Operations staff get a hard time. The lowly systems administrator (sysadmin), database administrator (DBA) and all the other operations engineering team members from cyber penetration specialists to user acceptance testing (UAT) and so on are generally unloved.
Delivering business-critical applications and code relies on two key factors; functionality and efficiency. Mock and Unit tests are a few industry standards that aim to ensure the correct functionality of your code, catching potential bugs and issues before deployment. These tests are vital to workflows, CI/CD pipelines, and the overall build and deployment process. While functionality may be sound, one key aspect that is oft-forgotten is the efficiency and performance of your code.
The explosion of APIs, devices, applications, and data sources has complicated the task of building connectivity across the enterprise. As organizations are connecting to applications outside of their four walls, they risk becoming fragmented. Moreover, existing on-premise systems, such as AS/400 and ERPs, need to be able to communicate both internally and externally.
Continuous Integration and Continuous Delivery (CI/CD) is perhaps best represented by the infinity symbol. It is something that is constantly ongoing, new integrations are rolled out while not interrupting the flow of information that is already running, as to stop systems in order to update them can be costly and inefficient. In order to ensure that you can successfully implement the latest builds into your system, it is important to know how well they will run alongside the components that are already installed and where there may be bottlenecks.
Artificial intelligence is already solving problems in all aspects of our lives, from animation filmmaking and tackling space exploration, to fast food recommendation systems that improve ordering efficiency. These real-world AI systems examples are just the beginning of what is possible in an AI Everywhere future and they are already testing the limits of compute power.
Instead of just opening up closed-off systems via APIs, many businesses are looking to use event-based triggers as well to react to changes in real time. Competitive pressures are driving the need for new thinking when it comes to developing applications that help a business react in real time. Being able to make real-time decisions based on events is at the heart of those efforts.
API stands for application programming interface, which is a set of definitions and protocols for building and integrating application software.
IBM MQ is a family of message-oriented middleware products that IBM launched in December 1993. It was originally called MQSeries and was renamed WebSphere MQ in 2002 to join the suite of WebSphere products. In April 2014, it was renamed IBM MQ. The products included in the MQ family are IBM MQ, IBM MQ Advanced, IBM MQ Appliance, IBM MQ for z/OS, and IBM MQ on IBM Cloud. MQ stands for MESSAGING AND QUEUEING.
Kafka is an open-source program for storing, reading, and analyzing streaming data. It is open-source, which means it’s free-to-use amongst a big community of users and developers contributing to new features, upgrades, and support on a regular basis. Kafka can run on multiple servers as a distributed system, allowing it to take advantage of each server’s processing power and storage capacity.
Learning Apache Kafka doesn’t have to be difficult. Read on to get a friendly explanation of the Apache Kafka fundamentals.
