In the age of constant digital transformation, organizations should strategize ways to increase their pace of business to keep up with — and ideally surpass — their competition. Customers are moving quickly, and it is becoming difficult to keep up with their dynamic demands. As a result, I see access to real-time data as a necessary foundation for building business agility and enhancing decision making.
Stream processing is at the core of real-time data. It allows your business to ingest continuous data streams as they happen and bring them to the forefront for analysis, enabling you to keep up with constant changes.
Apache Kafka and Apache Flink working together
Anyone who is familiar with the stream processing ecosystem is familiar with Apache Kafka: the de-facto enterprise standard for open-source event streaming. Apache Kafka boasts many strong capabilities, such as delivering a high throughput and maintaining a high fault tolerance in the case of application failure.
Apache Kafka streams get data to where it needs to go, but these capabilities are not maximized when Apache Kafka is deployed in isolation. If you are using Apache Kafka today, Apache Flink should be a crucial piece of your technology stack to ensure you’re extracting what you need from your real-time data.
With the combination of Apache Flink and Apache Kafka, the open-source event streaming possibilities become exponential. Apache Flink creates low latency by allowing you to respond quickly and accurately to the increasing business need for timely action. Coupled together, the ability to generate real-time automation and insights is at your fingertips.
With Apache Kafka, you get a raw stream of events from everything that is happening within your business. However, not all of it is necessarily actionable and some get stuck in queues or big data batch processing. This is where Apache Flink comes into play: you go from raw events to working with relevant events. Additionally, Apache Flink contextualizes your data by detecting patterns, enabling you to understand how things happen alongside each other. This is key because events have a shelf-life, and processing historical data might negate their value. Consider working with events that represent flight delays: they require immediate action, and processing these events too late will surely result in some very unhappy customers.
Apache Kafka acts as a sort of firehose of events, communicating what is always going on within your business. The combination of this event firehose with pattern detection — powered by Apache Flink — hits the sweet spot: once you detect the relevant pattern, your next response can be just as quick. Captivate your customers by making the right offer at the right time, reinforce their positive behavior, or even make better decisions in your supply chain — just to name a few examples of the extensive functionality you get when you use Apache Flink alongside Apache Kafka.
Innovating on Apache Flink: Apache Flink for all
Now that we’ve established the relevancy of Apache Kafka and Apache Flink working together, you might be wondering: who can leverage this technology and work with events? Today, it’s normally developers. However, progress can be slow as you wait for savvy developers with intense workloads. Moreover, costs are always an important consideration: businesses can’t afford to invest in every possible opportunity without evidence of added value. To add to the complexity, there is a shortage of finding the right people with the right skills to take on development or data science projects.
This is why it’s important to empower more business professionals to benefit from events. When you make it easier to work with events, other users like analysts and data engineers can start gaining real-time insights and work with datasets when it matters most. As a result, you reduce the skills barrier and increase your speed of data processing by preventing important information from getting stuck in a data warehouse.
IBM’s approach to event streaming and stream processing applications innovates on Apache Flink’s capabilities and creates an open and composable solution to address these large-scale industry concerns. Apache Flink will work with any Apache Kafka and IBM’s technology builds on what customers already have, avoiding vendor lock-in. With Apache Kafka as the industry standard for event distribution, IBM took the lead and adopted Apache Flink as the go-to for event processing — making the most of this match made in heaven.
Imagine if you could have a continuous view of your events with the freedom to experiment on automations. In this spirit, IBM introduced IBM Event Automation with an intuitive, easy to use, no code format that enables users with little to no training in SQL, java, or python to leverage events, no matter their role. Eileen Lowry, VP of Product Management for IBM Automation, Integration Software, touches on the innovation that IBM is doing with Apache Flink:
“We realize investing in event-driven architecture projects can be a considerable commitment, but we also know how necessary they are for businesses to be competitive. We’ve seen them get stuck all-together due to costs and skills constrains. Knowing this, we designed IBM Event Automation to make event processing easy with a no-code approach to Apache Flink It gives you the ability to quickly test new ideas, reuse events to expand into new use cases, and help accelerate your time to value.”
This user interface not only brings Apache Flink to anyone that can add business value, but it also allows for experimentation that has the potential to drive innovation speed up your data analytics and data pipelines. A user can configure events from streaming data and get feedback directly from the tool: pause, change, aggregate, press play, and test your solutions against data immediately. Imagine the innovation that can come from this, such as improving your e-commerce models or maintaining real-time quality control in your products.
This user interface not only brings Apache Flink to anyone that can add business value, but it also allows for experimentation that has the potential to drive innovation speed up your data analytics and data pipelines. A user can configure events from streaming data and get feedback directly from the tool: pause, change, aggregate, press play, and test your solutions against data immediately. Imagine the innovation that can come from this, such as improving your e-commerce models or maintaining real-time quality control in your products.
Source: ibm.com
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