Aiven

As the world enters the digital economy powered by Industry 4.0, digital transformation has reshaped the business landscape, increasing the degree of competition and leaving businesses which fail to embrace digital technologies at a huge disadvantage.

At the core of the digital transformation process is data management, the process of collecting, storing, and extracting valuable insights from the vast amount of data. To be specific, big data analysis can help businesses make strategic and data-driven decisions, enhance customer relationship management by uncovering consumer behavioural patterns and preferences, and eventually boost business performance and profitability.

In addition, big data also helps create new products and services by studying gaps in the market and understanding what consumers want, enabling businesses to tap into uncharted waters and identify business opportunities that were once deemed impossible. But big data analysis alone is not enough. In order to harness the full potential of data, we must ensure that insight and action from such information are rendered as swiftly as possible so that it can be used when it is at its most valuable: right now.

The limitations of traditional batch data processing systems

With an enormous amount of data generated within the digitally connected world, it is critical to process data in a timely manner to ensure effective decisions.

Traditionally, data is handled in a step-by-step workflow with raw data being collected, classified, usually stored in some database or storage system and then transformed, and analysed in iteration, before being presented in a manner for easy interpretation. On one hand, the traditional batch processing approach can facilitate periodic data transformation, logically extract valuable insights from the data, and inspire additional interactive data exploration to optimise the analytical process. On the other hand, one fundamental disadvantage of batch processing is the delay between data collection and data analysis, making insights generated from the data less relevant as time goes on.

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Despite the best efforts to minimise the waiting duration for batch processing, systems relying on this kind of data analytic process still suffer from inconsistencies and they might miss the golden opportunities for action that are presented by analysing data in real-time. Moreover, in many cases, users require such real-time processing capabilities to make decisions and respond to critical issues in a matter of seconds or even milliseconds such as detecting patterns, addressing inconsistencies or preventing financial fraud, and so on.

In these situations, processing data in real-time (otherwise referred to as ‘stream processing’) proves to be more beneficial since it allows data to be processed, transformed and analysed immediately upon arrival, creating real-time insights and solutions to challenges and setbacks at the time of event occurrence.

How Aiven’s Apache Kafka and its open-source streaming ecosystem enable businesses to connect and optimise their streaming data

Serving the mission of facilitating the experience for developers and perceiving the increasing need for real-time or event-driven applications (applications powered by the processing of events in real-time), Aiven, the open-source cloud data platform, launched the first and fully open-source event streaming ecosystem for Apache Kafka to help companies optimise their data streaming in near real-time.

As a data hub and event streaming platform, Apache Kafka delivers answers right at the moment when the question arises, moving companies from waiting to acting. This is becoming increasingly important in today’s digital world, where more and more companies are employing microservice architectures. In this aspect, Apache Kafka can simplify communication between services in the organisation, publishing their events as they happen, and making them available to any (or all) microservices that are dependent on them.

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Adopting Aiven’s Apache Kafka and its open-source streaming ecosystem can offer businesses several advantages. In the first place, using Apache Kafka as-a-service, businesses can easily set up clusters, deploy data streaming services, migrate their data between cloud regions for better data resiliency, and bring products and services to market faster, without worrying about managing the underlying data infrastructure.

One of the unique features of Aiven’s Apache Kafka is its ability to flexibly increase or decrease the computing power and storage of users as they scale up or scale down their operations. Additionally, with the use of a fully-managed service like Aiven’s Kafka, you benefit from Aiven’s enterprise uptime and SLA of 99.99% which ensures continuous operation of the customers’ activities. More importantly, by subscribing to Aiven’s Apache Kafka, businesses can also gain access to its comprehensive ecosystem and supporting services such as Aiven for Apache KafkaConnectⓇ, Aiven for Apache Kafka MirrorMarker2Ⓡ, and more supporting tools and services.

Aiven’s Apache Kafka customers’ success stories

Aiven for Apache Kafka is suitable for businesses of all shapes and sizes, ranging from startups to established enterprises that have a need for an integrated, open source-based data streaming environment that can easily scale and handle real-time data feeds. The product can be set up easily, connecting to the client’s tech stack via over 30 connectors and integrating to end users’ preferred tooling via APIs, the CLI client, and Terraform provider, among others. As such, the product is currently being enjoyed by a slew of high-profile companies.

Wolt, a Helsinki-based commerce company, has decided to build its real-time data infrastructure based on Aiven for Apache Kafka as the core technology to build its digital solutions, achieving significant savings which were then used towards improving its own products and services. Particularly, for Wolt, Aiven’s Kafka acts as a message bus to communicate between services and ingest data from different databases. Wolt complimented Aiven for Apache Kafka on its ease of use, scalability, and its superior tooling support and interoperability.

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Another long-term customer of the Apache Kafka service provided by Aiven is logistics business, Swift Solutions, which offers delivery and order fulfilment services in Indonesia. Swift Solutions has applied multiple services from Aiven’s product portfolio including its Apache Kafka service along with Aiven for MySQL and Aiven for RedisⓇ. The company has also adopted the Aiven Kubernetes Operator since its early development stage.

Moreover, GoTo Financial, a service within the tech giant Gojek, also noted the benefits of partnering with Aiven, particularly how the platform provided support for GoTo Financial in both its early attempts to separate Kafka instances for each product and function and its subsequent efforts to integrate every service into one single consolidated system with almost zero risk.

These are only some of the notable examples of businesses ranging from e-commerce, retail, to logistics, to fintech — all made better with the help of Aiven.

Interested in finding out more about how the different tools and technologies of Aiven’s ecosystem for Apache Kafka can help address your business challenges? Here’s a list of resources to get you started:

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This article is produced by the e27 team, sponsored by Aiven

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