IoFMT – Internet of Fleet Management Things
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American Water shares how bringing IoT to fleet management can provide value to the customer. In the utilities industry, fleet management plays a major part in the business. The front line is one of the largest parts of the business whether it is the field employees working on mains, or those working on the customers' property. American Water strives to provide the best customer experience and part of that includes improving the effectiveness of our fleet. Currently, there is no insight or active feedback on the effectiveness of the routes or driving behaviors. As a PoC, American Water leveraged NiFi to track metrics against a simulated truck, showing the initial values in capturing this type of data. Technologies: NiFi, Druid, Hive
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Large-scaled telematics analytics
The increasing availability of mobile phones with embedded GPS devices and sensors has spurred the use of vehicle telematics in recent years. Telematics provides detailed and continuous information of a vehicle such as the location, speed, and movement. Vehicle telematics can be further linked with other spatial data to provide context to understand driving behaviors at the detailed level. However, the collection of high-frequency telematics data results in huge volumes of data that must be processed efficiently. And the raw sensor and GPS data must be properly pre-processed and transformed to extract signal relevant to downstream processes. In addition, driving behavior often depends on the spatial context, and the analysis of telematics must be contextualized using spatial and real-time traffic data.
Our talk covers the promises and challenges of telematics data. We present a framework for large-scaled telematics data analysis using Apache big data tools (Hadoop, Hive, Spark, Kafka, etc). We discuss common techniques to load and transform telematics data. We then present how to use machine learning on telematics data to derive insights about driving safety.
Speakers
Yanwei Zhang, Senior Data Scientists II, Uber
Neil Parker, Senior Software Engineer, Uber
The Future of Hadoop by Arun Murthy, PMC Apache Hadoop & Cofounder Hortonworks
Arun Murthy will be discussing the future of Hadoop and the next steps in what the big data world would start to look like in the future. With the advent of tools like Spark and Flink and containerization of apps using Docker, there is a lot of momentum currently in this space. Arun will share his thoughts and ideas on what the future holds for us.
Bio:-
Arun C. Murthy
Arun is a Apache Hadoop PMC member and has been a full time contributor to the project since the inception in 2006. He is also the lead of the MapReduce project and has focused on building NextGen MapReduce (YARN). Prior to co-founding Hortonworks, Arun was responsible for all MapReduce code and configuration deployed across the 42,000+ servers at Yahoo!. In essence, he was responsible for running Apache Hadoop’s MapReduce as a service for Yahoo!. Also, he jointly holds the current world sorting record using Apache Hadoop. Follow Arun on Twitter: @acmurthy.
Presto Summit 2018 - 03 - Starburst CBO
Presto Cost-Based Optimizer (Karol Sobczak & Kamil Bajda-Pawlikowski, Starburst)
Presto Summit 2018 (https://www.starburstdata.com/technical-blog/presto-summit-2018-recap/)
Querying Druid in SQL with Superset
Druid is a high performance, column-oriented distributed data store that is widely used at Oath for big data analysis. Druid has a JSON schema as its query language, making it difficult for new users unfamiliar with the schema to start querying Druid quickly. The JSON schema is designed to work with the data ingestion methods of Druid, so it can provide high performance features such as data aggregations in JSON, but many are unable to utilize such features, because they not familiar with the specifics of how to optimize Druid queries. However, most new Druid users at Yahoo are already very familiar with SQL, and the queries they want to write for Druid can be converted to concise SQL.
We found that our data analysts wanted an easy way to issue ad-hoc Druid queries and view the results in a BI tool in a way that's presentable to nontechnical stakeholders. In order to achieve this, we had to bridge the gap between Druid, SQL, and our BI tools such as Apache Superset. In this talk, we will explore different ways to query a Druid datasource in SQL and discuss which methods were most appropriate for our use cases. We will also discuss our open source contributions so others can utilize our work. GURUGANESH KOTTA, Software Dev Eng, Oath and JUNXIAN WU, Software Engineer, Oath Inc.
Big Data Day LA 2015 - Introducing N1QL: SQL for Documents by Jeff Morris of ...
NoSQL has exploded on the developer scene promising alternatives to RDBMS that make rapidly developing, Internet scale applications easier than ever. However, as a trade off to the ease of development and scale, some of the familiarity with other well-known query interfaces such as SQL, has been lost. Until now that is...N1QL (pronounced ‘N1QL’) is a SQL like query language for querying JSON, which brings the familiarity of RDBMS back to the NoSQL world. In this session you will learn about the syntax and basics of this new language as well as Integration with the Couchbase SDKs.
Hadoop and Friends as Key Enabler of the IoE - Continental's Dynamic eHorizon
Hadoop and Friends as Key Enabler of the IoE - Continental's Dynamic eHorizonDataWorks Summit/Hadoop Summit
Hadoop and Friends as Key Enabler of the IoE - Continental's Dynamic eHorizonManaged Cluster Services
Slides from the August 2021 St. Louis Big Data IDEA meeting from Sam Portillo. The presentation covers AWS EMR including comparisons to other similar projects and lessons learned. A recording is available in the comments for the meeting.
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.
Realizing the promise of portable data processing with Apache Beam
The world of big data involves an ever changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the Big Data ecosystem together; it enables users to "run-anything-anywhere".
This talk will briefly cover the capabilities of the Beam model for data processing, as well as the current state of the Beam ecosystem. We'll discuss Beam architecture and dive into the portability layer. We'll offer a technical analysis of the Beam's powerful primitive operations that enable true and reliable portability across diverse environments. Finally, we'll demonstrate a complex pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Spark on Amazon Web Services, Apache Flink on Google Cloud, Apache Apex on-premise), and give a glimpse at some of the challenges Beam aims to address in the future.
A Day in the Life of a Druid Implementor and Druid's Roadmap
Benjamin Hopp (Solutions Architect) @ Imply:
Druid is an emerging standard in the data infrastructure world, designed for high-performance slice-and-dice analytics (“OLAP”-style) on large data sets.
This talk is for you if you’re interested in learning more about pushing Druid’s analytical performance to the limit.
Perhaps you’re already running Druid and are looking to speed up your deployment, or perhaps you aren’t familiar with Druid and are interested in learning the basics.
Some of the tips in this talk are Druid-specific, but many of them will apply to any operational analytics technology stack.
The most important contributor to a fast analytical setup is getting the data model right.
The talk will center around various choices you can make to prepare your data to get best possible query performance.
We’ll look at some general best practices to model your data before ingestion such as OLAP dimensional modeling (called “roll-up” in Druid), data partitioning, and tips for choosing column types and indexes.
We’ll also look at how more can be less: often, storing copies of your data partitioned, sorted, or aggregated in different ways can speed up queries by reducing the amount of computation needed.
We’ll also look at Druid-specific optimizations that take advantage of approximations; where you can trade accuracy for performance and reduced storage.
You’ll get introduced to Druid’s features for approximate counting, set operations, ranking, quantiles, and more.
And we will finish with the latest and greatest Druid news, including details about the latest roadmap and releases.
Storage Requirements and Options for Running Spark on Kubernetes
In a world of serverless computing users tend to be frugal when it comes to expenditure on compute, storage and other resources. Paying for the same when they aren’t in use becomes a significant factor. Offering Spark as service on cloud presents very unique challenges. Running Spark on Kubernetes presents a lot of challenges especially around storage and persistence. Spark workloads have very unique requirements of Storage for intermediate data, long time persistence, Share file system and requirements become very tight when it same need to be offered as a service for enterprise to mange GDPR and other compliance like ISO 27001 and HIPAA certifications.
This talk covers challenges involved in providing Serverless Spark Clusters share the specific issues one can encounter when running large Kubernetes clusters in production especially covering the scenarios related to persistence.
This talk will help people using Kubernetes or docker runtime in production and help them understand various storage options available and which is more suitable for running Spark workloads on Kubernetes and what more can be done
HBaseConAsia2018 Track2-2: Apache Kylin on HBase: Extreme OLAP for big data
ShaoFeng Shi of Kyligence
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Data Gloveboxes: A Philosophy of Data Science Data Security
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High Performance Data Lake with Apache Hudi and Alluxio at T3Go
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Protect your Private Data in your Hadoop Clusters with ORC Column Encryption
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The Future of Hadoop by Arun Murthy, PMC Apache Hadoop & Cofounder Hortonworks
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Big Data Day LA 2015 - Introducing N1QL: SQL for Documents by Jeff Morris of ...
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Hadoop and Friends as Key Enabler of the IoE - Continental's Dynamic eHorizon
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Realizing the promise of portable data processing with Apache Beam
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A Day in the Life of a Druid Implementor and Druid's Roadmap
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Storage Requirements and Options for Running Spark on Kubernetes
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HBaseConAsia2018 Track2-2: Apache Kylin on HBase: Extreme OLAP for big data
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FIWARE Global Summit - FIWARE Overview
Presentation by Juanjo Hierro
CTO, FIWARE Foundation
FIWARE Global Summit
8-9 May, 2018
Porto, Portugal
Using FME to Automate Data Integration in a City
Learn how the City of Coquitlam uses FME to solve diverse data integration challenges across multiple departments and projects, improving data sharing and accessibility between staff and contractors.
SAP CDC and NiFi Flow as a Service
Getting Denormalized Data from SAP in less than 15 minute intervals using NiFi, then automating the process so NiFi builds the flow for you using a database crawler, data type inference engine and api calls to build the NiFi flows.
ONF & iSDX Webinar
In the latest ONF and SDxCentral webianr, two of the researchers from the iSDX project, Nick Feamster, professor of computer science at Princeton University, and Arpit Gupta, leading researcher on iSDX at Princeton University. ONF's Technical Program Manager Rick Bauer will also present protion of the webinar, which will look at:
- why the industry needs iSDX
- how iSDX compares to other initiatives in the SDN ecosystem today
- a short demo of iSDX
- iSDX in practice – a customer viewpoint
http://bit.ly/1TAhUlA
Big Data Processing Beyond MapReduce by Dr. Flavio Villanustre
Data Centric Approach: Our platform is built on the premise of absorbing data from multiple data sources and transforming them to a highly intelligent social network graphs that can be processed to non-obvious relationships.
Hybrid Cloud Streaming and Modernising Payments at Lloyds Banking Group
"Lloyds Banking Group is one of the UK’s largest financial service providers with 26 million customers and 16 unique brands including Lloyds Bank, Halifax, Bank of Scotland and Scottish Widows. Driven by our purpose to Help Britain Prosper, we are moving more of our data processing to near real-time to enable industry-leading digital applications, improve our customers' experiences, and achieve more streamlined, efficient solutions, increasingly based on an event-driven architecture approach backed by Apache Kafka.
In the first part of this presentation we will talk about our journey with Kafka since we began around 2015, up to the current day as we continue the roll out of our hybrid and multi-cloud streaming capability, enabling us to connect event producers and consumers across any of our private and public cloud environments. Kafka is now being used across dozens of streaming applications throughout the Group, supporting key capabilities such as improving service resilience by replicating critical data to separate data stores, and keeping digital banking app users informed about activity on their accounts using push notifications.
In the second part we will describe our New Payments Architecture (NPA) platform which went into production in November 2023, and explain how it uses event-driven processing to help to achieve more resilient, transparent and future-proof payments capabilities for our customers.
"
The data streaming processing paradigm and its use in modern fog architectures
Invited lecture at the University of Trieste.
The lecture covers (briefly) the data streaming processing paradigm, research challenges related to distributed, parallel and deterministic streaming analysis and the research of the DCS (Distributed Computing and Systems) groups at Chalmers University of Technology.
Open Data Portals: 9 Solutions and How they Compare
Get a comparison of CKAN, Socrata, ArcGIS Open Data and other top open data solutions. Plus get answers to best practice questions such as: Which datasets are important to share? What are the approximate costs? Which file formats should the data be shared in? How often should the data get updated? And overall, how can we ensure success with our open data portal?
City of Atlanta Oracle Application Footprint
Presentation on how the City of Atlanta has grown its Oracle footprint
SAP Forum Ankara 2017 - "Verinin Merkezine Seyahat"
Ilker Tasdemir, MDS ap, Business Analytics Director, presentation
Velocity San Jose 2017: Traffic shifts: Avoiding disasters at scale
LinkedIn has evolved from serving live traffic out of one data center to four data centers spread geographically. Serving live traffic from four data centers at the same time has taken the company from a disaster recovery model to a disaster avoidance model, where an unhealthy data center can be taken out of rotation and its traffic redistributed to the healthy data centers within minutes, with virtually no visible impact to users.
As LinkedIn transitioned from big monolithic applications to microservices, it was difficult to determine capacity constraints of individual services to handle extra load during disaster scenarios. Stress testing individual services using artificial load in a complex microservices architecture wasn’t sufficient to provide enough confidence in data center’s capacity. To solve this problem, LinkedIn leverages live traffic to stress services site-wide by shifting traffic to simulate a disaster load.
Michael Kehoe and Anil Mallapur discuss how LinkedIn uses traffic shifts to mitigate user impact by migrating live traffic between its data centers and stress test site-wide services for improved capacity handling and member experience.
Enabling the Real Time Analytical Enterprise
Combining IOT, Customer Experience and Real-Time Enterprise Data within Hadoop. What if you could derive real-time insights using ALL of your data? Join us for this webinar and learn how companies are combining “new” real-time data sources (i.e. IOT, Social, Web Logs) with continuously updated enterprise data from SAP and other enterprise transactional systems, providing deep and up-to-the-second analytical insights. This presentation will include a demonstration of how this can be achieved quickly, easily and affordably by utilizing a joint solution from Attunity and Hortonworks.
CarStream: An Industrial System of Big Data Processing for Internet of Vehicles
As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for future transportation, de-signing large-scale IoV systems has become a critical task that aims to process big data uploaded by fleet vehicles and to provide data-driven services. The IoV data, especially high-frequency vehicle statuses (e.g., location, engine parameters), are characterized as large volume with a low density of value and low data quality. Such characteristics pose challenges for developing real-time applications based on such data. In this paper, we address the challenges in de-signing a scalable IoV system by describing CarStream, an industrial system of big data processing for chauffeured car services. Photon is deployed within Google Advertising System to join data streams such as web search queries and user clicks on advertisements. It produces joined logs that are used to derive key business metrics, including billing for advertisers. Our production deployment processes millions of events per minute at peak with an average end-to-end latency of less than 10 seconds. We also present challenges and solutions in maintaining large persistent state across geographically distant locations, and highlight the design principles that emerged from our experience. Rakshitha K. S | Radhika K. R"CarStream: An Industrial System of Big Data Processing for Internet of Vehicles" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14408.pdf http://www.ijtsrd.com/computer-science/database/14408/carstream-an-industrial-system-of-big-data-processing-for-internet-of-vehicles/rakshitha-k-s
Flink Forward Berlin 2017 Keynote: Ferd Scheepers - Taking away customer fric...
As many industries, banking is undergoing a fundamental change because of the software revolution. No longer are banks competing only on interest rates and having the best traders, these days customer experience and having the best engineers are the focus. In this changing world, banks compete with new start-ups, the so-called Fintechs, and with large platform organisations such as Google, Facebook and Apple. At ING, we believe that staying ahead of the game means changing how we interact with our customers, no longer a traditional model of waiting for the customers to come to the bank through our website or apps, but to actively reach out to the customer with information that is relevant to him or her in order to make their financial life frictionless. Many of these changes are driven by reacting to all events that are relevant to the customer, and using streaming analytics to be able to reach out to the customer in milliseconds after the event occurs. Apache Flink is key for ING to achieve this. This presentation addresses how ING approaches the challenge, the role that Apache Flink plays, and the consequences regulations have on how we work with Open Source in general, and with Apache Flink (and data Artisans) in particular. This keynote takes place at Kino 3.
Voxxed Days Thesaloniki 2016 - Streaming Engines for Big Data
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Big Data Processing Beyond MapReduce by Dr. Flavio Villanustre
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Hybrid Cloud Streaming and Modernising Payments at Lloyds Banking Group
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The data streaming processing paradigm and its use in modern fog architectures
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Open Data Portals: 9 Solutions and How they Compare
Open Data Portals: 9 Solutions and How they Compare
SAP Forum Ankara 2017 - "Verinin Merkezine Seyahat"
SAP Forum Ankara 2017 - "Verinin Merkezine Seyahat"
Velocity San Jose 2017: Traffic shifts: Avoiding disasters at scale
Velocity San Jose 2017: Traffic shifts: Avoiding disasters at scale
CarStream: An Industrial System of Big Data Processing for Internet of Vehicles
CarStream: An Industrial System of Big Data Processing for Internet of Vehicles
Flink Forward Berlin 2017 Keynote: Ferd Scheepers - Taking away customer fric...
Flink Forward Berlin 2017 Keynote: Ferd Scheepers - Taking away customer fric...
Voxxed Days Thesaloniki 2016 - Streaming Engines for Big Data
Voxxed Days Thesaloniki 2016 - Streaming Engines for Big Data
Voxxed days thessaloniki 21/10/2016 - Streaming Engines for Big Data
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Evolving Beyond the Data Lake: A Story of Wind and Rain
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Data Science Crash Course
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache Ratis
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
Managing the Dewey Decimal System
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Practical NoSQL: Accumulo's dirlist Example
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms.
To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.
Supporting Apache HBase : Troubleshooting and Supportability Improvements
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Security Framework for Multitenant Architecture
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google Cloud
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Computer Vision: Coming to a Store Near You
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
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The Census Bureau is the U.S. government's largest statistical agency with a mission to provide current facts and figures about America's people, places and economy. The Bureau operates a large number of surveys to collect this data, the most well known being the decennial population census. Data is being collected in increasing volumes and the analytics solutions must be able to scale to meet the ever increasing needs while maintaining the confidentiality of the data. Past data analytics have occurred in processing silos inhibiting the sharing of information and common reference data is replicated across multiple system. The use of the Hortonworks Data Platform, Hortonworks Data Flow and other open-source technologies is enabling the creation of a cloud-based enterprise data lake and analytics platform. Cloud object stores are used to provide scalable data storage and cloud compute supports permanent and transient clusters. Data governance tools are used to track the data lineage and to provide access controls to sensitive data.
Applying Noisy Knowledge Graphs to Real Problems
Knowledge graphs (KGs) have recently emerged as a powerful way to represent knowledge in multiple communities, including data mining, natural language processing and machine learning. Large-scale KGs like Wikidata and DBpedia are openly available, while in industry, the Google Knowledge Graph is a good example of proprietary knowledge that continues to fuel impressive advances in Google's semantic search capabilities. Yet, both crowdsourced and automatically constructed KGs suffer from noise, both during KG construction and during search and inference. In this talk, I will discuss how to build and use such knowledge graphs effectively, despite the noise and sparsity of labeled data, to solve real-world social problems such as providing insights in disaster situations, and helping law enforcement fight human trafficking. I will conclude by providing insight on the lessons learned, and the applicability of research techniques to industrial problems. The talk will be designed to appeal both to business and technical leaders.
Open Source, Open Data: Driving Innovation in Smart Cities
A city is a system of systems with independent objectives and governance for transportation, energy, healthcare, safety, security, and infrastructure. A smart city relies on data to be the connectivity between independent functions, and open data to be the building blocks for citizen-centered design, inclusion, and sustainability. Big Data is not about size – it is about finding new life-changing and transformational opportunities using data.
From Smart Mobility and Smart Energy to improved Public Health, Safety, & Governance – this session will discuss how cities are delivering better citizen services leveraging open source technology with a consistent governance and security framework that spans the data center and the public clouds.
Data integration is the key to ensuring that a city’s attempts to become an intelligent system of systems doesn’t result in a system of silos. A single view requires the capability to integrate transactional data from traditional data stores with person generated data, unstructured data, and machine sensor (IoT) data. The key to managing such a range of data is a capability that allows for both scaling analytic workloads and the preservation of detailed data with unexplored value, as both are vital to future growth potential.
Key Takeaways:
Understand the common use cases that tier 1, tier 2, and emerging cities are undertaking to deliver tactical results and progress towards policy objectives.
Understand the role of a shared catalog, unified security and consistent governance in building a secure, trusted, and connected capability.
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IoFMT – Internet of Fleet Management Things
- 1. Internet of Fleet Management Things Adam Michalsky Srilokh Moram
- 2. WHO WE ARE We serve a broad national footprint and a strong local presence. We provide services to approximately 15 million people in 46 states and Ontario, Canada. We employ 6,900 dedicated and active employees and support ongoing community support and corporate responsibility. We treat and deliver more than one billion gallons of water daily. We are the largest and most geographically diverse publicly traded water and wastewater service provider in the Unites States.
- 3. Problem Statement Establish a POC with Big Data technologies that provides insight into fleet driving behaviors and route effectiveness. Solution must work with mobile devices (iOS) and support offline work.
- 4. Environment 1 Management Node 12 Worker Nodes 5 Nodes (16x64GB) 3 Nodes (16x64GB) 3 Nodes (8x32GB) Streaming Analytics Manager 1 Nodes (8x32GB)
- 5. Environment 4 Nodes (16x256GB) 1 Node (32x256GB)
- 7. App Overview • Implemented in Objective C • Data is sent via site to site • Cocoa Framework • Queueing Mechanism • FMDB (3rd Party SQLite Interface)
- 8. Capturing Data Points Data Points • Device ID • Username • Event Time • Latitude & Longitude • Address
- 9. NiFi Flow – Overall Flow
- 10. NiFi Flow – Convert Record
- 11. NiFi Flow – Publish Kafka
- 12. SAM
- 13. SAM
- 14. SAM
- 15. Analytics
- 16. THANK YOU
Editor's Notes
- In HANA studio we can de-normalize the datasets.