Need for Speed: Benchmarking the Best Tools for Kafka to Delta Ingestion

Canadian Data Guy
7 min readJun 18, 2024


Welcome back to the second installment of our series on data ingestion from Kafka to Delta tables on the Databricks platform. Building on our previous discussion about generating and streaming synthetic data to a Kafka topic.

This blog post benchmarks three powerful options available on the Databricks platform for ingesting streaming data from Apache Kafka into Delta Lake: Databricks Jobs, Delta Live Tables (DLT), and Delta Live Tables Serverless (DLT Serverless). The primary objective is to evaluate and compare the end-to-end latency of these approaches when ingesting data from Kafka into Delta tables.

Latency is a crucial metric, as it directly impacts the freshness and timeliness of data available for downstream analytics and decision-making processes. It’s important to note that all three tools leverage Apache Spark’s Structured Streaming under the hood.

“Breaking the myth: Ingest from Kafka to Delta at scale in just 1.5 seconds with Delta Live Tables Serverless — up to 80% faster than traditional methods!”

Benchmark Setup

Benchmarking Criteria

The key metric measured was latency — the duration from when a row is produced…



Canadian Data Guy | Data Engineering & Streaming @ Databricks | Ex Amazon/AWS | All Opinions Are My Own