Consumer expectations for speed of service has only increased since the dawn of the information age. The ability to process information quickly and cost-effectively is no longer a luxury, it’s a necessity. Businesses across industries are racing to extract value from their data in real-time, and a transformative approach known as "shift left" is gaining traction. With streaming technologies, organizations can move data processing earlier in the pipeline to slash storage and compute costs, cut latency, and simplify operations. Let’s dive into what shift left means, why it’s a game-changer, and how it can reshape your data strategy.

Streaming Data: The Backbone of Modern Systems

Streaming data is ubiquitous in today’s tech ecosystem. From mobile apps to IoT ecosystems, real-time processing powers everything from convenience to security. Consider the scale of this trend: Uber runs over 2,500 Apache Flink jobs to keep ride-sharing seamless; Netflix manages a staggering 16,000 Flink jobs internally; Epic Games tracks real-time gaming metrics; Samsung’s SmartThings platform analyzes device usage on the fly; and Palo Alto Networks leverages streaming for instant threat detection. These examples highlight a clear truth: batch processing alone can’t keep pace with the demands of modern applications.

The Traditional ELT Approach: A Reliable but Rigid Standard

Historically, organizations have leaned on Extract, Load, Transform (ELT) pipelines to manage their data. In this model, raw data is ingested into data warehouses or lakehouses and then transformed for downstream use. Many adopt the "medallion architecture" to structure this process:

  1. Bronze Raw, unprocessed data lands here.
  2. Silver Data is cleansed, filtered, and standardized.
  3. Gold Aggregations and business-ready datasets are produced.

This approach has been a staple thanks to the maturity of batch processing tools and its straightforward design. However, ELT's limitations are glaring as data volumes grow and real-time needs intensify.

The Pain Points of ELT

  1. High Latency Batch jobs run on fixed hourly, daily, or worse schedules, leaving a gap between data generation and actionability. For time-sensitive use cases, this delay is a dealbreaker.
  2. Operational Complexity When pipelines fail, partial executions can leave a mess. Restarting often requires manual cleanup, draining engineering resources.
  3. Cost Inefficiency Batch processing recomputes entire datasets, even if only a fraction has changed. This overprovisioning unnecessarily inflates compute costs.

Shift Left: Processing Data in Flight

Enter the shift left paradigm. Instead of deferring transformations to the warehouse, this approach uses streaming technologies—like Apache Flink—to process data as it flows through the pipeline. By shifting computation upstream, organizations can tackle data closer to its source, unlocking dramatic improvements.

Why Shift Left Wins

  1. Reduced Latency Processing shrinks from hours or minutes to seconds—or even sub-seconds—making data available almost instantly.
  2. Lower Costs Incremental processing computes only what’s new, avoiding the waste of rehashing unchanged data. Reduced storage costs from data filtering before it lands and no redundant data copies.
  3. Simplified Operations Continuous streams eliminate the need for intricate scheduling and orchestration, reducing operational overhead.

A Real-World Win

Consider a company running batch pipelines in a data warehouse, costing $11,000 monthly. After shifting left to streaming, their warehouse bill dropped to $2,500. Even factoring in streaming infrastructure costs, they halved their total spend—while slashing latency from 30 minutes to seconds. This isn’t an outlier; it’s a glimpse of shift left’s potential.

Bridging the Expertise Gap

Streaming historically demanded deep expertise—think custom Flink jobs or Kafka integrations. That barrier is crumbling. Platforms like Delta Stream are democratizing stream processing with:

  • Serverless Options No need to manage clusters or nodes.
  • Automated Operations Fault tolerance and scaling are handled behind the scenes.
  • SQL-Friendly Interfaces Define transformations with familiar syntax, not arcane code.
  • Reliability Guarantees Exactly-once processing ensures data integrity without extra effort.

This shift makes streaming viable for teams without PhDs in distributed systems.

Transitioning Made Simple

Adopting shift left doesn’t mean scrapping your existing work. If your batch pipelines use SQL, you’re in luck: those statements can often be repurposed for streaming with minor tweaks. This means you can:

  1. Preserve your business logic.
  2. Stick with SQL-based workflows your team already knows.
  3. See instant latency and cost benefits.
  4. Skip the headache of managing streaming infrastructure.

For example, a batch query aggregating hourly sales could pivot to a streaming windowed aggregation with near-identical syntax—same logic, faster results.

The Future Is Streaming

Shifting left isn’t just an optimization, it’s a strategic evolution. As data grows and real-time demands escalate, clinging to batch processing risks falling behind. Thanks to accessible tools and platforms, what was once the domain of tech giants like Netflix or Uber is now within reach for organizations of all sizes. The numbers speak for themselves: lower costs, sub-second insights, and leaner operations. For competitive businesses, shifting left may soon transition from a smart move to a survival imperative. Ready to rethink your pipelines? Take a look at our on-demand webinar for more, Shift Left: Lower Cost & Reduce Latency of your Data Pipelines.