Feed Your Data Lake With Real-Time, Analytics-Ready Tables for 30-50% Lower Cost Using Tableflow

Organizations are under pressure to feed data lakes and lakehouses with fresher data while keeping a tight lid on cloud spend. The problem is that most ingestion stacks weren’t designed for the real-time, high-volume workloads that power modern analytics and artificial intelligence (AI). They rely on layers of connectors, ETL jobs, and maintenance processes that quietly inflate both infrastructure and operational costs.

Confluent’s Tableflow was built to change that equation. By turning Apache Kafka® topics directly into analytics-ready tables in formats such as Apache IcebergTM and Delta Lake, it dramatically simplifies legacy ETL architectures, reducing end‑to‑end total cost of ownership (TCO).

Across customer deployments and in modeled benchmarks comparing Tableflow with other stream-to-table solutions (e.g., data platforms and pipelines such as Snowflake Snowpipe, DBX Declarative pipelines, Amazon S3 Tables, and custom ETL pipelines), Tableflow typically delivers 30%–50% lower ingestion costs—with some workloads achieving even higher savings.

The Hidden Cost of Feeding Your Lake or Lakehouse

Feeding a data lake or lakehouse from Kafka sounds simple—“Get events into tables”—but in practice, it often looks like this:

• Ship data out of Kafka using sink connectors.

• Land raw records in cloud object storage.

• Run ETL jobs to parse, clean, and standardize data.

• Convert it to Apache ParquetTM, Iceberg, or Delta table formats.

• Manage schema evolution and change data capture (CDC) semantics.

• Continuously compact small files and tune clustering or partitioning.

• Publish and synchronize tables into one or more catalogs.

Every one of these steps has a cost:

• Connect infrastructure and data transfer: You pay to run and scale OSS/Kafka connectors and to cover egress charges out of your clusters.

• Ingestion and maintenance compute: Services such as Snowpipe, Delta Live Tables, Amazon S3 Tables, and BigQuery bill you for ingesting data and also for background maintenance (compaction, clustering, CDC materialization, and more).

• Duplicated storage: Many pipelines keep redundant copies of the same data—once in Kafka and again as raw files or internal tables in the warehouse or lake.

• Engineering and ops overhead: Teams must build, monitor, and constantly repair brittle chains of jobs and scripts just to keep tables fresh and usable.

The net result is an ingestion architecture that’s complex, fragile, and, most importantly, expensive.

Tableflow: Turn Apache Kafka® Topics into Analytics‑Ready Tables

Tableflow takes a fundamentally different approach. Instead of standing up a separate ingestion stack, it represents Kafka topics and their schemas directly as open table formats such as Iceberg and Delta Lake in just a few clicks.

Under the hood, Tableflow:

• Reuses Kafka segments stored in Confluent Cloud’s Kora engine and converts them into Parquet files

• Automatically handles schema evolution, type conversions, and CDC semantics

• Continuously compacts small files and maintains table metadata so that tables stay performant without manual tuning

• Publishes tables into catalogs such as Snowflake Open Catalog, AWS Glue Data Catalog, and Databricks Unity Catalog so that they’re immediately queryable by your preferred engines

Because Tableflow is part of Confluent’s data streaming platform, it also works hand‑in‑hand with Confluent Cloud for Apache Flink®. You can shift left your data processing—cleaning, joining, masking personally identifiable information (PII), and enriching data in-stream—so that what lands in your Iceberg or Delta tables is already analytics‑ready. This unified approach removes entire layers of redundant infrastructure, which is exactly where the 30%–50% savings come from.

4 Drivers of the 30%–50% Savings

Looking across the cost model and considering customer feedback, four main drivers explain why Tableflow is cheaper than traditional ingestion stacks:

1. Kafka services and storage: By leveraging architectural advancements within the Kora storage layer, Tableflow materializes tables off the same underlying Kafka segments. It can write into cloud object storage, such as Amazon S3 or Azure Data Lake Storage, thereby eliminating egress charges. Furthermore, the platform achieves a 67% reduction in Kafka topic storage charges by circumventing the requirement for triple data replication.

2. Connect infrastructure: Tableflow eliminates the requirement for independent sink connectors between Kafka and the lakehouse environment, thereby removing associated Kafka Connect task fees and data transfer expenditures. This structural simplification is a primary driver of overall cost optimization and effectively mitigates the architectural complexity inherent in legacy data pipelines.

3. Lakehouse compute: By using competing services, customers need significant additional compute to handle ingestion and post-landing processing (schema conversion, compaction, table maintenance). Tableflow’s pricing model bundles these responsibilities into a per‑topic‑hour fee plus a per‑GB processed charge, which has been tuned specifically for streaming workloads and reuse of Kafka storage rather than rewriting everything from scratch. That means fewer services to deploy, monitor, and over‑provision for peak load.

4. Lower engineering and operational overhead: Customers report that Tableflow significantly cuts the time their teams spend building and tending ingestion pipelines. Instead of stitching together connectors, jobs, and scripts, they enable Tableflow, point it at a topic, and let it handle table life cycle and catalog sync. Additionally, governance is simplified by unifying table and topic data access.

Taken together, these effects consistently translate into 30%–50% lower total ingestion costs for most streaming‑to‑lake workloads—often more at higher throughput or in architectures with especially brittle ETL chains.

Tableflow feeds your data lake for 30%–50% lower cost compared to available alternatives.

Real‑World Benchmarks vs Common Alternatives

Our comprehensive internal cost model rigorously analyzed all common lakehouses and stream-to-table solutions, such as Snowflake Snowpipe, Databricks Delta Live Tables, Amazon S3 Tables, Google BigQuery, and more. This analysis spanned scenarios ranging from low to very high throughput to provide a comparative TCO evaluation. These benchmarks demonstrate that traditional ingestion stacks incur substantial fragmented costs across core dimensions: Kafka services (data egress) and storage, connect infrastructure, and post-landing compute and maintenance.

For a representative medium throughput scenario we have seen across our enterprise customers, specifically modeling an architecture that uses a traditional sink connector pipeline on a workload of 10 topics with 1 MBps average ingress, 7-day retention, and 3x storage replication, the monthly costs are precisely quantified. This workload represents a monthly ingress of 25,290 GB and a topic storage requirement of 18,144 GB.

These calculations assume that a single connector task processes throughput at a rate of 5 MBps, necessitated by two tasks at an average cost of $0.10 per task-hour. Data transfer for Connect and Egress are billed at a standard rate of $0.025 per GB and $0.05 per GB respectively.  It should be noted that when customers assume the responsibility of self-managing these connectors, expenses generally increase by a margin of 40% to 60%. 

Tableflow incurs a fee of $0.10 per topic-hour and $0.04 per GB, exclusive of the additional 0.3x compaction factor required to sustain optimal read performance. While most managed solutions typically incur an average of $0.06 - $0.10 per GB for the conversion of streaming data into tables and compaction/maintenance and offer no storage savings, Tableflow provides significant financial advantages by waiving charges for 3x replication upon activation. This results in a 66.6% reduction in storage costs based on a standard rate of $0.08 per GB-month. 

Note that the preceding calculations assume optimized compute resources without accounting for idle time while using managed solutions. Consequently, when idle time is factored into the analysis, the realized savings are projected to be even higher.

Architectural and Operational Benefits Beyond Cost

While the 30%–50% savings are compelling, most teams adopt Tableflow for the architectural clarity it brings:

• Openness and vendor lock-in mitigation: Tableflow is designed to be vendor-neutral by supporting open table formats (Iceberg and Delta Lake) and allowing Bring Your Own Storage (BYOS). This external table approach decouples storage from metadata management, ensuring that customers control their data and can access it directly with any compatible engine, avoiding vendor lock-in.

• Operational simplicity and fully managed ease of use: Tableflow provides a serverless, production-ready platform that abstracts away complex ingestion and table management. The few-clicks user experience automates critical, error-prone tasks like schema evolution, type mapping, conversion to Parquet, and continuous compaction, eliminating the need to size and run separate clusters just for ingestion.

• Near–real-time data and analytics readiness: Tableflow ensures that tables are analytics-ready out of the box by automatically handling conversion and synchronization with external catalogs. It supports upsert materialization, enabling transactional views for use cases like customer 360 and fraud detection with near–real-time freshness.

• Shift-Left Processing & Governance: By working seamlessly with Confluent Cloud for Apache Flink®, teams can enforce silver/gold standard processing, data quality, PII masking, and governance policies on the stream before data lands in the lake, shrinking downstream risk and cleanup work.

These benefits compound the hard‑dollar savings. Your analytics teams spend less time wrangling pipelines and more time building value on top of trustworthy, real‑time data.

When You’ll See the Biggest Return on Investment

While almost any Kafka‑to‑lake workload can benefit from Tableflow, the strongest returns show up when you:

• Ingest high‑volume or 24×7 streams into your data lake or lakehouse

• Rely on CDC or upsert‑heavy workloads that require constant compaction and clustering

• Maintain multiple catalogs or compute engines (Snowflake, Databricks, Athena, etc.) that all need access to the same data

• Struggle with fragile, high‑maintenance ETL stacks that consume a disproportionate amount of engineering time

In these environments, consolidating ingestion and table management into Tableflow doesn’t just trim 30%–50% from your cloud bill; it simplifies how your entire data organization works.

Pricing That Automatically Scales as You Scale

The 30%–50% savings doesn’t stop there; Tableflow tiered pricing is coming later this month. Start small with a cost-effective entry tier that enables seamless proofs of concept and initial testing without a large up-front commitment. As your usage grows, automatically unlock deeper discounts—with up to a 90% discount at scale.

Contact us or your Confluent account team to start saving today.

Getting Started

If you’re already streaming business data with Confluent Cloud, turning on Tableflow is straightforward:

1. Identify the Kafka topics that feed your lake or warehouse today.

2. Enable Tableflow for those topics and choose Iceberg or Delta Lake as the target format.

3. Point Tableflow at your object storage bucket and register the resulting tables into your preferred catalog(s).

4. Start querying fresh, governed tables from Snowflake, Databricks, Amazon Athena, Amazon Redshift, or any other compatible engine—without rebuilding your pipelines.

From there, you can incrementally migrate additional workloads and decommission legacy ingest stacks as you go. Most teams start by targeting their highest‑volume or most brittle pipelines first, where the 30%–50% savings and operational relief are felt immediately.

To learn more about Tableflow’s key features and capabilities, check out the Tableflow documentation. To see it in action, watch our short introduction video or Tim Berglund's lightboard explanation.

Contact us today for a personalized demo and start unlocking the full potential of your data on Confluent Cloud. We’re incredibly excited to see how you use Tableflow to turn your real-time data streams into tangible business value!

The preceding outlines our general product direction and is not a commitment to deliver any material, code, or functionality. The development, release, timing, and pricing of any features or functionality described may change. Customers should make their purchase decisions based on services, features, and functions that are currently available.

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