Apache Flink for all: Making Flink consumable across all areas of your business

In an era of rapid technological advancements, responding quickly to changes is crucial. Event-driven businesses across all industries thrive on real-time data, enabling companies to act on events as they happen rather than after the fact. These agile businesses recognize needs, fulfill them and secure a leading market position by delighting customers.

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This is where Apache Flink shines, offering a powerful solution to harness the full potential of an event-driven business model through efficient computing and processing capabilities. Flink jobs, designed to process continuous data streams, are key to making this possible.

How Apache Flink enhances real-time event-driven businesses

Imagine a retail company that can instantly adjust its inventory based on real-time sales data pipelines. They are able to adapt to changing demands quickly to seize new opportunities. Or consider a FinTech organization that can detect and prevent fraudulent transactions as they occur. By countering threats, the organization prevents both financial losses and customer dissatisfaction. These real-time capabilities are no longer optional but essential for any companies that are looking to be leaders in today’s market.

Apache Flink takes raw events and processes them, making them more relevant in the broader business context. During event processing, events are combined, aggregated and enriched, providing deeper insights and enabling many types of use cases, such as: 

1. Data analytics: Helps perform analytics on data processing on streams by monitoring user activities, financial transactions, or IoT device data. 
2. Pattern detection: Enables identifying and extracting complex event patterns from continuous data streams. 
3. Anomaly detection: Identifies unusual patterns or outliers in streaming data to pinpoint irregular behaviors quickly. 
4. Data aggregation: Ensures efficient summarization and processing of continuous data flows for timely insights and decision-making. 
5. Stream joins: Combines data from multiple streaming platforms and data sources for further event correlation and analysis. 
6. Data filtering: Extracts relevant data by applying specific conditions to streaming data.
7. Data manipulation: Transforms and modifies data streams with data mapping, filtering and aggregation.

The unique advantages of Apache Flink

Apache Flink augments event streaming technologies like Apache Kafka to enable businesses to respond to events more effectively in real time. While both Flink and Kafka are powerful tools, Flink provides additional unique advantages:

• Data stream processing: Enables stateful, time-based processing of data streams to power use cases such as transaction analysis, customer personalization and predictive maintenance through optimized computing. 
• Integration: Integrates seamlessly with other data systems and platforms, including Apache Kafka, Spark, Hadoop and various databases. 
• Scalability: Handles large datasets across distributed systems, ensuring performance at scale, even in the most demanding Flink jobs.
• Fault tolerance: Recovers from failures without data loss, ensuring reliability.

IBM empowers customers and adds value to Apache Kafka and Flink

It comes as no surprise that Apache Kafka is the de-facto standard for real-time event streaming. But that’s just the beginning. Most applications require more than just a single raw stream and different applications can use the same stream in different ways.

Apache Flink provides a means of distilling events so they can do more for your business. With this combination, the value of each event stream can grow exponentially. Enrich your event analytics, leverage advanced ETL operations and respond to increasing business needs more quickly and efficiently. You can harness the ability to generate real-time automation and insights at your fingertips.

IBM® is at the forefront of event streaming and stream processing providers, adding more value to Apache Flink’s capabilities. Our approach to event streaming and streaming applications is to provide an open and composable solution to address these large-scale industry concerns. Apache Flink will work with any Kafka topic, making it consumable for all.

The IBM technology builds on what customers already have, avoiding vendor lock-in. With its easy-to-use and no-code format, users without deep skills in SQL, Java, or Python can leverage events, enriching their data streams with real-time context, irrespective of their role. Users can reduce dependencies on highly skilled technicians and free up developers’ time to accelerate the number of projects that can be delivered. The goal is to empower them to focus on business logic, build highly responsive Flink applications and lower their application workloads.

Take the next step

IBM Event Automation, a fully composable event-driven service, enables businesses to drive their efforts wherever they are on their journey. The event streams, event endpoint management and event processing capabilities help lay the foundation of an event-driven architecture for unlocking the value of events. You can also manage your events like APIs, driving seamless integration and control.

Take a step towards an agile, responsive and competitive IT ecosystem with Apache Flink and IBM Event Automation.

Source: ibm.com

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Maximizing your event-driven architecture investments: Unleashing the power of Apache Kafka with IBM Event Automation

In today’s rapidly evolving digital landscape, enterprises are facing the complexities of information overload. This leaves them grappling to extract meaningful insights from the vast digital footprints they leave behind.

Recognizing the need to harness real-time data, businesses are increasingly turning to event-driven architecture (EDA) as a strategic approach to stay ahead of the curve. 

Companies and executives are realizing how they need to stay ahead by deriving actionable insights from the sheer amount of data generated every minute in their digital operations. As IDC stated: as of 2022, 36% of IT leaders identified the use of technologies to achieve real-time decision-making as critical for business success, and 45% of IT leaders reported a general shortage of skilled personnel for real-time use cases.

This trend grows stronger as organizations realize the benefits that come from the power of real-time data streaming. However, they need to find the right technologies that adapt to their organizational needs. 

At the forefront of this event-driven revolution is Apache Kafka, the widely recognized and dominant open-source technology for event streaming. It offers businesses the capability to capture and process real-time information from diverse sources, such as databases, software applications and cloud services. 

While most enterprises have already recognized how Apache Kafka provides a strong foundation for EDA, they often fall behind in unlocking its true potential. This occurs through the lack of advanced event processing and event endpoint management capabilities.

Socialization and management in EDA

While Apache Kafka enables businesses to construct resilient and scalable applications, helping to ensure prompt delivery of business events, businesses need to effectively manage and socialize these events.

To be productive, teams within an organization require access to events. But how can you help ensure that the right teams have access to the right events? An event endpoint management capability becomes paramount in addressing this need. It allows for sharing events through searchable and self-service catalogs while simultaneously maintaining proper governance and controls with access based on applied policies.

The importance is clear: you can protect your business events with custom policy-based controls, while also allowing your teams to safely work with events through credentials created for role-based access. Do you remember playing in the sandbox as a kid? Now, your teams can learn to build sandcastles within the box by allowing them to safely share events with certain guardrails, so they don’t exceed specified boundaries. 

Therefore, your business maintains control of the events while also facilitating the sharing and reuse of events, allowing your teams to enhance their daily operations fueled by reliable access to the real-time data they need.

 

Also, granting teams reliable access to relevant event catalogs allows them to reuse events to gain more benefits from individual streams. This allows businesses and teams to avoid duplication and siloing of data that might be immensely valuable. Teams innovate faster when they easily find reusable streams without being hindered by the need to source new streams for every task. This helps ensure that they not only access data but also use it efficiently across multiple streams, maximizing its potential positive impact on the business. 

Level up: Build a transformative business strategy

A substantial technological investment demands tangible returns in the form of enhanced business operations, and enabling teams to access and use events is a critical aspect of this transformative journey.

However, Apache Kafka isn’t always enough. You might receive a flood of raw events, but you need Apache Flink to make them relevant to your business. When used together, Apache Kafka’s event streaming capabilities and Apache Flink’s event processing capabilities smoothly empower organizations to gain critical real-time insights from their data.

Many platforms that use Apache Flink often come with complexities and a steep learning curve, requiring deep technical skills and extensive knowledge of this powerful real-time processing platform. This restricts real-time event accessibility to a select few, increasing costs for companies as they support highly technical teams. Businesses should maximize their investments by enabling a broad range of users to work with real-time events instead of being overwhelmed by intricate Apache Flink settings.

This is where a low-code event processing capability needs to remove this steep learning curve by simplifying these processes and allowing users across diverse roles to work with real-time events. Instead of requiring skilled Flink structured query language (SQL) programmers, other business teams can immediately extract actionable insights from relevant events.

When you remove the Apache Flink complexities, business teams can focus on driving transformative strategies with their newfound access to real-time data. Immediate insights can now fuel their projects, allowing them to experiment and iterate quickly to accelerate time to value. Properly informing your teams and providing them with the tools to promptly respond to events as they unfold gives your business a strategic advantage. 

Finding the right strategic solution

As the need for building an EDA remains recognized as a strategic business imperative, the presence of EDA solutions increases. Platforms in the market have recognized the value of Apache Kafka, enabling them to build resilient, scalable solutions ready for the long term. 

IBM Event Automation, in particular, stands out as a comprehensive solution that seamlessly integrates with Apache Kafka, offering an intuitive platform for event processing and event endpoint management. By simplifying complex tech-heavy processes, IBM Event Automation maximizes the accessibility of Kafka settings. This helps ensure that businesses can harness the true power of Apache Kafka and drive transformative value across their organization. 

Taking an open, community-based approach backed by multiple vendors reduces concerns about the need for future migrations as individual vendors make different strategic choices, for example, Confluent adopting Apache Flink instead of KSQL. Composability also plays a significant role here. As we face a world saturated with various technological solutions, businesses need the flexibility to find and integrate those that enhance their existing investments seamlessly. 

As enterprises continue to navigate the ever-evolving digital landscape, the integration of Apache Kafka with IBM Event Automation emerges as a strategic imperative. This integration is crucial for those aiming to stay at the forefront of technological innovation. 

Source: ibm.com

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Level up your Kafka applications with schemas

Apache Kafka is a well-known open-source event store and stream processing platform and has grown to become the de facto standard for data streaming. In this article, developer Michael Burgess provides an insight into the concept of schemas and schema management as a way to add value to your event-driven applications on the fully managed Kafka service, IBM Event Streams on IBM Cloud.

What is a schema?

A schema describes the structure of data.

For example:

A simple Java class modelling an order of some product from an online store might start with fields like:

public class Order{

private String productName

private String productCode

private int quantity

[…]

}

If order objects were being created using this class, and sent to a topic in Kafka, we could describe the structure of those records using a schema such as this Avro schema:

{

"type": "record",

"name": “Order”,

"fields": [

{"name": "productName", "type": "string"},

{"name": "productCode", "type": "string"},

{"name": "quantity", "type": "int"}

]

}

Why should you use a schema?

Apache Kafka transfers data without validating the information in the messages. It does not have any visibility of what kind of data are being sent and received, or what data types it might contain. Kafka does not examine the metadata of your messages.

One of the functions of Kafka is to decouple consuming and producing applications, so that they communicate via a Kafka topic rather than directly. This allows them to each work at their own speed, but they still need to agree upon the same data structure; otherwise, the consuming applications have no way to deserialize the data they receive back into something with meaning. The applications all need to share the same assumptions about the structure of the data.

In the scope of Kafka, a schema describes the structure of the data in a message. It defines the fields that need to be present in each message and the types of each field.

This means a schema forms a well-defined contract between a producing application and a consuming application, allowing consuming applications to parse and interpret the data in the messages they receive correctly.

What is a schema registry?

A schema registry supports your Kafka cluster by providing a repository for managing and validating schemas within that cluster. It acts as a database for storing your schemas and provides an interface for managing the schema lifecycle and retrieving schemas. A schema registry also validates evolution of schemas.

Optimize your Kafka environment by using a schema registry.

A schema registry is essentially an agreement of the structure of your data within your Kafka environment. By having a consistent store of the data formats in your applications, you avoid common mistakes that can occur when building applications such as poor data quality, and inconsistencies between your producing and consuming applications that may eventually lead to data corruption. Having a well-managed schema registry is not just a technical necessity but also contributes to the strategic goals of treating data as a valuable product and helps tremendously on your data-as-a-product journey.

Using a schema registry increases the quality of your data and ensures data remain consistent, by enforcing rules for schema evolution. So as well as ensuring data consistency between produced and consumed messages, a schema registry ensures that your messages will remain compatible as schema versions change over time. Over the lifetime of a business, it is very likely that the format of the messages exchanged by the applications supporting the business will need to change. For example, the Order class in the example schema we used earlier might gain a new status field—the product code field might be replaced by a combination of department number and product number, or changes the like. The result is that the schema of the objects in our business domain is continually evolving, and so you need to be able to ensure agreement on the schema of messages in any particular topic at any given time.

There are various patterns for schema evolution:

• Forward Compatibility: where the producing applications can be updated to a new version of the schema, and all consuming applications will be able to continue to consume messages while waiting to be migrated to the new version.
• Backward Compatibility: where consuming applications can be migrated to a new version of the schema first, and are able to continue to consume messages produced in the old format while producing applications are migrated.
• Full Compatibility: when schemas are both forward and backward compatible.

A schema registry is able to enforce rules for schema evolution, allowing you to guarantee either forward, backward or full compatibility of new schema versions, preventing incompatible schema versions being introduced.

By providing a repository of versions of schemas used within a Kafka cluster, past and present, a schema registry simplifies adherence to data governance and data quality policies, since it provides a convenient way to track and audit changes to your topic data formats.

What’s next?

In summary, a schema registry plays a crucial role in managing schema evolution, versioning and the consistency of data in distributed systems, ultimately supporting interoperability between different components. Event Streams on IBM Cloud provides a Schema Registry as part of its Enterprise plan. Ensure your environment is optimized by utilizing this feature on the fully managed Kafka offering on IBM Cloud to build intelligent and responsive applications that react to events in real time.

Source: ibm.com

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Apache Kafka and Apache Flink: An open-source match made in heaven

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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