Close
We enable business and digital transformation decisions through the delivery of cutting-edge ICT solutions and products...
SOAP and REST offer different methods to invoke a web service. Learn the variations between the two approaches, including integration concerns and client choices.
Learn why graph database excels at exploring highly connected data, and how to leverage them in your organization. Key-value, document-oriented, column family, graph, relational.
Today we seem… Continue reading
Microsoft Power BI service can visualize a lot more than chart data; from asking questions to analyzing the services you use, here’s how to make the most of it.
Microsoft Power BI is… Continue reading
The adoption of highly scriptable cloud-based technologies, along with the emergence of continuous integration (CI) and continuous deployment (CD) tools, has created an environment in which every operations process should be 
scriptable and all manual processes targeted for automation. Organizations with a DevOps approach to application lifecycle management should automate every process imaginable, but they often hit a wall when they reach the persistence layer. Emerging technologies have the potential to make that limitation disappear.- Database automation
“Database release automation is a real problem,” Datical CTO Robert Reeves says. “You’ve got lots of great ways of automating the application and provisioning servers. But we are still asking DBAs (database administrators) to just work faster, work harder, as they do manual updates.”
So, why can’t we take the lessons we learned from Agile or the progress DevOps has made and apply them to the persistence layer?
“Because of state,” Reeves explains. Unlike applications, a database can’t simply be deleted and recreated on the fly as though you were deploying and undeploying a microservice packaged in a Docker container. “You can’t just zap it.”
The persistence layer presents one problem, but there are also unique regulatory, technical and corporate standards issues that affect databases. Continue reading
The adoption of highly scriptable cloud-based technologies, along with the emergence of continuous integration (CI) and continuous deployment (CD) tools, has created an environment in which every operations process should be
scriptable and all manual processes targeted for database automation. Organizations with a DevOps approach to application lifecycle management should automate every process imaginable, but they often hit a wall when they reach the persistence layer. Emerging technologies have the potential to make that limitation disappear.
“Database release automation is a real problem,” Datical CTO Robert Reeves says. “You’ve got lots of great ways of automating the application and provisioning servers. But we are still asking DBAs [database administrators] to just work faster, work harder, as they do manual updates.”
So, why can’t we take the lessons we learned from Agile or the progress DevOps has made and apply them to the persistence layer?
“Because of state,” Reeves explains. Unlike applications, a database can’t simply be deleted and recreated on the fly as though you were deploying and undeploying a microservice packaged in a Docker container. “You can’t just zap it.” Continue reading
A database is a collection of data that is stored for a specific purpose and organized in a manner that allows its 
contents to be easily accessed, managed, and updated. Although this definition includes stored data collections such as libraries, file cabinets, and address books, when we talk about databases we almost invariably mean a collection of data that is stored on a computer. There are two basic categories of a database. The most commonly encountered category is the transactional database, used to store dynamic data, such as inventory contents, which is subject to change on an ongoing basis. The other category is the analytical database, used to store static data, such as geographical or chemical test results, which is rarely altered.
Strictly speaking, a database is just the stored data itself, although the term is often used, erroneously, to refer to a database and its management system (DBMS). Continue reading
A database management system (DBMS) is system software for creating and managing databases. The DBMS provides users and programmers with a systematic way to create, retrieve, update and manage data.
A DBMS makes it possible for end users to create, read, update and delete data in a database. The DBMS essentially serves as an interface between the database and end users or application programs, ensuring that data is consistently organized and remains easily accessible.
The DBMS manages three important things: the data, the database engine that allows data to be accessed, locked and modified — and the database schema, which defines the database’s logical structure. These three foundational elements help provide concurrency, security, data integrity and uniform administration procedures. Typical database administration tasks supported by the DBMS include change management, performance monitoring/tuning and backup and recovery. Many database management systems are also responsible for automated rollbacks, restarts, and recovery as well as the logging and auditing of activity. Continue reading