Rolling With Riak
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The document provides an overview of NoSQL databases and focuses on MongoDB and Riak. It discusses how these databases address the needs of web applications by providing flexibility, scalability, and high performance. Riak is highlighted as using a distributed architecture with no single point of failure and tunable consistency properties. Its ability to link documents and handle high availability through replication is also summarized.
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All you didn't know about the CAP theorem
The document discusses the CAP theorem and related concepts like PACELC, ACID, and BASE. It analyzes how different database systems like PostgreSQL, MongoDB, and a hybrid PostgreSQL/Salesforce/Heroku Connect system fit within these models. While CAP classifications can be imprecise, the key aspects to understand are the consistency, availability, and partition tolerance tradeoffs that distributed systems must make.
Riak Core: Building Distributed Applications Without Shared State
Riak Core--an open-source Erlang library created by Basho Technologies that powers Riak KV and Riak Search--allows developers to build distributed, scalable, failure-tolerant applications based on a generalized version of Amazon's Dynamo architecture. In this talk, Rusty explains why Riak Core was built, discuss what problems it solves and how it works, and walk through the steps to using Riak Core in an Erlang application.
Introducing Riak
This document introduces Riak and summarizes its key features. Riak is a flexible storage engine that uses a key-value store model and supports document storage via JSON. It has a REST API and supports map/reduce functions. Riak is highly distributed, fault-tolerant, and optimized for availability. It aims to balance consistency and availability according to the CAP theorem by allowing tunable consistency on a per-request basis.
Riak in Ten Minutes
Riak is an open source, distributed key-value data store designed to be highly available, scalable and fault-tolerant. It stores data as keys mapped to values, with keys organized into buckets. Data can be queried using a map-reduce algorithm. Relationships between data can be represented using links. Riak is designed to scale horizontally across clusters of nodes and uses replication to achieve high availability even if some nodes fail.
Introduction to couchbase
This document provides an introduction and overview of Couchbase Server, a NoSQL document database. It describes Couchbase Server as the leading open source project focused on distributed database technology. It outlines key features such as easy scalability, always-on availability, flexible data modeling using JSON documents, and core features including clustering, replication, indexing and querying. The document also provides examples of basic write, read and update operations on a single node and cluster, adding nodes, handling node failures, indexing and querying capabilities, and cross data center replication.
Riak Training Session — Surge 2011
The document provides an overview of Riak, describing it as a key-value store that is distributed, horizontally scalable, fault-tolerant, and highly available. It was built for the web and was inspired by Amazon's Dynamo database. The tutorial then demonstrates how to install Riak, start a cluster, and perform basic PUT and GET operations to store and retrieve values.
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The document discusses NoSQL databases and the CAP theorem. It begins by providing an overview of NoSQL databases, their key features like being schemaless and supporting eventual consistency over ACID transactions. It then explains the CAP theorem - that a distributed system can only provide two of consistency, availability, and partition tolerance. It also discusses how Google's Spanner database achieves consistency and scalability using ideas from Lamport's Paxos algorithm and a new time service called TrueTime.
CAP Theorem - Theory, Implications and Practices
By Yoav Francis and Tomer Cagan, Concurrent and Distributed Computing Seminar, IDC 2012
Overview on the CAP Theorem, NoSQL and Distributed Databases
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Riak Core: Building Distributed Applications Without Shared State
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This document provides an overview of NoSQL databases and then discusses Amazon DynamoDB in more depth. It explains that NoSQL databases are an alternative to relational databases for certain data-intensive applications. It then discusses DynamoDB specifically, highlighting that it is a fully managed NoSQL database that provides fast and predictable performance, flexible data model, automatic scaling, and pay per request pricing. The document also provides examples of applications that were built on DynamoDB as part of a challenge.
DynamoDB Gluecon 2012
This document provides an overview of NoSQL databases and then discusses Amazon DynamoDB in more depth. It explains that NoSQL databases are an alternative to relational databases for certain data-intensive applications. It then discusses DynamoDB specifically, highlighting that it is a fully managed NoSQL database that provides fast and predictable performance, flexible data model, automatic scaling, and pay per request pricing. The document also provides examples of applications that were built on DynamoDB as part of a challenge.
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The document provides an introduction to NOSQL databases. It begins with basic concepts of databases and DBMS. It then discusses SQL and relational databases. The main part of the document defines NOSQL and explains why NOSQL databases were developed as an alternative to relational databases for handling large datasets. It provides examples of popular NOSQL databases like MongoDB, Cassandra, HBase, and CouchDB and describes their key features and use cases.
Front Range PHP NoSQL Databases
NoSQL databases are non-relational databases designed for large volumes of data across many servers. They emerged to address scaling and reliability issues with relational databases. While different technologies, NoSQL databases are designed for distribution without a single point of failure and to sacrifice consistency for availability if needed. Examples include Dynamo, BigTable, Cassandra and CouchDB.
Ml2
Microsoft R Server allows users to run R code on large datasets in a distributed, parallel manner across SQL Server, Spark, and Hadoop without code changes. It provides scalable machine learning algorithms and tools to operationalize models for real-time scoring. The document discusses how R code can be run remotely on Hadoop and Spark clusters using technologies like RevoScaleR and Sparklyr for scalability.
Intro to big data analytics using microsoft machine learning server with spark
Alex Zeltov - Intro to Big Data Analytics using Microsoft Machine Learning Server with Spark
By combining enterprise-scale R analytics software with the power of Apache Hadoop and Apache Spark, Microsoft R Server for HDP or HDInsight gives you the scale and performance you need. Multi-threaded math libraries and transparent parallelization in R Server handle up to 1000x more data and up to 50x faster speeds than open-source R, which helps you to train more accurate models for better predictions. R Server works with the open-source R language, so all of your R scripts run without changes.
Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business with full support for Python and R. Machine Learning Server meets the needs of all constituents of the process – from data engineers and data scientists to line-of-business programmers and IT professionals. It offers a choice of languages and features algorithmic innovation that brings the best of open source and proprietary worlds together.
R support is built on a legacy of Microsoft R Server 9.x and Revolution R Enterprise products. Significant machine learning and AI capabilities enhancements have been made in every release. In 9.2.1, Machine Learning Server adds support for the full data science lifecycle of your Python-based analytics.
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Big Data and NoSQL for Database and BI Pros
This document provides an agenda and overview for a conference session on Big Data and NoSQL for database and BI professionals held from April 10-12 in Chicago, IL. The session will include an overview of big data and NoSQL technologies, then deeper dives into Hadoop, NoSQL databases like HBase, and tools like Hive, Pig, and Sqoop. There will also be demos of technologies like HDInsight, Elastic MapReduce, Impala, and running MapReduce jobs.
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Big Data is everywhere these days. But what is it and how can you use it to fuel your business? Data is as important to organizations as labour and capital, and if organizations can effectively capture, analyze, visualize and apply big data insights to their business goals, they can differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line.
Join this session to understand the different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together.
Reasons to attend:
Learn how AWS can help you process and make better use of your data with meaningful insights.
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This document discusses running MongoDB and Kubernetes together to enable lean and agile development. It proposes using Docker containers to package applications and leverage tools like Kubernetes for deployment, management and scaling. Specifically, it recommends:
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2) Deploying to Kubernetes where services and replication controllers ensure high availability and scalability.
3) Treating databases specially by running them as "naked pods" assigned to labeled nodes with appropriate resources.
4) Demonstrating deployment of a sample MEAN stack application on Kubernetes with MongoDB and discussing future work around experimentation and blue/green deployments.
JAX 2013: Modern Architectures with Spring and JavaScript
This document discusses modern application architectures using Spring and JavaScript. It describes how typical applications have moved from monolithic architectures running on application servers with relational databases to more modular architectures with services, NoSQL databases, and rich client applications built with JavaScript running in browsers. It outlines challenges in developing modular applications with JavaScript on the client and RESTful services on the server using technologies like Spring, and opportunities to leverage platforms as a service.
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Spark has evolved its APIs and engine over the last 6 years to combine the best aspects of previous systems like databases, MapReduce, and data frames. Its latest structured APIs like DataFrames provide a declarative interface inspired by data frames in R/Python for ease of use, along with optimizations from databases for performance and future-proofing. This unified approach allows Spark to scale massively like MapReduce while retaining flexibility.
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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
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Bob Boule
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Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Rolling With Riak
- 1. (Yet-Another-NoSQL-DB) Presented By John Lynch [email_address]
- 2. Web App Developers ORM Focus on the App Speed of Development DB Agnostic(ish) Fixed Schema Limits design choices Migration Hell Scaling at DB layer Rails!
- 3. Shiny New Toys Decades of research and best practices Awesome ad-hoc query capability Zillions of vendors/tools/libraries/code Flexibility of schema-less design Ability to scale…. Web-scale
- 7. Scaling MongoDB Master, Slave, Replica Set, Replica Pair, Shard Server, Connection Pool, ack!
- 8. Scaling MongoDB
- 9. If all you want is NoSQL… NoSQL on MySQL Leverages all MySQL skills, tools, techniques, stability, dependability If you want NoSQL + Scalability… … not so much.
- 11. Riak speaks HTTP > curl –i http://host:8098/riak/bucket1/key1 HTTP/1.1 200 OK X-Riak-Vclock: awpcFAA== Content-Type: text/plain Content-Length: 9 Last-Modified: Wed, 01 Se… Etag: 45364657 I am a value Leverage existing HTTP infrastructure, tools, etc
- 12. Scaling Riak Riak Riak Riak Riak Riak Http Load Balancer Varnish (cache) Standard HTTP Protocol Rails Rails Rails Rails Rails
- 13. Scaling Riak (alt) Riak Rails Nginx Riak Rails Nginx Riak Rails Nginx Riak Rails Nginx Riak Rails Nginx Http Load Balancer Varnish (cache) Standard HTTP Protocol
- 17. Network Split
- 18. Network Split
- 19. Network Split
- 23. Any questions? (First round at Rock Bottom generously sponsored by Basho.com)