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SymmetricDS is web-enabled, database independent, data synchronization/replication software. It uses web and database technologies to replicate tables between relational databases in near real time. The software was designed to scale for a large number of databases, work across low-bandwidth connections, and withstand periods of network outage. By using database triggers, SymmetricDS guarantees that data changes are captured and atomicity is preserved. Support for database vendors is provided through a Database Dialect layer, with implementations for MySQL, Oracle, SQL Server, PostgreSQL, DB2, Firebird, HSQLDB, H2, and Apache Derby included. Synchronization can be configured to push data (trickle-back) or pull data (trickle-poll) at an interval. SymmetricDS allows for synchronization between two or more tiers of nodes, such as the following: A farm of web server nodes fronting an enterprise-class general office database A handful of regional servers for synchronizing from the general office to remote geographical areas 1000(s) of store server nodes using a departmental class database to sync with a regional node 10(s) of Point of Sale (POS) register nodes using an embedded database to sync with a store server Deployment options include the following: Web application archive (WAR) deployed to an application server such as Tomcat, Jetty, or JBoss Standalone service Embedded in an application SymmetricDS is written in Java and licensed as open source software under the GNU Lesser General Public License (LGPL).

DdlUtils is a small, easy-to-use component for working with Database Definition (DDL) files. These are XML files that contain the definition of a database schema, e.g. tables and columns. These files can be fed into DdlUtils via its Ant task or programmatically in order to create the corresponding database or alter it so that it corresponds to the DDL. Likewise, DdlUtils can generate a DDL file for an existing database. DdlUtils uses the Turbine XML format, which is shared by Torque and OJB. This format expresses the database schema in a database-independent way by using JDBC datatypes instead of raw SQL datatypes which are inherently database specific. An example of such a file is:

"The past year I've been learning a lot of Scala and I'm currently working on a new project using Scala. I use LiquiBase, which is a database-independent library for tracking, managing and applying database changes. I'm also using the simple-build-tool (SBT) for my project. So I've put together a little SBT plug-in for running LiquiBase maintenance commands (update, rollback, …) from within SBT. For example, whenever I want to apply new database changes with LiquiBase I can now simply run sbt liquibase-update which sets up a new instance of LiquiBase and executes the LiquiBase update command which migrates my database to the latest version. At the moment the plug-in supports the following commands: liquibase-update, liquibase-drop, liquibase-tag, liquibase-rollback and liquibase-validate. What are the benefits of using the plug-in and not just the LiquiBase CLI? * no download/install of LiquiBase * classpath handled by SBT * no need to provide a big list of parameters or writing shell scripts The plug-in is called liquibase-sbt-plugin and you can find it here on GitHub. Feel free to use it or fork it and suggest changes. I'm still relatively new to Scala and especially SBT so any remarks are very welcome."

There are many approaches available to scale PostgreSQL beyond running on a single server. An outline of the terminology and basic technologies involved is at High Availability and Load Balancing. There is a presentation covering some of these solutions. There is no one-size fits all replication software. You have to understand your requirements and how various approaches fit into that. For example, here are two extremes in the replication problem space: You have a few servers connected to a local network you want to always keep current for failover and load-balancing purposes. Here you would be considering solutions that are synchronous, eager, and therefore conflict-free. Your users take a local copy of the database with them on laptops when they leave the office, make changes while they are away, and need to merge those with the main database when they return. Here you'd want an asynchronous, lazy replication approach, and will be forced to consider how to handle conflicts in cases where the same record has been modified both on the master server and on a local copy. These are both database replication problems, but the best way to solve them is very different. And as you can see from these examples, replication has a lot of specific terminology that you'll have to understand to figure out what class of solution makes sense for your requirements. A great source for this background is in the Postgres-R Terms and Definitions for Database Replication. The main theoretical topic it doesn't mention is how to resolve conflict resolution in lazy replication cases like the laptop situation, which involves voting and similar schemes.

Blueprints is a collection of interfaces, implementations, ouplementations, and test suites for the property graph data model. Blueprints is analogous to the JDBC, but for graph databases. As such, it provides a common set of interfaces to allow developers to plug-and-play their graph database backend. Moreover, software written atop Blueprints works over all Blueprints-enabled graph databases. Within the TinkerPop software stack, Blueprints serves as the foundational technology for: Pipes: A lazy, data flow framework Gremlin: A graph traversal language Frames: An object-to-graph mapper Furnace: A graph algorithms package Rexster: A graph server

Daffodil Replicator is a Java tool for data synchronization, data migration, and data backup between various database servers. Daffodil Replicator works over standard JDBC driver and supports replication across heterogeneous databases. At present, it supports following databases: Microsoft SQL Server, Oracle, Daffodil DB, DB2, Derby, MySql, and PostGreSql. Daffodil Replicator is available in both enterprise and open source versions referred as Daffodil Replicator(E) and Daffodil Replicator(OS) respectively. For more details on Daffodil Replicator (E) or Daffodil Replicator (OS), click on the links on the left.

t just works - Migrate from any version (incl. an empty database) to the latest version of the schema Automatic migration on startup - Ship migrations together with the application and run them automatically on startup Convention Over Configuration - Classpath Scanning to automatically discover Sql and Java migrations Plain Old Sql - SQL scripts for regular migrations (incl. placeholder replacement). No proprietary XML formats, no lock-in. No limits - Java classes for advanced migrations Highly reliable - Safe for cluster environments (Multiple machines can migrate in parallel) Maven support - Maven plugin for migrating manually Fail fast - Inconsistent database or failed migration prevents app from starting. Schema Clean - Drop all tables, views, triggers, ... from a schema without dropping the schema itself

ModeShape (formerly "JBoss DNA") is a JCR 2.0 (JSR-283) implementation that provides access to content stored in many different kinds of systems. A ModeShape repository isn't yet another silo of isolated information, but rather it's a JCR view of the information you already have in your environment: files systems, databases, other repositories, services, applications, etc. To your applications, ModeShape looks and behaves like a regular JCR repository. Using the standard JCR API, applications can search, navigate, version, and listen for changes in the content. But under the covers, ModeShape gets its content by federating multiple back-end systems (like databases, services, other repositories, etc.), allowing those systems to continue "owning" the information while ensuring the unified repository stays up-to-date and in sync. ModeShape repositories can be used in a variety of applications. One of the most obvious ones is in provisioning and management, where it's critical to understand and keep track of the metadata for models, database, services, components, applications, clusters, machines, and other systems used in an enterprise. Governance takes that a step farther, by also tracking the policies and expectations against which performance can be verified. In these cases, a repository is an excellent mechanism for managing this complex and highly-varied information. But a ModeShape repository doesn't have to be large and complex: it could just manage configuration information for an application, or it could just provide a JCR interface on top of a couple of non-JCR systems.

Postgres-R is an extension to the relational database system Postgres providing efficient, fast and consistent database replication. Learn more about Postgres-R

Couchbase: The End of the Trade-off Game Before Couchbase, ops teams managing web applications faced tough compromises when it came to databases. Screaming speed or durable, safe data storage. Super scale out virtually overnight or scaling up with lots of hardware upgrades. Bleeding-edge NoSQL or safe, but inflexible relational solutions. Couchbase eliminates those trade-offs. We supply extraordinary speed with complete safety and incorruptibility.  A growth path that will never force you to rethink your application infrastructure from datacenter deployments to mobile devices - one continuous, synchronized flow. Solutions that complement your other systems - including your RDBMS.  Technology that meets the needs of developers and conforms to the mission-critical demands of ops teams. This really is the beginning of a new era of NoSQL solutions. Couchbase: database solutions for a post-relational world. Learn more.

The database administrators are extremely methodical professionals. Though the Enterprise Manager tool automates much of the gathering of information that is needed by database administrators

The CDO (Connected Data Objects) Model Repository is a distributed shared model framework for EMF models and meta models. CDO is also a model runtime environment with a focus on orthogonal aspects like model scalability, transactionality, persistence, distribution, queries and more. CDO has a 3-tier architecture supporting EMF-based client applications, featuring a central model repository server and leveraging different types of pluggable data storage back-ends like relational databases, object databases and file systems. The default client/server communication protocol is implemented with the Net4j Signalling Platform.

Apache CouchDB is a document-oriented database that can be queried and indexed in a MapReduce fashion using JavaScript. CouchDB also offers incremental replication with bi-directional conflict detection and resolution. CouchDB provides a RESTful JSON API than can be accessed from any environment that allows HTTP requests. There are myriad third-party client libraries that make this even easier from your programming language of choice. CouchDB's built in Web administration console speaks directly to the database using HTTP requests issued from your browser. CouchDB is written in Erlang, a robust functional programming language ideal for building concurrent distributed systems. Erlang allows for a flexible design that is easily scalable and readily extensible. See the introduction and the technical overview for more information.

"Neo4j is a graph database. It is an embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs rather than in tables. A graph (mathematical lingo for a network) is a flexible data structure that allows a more agile and rapid style of development."

A Scala ORM and DSL for talking with Databases with minimum verbosity and maximum type safety

Caching is the most important performance optimization technique. There are many things that can be cached in persistence, objects, data, database connections, database statements, query results, meta-data, relationships, to name a few. Caching in object persistence normally refers to the caching of objects or their data. Caching also influences object identity, that is that if you read an object, then read the same object again you should get the identical object back (same reference). JPA 1.0 does not define a shared object cache, JPA providers can support a shared object cache or not, however most do. Caching in JPA is required with-in a transaction or within an extended persistence context to preserve object identity, but JPA does not require that caching be supported across transactions or persistence contexts. JPA 2.0 defines the concept of a shared cache. The @Cacheable annotation or cacheable XML attribute can be used to enable or disable caching on a class.

In this topic, we will learn how to create a PHP MySQL Login System with the help of PHP and MySQL database. There are few steps given for creating a login system with MySQL database.

Spring 3.0.5 is now released and comes with the fixes of more than 80 minor issues and provided some enhancements and improvements to the Spring Expression Language (SpEL), annotation support, and embedded databases.

This business suite contains Oracle database technologies and a range of product which is acquired or developed by Oracle.

"Gremlin is a graph traversal language. The documentation herein will provide all the information necessary to understand how to use Gremlin for graph query, analysis, and manipulation. Gremlin works over those graph databases/frameworks that implement the Blueprints property graph data model. For example: TinkerGraph, Neo4j, OrientDB, DEX, Rexster, and Sail RDF Stores. 1 Please join the Gremlin users group at http://groups.google.com/group/gremlin-users for all TinkerPop related discussions. Finally, if you are a Gremlin user, please add to the Gremlin in the Wild wiki page with your specific Gremlin uses cases."