Carlos received his Masters CS at Salamanca University, Spain. He worked a few years there in a digital agency, gaining expertise on a very wide range of technologies before moving to London where he narrowed down the focus on to the backend and data engineering disciplines. The latest step in his professional career was to move back to Madrid to work for Job and Talent where he currently helps on building the best candidate-job opening matching technology. Aside from work he likes sharing as much as he can by public speaking, mentoring or getting involved in OSS or OpenData initiatives.
SASI: Cassandra on the Full Text Search Ride (DuyHai DOAN, DataStax) | C* Sum...DataStax
Since the introduction of SASI in Cassandra 3.4, it is way easier than before to query data. Now you can create performant indices on your columns as well as benefit from full text search capabilities with the introduction of the new `LIKE '%term%'` syntax.
This talk will show the architecture on a high level and exposes all the trade-offs so you can choose and use SAS wisely.
We also highlight some use-cases where SASI is not a good fit and should be avoided (there is no magic sorry)
To illustrate the talk, we'll use a sample database of 110 000 albums and artists and create indices on them
About the Speaker
DuyHai DOAN Apache Cassandra Evangelist, Datastax
DuyHai DOAN is an Apache Cassandra Evangelist at DataStax. He spends his time between technical presentations/meetups on Cassandra, coding on open source projects like Achilles or Apache Zeppelin to support the community and helping all companies using Cassandra to make their project successful. Previously he was working as a freelance Java/Cassandra consultant.
SMACK Stack 1.0 has been Spark, Mesos, Akka, Cassandra and Kafka working into different cohesive systems delivering different solutions for different use cases. Haven't heard about it before? Oh man! Where have you been? https://www.google.com/search?q=smack+stack+1.0
SMACK Stack 1.1 we go a step further Streaming, Mesos, Analytics, Cassandra and Kafka and Joe Stein will walk through in detail some of the different viable options for Streaming and Analytics with Mesos, Kafka and Cassandra.
Practical Large Scale Experiences with Spark 2.0 Machine Learning: Spark Summ...Spark Summit
Spark 2.0 provided strong performance enhancements to the Spark core while advancing Spark ML usability to use data frames. But what happens when you run Spark 2.0 machine learning algorithms on a large cluster with a very large data set? Do you even get any benefit from using a very large data set? It depends. How do new hardware advances affect the topology of high performance Spark clusters. In this talk we will explore Spark 2.0 Machine Learning at scale and share our findings with the community.
As our test platform we will be using a new cluster design, different from typical Hadoop clusters, with more cores, more RAM and latest generation NVMe SSD’s and a 100GbE network with a goal of more performance, in a more space and energy efficient footprint.
Scaling Data Analytics Workloads on DatabricksDatabricks
Imagine an organization with thousands of users who want to run data analytics workloads. These users shouldn’t have to worry about provisioning instances from a cloud provider, deploying a runtime processing engine, scaling resources based on utilization, or ensuring their data is secure. Nor should the organization’s system administrators.
In this talk we will highlight some of the exciting problems we’re working on at Databricks in order to meet the demands of organizations that are analyzing data at scale. In particular, data engineers attending this session will walk away with learning how we:
Manage a typical query lifetime through the Databricks software stack
Dynamically allocate resources to satisfy the elastic demands of a single cluster
Isolate the data and the generated state within a large organization with multiple clusters
Structuring Spark: DataFrames, Datasets, and StreamingDatabricks
As Spark becomes more widely adopted, we have focused on creating higher-level APIs that provide increased opportunities for automatic optimization. In this talk I given an overview of some of the exciting new API’s available in Spark 2.0, namely Datasets and Streaming DataFrames/Datasets. Datasets provide an evolution of the RDD API by allowing users to express computation as type-safe lambda functions on domain objects, while still leveraging the powerful optimizations supplied by the Catalyst optimizer and Tungsten execution engine. I will describe the high-level concepts as well as dive into the details of the internal code generation that enable us to provide good performance automatically. Streaming DataFrames/Datasets let developers seamlessly turn their existing structured pipelines into real-time incremental processing engines. I will demonstrate this new API’s capabilities and discuss future directions including easy sessionization and event-time-based windowing.
New Directions in pySpark for Time Series Analysis: Spark Summit East talk by...Spark Summit
Whether it’s Internet of Things (IoT), analysis of Financial Data, or Adtech, the arrival of events in time order requires tools and techniques that are noticeably missing from the Pandas and pySpark software stack.
In this talk, we’ll cover Two Sigma’s contribution to time series analysis for Spark, our work with Pandas, and propose a roadmap for to future-proof pySpark and establish Python as a first class language in the Spark Ecosystem.
2017.03.13 Financialisation as a Strategic Action Field: An Historically Info...NUI Galway
Seminar presentation by Sven Modell and ChunLei Yang of the Alliance Manchester Business School, UK, on Financialisation as a Strategic Action Field informed by corporate governance reforms in Chinese State-Owned Enterprises. This seminar was delivered to the Performance Management research cluster of the Whitaker Institute, NUI Galway on 13th march 2017.
2013.06.17 Time Series Analysis Workshop ..Applications in Physiology, Climat...NUI Galway
Professor Dimitris Kugiumtzis, Aristotle University of Thessaloniki, Greece, presented this workshop on linear stochastic processes as part of the Summer School on Modern Statistical Analysis and Computational Methods hosted by the Social Sciences Computing Hub at the Whitaker Institute, NUI Galway on 17th-19th June 2013.
“Prediction is hard, especially
about the future.” –Yogi Berra
Why is the future so hard
to predict?
- History is chaotic
- Physics is indeterminate
- Biology is contingent
- Humans are complex
- We do not have all the
facts
History is chaotic
- By “history” I mean the
changes in the world state
over time
- A simple system can
become complex
- A complex system can
become stable and simple
On average
- Single objects have
unpredictable paths
- Ensembles of objects can
be more easily predicted
- The “Mule” Effect
(Isaac Asimov,
Foundation series)
History has layers
- Physics – what is possible
- Biology – what is likely
- Society – what is
permitted
- Technology – what is
chosen
Physics is indeterminate
- We cannot predict very
far even with physics
- Complex systems
behave chaotically
- We do not have all the
information
- Anyway, quantum
Biology is contingent
- Evolution is not linear
- What can evolve need not
- Constraints on what can
evolve exist, but can be
changed
- Chance and adaptation
Humans are complex
- Admiral Rickover and the
thorium reactor
- The anti-nuclear
movement
- Social progressivism and
conservationism
- Result: Global warming
Technology’s hope
- Green Revolution – Norman
Borlaug and the new crops
- The failure of Ehrlich’s predictions
– “100s of millions will die by
1980”
- Ehrlich was right in his wrongness
Now we have a global population
of 7 billion, and oil has peaked
We don’t know what we
don’t know
- How can we predict the unpredictable?
- It isn’t through wish fulfilment (ad hoc thinking)
- If the future is uncertain, our solutions to it will not be
certain until they either fail or succeed (post hoc thinking)
Why don’t we know?
- We know the world more or less at
present
- However, we have degrees of uncertainty
(distance, information flow)
- Information is lost from the present and
the past
- We have insufficient information to
predict the future
What do we do?
- Don’t despair
- Don’t be over optimistic
- Don’t think good intentions
equal good outcomes
Video here: https://youtu.be/b2J4Yesrqrw
Further issues raised in this talk addressed during Q&A with Greg Adamson here: https://www.youtube.com/watch?v=L96f-j4s8t4
Held at Future day in Melbourne 2015
Big Data Meets IoT: Lessons From the Cloud on Polling, Collecting, and Analyz...StampedeCon
The collection and use of Big Data has become an important part of modern business practice. The Internet of Things (IoT) movement promises to provide new opportunities for businesses interested in the intersection of people and technology. It is also wrought with pitfalls for practitioners and researchers who struggle to make sense of an increasing cacophony of signals. How should they poll and collect data from millions of signals in a way that is manageable, scalable, and statistically valid? How should they analyze and predict using these data? This presentation will discuss these challenges with applied examples from monitoring and managing one of the world’s largest computers.
Hexawise Software Test Design Tool - "Vendor Meets User" at CAST Software Tes...Justin Hunter
This presentation was presented by Justin Hunter, Lanette Creamer, and Ajay Balamurugadas at CAST 2011. The focus of the presentation is using pairwise testing methods as well as other more sophisticated Design of Experiments based software test design methods.
The description of the presentation on the CAST site is:
"Vendor Meets User: The Hexawise Test Design Tool and a Tester who Tried to Use It in Real Life
Justin Hunter, Lanette Creamer, and Ajay Balamurugadas
Dr. William G. Hunter helped manufacturers create small numbers of prototypes that were each carefully designed to reveal as much actionable information as possible. He did this using Design of Experiments methods that he taught as a professor of Applied Statistics. Five years ago, while working at Accenture, Hunter’s son Justin began to apply some of these Design of Experiments-based methods to the software testing field. After seeing promising results from 17 pilot projects he helped manage at Accenture, Justin created Hexawise, a software test design tool that generates tests using Design of Experiments-based methods.
Justin will introduce the tool. But this is not the typical vendor talk. Testers Lanette Creamer and Ajay Balamurugadas each recently used Hexawise for the first time on a real project. They will share their experiences, covering both where it helped and where she experienced limitations of the tool and the test design technique.
Justin Hunter, Founder and CEO of Hexawise, is a test design specialist who has enjoyed teaching testers on six continents how to improve the efficiency and effectiveness of their test case selection approaches. The improbably circuitous career path that led him into the software testing field included working as a securities lawyer based in London and launching Asia’s first internet-based stock brokerage firm. The Hexawise test design tool is a web-based tool that is available for free to teams of 5 or fewer testers, as well as to non-profit organizations.
Lanette Creamer: After 10 years at Adobe, including working as a Quality Lead testing across the Creative Suites, Lanette is now a Senior Consultant with Sogeti. She is currently working as a Test Lead at Starbucks. Lanette has been evangelizing test collaboration and promoting advancement in human test ideas for the past 5 years. With a deep passion for collaboration as a way to increase test coverage, she believes it is a powerful solution when facing complex technical challenges. Lanette has presented at PNSQC, Better Software/Agile Development Practices, Writing About Testing, and STPCon in 2010. She’ll be participating at CAST 2011 in her home city of Seattle. She actively participates in the testing community and has written two technical papers and a published article on testing in ST&P Mag January 2010 (now ST&QA magazine)."
http://bit.ly/1ALVcwR – MapR Director of Architecture and Enterprise Strategy Jim Scott presented a session titled “Time Series Data in a Time Series World.” His session focused on working with time series data including single-value, geospatial and log time series data. By focusing on enterprise applications and the data center, OpenTSDB will be used as an example to explain some of the key time series core concepts including when to use different storage models.
Things Expo | San Jose, California - November 2014
AI&BigData Lab 2016. Сарапин Виктор: Размер имеет значение: анализ по требова...GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
Как устроить анализ данных 40 млн. человек за 5 лет так, чтобы это выглядело почти в реальном времени.
The introductory morning session will discuss big data challenges and provide an overview of the AWS Big Data Platform. We will also cover:
• How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
• Reference architectures for popular use cases, including: connected devices (IoT), log streaming, real-time intelligence, and analytics.
• The AWS big data portfolio of services, including Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR) and Redshift.
• The latest relational database engine, Amazon Aurora - a MySQL-compatible, highly-available relational database engine which provides up to five times better performance than MySQL at a price one-tenth the cost of a commercial database.
• Amazon Machine Learning – the latest big data service from AWS provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.
Azure Data Explorer deep dive - review 04.2020Riccardo Zamana
Full review 04.2020 about Azure Data Explorer service. Slide Desk is a sort of review od Kusto, in terms of usage, ingestion techniques, querying and exporting data, using anomaly detection and clustering methods.
Cortana Analytics Workshop: Real-Time Data Processing -- How Do I Choose the ...MSAdvAnalytics
Benjamin Wright-Jones, Simon Lidberg. Are you interested in near real-time data processing but confused about Azure capabilities and product positioning? Spark, StreamInsight, Storm (HDInsight) and Stream Analytics offer ways to ingest data but there is uncertainty about when and how we should use these capabilities. For example, what are the differences and key solution design decision points? Come to this session to learn about current and new near real-time data processing engines. Go to https://channel9.msdn.com/ to find the recording of this session.
Choose Right Stream Storage: Amazon Kinesis Data Streams vs MSKSungmin Kim
This presentation compares Amazon Kinesis Data Streams to Managed Streaming for Kafka (MSK) in both architectural perspective and operational perspective. In addition, it shows common architectural patterns: (1) Data Hub: Event-Bus, (2) Log Aggregation, (3) IoT, (4) Event sourcing and CQRS.
This presentation describes a intelligent IT monitoring solution that uses Nagios as source of information, Esper as the CEP engine and a PCA algorithm.
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.
Maginatics @ SDC 2013: Architecting An Enterprise Storage Platform Using Obje...Maginatics
How did Maginatics build a strongly consistent and secure distributed file system? Niraj Tolia, Chief Architect at Maginatics, gave this presentation on the design of MagFS at the Storage Developer Conference on September 16, 2013.
For more information about MagFS—The File System for the Cloud, visit maginatics.com or contact us directly at info@maginatics.com.
This is the course that was presented by James Liddle and Adam Vile for Waters in September 2008.
The book of this course can be found at: http://www.lulu.com/content/4334860
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, OathYahoo Developer Network
Offline and stream processing of big data sets can be done with tools such as Hadoop, Spark, and Storm, but what if you need to process big data at the time a user is making a request? Vespa (http://www.vespa.ai) allows you to search, organize and evaluate machine-learned models from e.g TensorFlow over large, evolving data sets with latencies in the tens of milliseconds. Vespa is behind the recommendation, ad targeting, and search at Yahoo where it handles billions of daily queries over billions of documents.
Astroinformatics 2014: Scientific Computing on the Cloud with Amazon Web Serv...Jamie Kinney
An overview of Amazon Web Services (AWS) and a survey of scientific computing applications of cloud computing. Examples come from the fields of Astronomy, High Energy Physics and include examples from CERN, NASA and others.
A guide through the Azure Messaging services - Update ConferenceEldert Grootenboer
https://www.updateconference.net/en/2019/session/a-guide-through-the-azure-messaging-services
A guide through the Azure Messaging services - Update Conference
Windows Azure - Uma Plataforma para o Desenvolvimento de AplicaçõesComunidade NetPonto
A plataforma Windows Azure abre espaço a desenvimento de aplicações utilizando o novo paradigma: "A Nuvem". Aplicações escaláveis, redundantes, e mais próximas do utilizador final. Isto tudo utilizando como base os conhecimentos que já tem e o novo Visual Studio 2010.
DataPalooza at the San Francisco Loft: In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
Similar to ML on Big Data: Real-Time Analysis on Time Series (20)
Building bots to automate common developer tasks - Writing your first smart c...Sigmoid
Human Communication
Online Communication
Messaging today
Why Messaging Apps might take over native apps
Why the sudden Bot uprising?
What is a Bot?
What makes a great bot?
Design principles
Common pitfalls
Before starting to develop a Bot
Helpful tools
Simple architecture
Demo: Uber Bot
References
Failsafe Hadoop Infrastructure and the way they workSigmoid
Impact
Different Kinds Of HA Configurations
HDFS HA - Necessary Hardware Resources
HDFS HA Architecture Using The Quorum Journal Manager
RM HA -Necessary Hardware Resources
Resource Manager HA Architecture
RM Failover
WEBSOCKETS AND WEBWORKERS
FOR LOW LATENCY INTERACTIVE WEB APPLICATIONS
By Prakriti Patra
AGENDA
Interactive Web Applications
Problems
Earlier Techniques
Polling
Long Polling
Streaming
HTML5 Web Sockets
How Web Sockets work
Comparison of Polling vs Web Socket
Support Chart
Event Looping in JavaScript
Non Blocking Threads
Web Workers
Building high scalable distributed framework on apache mesosSigmoid
By Mr. Rahul Kumar
Content
-Mesos Intro
-Software Projects Built on Mesos
-Create own Framework
-Why Mesos
-Protocol Buffer
-The Scheduler
-The Executor
-Mesos Endpoints
By Mr. Praveen R
Content
-What are we solving?
-Money weighted rate of
return (MWRR)
-MWRR Example
-Newton-Raphson Solver
-Demo
-Spark
-Apache Common Solvers
Session 2 : "Approaches to Text Analysis"
- Mr. Rohith Yeravothula (25 mins)
Introduction to Text Analytics
Introduction to News Documents text analysis
Introduction to our Architecture and its elements
Introduction to Compute Pipeline
Phase I Computation
Phase II Computation
Knowledge Graph
Introduction to News-Explorer
Introduction to Spark R with R studio - Mr. Pragith Sigmoid
R is a programming language and software environment for statistical computing and graphics.
The R language is widely used among statisticians and data miners for developing statistical
software and data analysis.
RStudio IDE is a powerful and productive user interface for R.
It’s free and open source, and available on Windows, Mac, and Linux.
• Distributed datasets loaded into named columns (similar to relational DBs or
Python DataFrames).
• Can be constructed from existing RDDs or external data sources.
• Can scale from small datasets to TBs/PBs on multi-node Spark clusters.
• APIs available in Python, Java, Scala and R.
• Bytecode generation and optimization using Catalyst Optimizer.
• Simpler DSL to perform complex and data heavy operations.
• Faster runtime performance than vanilla RDDs.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
3. Topics
Business use case
Training phase of the algorithm
Tech stack
Real time implementation
Demonstration on a force sensor
4. Data Model
We are currently working on these data models :
Unstructured data
Structured Data
Time series Data
For this talk we are going to concentrate on Time series data
5. Problem Statement
To build a reactive application which trains on limited amount of data.
6. Business use case
Main use case is in preventive maintenance systems.
Calendar based maintenance schedules and holding excessive inventory to reduce
downtime all lead to inefficiencies and increase costs.
Recent failures in machinery of oil rigs, car manufacturing plants have cost their
respective industries millions of dollars in down time and repairs.
Condition Based Monitoring systems are implemented with the goal of
eliminating unplanned downtime and reducing operations cost by maintaining the
proper equipment at the proper time.
As they say a stitch in time saves nine.
11. Time series analytics
Any analytics algorithm should be a mathematical model that should:
Data Compression: Compact representation of data
Signal Processing: extracting signal(sequences) even in presence of noise
Prediction: using model predict the future values of time series
12. Terminology
Patterns
Block of graph where values are within a
range
Patterns are grown from pairs of sequential
points till the block conform given
thresholds
Clusters
Similar type of patterns
13. Terminology
Sequences
A recurring series of patterns belonging to
a set of clusters.
Concepts
Sequences which are tagged as relevant to
the user.
Knowledge Base
Inference drawn from concepts.
This is the compressed representation of
the time series
14. Phases
Training phase
Objective is to build a Knowledge base.
Bulk historical data is given as input.
Parameters of the algorithm are fine tuned to match the use case.
Concepts are identified and assigned an action.
Validation Phase
Bulk
Bulk data is given.
Patterns are found and classified according to knowledge base.
Used to identify and tag scenarios over a known timeline.
15. Phases
Decision phase
Real Time
For example a Kafka source is provided.
Received data is processed in batches.
Patterns spanning multiples batches are stitched.
If a sequence is identified as a concept, the specified action is triggered.
20. Training phase output
Knowledge Base properties:
Data Compression: Compact representation of data
Signal Processing: extracting signal(sequences) even in presence of noise
Prediction: using model predict the future values of time series
21. Real time system
Light weight Computation framework
Ability to handle 3V’s (Volume, Velocity and Variety) of Big Data
Computation framework with micro batch processing architecture
23. Data Source
Data source that can keep the data from the source and ingest into computation
framework which can
Take Advantage of distributed computation framework
Store data in a fault tolerant manner
26. Connecting with IoT
Connect Mobile accelerometer to AWS IoT and stream data.
Train the system to predict an user’s behavior using accelerometer data.
28. Bottlenecks
Small File Issues: writing and reading huge number of small files.
Sharing data between batches.
29. Fix: Small Files Problem
Implemented a in memory queue to hold data for several batches and then
compile everything into a single file and write to storage system
Can also serve UI requests from in-memory queue.
This eliminates the extra read calls from storage system to serve UI requests
Allows the writes in first place to be asynchronous
30. Why Share data between batches
In Real time data ingestion, data can be broken into different batches depending
upon the batch size we choose
We need to take care of signals overflowing across batches
31. Sharing Data between batches
UpdateStateByKey
ssc.remember()
Spark Accumulators
In this section I am going go give brief introduction of architecture and business use case of our system.
Our goal is to make sense of any given sensor data may it be a pressure sensor in a valve or a camera on a self-driving car. By which we may be able to take smart decisions or make predictions about the future.
Unstructured data doesn't have relations between columns.
Whatever may be the data source, we want to make a generalized solution which can handle any type of variation and enable the user to get a specialized system for this own use case
Assume there is oil rig with 10 machine and 100 sensors each. Say, we know that a component in a machine needs maintainance every 3 months, but in many real life situations the component may breakdown pre maturely, which may cause the company millions in down time. Having a person monitor all the sensor outputs and determine whether any component needs maintainance is not a viable solution. Our system is built to handle this use case.
Please have a look this data from a pressure sensor in a valve. Say as an user we know that the first anomaly is caused by miss orientation of the spring and second is caused when seal of the valve is broken. Can you suggest any methods to isolate these 2 phenomenon.
Most of the traditional approaches wouldn't take in to consideration if a new type pattern emerges.
next slide our solution.
loss of similarity
Knowledge base is inferences drawn from the given data.
This is the pipeline all the phases of our application go through.
First you provide a data source, currently you can upload a local file in your computer or select it from your google chrome, formats supported are CSV and TSV.
Then the data is ingested using a schema provided. You can type cast variable, join columns from multiple files, etc.
Using this ingested data as our time series we can compute PCSC,
Simple example for say y=sin(x) time series model
Prediction on time series data is one of the use case for real time time series data analytics
talk 1 explains about how did we train the system and teach it the make decisions.
We use the trained system for real time analytics
We need some streaming or live computation framework
Spark streaming is a micro batch processing architecture
Collects stream data into small batches and processes it
Job Creation and scheduling overhead is in order of milliseconds
Batch interval can be as small as 1 Second
We can’t rely on original data source as it cannot provide recent data once lost
Apache kafka is a Distributed streaming platform
Publish and subscribe to streams of records.
Store streams of records in a fault-tolerant way
Streams of time series data will be pumping data into kafka
Spark will connect to kafka brokers and consume data
Processes data and store to database
UI server will pull data for visualization
Spark is the computation layer while kafka acts as data source for streaming data
We now have a streaming end to end application : a data source to stream, a compute framework and a storage system
We can connect any real time streaming source
One such demo: simple aws iot with moble sensors
Every batch is writing a lot of small files into storage system (HDFS)
We use parquet as it is one of the best compressed fomat of data available
Spark parquet format small files writing is adding up to extra overhead
Reading several small files from Storage to serve UI requests is also adding up to delay
Sharing data between batches
State maintain
Make one line
Basic problem with live streaming is data will be broken into batches.
Our mathematical model can’t rely upon a batch, needs to wait on for next batch to see if data is overflown