The Cassandra JDBC driver allows the users in the process of connecting with the live data of Cassandra. It allows direct connection from any application which is able to support the connectivity of the JDBC. It connects Java application with real-time Cassandra and NoSQL and Cloud services and databases. The user can use Apache Cassandra as in this case the big data is a backend and it powers the application of the Java and J2EE. It has certain features such as:
Cassandra 2.0 right through the 3.0 compatibility, in this case, the faster performance is leveraged and security of the Cassandra V3.0 is improved.
It delivers native CQL3 or the CQL2 data access to cluster Cassandra data
It has full support for data aggregation and SQL queries along with JOIN.
It has seamless integration with BI being the lead, and the ETL tools and the applications that are custom.
It has a flexible flattening on the NoSQL and has an automatic generation of schema and has flexible querying.
The user can connect to live Apache Cassandra data.
With the help of it, the user can write SQL, and even get Apache Cassandra data.
It is a codeless integration that has popular tools such as reporting, BI, and ETL.
It has full Unicode support the parameters, data as well as metadata.
It is a pure Java type 4/5 JDBC driver.
BI & Analytics
In the BI and Analytics drivers can provide the fastest and the most comfortable way for which the user can connect and get the information visualization technologies and Cassandra information all together. They're able to supply detailed access to the Cassandra information and metadata, functionality and it provides easy integration with the user's analytical tools.
ETL, Replication, & Warehousing
ETL is this correct to be a process in data warehousing and the full form of it is an extract, transform, and load. It is mainly a process by which a tool of ETL is extracting data from various sources of the data system, transforms it into an area that is staging, and then finally loads it back into the data warehouse system. The ETL is capable of covering a process in which the data is loaded from a source system. This transformation is possible and it occurs by the usage of the rules or by combining the data with some other data.
The process of data management is very important as in this case the security of data is regarded and it is very important and appropriate data management will help in ensuring that the vital data is safe and secure and never lost and it is protected within the organization. An essential part of data management is data security and it protects the employees as well as companies from various loss of data, breaches as well as thefts. Data management is a process that is administrative and it includes validating, acquiring, processing, protecting, as well as storing data and makes sure the data is accessible, relatable.
Workflow & Automation Tools
It allows the users to connect to the tools of BPM, ESB, iPaaS as well as information migration. In this stage, the adapters provide access from programs such as Microsoft flow, popular apps, SQL SSIS, and so on.
Developer Tools & Technologies
It is the easiest way in which the user can incorporate with the Cassandra from any place he desires. The Cassandra drivers offer the users of version for the Cassandra which simplifies integration developers to build and create software that has higher quality than what it was earlier.
Data virtualization can be considered as an alternative to data warehousing and ETL. It is an inherently aimed alternative which focused on producing quick and timely inside without the need to embark on major data with the extensive data storage and ETL. Data virtualization can be extended as well as adapted to serve the requirements of data warehousing and for this, the user needs to have an understanding of the data storage as well as history requirement alongside the planning and designing the process to incorporate the right type of data virtualization, storage strategies, and last but not least integration. Data virtualization has made it complimentary for all the existing data sources and it has increased the availability and the usage of the enterprise data.