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Podcast - Stream Processing using Kafka and Flink

Vaishnavi - Hi everyone, and welcome to Code & Deploy! I’m your host, Vaishnavi, and I’m thrilled to have you join us for today’s exciting episode. For those of you, new to the podcast, this is where we break down complex tech topics into bite-sized, insightful discussions that resonate with tech professionals and enthusiasts alike.

Today’s episode takes us into the world of data streaming platforms. We’ll unravel concepts like stream processing, stream governance, and how tools like Kafka and Flink power real-time data solutions. And to guide us on this fascinating journey, we have two experts from OpsTree Solutions:

  • Deepak Sood - Senior AI, Data, and DevOps Architect, who designs cutting-edge data pipelines and architectures.
  • Ramneek Kaur - A skilled Data Engineer specializing in real-time data applications.

Deepak and Ramneek, welcome to Code & Deploy! Before we get into the nitty-gritty, could you share what drew you to the field of data streaming and real-time analytics?

Getting into data streaming and real-time analytics?​

Deepak

  • What drew me to data streaming and real-time analytics is the sheer challenge and complexity of this field. The abundance of tools and technologies available, each with its own nuances and capabilities, makes selecting the right one for the right job both critical and fascinating.
  • I believe that "data has gravity"—it’s not easy to move or transform. Choosing the right data type and format at the outset is crucial because transforming petabytes of data later is a monumental challenge. This means that technological decisions made early in the process can have far-reaching implications.
  • With data growing exponentially and businesses needing to react to events in real time, batch processing is no longer sufficient. Streaming technologies enable us to process and respond to real-time events as they happen, which is both exciting and immensely rewarding to work on.

Ramneek

  • Thanks, Vaishnavi! Speaking about my experience, I was drawn to data streaming and real-time analytics because of its power to act instantly. The idea of processing data as it’s generated and turning it into quick, actionable insights fascinates me. Whether it’s detecting fraud, powering recommendations, or live traffic updates, the ability to create immediate impact is exciting. The field is dynamic and always evolving, which keeps me motivated to learn and innovate.
  • For me, working in this space means being part of something impactful, where the systems we design and develop have a direct and immediate effect on people’s lives. That combination of complexity, innovation, and impact is what makes this field so compelling to me.

Setting the Stage - What is Stream Processing?​

Vaishnavi - Let’s start at the basics. Stream processing is a buzzword, but not everyone understands what it really is. Deepak, could you give us a beginner-friendly definition of what stream processing is and how it differs from traditional batch processing?

Deepak - Explains stream processing in simple terms, emphasizing real-time data and continuous computation.

  • Stream processing is the method of continuously collecting, processing, and analyzing data in real time as it flows through a system. Unlike traditional batch processing, which works on large chunks of data at set intervals, stream processing operates on data as it’s generated — moment by moment.
  • This approach is particularly powerful for scenarios where immediate insights or actions are needed, such as fraud detection, real-time analytics, or monitoring systems. It emphasizes the ability to compute and react to data on the fly, ensuring businesses can stay agile and responsive to real-time events.

Vaishnavi - Ramneek, for someone stepping into this field, what’s a simple example of stream processing in action? Maybe a day-to-day scenario that our listeners can relate to.

Ramneek - Provides an example.

  • Of course, Vaishnavi! Uber or Ola is a fantastic example of stream processing in action, and it’s something many of us use regularly. When you open the app and request a ride, stream processing starts working immediately. First, the app captures your location in real time and continuously tracks nearby drivers' positions. This data is ingested into a stream processing platform, like Kafka, which handles the constant flow of location updates from thousands—sometimes millions—of users and drivers globally.
  • Another layer of stream processing comes into play with surge pricing. If there’s a high demand in your area, the system processes and analyzes this surge data in real time to dynamically adjust prices. All of this happens seamlessly, within seconds, while ensuring scalability and low latency—even during peak hours.

Challenges of Building a Data Streaming Platform​

Vaishnavi - Building a data streaming platform sounds exciting, but it’s definitely not without challenges. What are some of the biggest hurdles teams face when setting up such systems?

Deepak - Discusses technical challenges like scalability, data consistency, and latency.

  • Building a data streaming platform is indeed exciting, but it comes with its share of challenges. One of the biggest hurdles is scalability—ensuring the platform can handle an ever-increasing volume of data without compromising performance. As data grows exponentially, the architecture must scale seamlessly to meet the demand.
  • Another critical challenge is maintaining data consistency. In a distributed system, ensuring that data remains accurate and synchronized across multiple nodes and services is no small feat. This becomes even more complex when dealing with diverse data sources and formats.
  • Latency is also a significant concern. In real-time systems, even small delays can impact the user experience or decision-making process. Optimizing for low-latency processing while managing network and resource constraints is a constant balancing act.
  • Finally, teams must also address challenges like fault tolerance, handling out-of-order data, and selecting the right tools and frameworks. Each decision made has a ripple effect, and finding the right balance between flexibility, reliability, and performance is what makes this field both challenging and rewarding.

Vaishnavi - And Ramneek, from your experience, how do governance and maintaining data quality fit into this picture?

Ramneek - Explains the importance of governance, schemas, and compliance in streaming platforms.

  • This is really a nice question, Vaishnavi! As Deepak has mentioned about the challenges , in terms of data streaming, governance, schemas, and compliance are key to maintaining the integrity and security of the data as it flows through the system.
  • Governance is about setting up rules and controls to ensure data is used properly and securely. For example, in a ride-hailing app like Uber, governance ensures that only authorized users or systems can access sensitive data, like user payment information. It also helps track where data comes from and how it’s used, which is crucial in case something goes wrong or for auditing purposes.
  • Schemas define the structure of the data—like the blueprint for a building. They ensure that the data is formatted consistently so all systems understand it the same way. For instance, a driver’s location data might be structured with specific fields like latitude, longitude, and timestamp. If the schema is consistent, the app can process and use the data accurately across different services, like matching riders with drivers or calculating the ETA.
  • Compliance ensures that the data is being handled according to legal regulations, like GDPR . For example, in the case of Uber, compliance would ensure that personal information, such as a rider’s payment details, is stored and processed in a way that meets privacy laws and keeps the data secure.
  • In summary, governance sets the rules for data usage and security, schemas ensure data is structured correctly, and compliance ensures that the system adheres to legal and regulatory standards. Together, they ensure that data streaming systems run smoothly, securely, and in line with all required regulations.

Vaishnavi - Let’s talk tools! Kafka and Flink are often mentioned together in data streaming discussions. Deepak, could you explain why Kafka is the go-to choice for stream ingestion and what makes it so effective?

Deepak - Explains Kafka’s role

  • Kafka has become the go-to choice for stream ingestion because of its ability to handle massive amounts of data with high throughput and low latency. It acts as a distributed event streaming platform that decouples producers and consumers, allowing data to flow seamlessly between systems.
  • What makes Kafka so effective is its durability and fault-tolerant architecture. It stores data persistently, enabling replayability, which is critical for debugging or reprocessing historical events. Kafka’s partitioning mechanism also ensures horizontal scalability, making it capable of handling millions of events per second.
  • Flink complements Kafka by offering advanced stream processing capabilities. It excels at real-time event-time processing, aggregations, joins, and windowing with exactly-once state consistency and fault tolerance, making it ideal for complex data pipelines.
  • Kafka and Flink work seamlessly together. Kafka handles data ingestion and storage, while Flink processes and analyzes the data, enabling reliable, scalable, and efficient real-time pipelines.
  • This combination empowers businesses to unlock insights from streaming data, ensuring rapid decision-making and building robust, modern data architectures.

Vaishnavi - Ramneek, Flink complements Kafka by enabling real-time data processing. How does Flink fit into the architecture, and what are its standout features?

Ramneek - Highlights Flink’s event-time processing, fault tolerance, and APIs.

  • Absolutely, what Deepak has explained about Kafka is very true about its streaming and in very basic terms if I add, Flink is like the engine that takes the data streams ingested by Kafka and transforms them into actionable insights. One of Flink’s standout features is its ability to handle event-time processing, which ensures accurate results even if events arrive late or out of order. It’s also highly fault-tolerant, thanks to its state snapshotting and recovery mechanisms. Another big win is its rich APIs that cater to both developers who want to write low-level custom code and those who prefer higher-level abstractions for speed and simplicity. Together, Kafka and Flink form a powerful ecosystem for real-time applications.    Stream Governance- Why It Matters

Stream Governance​

Vaishnavi - We often hear about stream governance, but it’s not a term everyone’s familiar with. Deepak, could you tell us, what is this really? Why is governance crucial in data streaming?

Deepak - Explains governance, focusing on data lineage, access control, and schema management.

  • Stream governance refers to the practices and frameworks that ensure data in streaming systems is managed, controlled, and used effectively. It involves key aspects like data lineage, access control, and schema management to maintain trust and reliability in data.
  • Data lineage helps track the origin, flow, and transformations of data, ensuring transparency and enabling teams to debug and audit processes efficiently. Access control is crucial for securing sensitive data, allowing only authorized users or systems to interact with specific streams.
  • Schema management ensures that data formats remain consistent, preventing downstream errors when producers or consumers introduce changes. This is especially critical in real-time environments where any mismatch can disrupt processing pipelines.
  • Governance is essential in data streaming as it provides the foundation for building reliable, secure, and scalable systems. Without it, managing the growing complexity of real-time data flows becomes nearly impossible, risking compliance, security, and operational efficiency.

Vaishnavi - Ramneek, can you share a real-world example where governance played a critical role in a streaming project?

Ramneek - Provides an anecdote, possibly involving compliance or data quality issues.

  • Sure, Vaishnavi! A good example is real-time fraud detection in banking. As transactions are streamed, governance ensures that only authorized systems access sensitive financial data. Schemas ensure the data, like transaction amount and time, is structured consistently for accurate analysis. Compliance ensures adherence to regulations like PCI-DSS to protect customer data. Without proper governance, there could be data breaches or undetected fraudulent activities, risking both security and legal compliance.    Fun & Future of Data Streaming

Future / Trend in Data Streaming​

Vaishnavi - Let’s take a moment to geek out! What’s a future or trend in data streaming that excites you the most right now?

Deepak - Shares insights on AI in stream processing or serverless streaming tools.

  • One of the most exciting trends in data streaming right now is the integration of AI with stream processing. AI models, especially when combined with streaming platforms, enable real-time anomaly detection, predictive analytics, and dynamic decision-making. This opens up possibilities like detecting fraud as it happens or personalizing user experiences on the fly.
  • Another fascinating development is the rise of serverless streaming tools. Platforms like AWS Kinesis and Confluent Cloud are making it easier to build scalable, real-time pipelines without the need to manage infrastructure. This lowers the barrier for adoption, enabling teams to focus on building value rather than maintaining systems.
  • These trends are reshaping how businesses leverage data streams, making it not only more accessible but also smarter and more responsive. It’s thrilling to see how these advancements are pushing the boundaries of what’s possible in real-time analytics.

Vaishnavi - Ramneek, what’s one innovation or application you hope to see in the world of data streaming in the near future?

Ramneek - Discusses advancement

  • I’d love to see greater integration of AI and stream processing, especially in the form of intelligent stream management. Imagine systems that can self-optimize—adjusting processing logic, scaling resources dynamically, or even pre-emptively detecting potential bottlenecks—all without manual intervention. This would make streaming platforms even more robust and accessible for smaller teams or organizations without heavy engineering resources.     Wrapping Up

Recommendations​

Vaishnavi - Deepak and Ramneek, this has been a phenomenal discussion. To wrap things up, if someone wanted to start building a data streaming platform today, what’s the first step you’d recommend?

Deepak - Suggestions

  • If you're a small startup, I’d recommend taking a step back and reassessing whether a data streaming solution is really necessary. Many startups fall into the trap of the "shiny object syndrome," adding streaming tools and technologies that aren’t essential, which only increases complexity. It’s important to first evaluate how much data you're dealing with and whether streaming is the right approach to meet your goals.
  • If you’ve decided streaming is the way to go, start simple. Set up a small proof of concept (PoC) using Kafka and Flink, and ideally, run it directly on the cloud with free credits. This allows you to test if streaming is the right fit for your use case without heavy investment. This will also help you validate whether your business and technical objectives are being fulfilled, ensuring you're not over-engineering the solution before scaling.

Ramneek - Recommendations.

  • Start small and focus on understanding your use case.  Identify the key data sources, the type of insights or actions you want to derive, and your latency requirements. Once that’s clear, experiment with tools like Kafka and Flink using small datasets or simulations. Like Deepak and I work on projects where we need to make a decision to choose a tool for certain use case, so he has always guided me and often told me- “ramneek! Focus on why?”  so I would suggest to focus on the concept of “why” first rather than what tool u are using. There are excellent open-source resources and tutorials to help you get started. The key is to iteratively build, test, and refine your pipeline while keeping scalability and governance in mind for future growth. Vaishnavi - Thank you both for sharing your expertise and insights today. To our listeners, if you enjoyed this episode, make sure to subscribe and share it with your network. Let us know your thoughts in the comments and stay tuned for more tech deep dives. Until next time, keep exploring and keep building!