The Death of Lambda Architecture: Why the Modern Data Stack is Streaming-First

By:Anurag Jain

Lambda Architecture (Batch + Speed layers) was a necessary evil of the Hadoop era. Today, with tools like Apache Iceberg and Flink, we can finally build simple, unified streaming architectures.

“Complexity is the enemy of reliability. The goal of modern data engineering is to delete the Batch Layer entirely.”

The “Two Pipelines” Problem

For a decade, Data Engineers have suffered under the tyranny of the Lambda Architecture. To get both accurate historical data and low-latency real-time data, we had to build two separate pipelines:

The Batch Layer: Slow, accurate, comprehensive (e.g., nightly Spark jobs).
The Speed Layer: Fast, approximate, messy (e.g., Storm/Spark Streaming).

This meant writing the same business logic twice (once in SQL, once in Java/Scala), debugging two systems, and praying they converged.

Enter the “Kappa” and “Lakehouse” Era

At Digital Back Office, we advocate for a radical simplification. The modern stack allows us to treat all data as a stream, effectively killing the Batch Layer.

Key Enablers of the Unified Architecture:

Open Table Formats (Apache Iceberg / Delta Lake): These formats bring ACID transactions to the data lake. You can now stream data directly into your lakehouse and query it with SQL immediately. No more “waiting for the partition to close.”
Unified Compute Engines (Apache Flink / Spark Structured Streaming): Write your transformation logic once. Run it in streaming mode for real-time ingestion. Run the exact same code in batch mode for backfilling historical data.
Decoupled Storage & Compute: Store everything cheaply in S3/GCS. Spin up ephemeral compute clusters only when you need to query or process.

Why This Matters for Business

This isn’t just technical cleanup; it has massive business impact:

Data Freshness: Dashboards move from “Yesterday’s Data” to “Last Minute’s Data.”
Cost Reduction: You stop paying for a massive Hadoop cluster that sits idle 80% of the time.
Agility: Engineers spend time building new features, not fixing “drift” between the batch and speed layers.

The DBO Blueprint

When we modernize a client’s data stack, we typically move them to:

Ingest: Kafka / Redpanda
Process: Flink (for stateful stream processing)
Store: S3 + Apache Iceberg
Serve: Trino or StarRocks (for sub-second analytics)

Is your data architecture stuck in 2015? Let’s modernize it.

Relevant tags:

#Data Engineering#Architecture#Streaming#Big Data

Anurag Jain

Anurag is Founder and Chief Data Architect at Digital Back Office. He has over Twenty years of experience in designing and delivering complex, distributed systems and data platforms. At DBO, he is on mission to enable the businesses make best decision by leveraging data and AI.