Condense by Zeliot

Introduction

Apache Kafka has remained the backbone of event-driven architectures for over a decade. Its immutable log abstraction, scalable broker design, and stream-first philosophy have powered countless real-time systems—from fraud detection and e-commerce analytics to telematics ingestion and industrial automation.

But the world around Kafka has evolved. Data volumes have exploded. Cloud economics have shifted. Developer expectations have changed. And most critically, the business demands from real-time systems have moved far beyond what an isolated Kafka cluster can provide.

In 2025, continuing to operate Kafka as it was in the past, with manual management, loose integration, and layering with custom scripts, is increasingly unsustainable. Here are five deeply technical and operational reasons why modernizing the Kafka stack is no longer optional, but strategic.

1. Kafka Alone Isn’t a Platform

Running Kafka by itself delivers transport but not outcomes. Most real-time use cases depend on an entire ecosystem of critical components around Kafka, including:

Schema registries for versioned serialization

Stream processors for business logic execution

Connectors for integration with databases, filesystems, APIs, or telemetry streams

Monitoring agents to observe lag, consumer health, and throughput bottlenecks

Security layers for multi-tenant isolation, role-based access, and encryption

When these components are stitched together manually, organizations inherit the burden of lifecycle management: upgrades, patching, configuration drift, dependency mismatches, downtime orchestration, and incident response.

Modernizing the Kafka stack means adopting a cohesive, cloud-native runtime where these components work in unison—ideally under a single operational contract. This creates a predictable, observable, and sustainable foundation for stream-first workloads.

2. Developer Velocity Demands Better Abstractions

The Kafka ecosystem has traditionally favored infrastructure engineers and backend specialists. Defining stream joins, windowing logic, or repartitioning flows requires deep knowledge of Kafka Streams, KSQL, or Flink—plus careful handling of topic schemas, backpressure, and message formats.

As event-driven logic becomes part of core business applications—whether it’s scoring driver behavior, flagging transaction anomalies, or transforming IoT telemetry—developer experience becomes a bottleneck.

Modern stacks must support:

Low-code interfaces for operational workflows

GitOps workflows for versioned stream deployments

AI-assisted IDEs to auto-generate transformation templates

Live testing environments that simulate events before production rollout

Without these capabilities, real-time use cases become slower to deliver and harder to iterate, putting Kafka-centric architectures at odds with agile product cycles.

3. Cloud-Native Architecture Is Now Table Stakes

In 2025, most Kafka workloads run on cloud infrastructure—whether in VMs, managed Kubernetes clusters, or fully serverless runtimes. Yet traditional Kafka deployments often ignore cloud-native principles:

Manual node provisioning leads to overprovisioning or underperformance.

No support for autoscaling brokers or connectors based on demand.

Lack of integration with cloud IAM, logging, and billing complicates security and cost attribution.

Self-managed high availability adds operational tax for each region or zone.

Modern platforms treat Kafka as one component in a broader elastic data plane. Brokers auto-scale. Connectors spin up based on load. Stream processors run in serverless containers. Failovers are orchestrated automatically. Monitoring is pushed into existing cloud-native observability stacks.

Migrating to a cloud-aligned architecture reduces operational complexity, increases utilization efficiency, and enables faster scale-out for peak workloads, without human intervention.

4. Real-Time Use Cases Now Depend on Domain-Aware Processing

Kafka is a generic tool. But most real-time applications are domain-specific. Consider:

In mobility, real-time logic might involve VIN-based trip formation, geofence entry/exit events, and harsh braking classification.

In logistics, it may involve cargo temperature violation alerts, trip ETA updates, and route compliance tracking.

In finance, real-time use cases often involve transaction scoring, KYC triggers, or payment retry orchestration.

These patterns cannot be implemented through raw Kafka APIs or SQL-like interfaces. They demand prebuilt, domain-native transforms that understand context—e.g., how to interpret an OBD-II message, what constitutes a loading zone, or how to calculate SLA breach probability in transit.

Modern Kafka platforms incorporate verticalized logic libraries, deployable out-of-the-box, saving engineering months of effort while improving accuracy and operational trust.

5. Cost Optimization and BYOC Are Now Strategic Priorities

As enterprise cloud bills grow, organizations are rethinking the economics of managed Kafka. Traditional hosted platforms run Kafka inside the vendor’s cloud account, which leads to:

Double billing (vendor cost + unused cloud credits)

Lack of visibility into runtime costs

Inability to apply reserved instances or volume discounts

No control over data egress patterns or compliance enforcement

Modern Kafka platforms support Bring Your Own Cloud (BYOC), where all infrastructure runs in the enterprise’s cloud account, using its cloud credits and governance tools. This offers:

Full cost control and transparency

Better alignment with existing cloud agreements

Data sovereignty and compliance retention

Direct integration with internal monitoring, alerting, and IAM systems

BYOC is not just about infrastructure flexibility; now it is a financial, legal, and strategic enabler for Kafka adoption at scale.

Kafka Needs a Platform, Not Just Brokers

The technical power of Kafka is undiminished. But its role has changed. Kafka is no longer the end goal. It’s the foundation upon which real-time business logic, domain-aware intelligence, and operational outcomes are built.

Modernizing the Kafka stack means wrapping it with the necessary abstractions, integrations, and delivery systems required to thrive in production. The shift is from running brokers to delivering applications. From managing infrastructure to enabling decisions in motion.

Why Condense?

Condense is built for this new era of real-time streaming. It is a Kafka-native platform, delivered via BYOC, and tailored to industries like mobility, logistics, industrial automation, and connected infrastructure.

With prebuilt transforms, low-code development, AI-assisted IDEs, and full cloud integration, Condense reduces time-to-value while increasing platform trust. It brings together Kafka, stream logic, deployment tooling, and observability—without requiring a dedicated SRE team to keep things running.

In 2025, Kafka alone will no longer be enough. The future belongs to streaming platforms that don’t just deliver logs, but understand the domain behind every message. Systems where VINs aren’t just strings, but identifiers for operational context. Where a harsh brake isn't just a sensor value, but a signal that may affect safety, routing, or warranty.

Condense that transformation. It extends Kafka with domain semantics, real-time transforms pre-aligned with industry workflows, and infrastructure that runs inside the enterprise’s own cloud environment. Kafka becomes more than transport; it becomes the foundation for intelligent, outcome-driven applications that speak the language of the domain.

That’s why enterprises like Volvo, Eicher, Royal Enfield, Michelin, CEAT, and TVS have moved beyond generic Kafka clusters and toward streaming platforms like Condense, where real-time pipelines are not just technically correct but operationally meaningful.

Frequently Asked Questions (FAQ)

1. Is Apache Kafka being replaced?

No. Apache Kafka remains a foundational component for event streaming. What’s changing is the ecosystem around it. Modern organizations are moving away from raw Kafka clusters and toward integrated platforms that combine Kafka with stream processing, domain logic, observability, security, and deployment automation. The goal is not to replace Kafka, but to make it production-grade and outcome-oriented.

2. What does it mean to “modernize” a Kafka stack?

Modernization involves evolving from a loosely assembled set of Kafka services to a platform where stream processing is:

Domain-aligned (industry-specific logic and semantics)

Cloud-native (autoscaling, managed failover, integrated monitoring)

Developer-ready (GitOps, low-code, AI-assisted transforms)

Cost-efficient (BYOC, cloud credit utilization) It’s about increasing delivery speed and reducing operational burden, without losing Kafka’s core strengths.

3. Why is developer velocity relevant to Kafka architecture?

Infrastructure teams historically managed Kafka. But today, product and application teams are building on top of Kafka for use cases like real-time pricing, routing intelligence, maintenance prediction, and alerting. If the underlying stack requires custom JVM code or complex DSLs for every transformation, delivery slows down. Modern platforms provide abstractions that let domain experts and developers collaborate at speed, without needing to be Kafka internals experts.

4. What is the role of domain awareness in Kafka-based systems?

Raw Kafka doesn’t know the difference between a vehicle ID and a sensor type. But real-time systems increasingly depend on contextual interpretation: route IDs, fleet zones, compliance flags, shipment IDs, etc. Domain-aware platforms bring this intelligence closer to the data plane—embedding semantic understanding into transforms, alerting, and visualization. This eliminates the need to re-encode business logic downstream in BI tools or service code.

5. What is BYOC, and why does it matter for Kafka?

BYOC (Bring Your Own Cloud) allows the Kafka platform and supporting services to run fully inside the enterprise’s own cloud account (AWS, Azure, GCP). The platform is still vendor-operated but leverages the customer’s:

Cloud credits

IAM policies

Observability stack

Compliance posture This ensures data sovereignty, cost efficiency, and deep integration, without requiring the enterprise to self-manage Kafka infrastructure.

6. How does Condense modernize Kafka differently?

Condense builds on Kafka’s architecture but adds:

Prebuilt, domain-specific transforms (e.g., for mobility, logistics, energy)

A low-code/IDE interface for defining and deploying stream logic

CI/CD pipelines for stream application lifecycle management

Native BYOC deployment support across AWS, Azure, and GCP

Isolation by default, with full auditability and customer-bounded operations

It enables streaming-native applications to be built and deployed in days, not quarters, without requiring deep Kafka expertise or large ops teams.

7. What kinds of organizations are using Condense?

Condense is trusted by a broad spectrum of enterprises and system integrators operating in data-intensive, real-time environments. These span across:

Automotive OEMs – including Volvo, Royal Enfield, and TVS Motor - are using Condense for OTA updates, remote diagnostics, vehicle analytics, and feature lifecycle control.

Fleet & Mobility Platforms – such as Eicher, SML Isuzu, and Taabi Mobility, relying on Condense for trip intelligence, predictive maintenance, panic alerting, and live telematics processing.

Logistics & Transportation Networks – including Michelin and various freight, mining, and container mobility platforms using Condense for multi-modal tracking, cold chain eventing, and geofenced security.

Industrial & Manufacturing Operations – streaming real-time production telemetry, detecting bottlenecks, balancing workloads, and ensuring operational continuity using data from PLCs and SCADA systems.

Energy & Utilities – leveraging Condense to stream substation events, forecast demand, detect anomalies, and integrate directly with grid orchestration platforms in real time.

Financial Services – where Condense enables fraud detection pipelines, transaction anomaly flagging, and secure, compliant integration with downstream rule engines and audit layers.

Smart Cities & Public Infrastructure – powering streaming use cases in traffic signal networks, emergency response coordination, and public transportation tracking with millisecond latency.

Travel & Hospitality Systems – unifying data from property management systems (PMS), shuttle tracking, booking engines, and mobile apps to enable dynamic rate optimization, real-time availability, and multilingual customer notifications. Condense allows hotel chains, airport service providers, and hospitality tech platforms to detect and react to changes, such as flight delays, booking conflicts, or room state transitions in real time.

Introduction

Apache Kafka has become the backbone of modern real-time systems — enabling everything from sensor telemetry and financial transactions to personalized customer experiences. But Kafka, by itself, is complex to deploy, scale, and operate at production-grade levels. That’s why companies increasingly turn to managed or enhanced Kafka platforms to accelerate their streaming initiatives.

Among the top choices today are Confluent, Redpanda, and Condense — each with a distinct philosophy, architecture, and value proposition. This blog breaks down how they compare — not just on technical capabilities, but also on their strategic fit for businesses building streaming-powered products.

Architecture: Native Kafka vs Reimagined Kafka

At the core, Confluent and Condense are Kafka-native platforms. Redpanda, while Kafka API-compatible, reimagines the internals with a custom engine.

Confluent extends Apache Kafka with enterprise-grade tooling: tiered storage, schema registry, and ksqlDB. It retains the core Java-based architecture, making it ideal for teams already invested in Kafka.

Redpanda rewrites Kafka in C++ for performance. There is no ZooKeeper, no JVM, and the platform emphasizes ultra-low latency for financial and latency-critical workloads.

Condense builds on open-source Kafka but optimizes it for production out of the box. It retains the proven Kafka architecture, while simplifying orchestration, autoscaling, and fault tolerance — and adds industry-specific pre-tuned configurations.

If you want to stay within the Kafka ecosystem with full compatibility, Confluent and Condense are natural fits. Redpanda offers innovation but breaks away from Kafka’s internals — which may require deeper testing for enterprise-grade compatibility.

Operational Complexity: Who Handles the Burden?

Managing Kafka in production can be labor-intensive. It requires continuous monitoring, tuning, schema evolution handling, and scaling coordination.

Confluent Cloud simplifies this to an extent, but requires understanding the nuances of Kafka Connect, ksqlDB, and billing tied to usage. BYOC support is limited and often bundled with higher-tier plans.

Redpanda Cloud simplifies single-cluster ops, and its single-binary deployment is elegant. However, observability and governance tools are still maturing, and some enterprise workflows require deeper customization.

Condense is fully managed across any cloud — including full BYOC support. It not only runs Kafka but also handles connectors, transformations, schema evolution, stream routing, and monitoring — all within a unified control plane. Operations are declarative, GitOps-ready, and tuned for data sovereignty.

For teams with minimal Kafka expertise or limited DevOps bandwidth, Condense delivers Kafka-native capabilities with near-zero operational overhead — accelerating time-to-value without the infrastructure tax.

Ecosystem and Extensibility: Building Real Solutions

Having Kafka up and running is only the beginning. Real business outcomes come from what you can build on top of it — stream joins, enrichment, alerting, or real-time decisions.

Confluent provides Kafka Streams and ksqlDB, which are powerful but require teams to learn and manage new paradigms. It also offers a large connector marketplace, making integration easier across the board.

Redpanda is introducing WebAssembly-based stream transforms. This offers flexibility for engineers comfortable with writing low-level modules, but lacks high-level abstractions for product teams or business users.

Condense includes both prebuilt and programmable transforms. Users can choose between:

No-code blocks (e.g., split, filter, debounce, enrich)

Low-code logic (rule builders)

A built-in IDE to write custom logic in any language, backed by Git This creates a shared surface between developers and non-developers — aligning product, engineering, and operations.

Condense transforms Kafka from just a data pipe into a full application acceleration layer — dramatically reducing the time needed to go from raw events to production-grade workflows.

Deployment Control: SaaS vs BYOC vs Hybrid

Data locality, sovereignty, and infrastructure control are becoming essential across sectors like mobility, finance, government, and logistics.

Condense takes a BYOC-first approach. Everything — from Kafka brokers to transforms — runs in your VPC, ensuring data never leaves your cloud. This gives teams full control over compliance, cost optimization, and multi-region deployment strategies.

For businesses dealing with sensitive data or strict compliance mandates, Condense offers the strongest sovereignty and control without trading off simplicity.

Time-to-Market: From Pipeline to Product

Confluent and Redpanda provide the infrastructure to stream data. You build the business logic on top.

Condense takes a different approach: it offers a complete streaming platform — from ingestion and transformation to decision-making — with pre-integrated vertical building blocks. These include:

Mobility: Geofencing, driver behavior scoring, predictive maintenance

Finance: Fraud detection, KYC stream verification, microtransaction flagging

Manufacturing: Production line flow optimization, downtime analytics

By combining infrastructure, logic, and industry patterns, Condense reduces go-live time from months to weeks — or even days.

For companies looking to launch real-time features faster — without building everything from scratch — Condense delivers a 6x acceleration in go-to-market, with immediate ROI.

Making the Right Choice

Choose Condense if:

You're not just looking for a better Kafka deployment — you're building real-time, event-driven systems where speed, scale, and precision directly impact business outcomes. Condense is purpose-built for teams who want to move fast, stay in control, and avoid the complexity that traditional streaming stacks often impose.

Unlike platforms that stop at infrastructure, Condense offers a fully managed, Kafka-native foundation that extends all the way to production-ready applications. It’s optimized for organizations that value cloud sovereignty, automated operations, and rapid solution delivery — without trading off observability or reliability.

Whether you’re deploying in a regulated cloud environment, handling millions of events with sub-second responsiveness, or launching vertical-specific features like geofenced alerts, fraud detection, or sensor-driven automation — Condense compresses what would typically take months of engineering into a matter of weeks. And it does so without asking you to compromise on control, performance, or uptime.

With proven deployments across industries and a marketplace of reusable stream logic, Condense transforms Kafka from a streaming backbone into a real-time application platform — ready to scale with your ambitions.

Discover how Apache Kafka powers real-time streaming by boosting speed, reliability, and scalability for handling live data and events efficiently.

Why Kafka Became the Backbone of Real-Time Data

In today’s event-driven world, data no longer arrives in scheduled batches. It moves continuously — from app interactions, payment systems, vehicle telemetry, sensors, APIs, user sessions, and infrastructure events. Responding to this data in real-time is now a requirement across various industries, including mobility, finance, healthcare, manufacturing, and media.

Apache Kafka emerged as the foundational backbone for such systems. It provides a high-throughput, distributed commit log designed to handle streams of data with durability and fault tolerance. Whether it’s tracking thousands of financial transactions per second, handling IoT updates from a fleet of trucks, or processing live playback events during a sports stream, Kafka plays a critical role in making real-time data architectures possible.

Kafka’s Core Strengths

Kafka’s popularity stems from a set of core capabilities:

Durable, scalable message streaming

Kafka enables decoupling of producers and consumers while ensuring messages are reliably stored and delivered — even at massive scale.

Replayable data for stateful applications

Consumers can rewind streams to reprocess data, allowing for recovery, migration, testing, and stateful workflows.

High throughput with partitioning and horizontal scaling

Kafka supports millions of messages per second through partitioned topics, enabling systems to process data efficiently in parallel.

Strong ordering and delivery guarantees

Within a partition, Kafka ensures message order and supports at-least-once or exactly-once semantics, which is essential for financial or critical operations.

Extensive ecosystem integration

With support from tools like Kafka Connect, ksqlDB, and integration with Flink, Spark, and stream processors, Kafka has become the default substrate for building streaming pipelines.

But Running Kafka in Production Is Not Simple

Despite Kafka’s design strengths, many organizations struggle to run Kafka-based infrastructure at production scale.

Cluster provisioning and autoscaling

Kafka requires precise tuning of broker counts, partition sizes, replication factors, and storage volumes. Spiking workloads (e.g., IPL streaming or surge traffic during financial trading) can easily saturate under-provisioned clusters.

High operational overhead

Ensuring HA, handling broker failures, managing topic partitions, tuning I/O and memory — all require deep Kafka expertise. Small missteps lead to message loss or latency spikes.

Monitoring and observability

Kafka exposes a wide range of metrics but offers no built-in solution for high-level operational insights across producers, consumers, and delivery guarantees. Custom dashboards and logging pipelines are often needed.

Security and compliance

Kafka deployments must handle encryption, authentication, role-based access control, and data protection policies, which are non-trivial to implement across hybrid or multi-cloud environments.

Developer experience and integration cost

Kafka doesn’t include out-of-the-box support for schema evolution, business logic composition, or downstream delivery coordination — all of which must be built separately.

Condense: Streaming Infrastructure Built on Kafka — Without the Operational Burden

Condense is a fully managed, vertically optimized real-time application platform built on a Kafka core, abstracting away the complexity of provisioning, scaling, securing, and operating Kafka clusters.

Instead of offering Kafka as a raw broker, Condense delivers:

Managed Kafka with BYOC Support

Condense provides fully managed Kafka as part of its real-time execution environment. Organizations can run Condense in their cloud (AWS, GCP, Azure), giving them full sovereignty over data, networking, and access, without needing to maintain brokers, Zookeeper, or controller nodes. Kafka just works — scaled, secure, observable — with no cluster tuning or operator overhead.

Streaming-Native Development Platform

Condense layers stream-aware development tooling on top of Kafka:

Native ingestion from REST, MQTT, Kafka topics, or webhooks

Schema-bound event validation and version management

Transforms written in Python, Go, Java, or JavaScript in an integrated IDE

Visual logic builders (for merge, window, split, alert) to compose business workflows

GitOps support for versioned deployments, rollback, and traceability

Kafka becomes more than a broker — it becomes part of a production-grade application engine.

Observability and Operational Safety

Condense provides:

Per-event tracing through all transforms

Live stream viewers with structured logs

DLQ (Dead Letter Queues) for error handling

Auto retries and backoff strategies

Alerting mechanisms for message loss, latency breaches, or logic failures

This turns Kafka from an opaque system into an auditable, transparent platform for regulated or mission-critical use cases.

Streaming as a Service for Industry Use Cases

Condense is built not only to operate Kafka pipelines, but to accelerate use case realization across domains:

Mobility: CAN bus + GPS streaming for predictive maintenance

Finance: Real-time fraud detection and transaction flagging

Healthcare: Continuous vitals monitoring and alert orchestration

Media: Playback telemetry, personalization, and regional surge detection

Manufacturing: Conveyor checkpoint tracking and anomaly detection

Kafka alone doesn’t provide logic for these domains. Condense gives the infrastructure, developer tooling, and streaming semantics required to build these workflows efficiently.

Kafka Is the Engine. Condense Is the Control System.

Kafka’s distributed log architecture is ideal for powering high-throughput, low-latency streaming systems. But Kafka is only part of the story. Building actual applications on Kafka requires infrastructure scaffolding, orchestration, state tracking, and delivery management.

Condense brings these layers together in a single, real-time platform — abstracting Kafka complexity while maintaining Kafka power. With Condense, teams focus on building and deploying real-time logic, not managing brokers, tuning partitions, or wiring retry logic by hand.

Apache Kafka remains one of the most important foundational components in the real-time data ecosystem. Its durability, throughput, and integration breadth make it indispensable for modern data-intensive applications.

But scaling Kafka is a specialized skillset, and most teams need more than a message broker. They need a platform that combines ingestion, enrichment, transformation, and delivery, with governance, visibility, and developer control built in.

Condense delivers that.

Introduction

Apache Kafka is the backbone of modern real-time data architectures. It powers everything from user activity tracking to IoT telemetry, fraud detection, and microservices communication. As an open-source distributed log system, it promises high throughput, durability, and fault tolerance—making it an easy choice for engineering teams.

So, Apache Kafka has become the de facto standard for real-time data streaming. It’s fast, resilient, and open source—seemingly the ideal foundation for scalable event-driven systems.

But if you've ever tried running Kafka in production, you know the truth: Kafka is free like a puppy. The infrastructure may be open source, but the operational, engineering, and business costs of managing Kafka at scale are far from free.

Open Source Is Free—Until You Operate It

What often goes unspoken is this: Kafka is not truly free—especially not at scale. While the binaries cost nothing, the operational overhead, complexity, and long-term total cost of ownership (TCO) are anything but trivial. Organizations that adopt Kafka without fully accounting for these costs often find themselves fighting infrastructure, not building value.

Deploying Kafka in a development environment is easy. But running it reliably in production—across multiple environments, availability zones, and use cases—requires a supporting ecosystem and a dedicated operations strategy. This includes:

Kafka Connect: For integrating with external systems (databases, S3, etc.)

Kafka Streams / KSQL: For real-time data transformation and enrichment

Schema Registry: To manage data contracts and enforce serialization

Monitoring & Logging: Using Prometheus, Grafana, ELK/EFK, or OpenTelemetry

Security: SSL, SASL, ACLs, Role-Based Access Control

Disaster Recovery & Upgrades: For multi-cluster resilience and lifecycle management

24x7 Support: For SLA-driven production environments

Each of these layers brings its own configuration, observability, and maintenance requirements. And that complexity grows disproportionately with scale.

Engineering and Operational Overhead

Let’s quantify the engineering cost of running Kafka at even moderate scale (e.g., ~10 MBps throughput):RoleEffortTypical Monthly Cost (APAC Avg)Typical Monthly Cost (NA/EU Avg) Kafka Engg (1 FTE) Dev/Infra/Performance $4,000 $15,000 Kafka Admin (1 FTE) Cluster Ops, ACLs, Upgrades $4,000 $15,000 Cloud Infrastructure On-call, Incident Management $800 $800 Support (20% of 4 FTEs for 24x7 support) Compute, Network, Storage $2,000 $6,000 Cloud ops (30% of 2 FTEs) Terraform, CI/CD, Monitoring, Compliance $2,000 $6,000

Even with conservative estimates, Kafka operations often exceed $12,800 –$42,800 per month for production-grade setups. In cost-sensitive markets like APAC, the engineering cost may be lower in dollars—but the availability, skill gap, and churn introduce their own hidden risks.

One-Time Costs You’ll Never Budget For

Beyond monthly operational expenses, the initial setup and ecosystem build-out can quietly delay projects and inflate budgets. These include:

Logging & Monitoring Stack Integration: ~$5,000–$10,000

Kafka Connectors, Streams, Schema Registry Setup: ~$20,000+

Hardening for Prod (RBAC, backup, failover): Weeks of engineering time

Training, Hiring, and Retention: Especially difficult for Kafka specialists

Collectively, these non-trivial one-time costs extend time-to-market by several months—especially for teams without prior Kafka experience.

The Intangibles: What the Spreadsheet Doesn’t Show

Some of Kafka’s costs can’t be easily measured but are deeply felt:

Opportunity Cost: Every hour spent debugging partitions or tuning retention policies is an hour not spent improving your product.

Talent Risk: Kafka specialists are in high demand. Losing even one can stall a critical deployment.

Incident Fatigue: Kafka-related issues are often cascading—causing silent failures across entire pipelines.

Architecture Drift: Over time, DIY setups become inconsistent and brittle, making upgrades and audits painful.

In short, Kafka’s strength—its flexibility—can become a liability without the resources to manage it responsibly.

So What’s the Alternative?

Not every organization wants to build a data infrastructure team just to use Kafka. This is where fully managed Kafka-native platforms step in—not to replace Kafka, but to abstract away its operational complexity.

Enter Condense:

Kafka-native under the hood, but without provisioning brokers, connectors, or stream processors

No backend setup — deploy from cloud marketplaces (AWS, Azure, GCP)

No ops team required — observability, alerting, scaling, and support built-in

Includes the ecosystem — KSQL, Connect, Schema Registry equivalents are pre-integrated

Accelerates time-to-market by 6 months, with over 500 hours/month of engineering effort saved

For organizations that want Kafka’s power without managing Kafka itself, platforms like Condense offer a compelling alternative—especially in time- and cost-sensitive digital transformation journeys.

Comparing the Two Worlds: Self-Managed vs Fully Managed

Feature / Cost AreaOpen-Source KafkaCondense (Kafka-Native) Kafka Broker Setup Manual Fully abstracted Kafka Connect & Streams Setup Requires engineering Pre-integrated Monitoring, Alerting, Logging Requires setup & tuning Built-in Infra Scaling Manual via IaC Auto-scaled 24x7 Support In-house staffing Included CloudOps + SRE Headcount 3–4 FTEs typical 0 FTE Time-to-Market 6-12+ months Go live in weeks Monthly TCO (10 MBps) ~$12,800 (APAC) / ~$42,800 (NA/EU) ~$8,100 (APAC) / $10,300 (NA/EU) One-Time Setup Cost $28,471 $0 Intangible Cost Burden High None Net TCO Savings (3 years) — ~$4,700 (~40% in APAC) / ~$32,500 (~75% in NA/EU)

Condense is purpose-built for high-velocity teams that want the power of Kafka without turning into Kafka operations teams. It supports:

Native Kafka APIs (no client changes required)

BYOC model (runs on your AWS, Azure, or GCP)

Pre-integrated transforms, schema governance, and alerting

Visual logic builder and Git-backed IDE for custom workflows

Industry-specific use cases (mobility, fintech, industrial IoT, etc.)

Final Thoughts: Do You Want to Build a Platform or a Product?

Kafka is excellent infrastructure—but it’s still just that: infrastructure.

Unless you’re building a real-time data platform company, managing Kafka is a distraction. It demands talent, time, tools, and relentless vigilance. For most product-focused organizations, the cost of managing Kafka internally—financially and strategically—quickly outweighs its perceived benefits.

The better question is no longer “Can we manage Kafka?”

It’s: “Should we?”

With managed Kafka-native platforms like Condense, you can retain the power of Kafka without the overhead—freeing your teams to focus on what matters: building exceptional, data-driven products.

When Viewers Flood In — What Breaks First Isn’t the Video

When a high-stakes event, such as a major sports final or breaking news, occurs, media platforms can experience a sudden surge in users, jumping from thousands to millions in seconds. The video stream may be flawless, but the experience still crashes.

This isn’t about bad codecs or CDN failures.

The real culprit? Everything around the stream:

Sessions that can’t be created fast enough

Personalized UIs that lag or fail

Event-driven interactions that stutter or skip

Metrics and telemetry that back up like traffic jams

To stream at scale, you need more than bandwidth and buffering — you need real-time infrastructure that doesn’t crack under pressure.

World Cup 2022: When JioCinema Faced the Surge

In 2022, JioCinema offered free access to FIFA World Cup streams in India. The response was massive and overwhelming.

Viewers reported:

Frequent app crashes

Frozen or looping streams

Long delays in the playback startup

The video encoding was fine. CDNs were robust. But the backend systems — session management, real-time analytics, and personalization engines — couldn’t keep up with millions joining simultaneously.

By the time IPL 2023 rolled around, many of those issues had been fixed.

But the broader question remains:

How can any platform handle 5–10 million users logging in at once, not just for video, but for the real-time experiences around it?

The Hidden Layer Behind Every Stream

Let’s be clear — video delivery today is solid. Mature CDNs, adaptive bitrate streaming, and resilient encoding pipelines do their job.

But that’s not the problem.

What breaks under load:

Session creation and entitlement checks

Live metrics: stall events, buffer ratios, ping latency

Playback quality monitoring and QoE scoring

Real-time UI personalization and recommendations

CDN switching based on regional load

In-app engagement features like polls, trivia, and fan reactions

Fraud detection and churn prediction

These are all event-driven, stateful, and time-sensitive operations, and most platforms stitch together Kafka, Flink, Redis, Lambda, Airflow, and more to handle them. That works — until scale hits.

Condense: A Real-Time Streaming-Native Backend

Condense is a vertically optimized real-time platform designed to handle exactly this kind of pressure.

Not a video server. Not a CDN. But the layer that powers everything around the stream — sessions, telemetry, engagement, fraud detection — in real time.

What Condense Offers:

Ingestion Connectors: REST, Kafka, MQTT, Webhook

Streaming Transforms: Code your logic in Python, JS, Go, Java

Built-In State: Session-aware counters, window functions, regional aggregations

Low-Code or Code: Use visual logic blocks or the embedded IDE inside of Condense Application

Delivery Pipelines: Push to CRMs, dashboards, caches, and CDN APIs

Observability: Logs, retries, tracing, dead-letter queues, and replays

BYOC Deployment: Run inside your cloud with full data control and compliance

You define the business logic. Condense executes it at scale — with sub-second latency, no backend sprawl, and full observability.

IPL Final Simulation: 10 Million Concurrent Users

Let’s walk through a real-time scenario — moment by moment, to see how Condense handles a massive load, intelligently and effortlessly.

Minute 0–1: The Login Storm

3 million users open the app within 30 seconds. A torrent of sessions starts, and events flood in via REST and MQTT.

Condense handles:

Device & app info extraction

Token validation and entitlement

A/B group assignment

Session routing to the nearest CDN node

Live fraud checks (e.g., emulator detection)

Metadata updates to analytics and heatmaps

All logic runs inside the stream — no external API calls, no delay.

Minutes 2–5: Telemetry Overload

Playback begins. Clients emit over 30 million telemetry events per minute:

Buffering %

Resolution shifts

Playback stalls

Ping latencies

Condense computes in real time:

QoE scoring per session using sliding windows

Adaptive bitrate recommendations if stalls exceed a threshold

CDN switch alerts if regional stall rate >10%

Stateful logic is embedded in-stream.

Minutes 6–10: UI Personalization at Scale

As viewers interact (click, swipe, search), the user Activity events are streamed.

Condense joins activity with:

Watch history

Cohort data

Trending content

And responds instantly:

Personalizes homepage tiles

Surfaces relevant promos

Inserts live match overlays

UI reacts to behavior in milliseconds. No lag. No recompute.

Minutes 10–15: Load Spike in One Region

Sudden surge in North India — 2.5x new sessions.

Condense reacts live:

Aggregate sessions by region using window transforms

Reassigns new logins to the alternate CDN

Disables experimental UI for overloaded zones

Sends incident summary to the NOC dashboard

Just live streaming logic responding in real time.

Minutes 15–30: Real-Time Campaigns and Fan Engagement

A key moment: Virat hits a 6. The match event stream emits a match moment trigger.

Condense route engagement in real time:

Polls to only active users with >5 mins watch time

Trivia for Gen-Z users in metro cities

Celebratory effects skipped for low-bitrate sessions

Dynamic fan engagement — targeted, personalized, and instant.

Session End: Closing the Loop

At the session. End, Condense:

Finalizes QoE and engagement score

Streams metadata to GCS or S3

Sends churn likelihood to CRM

Adds session data to the fraud intelligence stream

No post-processing needed. Everything happens during the session.

Why Condense Works

Because Condense is streaming-native, built from the ground up to handle real-time event flows with:

Transforms versioned via Git

State embedded in the stream (not external caches)

Cloud, edge, or hybrid deployment

Live debugging, replays, and full traceability

No orchestration, servers, or glue logic required

You focus on business logic. Condense runs the rest — fast, reliable, and scalable.

Designed for Data Sovereignty and Production Safety

Run Condense inside your cloud (BYOC), with:

Full control over infrastructure and scaling

No cross-border data flows

Easy compliance with local regulations and retention policies

You own the data. You govern the flows. Condense powers the intelligence.

What JioCinema Would Have Built Today

If JioCinema were architecting for its IPL backend today, it wouldn’t create 40 microservices to handle session surges, QoE scoring, CDN decisions, or audience engagement.

It would use a unified real-time engine like Condense.

Because video delivery is only half the battle.

Everything around the stream is what makes or breaks the experience, and that’s where Condense shines.

Ready for Your Platform’s Breakout Moment?

If you’re preparing for:

A high-stakes sports final

A global political debate

A record-breaking OTT premiere

…your backend needs to move at the speed of your audience.

Condense is how you stay up, responsive, and intelligent — even when millions join at once.

Introduction

From Batch ETL to Real-Time Streaming — and Why Kafka Changed Everything

For decades, enterprises relied on batch-oriented ETL (Extract, Transform, Load) processes to move and prepare data for analysis. Batch ETL was designed in an era where data volumes were modest, real-time decision-making was rare, and overnight data refresh cycles were acceptable.

However, as digital interactions exploded and businesses shifted toward real-time engagement, batch ETL began to show critical limitations:

Latency between event generation and actionability,

Resource inefficiencies due to bursty processing,

Fragility in error handling and recovery,

Inability to support use cases like instant fraud detection or dynamic personalization.

The need for streaming architectures — where data could be processed continuously and transformations applied in motion — became urgent.

Kafka emerged in this context. Originally developed at LinkedIn to handle real-time data ingestion at internet scale, Kafka introduced a durable, high-throughput, distributed commit log architecture that enabled the decoupling of data producers and consumers—a critical foundation for event-driven architectures.

However, while Kafka solved the problem of real-time event transport, building full streaming ETL pipelines on Kafka remained operationally complex:

Managing brokers, partitions, replication, scaling,

Building connectors to numerous external systems,

Implementing transformations on the fly,

Ensuring observability and operational reliability.

This is where Condense reimagines the ecosystem — delivering a vertically optimized, fully managed streaming platform that transforms Kafka into a complete Streaming ETL solution.

Limitations of Traditional Batch ETL

Before exploring streaming ETL with Condense, it is important to recognize the challenges posed by batch ETL architectures:

Delayed Insights: Data is stale between batch cycles, making real-time decision-making impossible.

High Operational Risk: Failures during batch jobs often require rerunning entire pipelines.

Poor Resource Utilization: System resources are underutilized most of the time, then overloaded during batch windows.

Limited Agility: Adding new data sources or transformations requires heavy reengineering.

In an environment where customer expectations, security threats, and operational requirements evolve in real time, batch ETL imposes inherent limitations that no longer align with modern business needs.

Streaming ETL: A Paradigm Shift

Streaming ETL reimagines data pipelines as continuous, event-driven processes:

Events are ingested, transformed, and delivered immediately as they occur.

Errors affect only individual events, not entire pipelines.

Resource utilization is even and predictable.

New use cases — real-time fraud detection, dynamic inventory updates, predictive maintenance — become achievable.

Kafka provided the critical foundation for this shift by enabling real-time, durable, scalable event streaming.

However, Kafka alone is not sufficient to fully operationalize streaming ETL pipelines without significant custom development and operational management.

Condense bridges this gap — providing a complete, production-ready Streaming ETL platform built natively on Kafka's powerful backbone.

Condense: Streaming ETL, Fully Realized

Condense transforms Kafka from a raw event transport system into a vertically complete Streaming ETL platform, offering:

Fully managed Kafka clusters tuned for streaming workloads,

Real-time connectors to diverse source and sink systems,

Integrated low-code and custom-code transformations,

Full observability from pipeline to infrastructure,

Secure BYOC (Bring Your Own Cloud) deployments for data sovereignty.

Unlike traditional Kafka platforms that require assembling multiple services, Condense delivers an out-of-the-box, real-time ETL experience, enabling organizations to move from event ingestion to business action seamlessly.

Core Capabilities for Streaming ETL with Condense

Managed Kafka Backbone

Condense the abstracts of Kafka operations entirely:

Broker scaling, partition optimization, and replication management are fully automated.

Clusters deliver 99.95% uptime SLAs and elastic scaling.

KRaft metadata management simplifies architecture and improves reliability.

Enterprises gain Kafka’s real-time event streaming benefits without operational complexity.

Real-Time Connectors and Transformations

Condense provides prebuilt, streaming-native connectors to databases, cloud storage, SaaS platforms, and analytical engines.

Transformations can be implemented:

Using drag-and-drop low-code utilities for common operations (filtering, enrichment, validation),

Or with custom code development inside an integrated, AI-assisted IDE.

Streaming ETL pipelines built on Condense can perform complex event joins, schema mapping, aggregations, and enrichments dynamically, without batch orchestration.

End-to-End Observability

Streaming systems demand real-time operational insight.

Condense embeds full observability natively:

Kafka broker health and topic performance dashboards,

Pipeline visualization mapping connectors, transforms, topics, and consumers,

Real-time metrics: throughput, consumer lag, retry rates, partition health,

Log tracing and payload inspection for rapid debugging,

Seamless external integrations with Prometheus, Grafana, and Datadog.

Operational reliability is designed into every pipeline, not added retroactively.

Secure BYOC Deployments

Condense supports deployment directly into customer-owned cloud environments (AWS, Azure, GCP).

This ensures:

Full control over data residency and compliance,

Leverage of existing cloud credits,

Lower operational costs by avoiding double hosting,

No lock-in to external infrastructure providers.

Streaming ETL pipelines remain secure, compliant, and cost-effective.

Real-World Use Cases for Streaming ETL with Condense

Organizations across industries leverage Condense for critical real-time initiatives:

Financial Services: Continuous fraud detection pipelines monitoring transaction streams,

Retail and eCommerce: Real-time inventory synchronization and personalized promotions,

Manufacturing: Predictive maintenance pipelines ingesting IoT telemetry,

Healthcare: Patient monitoring and alert generation pipelines,

Telecommunications: Real-time network event monitoring for SLA assurance.

By enabling continuous ETL flows, Condense allows enterprises to operate based on current conditions, not outdated batch snapshots.

Conclusion

Batch ETL architectures, while foundational historically, can no longer keep pace with the demands of modern, real-time businesses.

Kafka initiated the transformation to event-driven architectures by solving the problem of durable, scalable event transport.

However, building production-grade streaming ETL pipelines on Kafka still required significant expertise and operational overhead.

Condense delivers the next evolution — a fully realized Streaming ETL platform, combining managed Kafka, real-time connectors, transformation capabilities, observability, and BYOC deployments into a seamless, production-ready solution.

Organizations adopting Condense for streaming ETL unlock:

Immediate time-to-insight,

Lower operational complexity,

Reduced data staleness and SLA risks,

Greater business agility and responsiveness.

In a real-time economy, batch is obsolete. Streaming is essential. Condense makes streaming ETL practical, scalable, and reliable for every enterprise.

FAQ

1. Why was Kafka important in the evolution of streaming ETL?

Kafka introduced scalable, durable, real-time event streaming, making it possible to decouple producers and consumers in data architectures and enabling continuous ETL flows.

2. What challenges exist when using Kafka alone for streaming ETL?

Kafka provides transport but lacks built-in capabilities for managing connectors, transformations, monitoring, and deployment, requiring significant custom engineering.

3. How does Condense improve Streaming ETL compared to open-source Kafka deployments?

Condense offers managed Kafka, integrated connectors, transformation engines, end-to-end observability, and BYOC deployment, simplifying and accelerating Streaming ETL adoption.

4. Does Condense support schema evolution during streaming transformations?

Yes. Condense integrates schema registry capabilities to ensure safe schema evolution and compatibility across transformations and downstream systems.

5. What industries can benefit from Streaming ETL with Condense?

Introduction

Modern enterprises increasingly operate in environments defined by continuous, high-volume event generation. Applications across industries — from financial services to connected vehicles, smart factories to media platforms — demand the ability to ingest, process, and respond to millions of streaming events per second, often with sub-second latencies.

At the heart of these architectures lies Apache Kafka, the open-source distributed event streaming platform that redefined how real-time data is moved at scale.

However, operating Kafka in high-throughput environments introduces unique performance challenges:

Broker saturation under variable traffic loads,

Partition and replication management overhead,

Consumer lag accumulation,

Backpressure propagation across services,

Operational complexity in scaling dynamically.

Condense, a fully managed, Kafka-native real-time platform, addresses these challenges by embedding autonomous optimization techniques across the streaming stack, ensuring that high-throughput pipelines remain performant, reliable, and resilient.

This blog explores the fundamental performance challenges in managing high-volume Kafka environments and how Condense systematically optimizes for throughput, scalability, and operational simplicity.

Understanding the Challenges of High-Throughput Kafka Workloads

Kafka’s design is inherently optimized for horizontal scalability and durability. However, in production environments characterized by unpredictable or surging workloads, specific bottlenecks emerge.

Key challenges include:

Broker Resource Saturation

Each Kafka broker handles a portion of the partitioned event load. Under high-ingestion scenarios:

Disk I/O saturation can cause broker-level backpressure,

Network throughput limits can bottleneck replication and consumer fetches.

Memory pressure can degrade page caching and increase disk reads.

Broker resource imbalance leads to uneven partition leadership distribution, degraded ingestion rates, and increased end-to-end latency.

Partition Skew and Consumer Lag

Efficient partition management is critical to Kafka performance. In high-throughput contexts:

Some partitions may receive disproportionate event volumes (hot partitions),

Consumers associated with overloaded partitions lag progressively,

Consumer rebalances introduce further disruption if triggered improperly.

Skewed partition workloads often remain undetected in basic monitoring setups, leading to hidden system inefficiencies.

Replication Overheads

Kafka's durability model depends on replication between brokers. High-throughput ingestion amplifies replication overheads:

ISR (In-Sync Replica) management becomes sensitive to network jitter and disk latency,

Replication throttling mechanisms can create ingestion stalls,

Ensuring write durability while maintaining low latency becomes increasingly complex.

Without optimized replication handling, durability guarantees may compete directly with ingestion throughput.

Operational Complexity in Scaling

Kafka was architected to scale horizontally, but scaling in production environments involves:

Adding brokers without disrupting leadership assignments,

Redistributing partition replicas across new brokers safely,

Avoiding cascading rebalances and service disruptions.

Manual scaling remains error-prone, slow, and disruptive without intelligent orchestration.

How Condense Optimizes Kafka for High-Throughput Streaming

Condense embeds autonomous optimization principles across its managed Kafka stack to address these high-throughput challenges systematically.

These optimizations focus on resilience, elasticity, and predictability at streaming scale.

Autonomous Broker Scaling and Partition Rebalancing

Condense implements autonomous broker scaling, where infrastructure resources dynamically expand or contract based on observed system load patterns.

Key mechanisms include:

Auto-scaling brokers based on CPU, disk I/O, and network utilization metrics,

Predictive scaling algorithms forecast resource needs based on historical and trending throughput,

Safe partition reassignment orchestration, ensuring rebalances are controlled, incremental, and non-disruptive.

Rather than reacting to broker failure or overload post-factum, Condense proactively scales Kafka clusters to absorb peak workloads seamlessly.

Hot Partition Detection and Dynamic Load Redistribution

Partition skew is one of the most insidious performance killers in high-throughput environments.

Condense continuously monitors:

Partition-level event rates,

Consumer lag distribution,

Leadership assignment imbalances.

Upon detecting hot partitions, Condense:

Dynamically reassigns partition leadership to underutilized brokers,

Suggests or automates partition splitting (where upstream support exists),

Rebalances consumer groups where needed to spread the consumption load more evenly.

This dynamic load redistribution ensures uniform resource utilization and minimizes consumer lag accumulation.

Intelligent Replication and ISR Management

Condense optimizes replication performance to maintain durability without sacrificing throughput:

Replication throttling is applied adaptively based on broker health,

ISR set monitoring identifies and flags lagging replicas before triggering ISR shrinkage.

Network-aware replica placement ensures replication paths minimize inter-zone latency.

Fast leader election policies minimize producer and consumer disruptions during broker failures.

These replication strategies ensure Kafka’s durability model scales with ingestion volume without introducing unnecessary backpressure.

End-to-End Stream Backpressure Management

Backpressure, once introduced at any point in a streaming system, propagates rapidly.

Condense enforces end-to-end backpressure observability and control, including:

Monitoring event queue depths at connectors, brokers, and consumer applications,

Providing auto-tuning recommendations for producer batch sizes, linger.ms, and consumer fetch parameters,

Integrating with connector frameworks to apply rate limiting or pause/resume semantics gracefully during congestion scenarios.

This holistic backpressure management prevents system overloads, ingestion stalls, and message loss even under extreme load conditions.

Predictive Observability and Alerting

High-throughput optimization is not purely reactive.

Condense integrates predictive observability features that allow early detection of performance anomalies:

Trend-based alerting on throughput anomalies, lag growth rates, and replication instability,

Anomaly detection models for partition throughput skew,

Resource forecasting dashboards enabling proactive capacity planning.

Operators and architects gain not just visibility into current system health, but insights into impending stress conditions, allowing preventive action.

Real-World Outcomes: High-Throughput Streaming in Action

Organizations leveraging Condense for high-throughput streaming ETL, fraud detection, IoT telemetry ingestion, and real-time analytics have reported:

Almost no latency in the consumer during ingestion peaks,

Zero downtime scaling events, with rolling broker additions during peak loads,

Consistent throughput even during replication-intensive workloads,

Significant reductions in operator intervention and incident escalations.

By embedding intelligent, autonomous optimizations directly into its managed Kafka architecture, Condense enables enterprises to operate real-time data systems at massive scale, with reliability typically associated with traditional, tightly controlled batch systems, but at real-time velocity.

Conclusion

Managing high-throughput data streams requires more than simply deploying Kafka clusters and scaling infrastructure manually.

Optimal performance at streaming scale demands:

Autonomous resource scaling,

Dynamic partition and consumer load balancing,

Intelligent replication and ISR management,

End-to-end backpressure detection and handling,

Predictive observability and proactive incident prevention.

Condense delivers these capabilities natively, transforming Kafka into a fully resilient, self-optimizing streaming backbone for enterprises operating at the highest levels of data intensity.

In a world increasingly defined by real-time expectations and exponential data growth, Condense provides the foundation for high-throughput, low-latency, resilient streaming pipelines — without operational friction.

FAQ

1. How does Condense handle Kafka scaling during sudden traffic spikes?

Condense employs autonomous broker scaling based on resource utilization trends, combined with controlled partition reassignment to prevent consumer disruption during scaling.

2. What techniques does Condense use to prevent hot partition issues?

Condense monitors partition event rates, detects skew early, dynamically reassigns leadership, and optimizes consumer group balancing to distribute load evenly.

3. How does Condense ensure replication durability without affecting throughput?

Condense dynamically adapts replication throttling, monitors ISR health continuously, and minimizes cross-zone replication latency through intelligent broker placement.

4. Can Condense detect backpressure across the full streaming pipeline?

Yes. Condense captures queue depth metrics across connectors, brokers, and consumers, applies rate control dynamically, and enables auto-tuning of producer/consumer parameters.

5. Does Condense provide predictive scaling insights?

Introduction

Apache Kafka has become the de facto standard for real-time data streaming, powering everything from payment processing to connected vehicles and smart grids.

But anyone who has tried to deploy and manage Kafka in production knows the truth:

Kafka is powerful, but running it reliably is complex, resource-intensive, and costly.

Setting up brokers, tuning partitions, configuring ZooKeeper (or KRaft), securing clusters, handling failovers, scaling across clouds — it's a massive operational burden.

That’s why we built Condense.

Condense simplifies Kafka deployment dramatically by offering a fully managed, verticalized, BYOC Kafka native streaming platform — now available directly from the AWS, Azure, and GCP marketplaces.

In this blog, we’ll explore:

Why DIY Kafka deployments are so painful

How Condense removes the operational complexity

The advantages of BYOC (Bring Your Own Cloud) Kafka deployment

Why Condense prebuilt vertical ecosystem accelerates time-to-value

How Condense delivers lower TCO and faster GTM

The Hidden Complexity of Running Kafka Yourself

Self-hosting Kafka seems simple at first: spin up some brokers, connect producers and consumers, and start streaming events.

But reality hits hard:ChallengeImpactCluster Scaling Manual partition management, rebalance overhead Storage Management Disk throughput tuning, retention policy complexity Monitoring and Observability DIY Prometheus/Grafana setups, incomplete visibility Security and Access Control Complex ACLs, encryption management Multi-Cloud or Hybrid Deployments High network engineering complexity Downtime and Failures Risk of cascading failures, difficult disaster recovery Expertise Requirements Need dedicated Kafka SREs and platform engineers

Kafka isn’t a set-and-forget system.

Without deep operational expertise, outages, latency issues, and data loss are inevitable.

This leads to higher costs, slower go-to-market timelines, and frustrated engineering teams.

Introducing Condense: Kafka Deployment Without the Headaches

Condense changes the game by offering:

Fully managed Kafka clusters — no setup, tuning, or scaling worries.

BYOC deployment — Condense installs directly into your AWS, Azure, or GCP account via marketplace listings.

Instant leverage of your cloud credits — reducing net new costs.

Data sovereignty and compliance — since the Kafka clusters run in your controlled environment.

End-to-end managed services — with 24x7 support, built-in observability, and proactive monitoring.

In short: you stream, we manage everything else.

How BYOC Deployment with Condense Works

With Condense cloud marketplace listings, deploying Kafka becomes a one-click operation:

Find Condense in AWS Marketplace, Azure Marketplace, or GCP Marketplace

Deploy Condense into your own VPC, under your cloud account.

Use your existing cloud credits to offset costs.

Instant access to the Condense ecosystem, which offers managed Kafka, prebuilt connectors, KSQL, schema registry, and observability tools.

No heavy lifting. No infra setup. No vendor lock-in.

Your Kafka, running in your cloud, under your control, with Condense operational excellence built in.

Advantages of Condense by Fully Managed Kafka

FeatureBenefitAuto-Scaling Brokers, partitions, and consumers scale automatically with load. Built-in Observability Real-time dashboards for lag, throughput, partition health. High Availability 99.95% uptime SLA with zone redundancy and self-healing. Secure by Default End-to-end encryption, RBAC, fine-grained ACLs. Zero Downtime Upgrades Continuous availability even during maintenance.

Unlike DIY Kafka, Condense gives you production-grade Kafka without the traditional ops burden — freeing up your teams to focus on innovation.

Beyond Kafka: Condense’s Full-Stack Streaming Ecosystem

Condense isn’t just managed by Kafka. It’s a full, verticalized real-time data platform.LayerCapabilitiesConnectors Prebuilt industry-specific source and sink connectors Low-Code/No-Code Utilities Drag-and-drop stream transformations Built-In IDE Develop and deploy custom transforms with code KSQL and Schema Registry Native support for stream queries and schema evolution AI Assistant Integrated help for building pipelines, writing code, and optimizing performance

With Condense, you can ingest, process, transform, and stream data without leaving the platform, dramatically accelerating your time to value.

Why Enterprises Choose Condense Over DIY Kafka

DimensionDIY KafkaCondense Deployment Speed Weeks to months Minutes via Marketplace Cloud Credits Usage No Yes — leverage AWS/Azure/GCP credits Data Sovereignty Depends on setup Guaranteed (your cloud, your control) Operational Overhead High Zero (fully managed) TCO (Total Cost of Ownership) High (infra + manpower) Up to 60% lower Time to Market Slow 6x faster Feature Set Only core Kafka Kafka + connectors + low-code + IDE Support and SLAs DIY or third-party 24x7 enterprise-grade support

Bottom Line:

Condense helps you go from Kafka dreams to production reality in record time — without ops pain, without runaway costs, and without vendor lock-in.

Conclusion

Running Kafka shouldn’t require building a second infrastructure company inside your company.

With Condense, you get:

One-click BYOC deployment into AWS, Azure, or GCP

Fully managed Kafka tuned for high performance

Zero operational burden

Up to 60% lower TCO

6x faster go-to-market with prebuilt connectors and vertical integrations

Integrated AI developer tools for faster innovation

No more operational headaches. No more Kafka firefighting.

Just seamless real-time data streaming — built for modern enterprises.

Ready to depoy Kafka the smart way?

Book a meeting now!

FAQ

1. How is Condense different from other managed Kafka services?

Condense offers not just Kafka hosting but an entire real-time data ecosystem with industry-specific verticalization ecosystem like prebuilt connectors, Transforms, low-code tools like Utility for conditional logic, Built-in IDE to write complex logic to realize custom use case, KSQL, schema registry — all deployed in your cloud environment with full control.

2. What does BYOC (Bring Your Own Cloud) mean?

It means Condense gets deployed along with fully managed Kafka, a verticalized ecosystem, and supporting services inside your AWS, Azure, or GCP account. You retain full ownership, governance, and compliance, while Condense handles operations.

3. Can I use my AWS, Azure, or GCP credits with Condense?

Yes! Condense is available via marketplace listings, allowing enterprises to fully leverage existing cloud credits, improving budget efficiency.

4. How does Condense lower Kafka TCO by 60%?

By removing the need for specialized Kafka ops teams, automating scaling and management, offering prebuilt integrations, and enabling faster deployments, Condense dramatically cuts both infrastructure and operational costs.

5. Does Condense offer 24/7 support and SLAs?