RaptorDB License Key ⏵

RaptorDB will help developers by making their jobs easier because developing software products is hard enough without complete requirements.
 
That becomes even harder when requirements and minds change as they only do in the real world. With RaptorDB you know you can benefit from easier development, faster change perfoming, low knowledge requirements, simpler maintenance, minimal time and cost or setup as well as speed of execution.

Bridging the gap between collection of the nature and data storage with the requirements of intelligence, RaptorDB 2022 Crack has been developed with the objective of helping you take your work to new heights. RaptorDB uses agility to provide high scalability, making it a preferred choice for repositories of large data sets. No need to worry about the scalability of the data. RaptorDB will scale up and down for you as needed without having to worry about it again. Go big or go home. Create massive repositories of large datasets with minimal setup in a few clicks using the cloud and centralize your data so that you can access it anywhere and at any time.

RaptorDB Features:

See what data you want to run an effective analysis with out knowing the details of how to get the data, understand how to collect, centralize and maintain your data storage, use powerful SQL without having to worry about how to write a query or SQL expressions, apply rational queries on the collection of data through intelligent platform, run the analysis without the setting up and maintenance of a computer cluster and manage the results through the central repository.
I (Paulus) have been following and using both MongoDB and Cassandra for quite a while, plus have used MongoDB using SOLR as well. RaptorDB was a new entrant to the MongoDB scene and I took it for a spin and decided to compare it to the others (Cassandra and SOLR).
My experience with RaptorDB:
RaptorDB:

Has a much better and simpler to use “data collection” module than SOLR.
Collections can be viewed, added and removed via the web interface.
Collections can be created with a standard SQL syntax, the default query engine is jMongo.
The collection creation and data “modification” happens in a centralized manner.
Query syntax is based on SQL.
The data collection does a better job in handling the query. I would say it is similar to SOLR.
Query results are stored in a database that is shared with other databases.
Collections can be queried with central authorization.
Size limits are gracefully handled for collections.
Your key data is placed in the same database.
Database creation and data collection is a simple matter of clicking a button.
All the important data is contained in a single document and have the same fields as in the “real” db.

The Databases:

MongoDB:

RaptorDB Activation Code is a next generation database to store very large amounts of structured data, up to several exabytes. With typical entry points of 10 million structs per hour it is ideal for big scale social media, e-commerce, credit card data, health care and image processing.
RaptorDB, Apache Ignite, and BigTable are designed to solve problems that large companies have struggled with for decades: large data sets and where to store them.
Flexibility
The database performance can be adjusted by setting a load factor. By default RaptorDB uses a load factor of 50%.
Apache Ignite and BigTable can scale a lot faster than RaptorDB. When using them to load data, the load factor can reach down to 1%.
RaptorDB becomes even faster than Apache Ignite with your own partitions. 
You can use RaptorDB locally and on a server without having to worry about cost, firewall restrictions, single points of failure, or how to configure replication or distributed storage.

BIGTABLE (BIG Data Tables): An open-source technology based on Google BigTable, but instead of cells, it uses columns for storing rows.
Ignite (Apache Ignite): A technology used to manage large and complex distributed datasets.
RaptorDB: A distributed database written in Java.

Why use RaptorDB?
1.) High performance
2.) You can use both Apache Spark and Ignite with RaptorDB
3.) Easy integration with existing Java code
4.) Minimal costs
5.) It comes with an easy to use UI to add/edit data, so you don’t have to worry about deployment
6.) Fast setup and deployment
7.) Easy maintenance and changes
8.) Full Apache 2.0
9.) Simple, modern database schema to get started quickly
10.) Prototype and prototype more quickly
11.) Helps you quickly create and deploy prototypes for a Web app
12.) Multiple write-back caches to help speed up DB writes
13.) Multiple cache expiry timeouts to help scale your DB
14.) Compact Java objects, which are more compact than JAVA Strings
15.) HBase compatibility for Hadoop and Spark programmers
16.) MapReduce compatibility to help Hadoop and Spark programmers develop faster
17.) RESTful Java APIs to make it easy to access
18.) K-V (key-value
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RaptorDB is a next generation document oriented database. RaptorDB supports the Document/Object paradigm for representation of documents with element-oriented support. The RaptorDB implementation of the Document/Object paradigm is inspired by the new specifications of the JSR 133.
The new specifications allow the developer to not only define very complex structures as document, but also to model them in a very flexible way. So, the developers can define very complex documents by combining multiple parts. Part of the specification is supported by the RaptorDB, namely the REST and Atom part.
RaptorDB is easy to use since its implementation is based on Java. Most all methods are supported. Including large parts of JDOM or DOM. You can use the very simple API via JDOM. You also can use the high performance API: Streaming/Non-Streaming.
What is New in RaptorDB:
The following is a list of some of the new features and functionality.
* Support for JSR 133
* Support for composite keys
* Sorted Nested Collections
* Support for GeoJSON
* Support for range queries
* Less restrictive schema
* Built-in JSON parser
* Extended annotation support
* JSON, CSV, and URI as type of element
* Support for “parent” element of elements
* support for both JSR 133 and JSR 219
* support for REST and Atom
* Support for streaming queries
* Support for JSON-style elements
* Support for multiple (boost and negative boost)
* Support for multiple group by
* Support for XML attributes
* Support for complex/nested collections
* Support for both class and element as name of element
* First class support of JSR 133
* A better first line error response
* Indices and an indexed-views for multiple columns
* StringBuilder and StringBuffer support
* Dedicated collections and classes for date
* Simple-to-use extension mechanisms
* Multi-language Support
* REST and Atom API: faster, simpler, more powerful
* Improved performance
* SQL and GAQL-like SQL
* Multidimensional array/map support
* Extensible collection classes
* Basic support for querying with database server
* Query-by-Example, normalizes and aggregates
* Stream-like insert
* Stream-like update
* Stream-like delete
* Support for hot data integration
* Support for type checking
* Support

RaptorDB is a server-side binary database as an abstraction over existing databases. It is installed on a production server in a production environment. The database has been made according to the Apache 2.0 license to make it available to free open source software as public domain. 

What RaptorDB is not:

A database server
An ORM (Object-Relational mapping)
A database management system (DBMS)
A CUBRID API/library (CUBRID being Oracle’s RDBMS)
A database service
An application server
A platform to develop applications
An ORM
An XML database
An object-relational or object-data mapping tool
A framework
A library
A web application 

What RaptorDB can provide:

Relational database abstraction
NoSQL database, In-memory, Document-oriented, column oriented, graph oriented
Migrating to and from an existing RDBMS
Object-data mapping with ease
Data persistence, as an entry point for data wrangling, data imports and data exports
Robust server-side abstraction (No Object-relational mapping)
Strong object orientation and inferencing
Data archetypes (essentially like schemas)
Data validation, indexation
Complex insert and update statements
Dynamic queries
Cache data
In memory, database, or table level indexes
Built-in support for fulltext
ACL (Access control lists)
Plug-in support for any other system
ODBC, JDBC or Hibernate database interaction
SQL Developer to RaptorDb
Easy development, installation and management
Data-oriented application
Design data structure
Subset data exploration
API for application development
Open source project

RaptorDB Server:

RaptorDB is a scalable, pluggable database server. It can store data on disk, in memory, be in sync with any other server on the same network or any relational database and is used by many popular open source applications. 

RaptorDB has been implemented with Apache 2.0 licence. The source code is hosted at GitHub repository:

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