Netflix's shift to a layered data movement architecture demonstrates how decoupling metadata and using a single source of truth (S3) can drastically reduce costs (40%) and improve performance (50%) for massive-scale Cassandra-to-Iceberg pipelines.
By Guil Pires, Jennifer Prince, Jose Camacho, Ken Kurzweil, Phanindra Chunduru
In a previous post, we introduced Data Bridge, a unified management plane for batch Data Movement at Netflix. Historically, several bespoke Data Movement connectors were developed across different engineering organizations to fulfill their specific requirements. Over the last few years, the Data Movement team has started centralizing these offerings through an abstraction that provides a catalog of connectors, along with simple UI and APIs to initiate Data Movement jobs.
One such case is the Cassandra to Iceberg connector. Apache Cassandra powers mission critical applications at Netflix, including Member, Billing, Recommendations, Subscriptions and many more. These use cases heavily leverage Data Movement to Apache Iceberg for many analytics and operational tasks, and central to this movement was a connector for Cassandra to Iceberg built in-house named Casspactor. As many Cassandra based Data Abstractions emerged, such as Key Value, Time Series and Graph — the need for larger and more complex Data Movement with transformations became more critical to the business.
Data movements are fundamentally fulfilled by leveraging the existing Cassandra backup infrastructure. Regularly scheduled backups are performed directly on the Apache Cassandra nodes, via a sidecar process managing the upload of all necessary SSTables and associated Metadata files directly into Amazon S3. When a Data Movement job is initiated, the job constructs the specific backup structure it needs by referencing the S3 based metadata, allowing it to precisely locate the SSTable files. The engine then downloads these files, performs the required mutation compaction and processing, and finally writes the fully transformed, compacted data directly into the target Apache Iceberg tables.
Casspactor processed roughly 1,200 data movements per day, transferring approximately 3 PB of data from Apache Cassandra into Apache Iceberg tables. It served some of the most critical workloads at Netflix. For years, it worked. Then, two compounding challenges made it clear we needed a fundamentally different architecture.
Before Casspactor could move a single record, it needed to answer a deceptively simple question: which backup exists, is it complete, and what does it contain?
Casspactor assembled this answer from multiple independent systems:
Each system had its own failure modes, update cadences, and accuracy guarantees. Casspactor’s view of the world was a composite, and composites diverge from reality.
Metadata fell out of sync with actual backups, causing Casspactor to read stale or incorrect data silently. Routine maintenance on the Cassandra Clusters triggered uncoordinated snapshots, and because Casspactor required all nodes in a region to snapshot at the same clock second, a single node replacement could break data movement for an entire region.
The fix was hiding in plain sight. The answer to “which backup exists and is it complete?” already lived in the backup storage layer (Amazon S3) itself. By reading metadata directly from the backup files, we could replace the entire dependency chain with a single source of truth.
Cassandra at Netflix does not just store raw tables. It backs higher level data abstractions, such as Key Value, Time Series, and others, each with its own data model, access patterns, and semantics. When any of these abstractions needed to move data to Iceberg, they all funneled through Casspactor.
Every abstraction inherited Casspactor’s constraints:
We needed something fundamentally different: an engine that reads directly from backups in S3, produces standard Spark DataFrames, and lets each data abstraction build its own connector with full awareness of its data model. One foundation, many connectors.
The new architecture, built upon the foundation of Apache Cassandra Analytics and the in-house Move Data framework, represents a fundamental shift toward a layered, purpose-built stack designed for reuse and maintainability. This new engine was conceived with clear separation of concerns, moving away from Casspactor’s monolithic design. The architecture is intentionally layered with the foundation being a core S3 reading capability: the Cassandra Analytics Wrapper, which is built on top of the Open Source Cassandra Analytics with Netflix’s internal backup representation and an S3 Client.
This layer handles the raw data retrieval from backups, translating it into standard Spark DataFrames. Sitting atop this foundation is a “Connector Factory” model, via both Java UDFs and transforms which allows individual data abstractions (Key Value, Time Series, others) to build highly optimized, data model aware connectors that process the generic Spark DataFrames, avoiding the need for complex, expensive, and failure-prone post-processing steps. This layered approach ensures that improvements to the core reading engine benefit all connectors, while the connectors themselves are focused solely on data transformation.
The successful validation of the new stack was the critical first step, but it only marked the beginning of the most challenging phase: the migration. Large scale data migrations are inherently complex, high-risk undertakings that can be time consuming and often result in customer frustration and service disruption. To navigate the high stakes of decommissioning a mission-critical system like Casspactor and seamlessly replacing it, we needed a strategy that prioritized reliability and transparency above all else.
The migration was fundamentally enabled by a Like-for-Like strategy, which served as the cornerstone of our Platform Engineering philosophy, abstracting complexity. The core tenet was to maintain absolute consistency across the user-facing interface, the output contract, and the final data artifact. This meant ensuring that the data movement parameters defined via the Data Bridge abstraction remained unchanged, and, critically, the schema, metadata, and data within the destination Iceberg tables were identical to the legacy output. By preserving these external contracts, we eliminated the need for complex, time-consuming coordination with dozens of internal teams who relied on these data pipelines. This approach transformed the migration from a distributed, high-risk, multi-team effort into an internal platform implementation detail, allowing us to achieve a transparent, zero-impact transition and accelerate the retirement of the legacy system without requiring any code changes or validation from downstream users.
To navigate this migration, we developed a strategy anchored by three core pillars that serve as a blueprint for successful, large-scale data migrations:
The next section will provide a detailed exploration of these key pillars.
Trust is earned, and in data migration, it is earned one row at a time. The first pillar is the most critical: providing a measurable guarantee to users and partners that the data produced by the new system is an exact, row-by-row replica of the data produced by the old one.
Our foundational tactic was deploying the new Move Data connector in a “shadow” testing that ran in parallel with the production Casspactor jobs. This allowed us to validate the new system with real-world, production workloads without any customer impact.
The test for trust: prove that C = M. This required continuously checking for two conditions:
Any result where the cardinality of these difference sets (the number of differing rows) was greater than zero triggered an immediate, high-priority investigation. The target was 100% similarity.
The shadow mode quickly became a powerful forensic tool, exposing “unknown unknowns”, subtle discrepancies that were not bugs in the new system but rather differences in behavior between the new and old systems. Resolving these was the core work of building trust. For each problem we initiated an investigation log where we captured the details, logs, queries that allowed us to diagnose. Based on the assessment the issues were categorized so that similar differences on other datasets were later resolved affecting many of the shadow pipelines.
Maintaining an investigation log was critical to organize the outstanding issues and effectively communicate to stakeholders the progress and confidence of the new connector so that we effectively measure the appropriate level of “confidence” to initiate the migration.
We observed differences in how connectors leverage reference timestamps for Time-to-Live, Consistency Levels, backup selection, and various internal business logic. This continuous, data-driven cycle of discovery and resolution was the mechanism by which we built confidence in the new architecture.
Trust is built in the background, but an active migration requires real-time insight: Visibility. The second pillar involves instrumenting the system to provide an unambiguous, clear understanding of operational health and migration progress.
We extended our instrumentation to the overall migration workflow and its dependencies:
This comprehensive instrumentation allowed the team to be proactive, fix issues as they emerged during the migration, and gain the necessary confidence to accelerate the migration timeline.
Even with perfect data correctness and enhanced visibility, the third pillar, Safety is required for a zero-impact migration. The challenge is ensuring that when a system inevitably fails, the user experience is uninterrupted. Our strategy centered on decoupling the user’s workflow from the underlying connector implementation.
To achieve a transparent swap, we leveraged the Maestro workflow orchestration platform to implement the Decider pattern:
This abstraction gave our team complete control. We could upgrade or rollback any connector for any data movement instantly by simply updating a configuration in the controller, with zero modification required to the thousands of downstream customer workflows. Crucially, this abstraction guaranteed the critical safety net: a conditional step in the Maestro workflow logic ensured that if the Move Data step fails, it would immediately execute the Casspactor step.
This pattern would increase the chances that the user’s data movement completes successfully, even if the new connector encountered a bug or transient failure during the initial rollout phases. User impact was completely eliminated; they might see a slightly longer runtime in the event of a failure and fallback, but they would never see a migration failure or suffer from stale data.
Beyond the workflow, the new system architecture itself was inherently more resilient. By building the new data movement connector on Cassandra Analytics and reading backups directly from S3, we removed fragile dependencies on deprecated internal services.
The migration from Casspactor to the new, layered architecture built on Cassandra Analytics and the Move Data connector was more than a typical “tech debt” project; it was a fundamental shift in our approach to data movement reliability and scalability at Netflix.
The legacy system, while serving us well for years, was ultimately constrained by monolithic design, fragile metadata dependencies, and an inability to handle the complexity of modern data abstractions. The new stack resolves these issues by delivering a robust, cost-efficient, and inherently more resilient solution that reads directly from S3, handles wide partitions gracefully, and eliminates costly intermediate tables.
Our blueprint for the migration, anchored by the three pillars of Validation, Visibility, and Safety, ensured a transparent and high-confidence transition. Through rigorous shadow testing and a data-driven audit framework, we achieved the desired data consistency. Enhanced dashboards and alerting provided the real-time operational insight necessary to manage risk. Most critically, the implementation of the Decider pattern within our workflow abstraction minimized the impact for all downstream users.
This successful migration validates a core philosophy: by abstracting complexity at the platform level, we can perform large system migrations without burdening our product engineering partners. The new foundation is now ready to support the next generation of Netflix’s data abstractions.
This foundational work on the Cassandra Data Movement stack has done more than just replace a legacy system: it has become an accelerator for innovation across the entire Data Movement organization. By providing a reliable, performant engine that standardizes data retrieval into Spark DataFrames, we’ve enabled the rapid development of new, highly optimized connectors. This new “Connector Factory” approach has already delivered a dedicated Key-Value to Iceberg and Time Series connectors, both of which are fully aware of their respective data models, eliminating costly post-processing. This architecture is also paving the way for ambitious new initiatives, including the development of a solution for bulk loading data into Cassandra itself, effectively completing the data movement cycle, and enabling safer fleetwide connector rollout with canaries inspired by the Decider Pattern.
We are incredibly grateful for the extensive collaboration among the Data Movement, Data Bridge, Online Data Stores, Membership, Billing, Subscriber and Ads platform teams at Netflix; this work simply couldn’t have been accomplished without their partnership!
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