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Real-Time Data Pipelines That Replace Batch ETL

Revolutionizing Streaming Data Pipelines with the Power of Real-Time Insights

Organizations across industries use StreamNative

• CHALLENGES

Why Batch ETL Breaks Down

Batch ETL Fragility

Overnight batch jobs fail silently, miss SLAs, and leave downstream systems with stale data. Recovery means re-running entire pipelines.

Connector Sprawl

Managing separate Kafka Connect clusters, Pulsar IO instances, and custom glue code across teams. Each connector is another operational burden.

Data Inconsistency

Batch windows create gaps. Different systems see different versions of the truth depending on when their last extract ran.

Scaling Bottlenecks

Traditional ETL tools weren't built for real-time volume. Scaling means bigger batch windows or expensive infrastructure upgrades.

• THE WAY OUT

The Game-Changing Paradigm: Streaming-Augmented Lakehouse (SAL)

The Streaming-Augmented Lakehouse is the next evolution in data architecture, blending the best of both worlds: lakehouses and real-time data streaming.

Streaming Data Pipeline architecture diagram

Continuous Data Movement

Every event in Kafka or Pulsar simultaneously lands as a row in Iceberg or Delta tables. No staging, no connectors.

Unified Connector Interface

Iceberg and Delta Lake tables work with Databricks, Snowflake, Trino, and Spark. No format lock-in.

Self-Healing Pipelines

BI dashboards and ML pipelines see fresh data within seconds, not hours.

• RELATED TOOL

Flink Managed Services

Run Apache Flink on StreamNative's fully managed service — tightly integrated with your streaming pipelines for real-time transformations, enrichment, and analytics.