In the ever-evolving landscape of financial services, institutions are under constant pressure to ensure their messaging infrastructures comply with a myriad of global regulatory requirements. Compliance with regulations such as the General Data Protection Regulation (GDPR), the Payment Services Directive 2 (PSD2), and other localized financial regulations is not just a legal necessity but a cornerstone for maintaining trust and integrity in the financial sector.
In today’s digital landscape, the performance and reliability of messaging systems are paramount for business operations. Systems like IBM MQ play a crucial role in ensuring seamless communication between different parts of an application, impacting everything from transaction processing to customer experiences.
You probably have seen ads where someone claims that their app can save you money by finding subscriptions you forgot about. I have a hard time imaging someone with $100s of dollars of expenses they forgot about, but I have had the occasional one that was missed.
There was a time not too long ago, before the cloud was a part of every enterprise technology conversation, when integration work was considered the purview of a specific architecture and engineering group.
If messages failed to send, or services failed to respond, application stakeholders would create a trouble ticket for the integration team to address.
Distributed transaction tracing (DTT) is a way of following the progress of message requests as they permeate through distributed cloud environments. Tracing the transactions as they make their way through many different layers of the application stack, such as from Kafka to ActiveMQ to MQ or any similar platform, is achieved by tagging the message request with a unique identifier that allows it to be followed.
In today’s rapidly evolving technological and business landscapes, staying competitive requires more than just a great product or service. It demands a technological edge that can drive efficiency, innovation, and overall growth. This is where partnering comes into play – it’s like turbocharging your business engine.
Most companies in today’s business landscape that deal with large amounts of data want to integrate their applications so that they can pass data between them seamlessly and easily. Being able to ensure that you can see exactly what is happening at every stage of the process is key, and this is where approaching the process with observability in mind can make a real difference.
When businesses look at how best to understand the performance levels of their platforms, some of the best incident management metrics to look at are Mean Time Between Failures (MTBF) and Mean Time To Resolution (MTTR). These two measurements will give an excellent indication of the health and speed of the system, as well as the ability of the platform to take care of any anomalies that have been detected or to flag them up for others to take action to resolve them.
The last decade has been nothing but a roller coaster ride for the airline industry. The pandemic has transformed it forever and now it needs to reevaluate its digital transformation priorities on how to manage traveler expectations. Taking it a step further, travelers buying behavior is changing farther as now they will want to book tickets while chatting with an AI interface.
Apache Kafka has come a long way since its initial development at LinkedIn in 2010 and its release as an open-source project the following year. Over the past decade, it has grown from a humble messaging bus used to power internal applications into the world’s most popular streaming data platform.