#confluent-kafka

[ryanvgates]

AWS MSK Create & List Topics

Problem I needed to created topics in Amazon Web Services(AWS) Managed Streaming for Apache Kafka(MSK) and I wanted to list out the topics after they were created to verify. Solution This solution is written in python using the confluent-kafka package. It connects to the Kafka cluster and adds the new topics. Then it prints out all of the topics for verification This file contains…

[heart-ghost-studyblr]

Currently studying kafka and make my way to the Confluent Certificate Developer Apache Kafka (CCDAK) which is primary use Java and Kafka.

[bcrispin]

Proud to have contributed to this.

[mordorintelligencemarketresearch]

Event Stream Processing: Powering the Next Evolution in Market Research.

What is Event Stream Processing?

At its core, Event Stream Processing is the technology that allows you to process and analyze data in motion. Unlike traditional batch processing, ESP enables organizations to ingest, filter, enrich, and analyze live data streams—in milliseconds. Technologies like Apache Kafka, Apache Flink, Spark Streaming, and proprietary platforms like Confluent and Azure Stream Analytics are powering this real-time revolution.

🌍 Overview of the Event Stream Processing Market

According to recent industry reports:

The global ESP market is projected to grow from $800M in 2022 to nearly $5.7B by 2032, with a CAGR exceeding 20%.

The drivers include growth in IoT devices, real-time analytics demand, AI/ML integration, and cloud-native infrastructure.

ESP is already being adopted in industries like finance, retail, telecom, and increasingly, in data-driven research sectors.

So how does this affect market research?

🧠 How ESP is Reshaping Market Research

The market research industry is undergoing a paradigm shift—from long cycles of surveys and focus groups to continuous consumer intelligence. ESP offers the foundation to make this real-time, automated, and infinitely scalable.

1. Always-On Consumer Listening

Traditional market research works in waves. ESP enables constant monitoring of consumer conversations, behaviors, and sentiments across social media, websites, mobile apps, and even connected devices.

2. Real-Time Behavioral Segmentation

Instead of waiting for post-campaign analysis, ESP enables dynamic audience segmentation based on live behavior. Imagine updating customer personas on the fly as users interact with a product or ad in real time.

3. Instant Trend Detection

With ESP, market researchers can spot emerging trends, spikes in brand mentions, or negative sentiment as it happens, giving companies the edge to react and innovate faster.

4. Improved Campaign Feedback Loops

By streaming campaign data into ESP systems, researchers can assess performance metrics like engagement, bounce rates, or purchase behavior in real time—enabling agile marketing and live optimization.

5. Enriching Traditional Research

Even classic survey research can be elevated. ESP can feed in contextual data (e.g., weather, location, digital footprint) to enhance response interpretation and modeling accuracy.

🚀 Emerging Use Cases

Use CaseESP in ActionSocial Listening at ScaleReal-time monitoring of tweets, posts, or mentions for brand perceptionVoice of the Customer (VoC)Processing live feedback from chat, call centers, or in-app surveysRetail Behavior AnalyticsStreaming in-store or ecommerce interaction data for buyer journey insightsAd Performance TrackingMeasuring campaign impact in real time and adjusting targeting dynamicallyGeo-Contextual SurveysTriggering location-based surveys in response to real-world events

🔍 Market Research Firms Tapping into ESP

Forward-thinking agencies and platforms are now building ESP pipelines into their solutions:

Nielsen is exploring real-time TV and digital media tracking.

Qualtrics and SurveyMonkey are integrating APIs and live data feeds to automate feedback systems.

Custom research agencies are partnering with ESP tech vendors to develop always-on insight platforms.

📈 Strategic Value for Researchers & Brands

Integrating ESP with market research doesn’t just speed things up—it changes the value proposition:Traditional ResearchESP-Enabled ResearchBatch, retrospectiveContinuous, real-timeManual analysisAutomated insightsSample-basedFull-data streamStatic reportsLive dashboardsReactive strategyProactive action

⚠️ Challenges to Consider

Data Overload: Without the right filters and models, ESP can create noise rather than insight.

Technical Skills Gap: Researchers may need to upskill or collaborate with data engineers.

Compliance Risks: Real-time processing must adhere to privacy laws like GDPR and CCPA.

Cost & Infrastructure: ESP requires robust architecture—cloud-native and scalable.

🔮 The Future: Market Research as a Streaming Platform

As ESP becomes more affordable and accessible via cloud platforms, we’ll see the rise of Insight-as-a-Stream—where brands and researchers subscribe to live feeds of behavioral, attitudinal, and transactional data, powered by AI and ESP pipelines.

In this new era, agility becomes a competitive advantage, and ESP is the engine behind it.

Final Thoughts

Event Stream Processing is no longer just for tech giants or financial firms—it’s the future backbone of modern market research. From real-time sentiment analysis to dynamic targeting and predictive behavioral modeling, ESP is enabling insights that are faster, smarter, and more actionable than ever before.

Market researchers who adopt ESP today won't just keep up—they'll lead. The Event Stream Processing market is poised for substantial growth, driven by technological advancements and the increasing need for real-time data analytics across various industries. For a detailed overview and more insights, you can refer to the full market research report by Mordor Intelligence: https://www.mordorintelligence.com/industry-reports/event-stream-processing-market

[aitoolswhitehattoolbox]

AWS Python Developer

AWS Python DeveloperJD4-5 years of overall IT experience and 3+ years in expert level development experience with Python and scalable solutions. – Expert level development experience with Python AND/OR Java for scalable solutions. – Experience with Kafka and Kafka Streams (confluent or Apache Kafka or MSK) is a must. – Expert level development experience with Python and scalable solutions. –…

[sapientsapiens]

Taking up the last module of the #DEZoomcamp @DataTalksClub regarding Stream Processing. About to get into Kafka <--> its configuration, producer, consumer, Confluent cloud. We shall also see how to use PyFlink to consume data from a Kafka stream.

[strategictech]

Kora: A cloud-native redesign of the Apache Kafka engine

Five key innovations that increased the performance, availability, and cost-efficiency of the engine at the heart of Confluent’s managed Apache Kafka service.  

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[killexamz]

Actual CCDAK Confluent Certified Developer for Apache Kafka exam questio...

[heart-ghost-studyblr]

I try to make a balance between reading the book "Kafka: The Definitive Guide - 2nd Edition," doing Confluent course lab exercises, and a little bit of Udemy projects with Kafka as well. In the middle of the week, I'm making my homepage to showcase some portfolio stuff, which is not my priority at this time, but it involves a lot of coding as well.

Feeling like I can answer any interview questions about Kafka at this point, including the fundamentals, use cases, and examples of writing a pub/sub system in Java.

It's all about studying; it magically changes you inside and out. You're the same person, in the same place, but now capable of creating really good software with refined techniques.

[varunsngh007]

Does Apache Kafka handle schema?

Apache Kafka does not natively handle schema enforcement or validation, but it provides a flexible and extensible architecture that allows users to implement schema management if needed. Kafka itself is a distributed streaming platform designed to handle large-scale event streaming and data integration, providing high throughput, fault tolerance, and scalability. While Kafka is primarily concerned with the storage and movement of data, it does not impose any strict schema requirements on the messages it processes. As a result, Kafka is often referred to as a "schema-agnostic" or "schema-less" system.

However, the lack of schema enforcement may lead to challenges when processing data from diverse sources or integrating with downstream systems that expect well-defined schemas. To address this, users often implement external schema management solutions or rely on schema serialization formats like Apache Avro, JSON Schema, or Protocol Buffers when producing and consuming data to impose a degree of structure on the data. Apart from it  by obtaining Apache Kafka Certification, you can advance your career as a Apache Kafka. With this course, you can demonstrate your expertise in the basics of afka architecture, configuring Kafka cluster, working with Kafka APIs, performance tuning and, many more fundamental concepts.

By using these serialization formats and associated schema registries, producers can embed schema information into the messages they produce, allowing consumers to interpret the data correctly based on the schema information provided. Schema registries can store and manage the evolution of schemas, ensuring backward and forward compatibility when data formats change over time.

Moreover, some Kafka ecosystem tools and platforms, like Confluent Schema Registry, provide built-in support for schema management, making it easier to handle schema evolution, validation, and compatibility checks in a distributed and standardized manner. This enables developers to design robust, extensible, and interoperable data pipelines using Kafka, while also ensuring that data consistency and compatibility are maintained across the ecosystem. Overall, while Apache Kafka does not handle schema enforcement by default, it provides the flexibility and extensibility needed to incorporate schema management solutions that align with specific use cases and requirements.

[ericvanderburg]

Ably Unveils Kafka Connector 3.0 with Enhanced Throughput, Error Handling, and Confluent Cloud Accreditation

http://i.securitythinkingcap.com/St3GQ0

[valuebound]

Apache Kafka: The Future of Real-Time Data Processing.

Apache Kafka is an open-source software platform that functions as a distributed publish-subscribe messaging system allowing the exchange of data between applications, servers, and processors, while also providing a robust queue that can handle a high volume of data and enables messages to be passed from one end-point to another.

Apache Kafka was originally developed by LinkedIn, later it was donated to the Apache Software Foundation and became an open-sourced Apache project in early 2011. Currently, it is maintained by Confluent under Apache Software Foundation. Kafka is written in Scala and Java. More than 80% of all Fortune 100 companies trust and use Kafka.                     

[reportwire]

How Does Kafka Perform When You Need Low Latency?

How Does Kafka Perform When You Need Low Latency?

Most Kafka benchmarks appear to test high throughput but not low latency. Apache Kafka was traditionally used for high throughput rather than latency-sensitive messaging, but it does have a low-latency configuration. (Mostly setting linger.ms=0 and reducing buffer sizes). In this configuration, you can get below 1-millisecond latency a good percentage of the time for modest…