The document is a presentation on NoSQL databases given by Nick Dimiduk. It begins with an introduction of the speaker and their background. The presentation then covers what NoSQL is not, the motivations for NoSQL databases, an overview of Hadoop and its components, and a description of HBase as a structured, distributed database built on Hadoop.
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
As Hadoop graduates from pilot project to a mission critical component of the enterprise IT infrastructure, integrating information held in Hadoop and in Enterprise RDBMS becomes imperative. We’ll look at key scenarios driving Hadoop and RDBMS integration and review technical options. In particular, we’ll deep dive into the Apache SQOOP project, which expedites data movement between Hadoop and any JDBC database, as well as providing an framework which allows developers and vendors to create connectors optimized for specific targets such as Oracle, Netezza etc.
Introduction to Hadoop Ecosystem was presented to Lansing Java User Group on 2/17/2015 by Vijay Mandava and Lan Jiang. The demo was built on top of HDP 2.2 and AWS cloud.
http://bit.ly/1BTaXZP – Hadoop has been a huge success in the data world. It’s disrupted decades of data management practices and technologies by introducing a massively parallel processing framework. The community and the development of all the Open Source components pushed Hadoop to where it is now.
That's why the Hadoop community is excited about Apache Spark. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Sparkstreaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis.
This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop.
Keys Botzum - Senior Principal Technologist with MapR Technologies
Keys is Senior Principal Technologist with MapR Technologies, where he wears many hats. His primary responsibility is interacting with customers in the field, but he also teaches classes, contributes to documentation, and works with engineering teams. He has over 15 years of experience in large scale distributed system design. Previously, he was a Senior Technical Staff Member with IBM, and a respected author of many articles on the WebSphere Application Server as well as a book.
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
As Hadoop graduates from pilot project to a mission critical component of the enterprise IT infrastructure, integrating information held in Hadoop and in Enterprise RDBMS becomes imperative. We’ll look at key scenarios driving Hadoop and RDBMS integration and review technical options. In particular, we’ll deep dive into the Apache SQOOP project, which expedites data movement between Hadoop and any JDBC database, as well as providing an framework which allows developers and vendors to create connectors optimized for specific targets such as Oracle, Netezza etc.
Introduction to Hadoop Ecosystem was presented to Lansing Java User Group on 2/17/2015 by Vijay Mandava and Lan Jiang. The demo was built on top of HDP 2.2 and AWS cloud.
http://bit.ly/1BTaXZP – Hadoop has been a huge success in the data world. It’s disrupted decades of data management practices and technologies by introducing a massively parallel processing framework. The community and the development of all the Open Source components pushed Hadoop to where it is now.
That's why the Hadoop community is excited about Apache Spark. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Sparkstreaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis.
This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop.
Keys Botzum - Senior Principal Technologist with MapR Technologies
Keys is Senior Principal Technologist with MapR Technologies, where he wears many hats. His primary responsibility is interacting with customers in the field, but he also teaches classes, contributes to documentation, and works with engineering teams. He has over 15 years of experience in large scale distributed system design. Previously, he was a Senior Technical Staff Member with IBM, and a respected author of many articles on the WebSphere Application Server as well as a book.
There is a lot more to Hadoop than Map-Reduce. An increasing number of engineers and researchers involved in processing and analyzing large amount of data, regards Hadoop as an ever expanding ecosystem of open sources libraries, including NoSQL, scripting and analytics tools.
The Future of Hadoop: A deeper look at Apache SparkCloudera, Inc.
Jai Ranganathan, Senior Director of Product Management, discusses why Spark has experienced such wide adoption and provide a technical deep dive into the architecture. Additionally, he presents some use cases in production today. Finally, he shares our vision for the Hadoop ecosystem and why we believe Spark is the successor to MapReduce for Hadoop data processing.
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Proc...Agile Testing Alliance
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Processing by "Sampat Kumar" from "Harman". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
Strata + Hadoop World 2012: Data Science on Hadoop: How Cloudera Impala Unloc...Cloudera, Inc.
This talk will cover what tools and techniques work and don’t work well for data scientists working on Hadoop today and how to leverage the lessons learned by the experts to increase your productivity as well as what to expect for the future of data science on Hadoop. We will leverage insights derived from the top data scientists working on big data systems at Cloudera as well as experiences from running big data systems at Facebook, Google, and Yahoo.
Top Hadoop Big Data Interview Questions and Answers for FresherJanBask Training
Top Hadoop Big Data Interview Questions and Answers for Fresher , Hadoop, Hadoop Big Data, Hadoop Training, Hadoop Interview Question, Hadoop Interview Answers, Hadoop Big Data Interview Question
This talk gives an introduction into Hadoop 2 and YARN. Then the changes for MapReduce 2 are explained. Finally Tez and Spark are explained and compared in detail.
The talk has been held on the Parallel 2014 conference in Karlsruhe, Germany on 06.05.2014.
Agenda:
- Introduction to Hadoop 2
- MapReduce 2
- Tez, Hive & Stinger Initiative
- Spark
This talk examines HBase client options available to application developers working with HBase. The focus is framed on, but not limited to, building webapps.
There is a lot more to Hadoop than Map-Reduce. An increasing number of engineers and researchers involved in processing and analyzing large amount of data, regards Hadoop as an ever expanding ecosystem of open sources libraries, including NoSQL, scripting and analytics tools.
The Future of Hadoop: A deeper look at Apache SparkCloudera, Inc.
Jai Ranganathan, Senior Director of Product Management, discusses why Spark has experienced such wide adoption and provide a technical deep dive into the architecture. Additionally, he presents some use cases in production today. Finally, he shares our vision for the Hadoop ecosystem and why we believe Spark is the successor to MapReduce for Hadoop data processing.
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Proc...Agile Testing Alliance
Introduction To Big Data with Hadoop and Spark - For Batch and Real Time Processing by "Sampat Kumar" from "Harman". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
Strata + Hadoop World 2012: Data Science on Hadoop: How Cloudera Impala Unloc...Cloudera, Inc.
This talk will cover what tools and techniques work and don’t work well for data scientists working on Hadoop today and how to leverage the lessons learned by the experts to increase your productivity as well as what to expect for the future of data science on Hadoop. We will leverage insights derived from the top data scientists working on big data systems at Cloudera as well as experiences from running big data systems at Facebook, Google, and Yahoo.
Top Hadoop Big Data Interview Questions and Answers for FresherJanBask Training
Top Hadoop Big Data Interview Questions and Answers for Fresher , Hadoop, Hadoop Big Data, Hadoop Training, Hadoop Interview Question, Hadoop Interview Answers, Hadoop Big Data Interview Question
This talk gives an introduction into Hadoop 2 and YARN. Then the changes for MapReduce 2 are explained. Finally Tez and Spark are explained and compared in detail.
The talk has been held on the Parallel 2014 conference in Karlsruhe, Germany on 06.05.2014.
Agenda:
- Introduction to Hadoop 2
- MapReduce 2
- Tez, Hive & Stinger Initiative
- Spark
This talk examines HBase client options available to application developers working with HBase. The focus is framed on, but not limited to, building webapps.
We start by looking at distributed database features that impact latency. Then we take a deeper look at the HBase read and write paths with a focus on request latency. We examine the sources of latency and how to minimize them.
If you've used a modern, interactive map such as Google or Bing Maps, you've consumed "map tiles". Map tiles are small images rendering a piece of the mosaic that is the whole map. Using conventional means, rendering tiles for the whole globe at multiple resolutions is a huge data processing effort. Even highly optimized, it spans a couple TBs and a few days of computation. Enter Hadoop. In this talk, I'll show you how to generate your own custom tiles using Hadoop. There will be pretty pictures.
Let Spark Fly: Advantages and Use Cases for Spark on HadoopMapR Technologies
http://bit.ly/1BTaXZP – Apache Spark is currently one of the most active projects in the Hadoop ecosystem, and as such, there’s been plenty of hype about it in recent months, but how much of the discussion is marketing spin? And what are the facts? MapR and Databricks, the company that created and led the development of the Spark stack, will cut through the noise to uncover practical advantages for having the full set of Spark technologies at your disposal and reveal the benefits for running Spark on Hadoop
This presentation was given at a webinar hosted by Data Science Central and co-presented by MapR + Databricks.
To see the webinar, please go to: http://www.datasciencecentral.com/video/let-spark-fly-advantages-and-use-cases-for-spark-on-hadoop
Apache Apex & Apache Geode are two very promising incubating open source projects, combined they promise to fill gaps of existing big data analytics platforms.
Apache Geode provides a database-like consistency model, reliable transaction processing and a shared-nothing architecture to maintain very low latency performance with high concurrency processing.
In this session we will talk about use cases and on-going efforts of integrating Apex and Geode to build scallable & fault tolerant RealTime streaming applications that ingest from various sources and egress to Geode.
Use case 1 - Geode as data store to write streaming processed data computed by Apex which is powering user applications or dashboards.
Use case 2 - Apex application reading data from Geode cache and use it for data processing.
Use case 3 - Apex platform's operator checkpointing in Geode to improve performance of Apex batch operations.
Presented by Ashish Tadose at Apex Meetup on 03/17/16
Apache Spark 2.0: Faster, Easier, and SmarterDatabricks
In this webcast, Reynold Xin from Databricks will be speaking about Apache Spark's new 2.0 major release.
The major themes for Spark 2.0 are:
- Unified APIs: Emphasis on building up higher level APIs including the merging of DataFrame and Dataset APIs
- Structured Streaming: Simplify streaming by building continuous applications on top of DataFrames allow us to unify streaming, interactive, and batch queries.
- Tungsten Phase 2: Speed up Apache Spark by 10X
Introduction to Apache Spark Developer TrainingCloudera, Inc.
Apache Spark is a next-generation processing engine optimized for speed, ease of use, and advanced analytics well beyond batch. The Spark framework supports streaming data and complex, iterative algorithms, enabling applications to run 100x faster than traditional MapReduce programs. With Spark, developers can write sophisticated parallel applications for faster business decisions and better user outcomes, applied to a wide variety of architectures and industries.
Learn What Apache Spark is and how it compares to Hadoop MapReduce, How to filter, map, reduce, and save Resilient Distributed Datasets (RDDs), Who is best suited to attend the course and what prior knowledge you should have, and the benefits of building Spark applications as part of an enterprise data hub.
HP Microsoft SQL Server Data Management SolutionsEduardo Castro
In this presentation was used in the MSDN WebCast and we cover some details about the hardware offerings to run SQL Server DataWarehouse, some detail about HP Hardware is shown.
Best Regards,
Ing. Eduardo Castro Martinez
http://ecastrom.blogspot.com
Apresentação do Vitor Tomaz sobre a Arquitectura dos Serviços da plataforma Windows Azure na 4a Reunião Presencial da Comunidade NetPonto em Coimbra (http://netponto.org).
[NetPonto] Arquitectura dos Serviços da plataforma Windows AzureVitor Tomaz
O Windows Azure é uma plataforma que fornece serviços de alta disponibilidade e escalabilidade. Nesta sessão iremos abordar a arquitectura dos serviços base desta plataforma (Compute, Storage e SQLAzure) de modo a entendermos de que forma é que a escalabilidade e alta disponibilidade são conseguidas. Iremos ver as diferenças para as plataformas "tradicionais" e algumas consequências no desenvolvimento de soluções para este ambiente.
This slide deck was used as part of a conference session providing an introduction to desktop virtualization presented by Simon Bramfitt , founder of Entelechy Associates.
The session was held at the Virtualization Solutions Exchange (VSX 2012) November 2, 2012 at the Computer History Museum, Mountain View, California.
Information on VSX 2012 can be found here http://www.vsx2012.com/index.cfm
The PowerPoint file is available for download from Entelechy Associates here http://entelechy-associates.com/
Hydrologic Information Systems and the CUAHSI HIS Desktop ApplicationACSG Section Montréal
The U.S. National Science Foundation supported Consortium of Universities for the Advancement of Hydrologic Sciences (CUAHSI) Hydrologic Information System (HIS) project includes extensive development of data storage and delivery tools and standards including WaterML (a language for sharing hydrologic data sets via web services); and HIS Server (a software tool set for delivering WaterML from a server); These and other CUASHI HIS tools have been under development and deployment for several years and together, present a relatively complete software “stack” to support the consistent storage and delivery of hydrologic and other environmental observation data. This presentation describes the development of a new HIS software tool called “HIS Desktop” and the development of an online open source software development community to update and maintain the software. HIS Desktop is envisioned as a local (i.e. not server-based) client side software tool that will run on multiple operating systems and will provide a highly usable level of access to HIS services. The software will provide many key capabilities including data query, map-based visualization, data download, local data maintenance, editing, graphing, data export to selected model-specific data formats, linkage with integrated modeling systems such as OpenMI, and potentially upload to the HIS server from the local desktop software. As the software is presently in the early stages of development, this presentation will focus on design approach and paradigm and is viewed as an opportunity to encourage participation in the open development community. Indeed, recognizing the value of community based code development as a means of ensuring end-user adoption, this project has adopted an “iterative” or “spiral” software development approach which will be described in this presentation.
3. whoami
Computer Science & Engineering at Ohio State:
Artificial Intelligence, Programming Languages, Systems
Engineering
Applied Technical Systems: Hierarchical, non-relational
data storage and analysis systems (no-sql before there was
NoSQL). Information Retrieval, Wire Serialization/RPC
(before there was Thrift/Avro), Data Visualization (GB's)
Visible Technologies: Social Media Storage, Processing,
Analytics. Monitoring, Engagement, Warehousing, and BI. (TB's)
Drawn to Scale: Big Data Storage, Processing, Retrieval,
Analytics (TB's, PB's)
22. vertical partitioning
Data Server Villages of People Data Server Villages of People
App Servers App Servers
Data Server Villages of People Data Server Villages of People
App Servers App Servers
no central point of organization
no committee or standardizing body
no plan/strategy/illuminati to take down the RDBMS; lots of "in-fighting"
central tenant - there IS NO one-size-fits-all
unlike RDBMS assumptions, each engineering effort must be evaluated for data needs
is it “anti-RDBMS”?
not so much
will not magically solve all your data or performance problems
applications won’t magically stop crashing, data corruption, etc.
Big Data is still hard. These tools make it possible/affordable/approachable
data persistence comes down to garantees
why are we here?
"web scale"
more users, content, connections
more trends, insight, knowledge
Atomicity: fault-tolerance is moving to the application layer - smaller atomic units
Consistency: yes! but not necessarily immediate - "availability" (latency, reads) is more important.
Isolation: smaller atomic units (multi-step transaction vs. compare-and-swap), greater availability, denormalization => reduced dependency on isolation
Durability: some things are more important that getting every last detail, i.e. latency of response, view in aggregate
Basically Available: is the data layer up or not? are we serving content to our users or not?
Soft State: shifting burden of "correctness" up to application layer. availability is more important than precision. accuracy (correct) vs. precision (repeatable).
Eventual Consistency: all operations are recorded and ordered. played back as resources permit.
agile dev moves too fast for schema and constraints - this isn’t waterfall
data models change quickly
up-front schema modeling is akin to waterfall development - not always practical/feasible/possible
data is messy - record what you have and leave constraints up to the application
at scale, data services look like a DHT anyway!
isolated independent services
introduced caching layers
partitioned data by logical and range boundaries.
webapp
app servers/session self-contained - load-balanced
data’s in one spot - what do you do?
37-signals approach - DHH “scaling is a good thing because scaling => users => $$$”
more users, more instances. easy!
doesn’t work for social applications:
- users cannot interact
- old MMO’s vs. new social games
redesign data server as “data services”
separate independent logical components
knowing each service by name becomes “vexing”
configuration/logistical nightmare!
abstractions!
wouldn’t it be nice if...
Distributed Computing Made Easy Less Hard
programming model/API for parallel computing
Google's MapReduce paper
replicated, high throughput, fairly UNIX-y (not POSIX).
Google FS Paper
Distributed Group Services - coordination, synchronization, configuration, naming.
Google Chubby Paper
efficient, cross-language messaging
Facebook/Apache Thrift
Google Protobufs
Google BigTable
Addresses limitations of Raw M/R, HDFS access
request by key: vs. hdfs sequential reads
low-latency, ms response times vs. m/r high-latency