Alexandre Vasseur - Evolution of Data Architectures: From Hadoop to Data Lake in becoming Data Driven. - NoSQL matters Paris 2015

Mar 27, 20150 likes1,350 views

Come to this deep dive on how Pivotal's Data Lake Vision is evolving by embracing next generation in-memory data exchange and compute technologies around Spark and Tachyon. Did we say Hadoop, SQL, and what's the shortest path to get from past to future state? The next generation of data lake technology will leverage the availability of in-memory processing, with an architecture that supports multiple data analytics workloads within a single environment: SQL, R, Spark, batch and transactional.

Evolution of Data
Architectures:
From Hadoop to Data Lake
in becoming Data Driven
Alexandre Vasseur, Pivotal
@PivotalFrance

© Copyright 2015 Pivotal. All rights reserved.
If you have one thing to do
Store Massive
Data Sets
Achieve Continuous
Innovation at Scale
Becoming Data
Driven with Apps

Data Driven Apps
AGILE
DEV & DATA
SCIENCE
MODERN,
COLLABORATIVE
APP & DEV
PLATFORM:
MODERN,
CLOUD-ORIENTED
& OPEN
DATA FABRIC:
MODERN
CLOUD-ORIENTED
& OPEN

© Copyright 2015 Pivotal. All rights reserved.
The Big Data Problem
Fragmentation ContraintsComplexity

© Copyright 2015 Pivotal. All rights reserved.
Pivotal + Hortonworks Alliance
•  Started July 2014 around Ambari collaboration
•  Announcing Pivotal Big Data Suite
on Hortonworks Data Platform
•  Advanced support from world’s leading Hortonworks
support services
•  Joint engineering efforts and enhanced Pivotal HD

© Copyright 2015 Pivotal. All rights reserved.
ODP - Standardize Hadoop Ecosystem
•  Deliver ODP Core to build a versionned, packaged,
tested set of Hadoop components.
•  Focus on developing a platform, rather than projects
•  Initial scope on Apache Hadoop
HDFS / MR / Yarn / Ambari
Remove
vendors lock-in
Ecosystem
Effect
Shorter
Innovation Cycles
http://opendataplatform.org
…

© Copyright 2015 Pivotal. All rights reserved.
Open Sourced but not just Hadoop
•  Open sourcing all Pivotal Big Data Suite components
–  Pivotal GemFire - premium in-memory NoSQL database
–  Pivotal HAWQ - world’s leading SQL compliant enterprise
SQL on Hadoop
–  Pivotal Greenplum Database - advanced enterprise MPP
analytic database with Hadoop interconnect
– SpringXD - Unified, distributed, and extensible system for
data driven application development

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HAWQ SQL on Hadoop
PROVEN AT SCALE
PRODUCTIVE
NATIVE on HADOOP / ODP
OPEN & EXTENSIBLE

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HAWQ SQL on Hadoop
10+ years R&D in Massively Parallel SQL
SQL engine at peta scale analytics in world’s largest industries
Mature cost based query optimizer
Full SQL semantics
Rich ecosystem of ELT/dataviz/BI & partners
PL/*, build in analytics, R native framing
All Hadoop formats (gz, Parquet, HAWQ etc)
Data node short circuit reads (colocated, not M/R based)
Predicate pushdown to Hive, HBase
HAWQ PXF: Query federation to NoSQL, DB, etc

© Copyright 2015 Pivotal. All rights reserved.
SpringXD
Data from anywhere, to anywhere
Real time & batch
Ingest + analytics
+ jobs orchestration
Developer friendly
Built in connectors
With / without Spark
DSL
Your choice of Hadoop
Your choice of messaging
Standalone, YARN & outside Hadoop

© Copyright 2015 Pivotal. All rights reserved.
Simplify Data Driven Applications
•  PaaS with NoSQL & Big Data choices built-in
•  Emergence of vertical services: Mobile, IoT, …
Data centric runtimes built in
Java/PHP/Node.js/Ruby
Python
R/Shiny
Scala
SpringXD
Large choice of data services
DB, clustered MySQL etc
Memcache, Redis etc
GemFire, Cassandra etc
Hadoop, GreenPlum etc
Can run virtualized inside PaaS
Can run multi-tenant-ified alongside PaaS

© Copyright 2015 Pivotal. All rights reserved.
DEMO
PHD (or any ODP Core-based Hadoop Distribution)
HDFS
HAWQ
(SQL on Hadoop)
GreenplumDB
(Analytics DW)
GemFire
(JSON/Object
in memory data grid)
Redis
(Key Value Store)
RabbitMQ
SpringXD
(Stream Processing/scoring)
SpringXD
CloudFoundryDataServices
HBase Hive
PXF
(Filtered Pushdown)
Direct Store
Federated
GPHDFS
Write behind
Persistence
Analytic AppsOnline Apps
Pivotal
Big Data Suite
Spark

© Copyright 2015 Pivotal. All rights reserved.
The New Data Imperatives
Converged
Data & Cloud
OpenData-Driven
Apps

A NEW PLATFORM FOR A NEW ERA
Meet us at the booth !
Come to do a “HAWQ in 2 min” lab
Win a Solo2 Beats Headphone !

1) Hadoop enables modern data architectures that can process both traditional and new data sources to power business analytics and other applications. 2) By 2015, organizations that build modern information management systems using technologies like Hadoop will financially outperform their peers by 20%. 3) Hadoop provides an agile "data lake" solution that allows organizations to capture, process, and access all their data in various ways for business intelligence, analytics, and other uses.

Big data architectures and the data lakeJames Serra

The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include: - Defining top-down and bottom-up approaches to data management - Explaining what a data lake is and how Hadoop can function as the data lake - Describing how a modern data warehouse combines features of a traditional data warehouse and data lake - Discussing how federated querying allows data to be accessed across multiple sources - Highlighting benefits of implementing big data solutions in the cloud - Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (

Verizon: Finance Data Lake implementation as a Self Service Discovery Big Dat...DataWorks Summit

Finance Data Lake objective is to create a centralized enterprise data repository for all Finance and Supply Chain data. It serves as the single source of truth. It enables a self-service discovery Analytics platform for business users to answer adhoc business questions and derive critical insights. The data lake is based on open source Hadoop big data platform and a very cost effective solution in breaking the ERP data silos and simplifying the data architecture in the enterprise. POCs were conducted on in-house Hortonworks Hadoop data platform to validate the cluster performance for Production volumes. Based on business priorities, an initial roadmap was defined using 3 data sources including 2 SAP ERPs and Peoplesoft (OLTP systems). Development environment was established in AWS Cloud for agile delivery. The near real time data ingestion architecture for the data lake was defined using replication tools and custom SQOOP based micro-batching framework and data persisted in Apache Hive DB in ORC format. Data and user security is implemented using Apache Ranger and sensitive data stored at rest in encryption zones. Business data sets were developed in Hive scripts and scheduled using Oozie. Multiple reporting tools connectivity including SQL tools, Excel and Tableau were enabled for Self-service Analytics. Upon successful implementation of the initial phase, a full roadmap is established to extend the Finance data lake to over 25 data sources and enhance data ingestion to scale as well as enable OLAP tools on Hadoop.

Planing and optimizing data lake architectureMilos Milovanovic

Data lake benefitsRicky Barron

Open-BDA Hadoop Summit 2014 - Mr. Slim Baltagi (Building a Modern Data Archit...Innovative Management Services

Mr. Slim Baltagi is a Systems Architect at Hortonworks, with over 4 years of Hadoop experience working on 9 Big Data projects: Advanced Customer Analytics, Supply Chain Analytics, Medical Coverage Discovery, Payment Plan Recommender, Research Driven Call List for Sales, Prime Reporting Platform, Customer Hub, Telematics, Historical Data Platform; with Fortune 100 clients and global companies from Financial Services, Insurance, Healthcare and Retail. Mr. Slim Baltagi has worked in various architecture, design, development and consulting roles at. Accenture, CME Group, TransUnion, Syntel, Allstate, TransAmerica, Credit Suisse, Chicago Board Options Exchange, Federal Reserve Bank of Chicago, CNA, Sears, USG, ACNielsen, Deutshe Bahn. Mr. Baltagi has also over 14 years of IT experience with an emphasis on full life cycle development of Enterprise Web applications using Java and Open-Source software. He holds a master’s degree in mathematics and is an ABD in computer science from Université Laval, Québec, Canada. Languages: Java, Python, JRuby, JEE , PHP, SQL, HTML, XML, XSLT, XQuery, JavaScript, UML, JSON Databases: Oracle, MS SQL Server, MYSQL, PostreSQL Software: Eclipse, IBM RAD, JUnit, JMeter, YourKit, PVCS, CVS, UltraEdit, Toad, ClearCase, Maven, iText, Visio, Japser Reports, Alfresco, Yslow, Terracotta, Toad, SoapUI, Dozer, Sonar, Git Frameworks: Spring, Struts, AppFuse, SiteMesh, Tiles, Hibernate, Axis, Selenium RC, DWR Ajax , Xstream Distributed Computing/Big Data: Hadoop, MapReduce, HDFS, Hive, Pig, Sqoop, HBase, R, RHadoop, Cloudera CDH4, MapR M7, Hortonworks HDP 2.1

Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo

Hadoop data-lake-white-paperSupratim Ray

This document discusses how Apache Hadoop provides a solution for enterprises facing challenges from the massive growth of data. It describes how Hadoop can integrate with existing enterprise data systems like data warehouses to form a modern data architecture. Specifically, Hadoop provides lower costs for data storage, optimization of data warehouse workloads by offloading ETL tasks, and new opportunities for analytics through schema-on-read and multi-use data processing. The document outlines the core capabilities of Hadoop and how it has expanded to meet enterprise requirements for data management, access, governance, integration and security.

Ambari Meetup: 2nd April 2013: Teradata Viewpoint Hadoop Integration with AmbariHortonworks

Teradata Viewpoint provides a unified monitoring solution for Teradata Database, Aster, and Hadoop. It integrates with Ambari to simplify monitoring Hadoop. Viewpoint uses Ambari's REST APIs to collect metrics and alerts from Hadoop and store them in a database for trend analysis and visualization. This allows Viewpoint to deliver comprehensive Hadoop monitoring without having to understand its various monitoring technologies.

Data Governance for Data LakesKiran Kamreddy

Hadoop based data Lakes have become increasingly popular within today’s modern data architectures for their ability to scale, handle data variety and low cost. Many organizations start slow with the data lake initiatives but as they grow bigger, they suffer with challenges on data consistency, quality and security, resulting in losing confidence in their data lake initiatives. This talk will discuss the need for good data governance mechanisms for Hadoop data lakes and it relationship with productivity and how it helps organizations meet regulatory and compliance requirements. The talk advocates carrying a different mindset for designing and implementing flexible governance mechanisms on Hadoop data lakes.

Data Lake for the Cloud: Extending your Hadoop ImplementationHortonworks

As more applications are created using Apache Hadoop that derive value from the new types of data from sensors/machines, server logs, click-streams, and other sources, the enterprise "Data Lake" forms with Hadoop acting as a shared service. While these Data Lakes are important, a broader life-cycle needs to be considered that spans development, test, production, and archival and that is deployed across a hybrid cloud architecture. If you have already deployed Hadoop on-premise, this session will also provide an overview of the key scenarios and benefits of joining your on-premise Hadoop implementation with the cloud, by doing backup/archive, dev/test or bursting. Learn how you can get the benefits of an on-premise Hadoop that can seamlessly scale with the power of the cloud.

Artur Fejklowicz - “Data Lake architecture” AI&BigDataDay 2017Lviv Startup Club

Beyond TCODataWorks Summit/Hadoop Summit

This document discusses architecting Hadoop for adoption and data applications. It begins by explaining how traditional systems struggle as data volumes increase and how Hadoop can help address this issue. Potential Hadoop use cases are presented such as file archiving, data analytics, and ETL offloading. Total cost of ownership (TCO) is discussed for each use case. The document then covers important considerations for deploying Hadoop such as hardware selection, team structure, and impact across the organization. Lastly, it discusses lessons learned and the need for self-service tools going forward.

Incorporating the Data Lake into Your Analytic ArchitectureCaserta

Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization. Joe Caserta presented Incorporating the Data Lake into Your Analytics Architecture. For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.

Data lake – On Premise VS CloudIdan Tohami

The document discusses Ido Friedman and his background working with various data technologies. It then discusses the concept of a data lake and how it serves as a single store for raw and transformed data used for reporting, analytics, and machine learning. The rest of the document discusses how traditional tools like SQL have changed with the rise of Hadoop and cloud storage. It provides examples of performance and cost differences between running data workloads on Hadoop clusters versus cloud-based data processing services like BigQuery and Dataproc. The document concludes that a large data lake is now possible in the cloud and discusses various deployment options to consider.

Big Data 2.0: ETL & Analytics: Implementing a next generation platformCaserta

In our most recent Big Data Warehousing Meetup, we learned about transitioning from Big Data 1.0 with Hadoop 1.x with nascent technologies to the advent of Hadoop 2.x with YARN to enable distributed ETL, SQL and Analytics solutions. Caserta Concepts Chief Architect Elliott Cordo and an Actian Engineer covered the complete data value chain of an Enterprise-ready platform including data connectivity, collection, preparation, optimization and analytics with end user access. Access additional slides from this meetup here: http://www.slideshare.net/CasertaConcepts/big-data-warehousing-meetup-january-20 For more information on our services or upcoming events, please visit http://www.actian.com/ or http://www.casertaconcepts.com/.

Why Data Lake should be the foundation of Enterprise Data ArchitectureAgilisium Consulting

A Reference Architecture for ETL 2.0 DataWorks Summit

More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.

Solving Big Data Problems using Hortonworks DataWorks Summit/Hadoop Summit

Hortonworks provides an open source Apache Hadoop data platform for managing large volumes of data. It was founded in 2011 and went public in 2014. Hortonworks has over 800 employees across 17 countries and partners with over 1,350 technology companies. Hortonworks' Data Platform is a collection of Apache projects that provides data management, access, governance, integration, operations and security capabilities. It supports batch, interactive and real-time processing on a shared infrastructure using the YARN resource management system.

IlOUG Tech Days 2016 - Unlock the Value in your Data Reservoir using Oracle B...Mark Rittman

Mark Rittman from Rittman Mead presented on Oracle Big Data Discovery. He discussed how many organizations are running big data initiatives involving loading large amounts of raw data into data lakes for analysis. Oracle Big Data Discovery provides a visual interface for exploring, analyzing, and transforming this raw data. It allows users to understand relationships in the data, perform enrichments, and prepare the data for use in tools like Oracle Business Intelligence.

Building the Enterprise Data Lake: A look at architecturemark madsen

The document discusses considerations for building an enterprise data lake. It notes that traditional data warehousing approaches do not scale well for new data sources like sensors and streaming data. It advocates adopting a data lake approach with separate systems for data acquisition, management, and access instead of a monolithic architecture. A data lake requires a distributed architecture and platform services to support various data flows, formats, and processing needs. The data architecture should not enforce models or limitations upfront but rather allow for evolution and change over time.

Hadoop Integration into Data Warehousing ArchitecturesHumza Naseer

Modern Data Architecture for a Data Lake with Informatica and Hortonworks Dat...Hortonworks

How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services? Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business. Join Hortonworks and Informatica as we discuss: - What is a data lake? - The modern data architecture for a data lake - How Hadoop fits into the modern data architecture - Innovative use-cases for a data lake

5 Steps for Architecting a Data LakeMetroStar

Building a Data Lake - An App Dev's PerspectiveGeekNightHyderabad

Speaker: Geetha Balasundaram, Developer at ThoughtWorks From tools and technology to people and requirements, what's different in the data engineering space? App development is traditional now. All enterprises want to become data-guided. Data lake is good start yet the know-hows and do-hows are so many. Experiences from building a data lake in the retail domain, the talk will be covering. - What is this vast new space of data engineering, - Why it is critical to think in terms of data rather than features - How important it is to understand these technologies and create a data lake that is usable and insightful to business

Data Lake ArchitectureDATAVERSITY

Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes. This will include: The Lab and the factory The base environment for batch analytics Critical governance components Additional components necessary for real-time analytics and ingesting streaming data

The Warranty Data Lake – After, Inc.Richard Vermillion

The Emerging Data Lake IT StrategyThomas Kelly, PMP

Meaning making – separating signal from noise. How do we transform the customer's next input into an action that creates a positive customer experience? We make the data more intelligent, so that it is able to guide our actions. The Data Lake builds on Big Data strengths by automating many of the manual development tasks, providing several self-service features to end-users, and an intelligent management layer to organize it all. This results in lower cost to create solutions, "smart" analytics, and faster time to business value.

Extensible Database APIs and their role in Software ArchitectureMax Neunhöffer

This event will start with a presentation on “Extensible database APIs and their role in software architecture”, centered around JavaScript. This will be followed by a hands-on interactive workshop. Participants with their own computers will learn how to create a small web application with a database backend, within the session, using only JavaScript. This will be a guided hands-on session using the multi-model NoSQL database ArangoDB and its Foxx JavaScript extension framework. Presenting this workshop will be Max Neunhöffer from https://www.arangodb.com/.

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The proliferation of machine sensors, interaction, and transaction data is driving a significant transformation within the oil and gas industry. Some industry analysts estimate that correctly implementing big data analytics can provide a 4-8% improvement in operational efficiency for oil companies. Other research shows that nearly 90% of oil industry executives rate big data analytics as a top priority, while fewer than a third have implemented solutions.

Real-Time Processing in Hadoop for IoT Use Cases - Phoenix HUGskumpf

The document discusses real-time processing in Hadoop using the Hortonworks Data Platform (HDP). It provides an overview of using HDP for real-time streaming analytics in a logistics scenario. Example applications and architectures are presented, including using Kafka for ingesting sensor data, Storm for stream processing, and HBase for real-time querying. Demos will also illustrate integrating predictive analytics into streaming scenarios.

Verizon Centralizes Data into a Data Lake in Real Time for AnalyticsDataWorks Summit

Verizon – Global Technology Services (GTS) was challenged by a multi-tier, labor-intensive process when trying to migrate data from disparate sources into a data lake to create financial reports and business insights. Join this session to learn more about how Verizon: • Easily accessed data from multiple sources including SAP data • Ingested data into major targets including Hadoop • Achieved real-time insights from data leveraging change data capture (CDC) technology • Reduced costs and labor

Storm Demo Talk - Colorado Springs May 2015Mac Moore

The document discusses real-time processing capabilities in Hadoop and Hortonworks Data Platform (HDP). It begins with an introduction to Hortonworks and an overview of real-time streaming architectures on HDP. It then demonstrates streaming capabilities with and without predictive analytics additions. The document highlights how HDP provides a centralized architecture and open data platform to enable real-time and batch processing of any type of data for analytics applications.

Hadoop uk user group meeting finalSkills Matter

The document summarizes Pentaho's open source business intelligence and data integration products, including their new capabilities for Hadoop and big data analytics. It discusses Pentaho's partnerships with Amazon Web Services and Cloudera to more easily integrate Hadoop data. It also outlines how Pentaho helps users analyze and visualize both structured and unstructured data from Hadoop alongside traditional data sources.

Talend Big Data Capabilities OverviewRajan Kanitkar

This document provides an overview of Talend's big data solutions. It discusses the drivers of big data including volume, velocity, and variety. It then describes the Hadoop ecosystem, including core components like HDFS, MapReduce, Hive, Pig, and HBase. The document outlines Talend's big data product strategy, including solutions for big data integration, manipulation, quality, and project management. It introduces Talend Open Studio for Big Data, an open source tool for designing Hadoop jobs with a graphical interface. Finally, it briefly discusses Talend's partnerships around Hadoop distributions.

Apache Hadoop on the Open CloudHortonworks

1) The webinar covered Apache Hadoop on the open cloud, focusing on key drivers for Hadoop adoption like new types of data and business applications. 2) Requirements for enterprise Hadoop include core services, interoperability, enterprise readiness, and leveraging existing skills in development, operations, and analytics. 3) The webinar demonstrated Hortonworks Apache Hadoop running on Rackspace's Cloud Big Data Platform, which is built on OpenStack for security, optimization, and an open platform.

Optimizing your Modern Data Architecture - with Attunity, RCG Global Services...Hortonworks

This document discusses optimizing a traditional enterprise data warehouse (EDW) architecture with Hortonworks Data Platform (HDP). It provides examples of how HDP can be used to archive cold data, offload expensive ETL processes, and enrich the EDW with new data sources. Specific customer case studies show cost savings ranging from $6-15 million by moving portions of the EDW workload to HDP. The presentation also outlines a solution model and roadmap for implementing an optimized modern data architecture.

Transform You Business with Big Data and HortonworksHortonworks

This document summarizes a presentation about Hortonworks and how it can help companies transform their businesses with big data and Hortonworks' Hadoop distribution. Hortonworks is the sole distributor of an open source, enterprise-grade Hadoop distribution called Hortonworks Data Platform (HDP). HDP addresses enterprise requirements for mixed workloads, high availability, security and more. The presentation discusses how Hortonworks enables interoperability and supports customers. It also provides an overview of how Pactera can help clients with big data implementation, architecture, and analytics.

Transform Your Business with Big Data and Hortonworks Pactera_US

Create a Smarter Data Lake with HP Haven and Apache HadoopHortonworks

An organization’s information is spread across multiple repositories, on-premise and in the cloud, with limited ability to correlate information and derive insights. The Smart Content Hub solution from HP and Hortonworks enables a shared content infrastructure that transparently synchronizes information with existing systems and offers an open standards-based platform for deep analysis and data monetization. - Leverage 100% of your data: Text, images, audio, video, and many more data types can be automatically consumed and enriched using HP Haven (powered by HP IDOL and HP Vertica), making it possible to integrate this valuable content and insights into various line of business applications. - Democratize and enable multi-dimensional content analysis: - Empower your analysts, business users, and data scientists to search and analyze Hadoop data with ease, using the 100% open source Hortonworks Data Platform. - Extend the enterprise data warehouse: Synchronize and manage content from content management systems, and crack open the files in whatever format they happen to be in. - Dramatically reduce complexity with enterprise-ready SQL engine: Tap into the richest analytics that support JOINs, complex data types, and other capabilities only available with HP Vertica SQL on the Hortonworks Data Platform. Speakers: - Ajay Singh, Director, Technical Channels, Hortonworks - Will Gardella, Product Management, HP Big Data

Hortonworks and Red Hat Webinar - Part 2Hortonworks

A Comprehensive Approach to Building your Big Data - with Cisco, Hortonworks ...Hortonworks

Munich HUG 21.11.2013Emil Andreas Siemes

Pivotal Strata NYC 2015 Apache HAWQ LaunchVMware Tanzu

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Level Up – How to Achieve Hadoop AccelerationInside Analysis

Introduction to HadoopPOSSCON

Building a Modern Data Architecture with Enterprise HadoopSlim Baltagi

Hortonworks - What's Possible with a Modern Data Architecture?Hortonworks

The Big Data Gusher: Big Data Analytics, the Internet of Things and the Oil B...Platfora

Real-Time Processing in Hadoop for IoT Use Cases - Phoenix HUGskumpf

Verizon Centralizes Data into a Data Lake in Real Time for AnalyticsDataWorks Summit

Storm Demo Talk - Colorado Springs May 2015Mac Moore

Hadoop uk user group meeting finalSkills Matter

Talend Big Data Capabilities OverviewRajan Kanitkar

Apache Hadoop on the Open CloudHortonworks

Optimizing your Modern Data Architecture - with Attunity, RCG Global Services...Hortonworks

Transform You Business with Big Data and HortonworksHortonworks

Transform Your Business with Big Data and Hortonworks Pactera_US

Create a Smarter Data Lake with HP Haven and Apache HadoopHortonworks

Hortonworks and Red Hat Webinar - Part 2Hortonworks

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While metrics generated by static code analysis are well established as predictors of possible future defects, there is another untapped source of useful information, namely your source code revision history. This presentation will discuss converting this revision information into a graph representation, various defect prediction models and how to generate their related change metrics through graph traversal, as well as the potential applications and benefits of these graph enabled prediction models.

Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matt...NoSQLmatters

Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015NoSQLmatters

Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spar...NoSQLmatters

Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...NoSQLmatters

Data analysis is an exploratory process that requires a variety of tools and a flexible data store. Data analysis projects are easy to start but quickly become difficult to manage and error prone when depending on file-based data storage. Relational databases are poorly equipped to accommodate the dynamic demands complex analysis. This talk describes best practices for using MongoDB for analytics projects. Examples will be drawn from a large scale text mining project (approximately 25 million documents) that applies machine learning (neural networks and support vector machines) and statistical analysis. Tools discussed include R, Spark, Python scientific stack, and custom pre-processing scripts but the focus is on using these with the document database.

Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015NoSQLmatters

Sometimes we need to step back and take a look at the bigger picture - not just counting huge piles of individual log records, but reasoning about the behaviors of the people who are ultimately generating this firehose of data. While your DevOps folks care deeply about log records from a machine utlization perspective, marketing wants to know what these records tell us about the customers' needs. Elasticsearch Aggregations are a great feature but are not a panacea. We can happily use them to summarise complex things like the number of web requests per day broken down by geography and browser type on a busy website, but we would quickly run out of memory if we tried to calculate something as simple as a single number for the average duration of visitor web sessions when using the very same dataset. Why does this occur? A web session duration is an example of a behavioural attribute not held on any one log record; it has to be derived by finding the first and last records for each session in our weblogs, requiring some complex query expressions and a lot of memory to connect all the data points. We can maintain a more useful joined-up-picture if we run an ongoing background process to fuse related events from one index into ?entity-centric? summaries in another index e.g: • Web log events summarised into ?web session? entities • Road-worthiness test results summarised into ?car? entities • Reviews in a marketplace summarised into a ?reviewer? entity Using real data, this session will demonstrate how to incrementally build entity-centric indexes alongside event-centric indexes by using simple scripts to uncover interesting behaviours that accumulate over time. We'll explore: • Which cars are driven long distances after failing roadworthiness tests? • Which website visitors look to be behaving like ?bots?? • Which seller in my marketplace has employed an army of ?shills? to boost his feedback rating? Attendees will leave this session with all the tools required to begin building entity-centric indexes and using that data to derive richer business insights across every department in their organization.

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Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015NoSQLmatters

Just a few years ago all software systems were designed to be monoliths running on a single big and powerful machine. But nowadays most companies desire to scale out instead of scaling up, because it is much easier to buy or rent a large cluster of commodity hardware then to get a single machine that is powerful enough. In the database area scaling out is realized by utilizing a combination of polyglot persistence and sharding of data. On the application level scaling out is realized by microservices. In this talk I will briefly introduce the concepts and ideas of microservices and discuss their benefits and drawbacks. Afterwards I will focus on the point of intersection of a microservice based application talking to one or many NoSQL databases. We will try and find answers to these questions: Are the differences to a monolithic application? How to scale the whole system properly? What about polyglot persistence? Is there a data-centric way to split microservices?

Chris Ward - Understanding databases for distributed docker applications - No...NoSQLmatters

In this talk we'll focus on the use of Crate alongside Weave in Docker containers, the technical challenges, best practices learned, and getting a big data application running alongside it. You'll learn about the reasons why Crate.IO is building "yet another NoSQL database" and why it's unique and important when running web scale containerized applications. We'll show why the shared-nothing architecture is so important when deploying large clusters in containers and how it addresses the issues and fears of a Docker-based persistence layer. You will learn how to deploy a Crate cluster in the cloud within minutes using Docker, some of the challenges you'll encounter, and how to overcome them in order to scale your backends efficiently. We focused on super simple integration with any cloud provider, striving it to be as turnkey as possible with minimal up-front configuration required to establish a cluster. Once established, we'll show how to scale the cluster horizontally by simply adding more nodes. The session will also give you examples when you should use Crate compared to other similar technologies such as MongoDB, Hadoop, Cassandra or FoundationDB. We'll talk about this approach's strengths and what types of applications are well-suited for this type of data store, as well what is not. Finally we'll outline how to architect an application that is easy to scale using Crate and Docker.

Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters...NoSQLmatters

More than two years ago we faced the decision whether to run our MongoDB database on Amazon's EC2 ourselves or to rely on a Database as a Service provider. Common wisdom told us that a well known provider, focusing all its knowledge and energy on running MongoDB, would be a better choice than us trying it on the side. Well, this talk describes what can go wrong, since we have seen a lot of interesting minor and major hiccups — including stopped instances, broken backups, a major security incident, and more broken backups. Additionally, we discuss some reasons why a hosted solution is not always the better choice and which new challenges arise from it.

Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters...NoSQLmatters

What if we would try to make Elasticsearch SQL 92 compliant (http://www.contrib.andrew.cmu.edu/~shadow/sql/sql1992.txt)? This wouldn't serve that much nowadays, you would say. Well, we actually tried to do the exercise and we have some interesting conclusions. While we take Elasticsearch as an example for this "side by side", the issues we are addressing also apply to nosql in general. With this unusual exercise, we take the occasion to compare relational databases / sql with Elasticsearch / nosql on all the levels : functionality, semantics, performance and user experience.

Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015NoSQLmatters

DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Pa...NoSQLmatters

Apache Spark is a general data processing framework which allows you perform map-reduce tasks (but not only) in memory. Apache Cassandra is a highly available and massively scalable NoSQL data-store. By combining Spark flexible API and Cassandra performance, we get an interesting alternative to the Hadoop eco-system for both real-time and batch processing. During this talk we will highlight the tight integration between Spark & Cassandra and demonstrate some usages with live code demo.

Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...NoSQLmatters

When deploying your service to Microsoft Azure, you have a number of options in terms of noSQL: you can install databases on Linux or Windows virtual machines by yourself, or via the marketplace, or you can use open source databases available as a service like HBase or proprietary and managed databases like Document DB. After showing these options, we'll show Document DB in more details. This is a noSQL database as a service that stores JSON.

David Pilato - Advance search for your legacy application - NoSQL matters Par...NoSQLmatters

How do you mix SQL and NoSQL worlds without starting a messy revolution?This live coding talk will show you how to add Elasticsearch to your legacy application without changing all your current development habits. Your application will have suddenly have advanced search features, all without the need to write complex SQL code!David will start from a Spring, Hibernate and Postgresql based application and will add a complete integration of Elasticsearch, all live from the stage during his presentation.

Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015NoSQLmatters

During this live-coding session, Tugdual will move an old fashion full SQL application (JavaEE) to the new NoSQL world.Using MongoDB, and REST, he will show the benefits of this new architecture: * Easyness * Flexibility * High availability * Scalability; During this presentation, you will learn more about: * Document Oriented Model * JSON * REST * Iterative development; This demonstration is also a good opportunity to see how you can migrate data from a relational database, and the various schema options.

Gregorry Letribot - Druid at Criteo - NoSQL matters 2015NoSQLmatters

How do you monitor performance for one of your clients on a specific user segmentation when dealing with billions of events a day ? With over 2 billion ads served and 230Tb of data processed a day, we at Criteo have a comprehensive need for an interactive analytics stack. And by interactive, we mean a querying system with dynamic filtering to drill down over multiple dimensions, answering within sub-second latency. This session will take you on our journey with Druid, ""an open-source data store designed for real-time exploratory analytics on large data sets"". We will explore Druid's architecture and noticeable concepts, how relevant they are for some use cases and how it really performs.

Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQ...NoSQLmatters

In many modern applications the database side is realized using polyglot persistence – store each data format (graphs, documents, etc.) in an appropriate separate database. This approach yields several benefits, databases are optimized for their specific duty, however there are also drawbacks: * keep all databases in sync * queries might require data from several databases * experts needed for all used systems A multi-model database is not restricted to one data format, but can cope with several of them. In this talk i will present how a multi-model database can be used in a polyglot persistence setup and how it will reduce the effort drastically.

Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015NoSQLmatters

The impact that NoSQL has had on the technology community cannot be overstated. The proliferation of new and exciting data systems has led to a slew of interesting solutions to problems that were once solved the relational way. In this session we explore all that is great and good about NoSQL: the innovative software, the clever storage paradigms and the reigniting of developer interest in data access. It is unfortunate that NoSQL is not only a force for good in our community. We'll explore some of the darker corners of NoSQL: the disregard for years of proven technology, the overbearing hype, the overblown marketing and the ever present arguments over which technology is best. We close the session by exploring what can be done to extract even more value from the NoSQL movement, where we can improve how the community interacts with the larger technology community and what the future holds for data access technologies.