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By Use Case

AIOps for Transaction Tracking

Track transactions across multiple middlewares and infrastructures, stitching related messages together to visualize the business flow, identify, and alert on anomalies, latency, lost and diverted messages using real-time big data analytics. Transactions can include applications, IBM MQ, Apache Kafka, Solace, RabbitMQ, ActiveMQ and many more.

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Mastering Your Transaction Flow

Business transactions involve long-running, multi-step asynchronous message exchanges across a distributed and diverse IT infrastructure. These complex transactions defy standard tracking and analysis via tagging or statistical sampling techniques. The meshIQ platform stitches together complete end-to-end transactions by examining method calls and individual message payload contents, correlating them and presenting intuitive visualizations of any breaches in behavior and performance.

Messages and Transactions

End-to-End Monitoring

Effective Transaction Monitoring
To effectively monitor a transaction, you must monitor each system that the transaction passes through. If you have gaps in your knowledge, each event or outage will take much longer to resolve. You will need to convene a “war room” meeting, calling the owners of each system together to hear how each team is convinced they’re running within defined SLA’s. Every minute spent looking for the root cause of an issue is lost business.

A Complete View of the Enterprise Application

Problem

  • You need to see inside each system
  • Across every platform, including mainframe & distributed, data centers & clouds

From mobile apps to enterprise payment systems, digital business services depend upon fast and seamless exchanges of electronic business transactions. When transaction performance lags, business suffers. The meshIQ can monitor every form of machine data and tracks, analyzes, and visualizes the status and integrity of every transaction, from end-to-end, enabling fast problem resolution before users are affected.

Track Each Transaction

Visualize each transaction

The meshIQ platform is the most powerful solution available for determining and linking transaction performance to your business performance, in real-time.

Real-time message and transaction flow visualization and alerting provides instant awareness of any emerging performance issues that can negatively impact your business.

Discover the Pathway a Transaction Takes

Stitch together all the message data to show the pathway a transaction takes

The meshIQ platform stitches together complete end-to-end transactions across the enterprise correlating user transactions with applications and middleware, from mobile to mainframe.

Across on-premises and multi-cloud environments
  • Distributed (Windows, Linux, Unix, iSeries)
  • Mainframe
  • Appliances, VMs, Containers
  • Blockchains
  • On Premises
  • Public, Private, and Hybrid Clouds

Analyze the Data

Explore the Data and Compare with History in Real-Time

  • The meshIQ platform provides machine learning based artificial intelligence to analyze the data and present a visual model of the business, with charting and reports showing the continuous performance of transactions as they run.
  • Real-time queries can be invoked to see historical performance.
  • Automation proactively fixes problems as they arise.
  • With simple point and click exploration, views and charts are created dynamically allowing root cause analysis in real time. This makes meshIQ XRay the first choice for companies who depend on middleware (vs Prometheus, Grafana, Zabbix, Nagios, Zabbix etc) which require you to know the problem before you build the monitoring).
Middlewares

Track Across Multiple Combinations of Messaging Middleware

  • IBM MQ (MQSeries, WebSphere MQ, WMQ)
  • Kafka (Apache, Confluent, Cloudera, IBM Event Streams, Amazon MSK)
  • TIBCO EMS
  • Solace PubSub+
  • RabbitMQ, ActiveMQ, Amazon MQ
  • IBM App Connection Enterprise (ACE, IIB)
  • Managed File Transfer (MFT)
  • IBM Cloud Pak for Integration (CP4I) and components – DataPower, MQ, ACE, Kafka in OpenShift
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