Data is the fuel for a modern business.
Capitalize the value of your data.
Endless possibilities of intelligent data streaming with Apache Kafka and Confluent
Today’s integration strategy presents data challenges and governance issues
In the dynamic landscape of the modern business world, data holds the key to success. To unlock its full potential, businesses must capitalize on the value of their data.
However, the reality with today’s data integration strategy presents challenges, with data silos and monolithic point-to-point connections leading to data fidelity and governance issues.
The reliance on batch processing in the so-called “modern” data stack may hinder businesses from harnessing real-time operational and business intelligence insights.
The Reality with Today’s Data Integration Strategy
Today’s Data Integration Approaches Create a Chaotic and Unscalable Data Foundation
Use a Scalable and Completely Decoupled Architecture for High-Quality, Self-Service Access to Real-Time Data Streams
Discover, Understand, and Trust Your Data Streams
Next-gen Data Lifecycle with Apache Kafka fundamentally shifts the perspective on data
Get Your Data to the Right Place, in the Right Format, at the Right Time to Unlock Endless Use Cases
The Reality with Today’s Data Integration Strategy
Coming from data silos in many independent systems, the current data integration strategy creates a giant spiderweb of monolithic point-to-point connections, presenting data fidelity and governance challenges.
The reliance on batch processing in the so-called “modern” data stack limits businesses from unlocking real-time insights.
Today’s Data Integration Approaches Create a Chaotic and Unscalable Data Foundation
Challenges associated with today’s data integration approaches
1 |
Over-reliance on Central Data Teams: Batch-based, Low Fidelity Stale Data Traditional batch processing often leads to low-fidelity, stale data that is unsuitable for real-time operational and business intelligence use cases. In a rapidly evolving business landscape, real-time insights are crucial for making informed decisions. |
2 |
Over-reliance on Central Data Teams: Relying heavily on central data teams with limited domain knowledge can become a bottleneck for innovation. Empowering domain-specific teams to create and share data streams allows for agility and faster data-driven solutions. |
3 |
Immature Governance and Observability: Lack of mature governance and observability can result in data access conflicts between IT operations and engineering teams. Implementing self-service search and discovery with proper security and compliance measures can foster collaboration and efficiency. |
4 |
Infra-heavy Data Processing: Resource-intensive data processing infrastructures can pose scale and performance challenges, leading to higher overall Total Cost of Ownership (TCO). |
5 |
Inflexible Monolithic Design: Monolithic data architectures create multiple siloed, purpose-built pipelines, resulting in data sprawl and complexity that impedes scalability and maintainability. |
5 Fundamental Principles for Better Data Pipelines
To address these challenges and build a solid foundation for data-driven excellence,
businesses can adopt fundamental principles for better data pipelines
Continuously capture, evolve, and share high-fidelity real-time data for all your use cases, enabling quick and decisive actions.
Streaming
Streaming
Decentralized
Bring software engineering practices to build multiple models, experiment, test, and deploy data solutions in an agile manner, accelerating time-to-market and fostering continuous improvement.
Developer-oriented
Developer-oriented
Bring software engineering practices to build multiple models, experiment, test, and deploy data solutions in an agile manner, accelerating time-to-market and fostering continuous improvement.
Declarative
Build reusable and performant data flows by separating data topology definition from data processing infrastructure, simplifying data operations and reducing redundancies.
Enable self-service search and discovery while maintaining security, observability, and compliance, promoting data accessibility within a controlled framework.
Governed
Use a Scalable and Completely Decoupled Architecture for High-Quality, Self-Service Access to Real-Time Data Streams
By adopting a scalable and completely decoupled architecture, businesses can ensure high-quality, self-service access to real-time data streams. This approach enables seamless data sharing and reuse, enhancing overall data effectiveness.
Discover, Understand, and Trust Your Data Streams
Understanding the origin, transformations, and destinations of data streams is crucial for
maintaining data integrity and trust. Implementing Stream Catalog, Stream Lineage, and Stream Quality allows organizations to increase collaboration, productivity, gain deeper insights into complex data relationships and shorten onboarding time of new team members.
Stream Catalog
Build and easily maintain an encyclopedia of how data is available and what they represent. For instance your UML class diagram can be converted into schema definitions and POJO class libraries.
Stream Lineage
Maintain oversight of, from where data originates, how it is enriched and processed from its origin to where you want to use it.
Stream Quality
By elevating data as prime quality output of each project implementation, your team has ownership and responsibility of ensuring and evaluating its contribution to your overall business.
Apache Kafka fundamentally shifts the perspective on data
When gradually implementing Kafka in your infrastructure, you begin to realize the ability easily to establish data-streams (think IIOT, databases, current and legacy business systems, company website, web-shop a.o. ) through a continuously growing number of connectors in a governed manner.
This ability creates an unprecedented opportunity to quickly and easily investigate and qualify assumptions and/or suspicions through e.g. the familiar SQL syntax. KSQL real-time processes incoming data and generates aggregated and derived events and values across multiple data-streams.
Once qualified, the “now knowledge”, can be monitored and the desired actions can be set in place to support decisions on how to respond.
Through the investigative period you may even have acquired sufficient data to do the initial training of a machine learning model, which then can be further trained on the real-time data coming from the data-streaming sources.
Viewing this as building-blocks, you now have the foundation to gain deeper insight in to your processes, where they excel and where to improve.
Next-gen Data Lifecycle with Apache Kafka and Confluent
Incorporating Apache Kafka and Confluent Platform in the data lifecycle
unlocks the true potential of intelligent data streaming:
Connect:
Continuously stream data to Apache Kafka, ensuring a constant flow of high-quality data.
Govern:
Tag and secure data streams ensuring data is persisted and stored for further processing
Enrich:
Process and cleanse data to enhance its quality and relevance using familiar tools, e.g. KSQL
Build:
Create ready-to-share, read-to-use data products, providing valuable insights across the organization.
Share:
Multicast data to any destination, empowering teams with the data they need to make informed decisions.
Get Your Data to the Right Place, in the Right Format, at the Right Time to Unlock Endless Use Cases
Businesses can unlock endless use cases by adopting intelligent
data streaming practices, leading to a range of benefits:
Speed up Time to Market:
Enable self-service search and discovery to trustworthy data products, accelerating the delivery of valuable use cases.
Bridge the Data Divide:
Power all your operational, analytical, and SaaS use cases with high-quality real-time data streams.
Build for Scale and Performance:
Describe your data flow and transformation logic while the infrastructure flexes automatically to process data at scale.
Develop with Agility:
Easily iterate, evolve, and reuse pipelines to meet changing business and data needs of the organization.
Maintain Trust and Compliance:
Track where your data goes, how it got there, and who has access to it with end-to-end governance.
Data is indeed the fuel for
a modern business.
By capitalizing on the value of data and adopting intelligent data streaming practices with Apache Kafka and Confluent, you can overcome data integration challenges and unlock the true potential of your data.
Embracing a future of real-time insights, agility, and informed decision-making will position businesses at the forefront of the data-driven revolution, securing their competitive advantage in the digital era.
Embrace the power of intelligent data streaming and fuel your business to new heights with us as Kafka Confluent partner.
Claus Hein
Sales Manager
