Spring data
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Spring Data is an umbrella project that aims to provide a consistent programming model for data access in various storage systems. It includes subprojects for major data access domains like relational and non-relational databases. The subprojects provide a repository abstraction for storing and retrieving data across various persistence stores using their native APIs in a consistent way. Spring Data is an open source project maintained by Spring and VMWare.
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Once you begin developing with NoSQL technologies you will quickly realize that accessing data stores or services often requires in-depth knowledge of proprietary APIs that are typically not designed for use in enterprise Java applications. Sooner or later you might find yourself wanting to write an abstraction layer to encapsulate those APIs and simplify your application code. Luckily such an abstraction layer already exits: Spring Data.
Big Data Israel Meetup : Couchbase and Big Data
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Spring Data Neo4j Intro SpringOne 2011
Spring Data Graph is an integration library for the open source graph database Neo4j and has been around for over a year, evolving from its infancy as brainchild of Rod Johnson and Emil Eifrem. It supports transparent AspectJ based POJO to Graph Mapping, a Neo4jTemplate API and extensive support for Spring Data Repositories. It can work with an embedded graph database or with the standalone Neo4j Server.
The session starts with a short introduction to graph databases. Following that, the different approaches using Spring Data Graph are explored in the Cineasts.net web-app, a social movie database which is also the application of the tutorial in the Spring Data Graph Guidebook. The session will also cover creating a green-field project using the Spring Roo Addon for Spring Data Graph and deploying the App to CloudFoundry.
Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and An...
We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down.
Connected data is the key to your business succeeding and growing in today’s connected world.
Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.
Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Knowledge graphs provide:
- Increased visibility between internal groups
- Efficiency gains
- Cross-functional data collaboration
- Core complete and reliable business insights
- Better customer engagement
The live presentation and discussion can be found here: https://youtu.be/7vBdlXzhs_4
Additional reading on why connected data is beneficial: https://www.graphgrid.com/why-connected-data-is-more-useful/
Connected data solutions available by Benjamin and his team via GraphGrid and AtomRain: https://www.graphgrid.com and https://www.atomrain.com
springdatajpatwjug-120527215242-phpapp02.pdf
This document provides an overview and introduction to Spring Data JPA, including:
- Refactoring a plain JPA and Spring application to use Spring Data JPA repositories to reduce boilerplate code.
- The Spring Data repository abstraction and how it simplifies data access layers.
- Advanced topics like JPA specifications and QueryDSL for more complex queries.
- Configuring Spring Data JPA in Spring applications.
NoSQL and MapReduce
The document discusses NoSQL databases and MapReduce. It provides historical context on how databases were not adequate for the large amounts of data being accumulated from the web. It describes Brewer's Conjecture and CAP Theorem, which contributed to the rise of NoSQL databases. It then defines what NoSQL databases are, provides examples of different types, and discusses some large-scale implementations like Amazon SimpleDB, Google Datastore, and Hadoop MapReduce.
Architecting Your First Big Data Implementation
This document provides an overview of architecting a first big data implementation. It defines key concepts like Hadoop, NoSQL databases, and real-time processing. It recommends asking questions about data, technology stack, and skills before starting a project. Distributed file systems, batch tools, and streaming systems like Kafka are important technologies for big data architectures. The document emphasizes moving from batch to real-time processing as a major opportunity.
20130204 graph to-pacer-xml
This document provides an introduction to GraphTO, a graph database conference. It discusses who the organizers are and provides an introduction to graph databases and concepts. It highlights how graph databases are better suited than SQL for complex, connected data and provides examples of querying and visualizing graph data using technologies like Neo4j, Cypher, and SPARQL. Finally, it discusses loading patent grant data from XML into a graph and available resources for working with graph databases.
MongoDB + Java + Spring Data
This document provides an overview of MongoDB, Java, and Spring Data. It discusses how MongoDB is a document-oriented NoSQL database that uses JSON-like documents with dynamic schemas. It describes how the Java driver can be used to interact with MongoDB to perform CRUD operations. It also explains how Spring Data provides an abstraction layer over the Java driver and allows for object mapping and repository-based queries to MongoDB.
Size does not matter (if your data is in a silo)
The document discusses the challenges of working with large, distributed datasets from multiple sources. It summarizes that data is often siloed without common semantics, making integration and reuse difficult. It proposes that semantic web technologies can help by providing shared descriptions of data through ontologies, treating physical data schemas as virtual views onto semantic models. This would allow automation, discovery, and flexible use of large-scale data.
Neo4j_allHands_04112013
This document provides an overview of Neo4j GraphStore for modeling linked data. It discusses connected data and the NoSQL movement, modeling linked data using graphs and property graphs, and how Neo4j is a graph-based database that allows querying through both traversal-based and pattern-matched approaches. Key aspects of Neo4j covered include its architecture, accessible API, querying via Cypher, and benefits like plugins while noting some limitations around performance and scaling.
NOSQL Overview Lightning Talk (Scalability Geekcruise 2009)
This document discusses trends driving the emergence of NoSQL databases and provides an overview of NoSQL. The key trends include: (1) rapidly increasing data set sizes, (2) greater connectivity of data, and (3) more semi-structured and decentralized content. These trends have challenged the performance and architecture of traditional relational databases. NoSQL databases emerged in response and come in four main categories: key-value stores, BigTable clones, document databases, and graph databases. Each has a different data model suited to different types of use cases. Looking ahead, the best approach may be "polyglot persistence" using both SQL and NoSQL solutions.
NoSQL databases
This document provides an overview of NoSQL databases. It begins with a brief history of relational databases and Edgar Codd's 1970 paper introducing the relational model. It then discusses modern trends driving the emergence of NoSQL databases, including increased data complexity, the need for nested data structures and graphs, evolving schemas, high query volumes, and cheap storage. The core characteristics of NoSQL databases are outlined, including flexible schemas, non-relational structures, horizontal scaling, and distribution. The major categories of NoSQL databases are explained - key-value, document, graph, and column-oriented stores - along with examples like Redis, MongoDB, Neo4j, and Cassandra. The document concludes by discussing use cases and
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Git rerere
This document discusses how to enable and use git's rerere feature to reuse recorded resolutions of merge or rebase conflicts. It recommends enabling rerere globally, then shows how rerere records the preimage used to resolve a conflict during a merge. Later, when rebasing the branch with the same conflict, rerere will reuse the previous resolution so the user does not need to resolve the conflict again.
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This document provides an overview of Redis, an open source key-value store. It discusses Redis' features like speed, persistence, data types including strings, lists, sets and more. It also demonstrates how to use Redis via its command line interface and with Java libraries like Jedis. Finally, it shows how Redis can be used with Spring through interfaces, templates and drivers.
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The document discusses the ePUB format which uses XHTML for content, CSS for layout and formatting, and XML for the manifest, table of contents, and metadata. It specifies that ePUB uses Open Publication Structure (OPS) for formatting content, Open Packaging Format (OPF) for the XML .epub format, and Open Container Format (OCF) to wrap files in a ZIP archive.
트렌드 코리아 2011
The document discusses trends from 2007 to 2011 based on keywords. It covers past, present and future trends related to Korean chic, neighborhoods, technology, aging and products. Some of the keywords mentioned include times for Korean chic, into our neighborhood, good to be geeks, end of taboo, ready made order made, omni-U solution and manner matters.
Future Health Care 3
The document discusses future scenarios for health management systems with ubiquitous environments. It describes personalized health care tailored to each individual and focuses on estimation and prevention, care and healing, and improving accessibility. Three scenarios are presented for estimation and prevention: monitoring from birth and genes, youth practices, and lifestyle factors for adults like food. The system aims to go beyond traditional health insurance and fund management to improve overall health quality.
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The document discusses the future of health management systems with ubiquitous equipment. It describes how personal health management will be portable, networked to allow sharing of information, and focused on health quality. It discusses trends towards well-being, welfare, and aging life. Future health care could utilize computer, communication, imaginary interfaces, and robotics. Scenarios shown include estimation and prevention through tagging and monitoring from birth through adulthood, care and healing through personalized at-home treatment, and accessibility through robotics and remote assistance. The goal is personalized health care and management focused on high health quality.
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Spring data
- 1. Spring Data Sean@Weaveus May 2011
- 2. Goal • !" #$% RDB(JPA) &' • () *+, Data -. /0 &' • Cloud, Column, Document, Graph, KeyValue, MapReduce Monday, May 9, 2011 2
- 3. Umbrella Project • 1 234 56 SubProject) 78 • SpringData-JPA • SpringData-MongoDB • And so on... • Spring Data6 9: ;<= >? Monday, May 9, 2011 3
- 4. SubProjects Category SubProject Blob Blob(Amazon S3) Column HBase Cassandra Document CouchDB MongoDB Graph Neo4j KeyValue Riak Redis Membase MapReduce Hadoop RDB JDBC(Oracle) JPA Commons Mapping Commons Monday, May 9, 2011 4
- 5. • http://www.springsource.org/spring-data • @SpringData • http://blog.springsource.com/category/data- access/ • OpenSource by Spring,VMWare • @A3B) 1.0.0 Milestone $C SubProjectD E@ F) Monday, May 9, 2011 5
- 6. Data Access of Spring Monday, May 9, 2011 6
- 7. • SpringData - JPA • SpringData Document - MongoDB • SpringData KeyValue - Redis • SpringData Other Data Access • SpringData Commons, Mapping Monday, May 9, 2011 7