Why ELT Can't Keep Up in the Era of High-Scale Data Engineering

While winning in artificial intelligence (AI) is critical to the future of business, old-school analytics—visualizations, dashboards, and infrequent reports—are still core to an organization's data needs. Behind the scenes, this analytics ecosystem remains heavily hydrated by batch-based ELT data integration. For a long time, this made perfect sense, as data sources were fewer, data volumes were manageable, and analytics consumers were limited.

Until recently, streaming data pipelines had been considered only for specific use cases that were deemed to need real-time data, such as fraud detection, user personalization, and predictive maintenance. However, they’re particularly important for the growing number of high-scale data movement and integration workloads that go well beyond ultra–low-latency use cases.

In this blog post, we’ll explore how a  unified open data architecture—centered on a foundation of Apache Kafka® + open table formats such as Apache Iceberg and Delta Lake + any compatible transformation/query engine—can unlock both operational and analytical use cases at high-scale, efficiently. Let’s get started.

What You Need for High-Scale Data Engineering

In the past decades, data has scaled, not just in volume but also variety, velocity, and update frequency. And now all that data must be accessible across multiple systems.

Gone are the days when a single proprietary data warehouse, holding one type of data for one type of analysis, could satisfy an entire organization. At the same time, expectations for data teams have skyrocketed as teams are tasked with driving decision-making, enhanced product experiences, and automation across the enterprise with limited resources and mounting tech debt.

Data engineering can no longer be about simply moving data from point A to point B. Instead it should be about designing and supporting data platforms that can support complex, high-scale data needs and help drive strategic business decisions and outcomes. AI has only exacerbated this challenge and added urgency to the problem.

To address all these demands, you need to build data architectures that are:

• Scalable – The architecture needs to scale seamlessly as data volume and velocity increases.

• Fast – The system should be able to handle high-throughput of large data volume efficiently with consistent high performance.

• Reusable – Data should be ingested once and reused across multiple teams, destinations, and workloads without rebuilding pipelines or duplicating ingestion logic.

• Open, Flexible, and Interoperable – The architecture should support open standards to avoid lock-in, run on any cloud or multiclouds, and, more importantly, support multiple query engines.

• Reliable – Consistent and trustworthy data is the foundation for accurate and reliable insights, preventing the waste of time, effort, and resources on fixing bad data and—even worse—on wrong business decisions. 

• Cost-Effective – In the cloud world, teams can’t continue to throw compute at a problem without incurring exorbitant costs, many times without notice. An appropriate approach to high-scale data should minimize costs while maintaining performance.

The Rise and Lingering Flaw of Traditional ELT Data Pipelines

Data pipelines exist to move operational data into analytical storage.

This started with extract-transform-load (ETL) in the 1980s, which transformed data early in staging areas before loading cleaned, filtered data into central repositories to avoid burdening expensive, resource-constrained source databases and repositories. But the tightly coupled ETL pipelines quickly turned into a “spaghetti mess.”

In the 2010s, as data storage got cheaper and cloud data warehouses separated storage and compute, data teams found extract-load-transform (ELT) easier—loading all raw data into the warehouse (EL) to "figure out the rest later" (T), often through multi-hop medallion architectures. The idea was to allow teams to write their own distinct logic against centralized data (i.e., transform) instead of new extract-and-load pipelines.

The modern data stack evolved by combining specialized cloud-based, fit-for-purpose user interface (UI)- and SQL-based tools such as Fivetran (i.e., extract and load), data build tool (i.e., transform), and Snowflake (i.e., data warehouse), allowing organizations to mix and match the best vendors for each layer of the stack. This model succeeded because it lowered the barrier to entry for analytics: minimal up-front engineering, fast time to first dashboard, and little operational ownership.

Driven by data scale, complexity, and the demand for AI/machine learning (ML) along with traditional reporting, the data lakehouse gained popularity by bringing the reliability of warehouses to low-cost object storage. Unlike traditional warehouses, it leverages open table formats (e.g., Delta Lake and Apache Iceberg) to decouple data from proprietary query engines.

The modern data stack made great strides in data access and scale, but the underlying integration methods remain archaic—batch-based, point-to-point ELT data pipelines dropping everything into one repository to “figure it out later”, built on tools offering limited visibility into how data is actually moved.

Why ELT Pipelines Break Down at Scale

The batch-based ELT integration model extracts data repeatedly and reloads it for every new consumer on its own schedule, with little reuse across pipelines.

This works well for low-volume, highly structured data with few sources and one destination. However, as data estates grow with volume, variety, sources, destinations, and use cases, this point-to-point way of connecting data to a siloed repository with poor governance and access controls, simply doesn’t scale. 

Cascading Failures and Schema Drift

APIs update, databases undergo migrations, and column names change. In a point-to-point system, a single change in an upstream system can break dozens of separate pipelines simultaneously. Because the integration logic is duplicated across multiple scripts, data engineers have to hunt down and update the code in every single pipeline that touches that source.

Resource Inefficiency

As data volumes grow, extracting data at the required rate, especially in batch mode, needs high processing power—and it can be limiting. Inconsistent workload variations make it difficult to predict resource requirements, resulting in scale and performance challenges and high operating expenses.

Complexity Problem and Orchestration Chaos

Adding more pipelines significantly increases complexity and operational overhead. The sheer volume of code, scheduling rules, and credentials becomes physically impossible for a data team to maintain without scaling headcount at the same rate as pipeline growth.

Black Box Pipelines

Because GUI data pipelines hide their underlying code, modifying them is risky and complex. To avoid breaking things, engineers often just build new pipelines for new requirements, which accelerates pipeline sprawl and technical debt.

What was missing from data architectures wasn’t better storage or compute; it was instead a durable, scalable, reusable data movement layer. Kafka filled this gap by enabling data to be ingested once and consumed many times, reliably and at scale. Kafka enables an integration fabric, not simply a real-time data movement.

A Streaming-First Data Integration Architecture

The alternative approach is to get the best of both operational and analytics in a streamlined but composable solution.

• Data Movement and Ingestion: Kafka + connectors + Tableflow

• Data Quality and Consistent Context: Schema Registry  and Data Quality Rules

• Table Storage: Iceberg/Delta Lake on object storage (such as Amazon S3 or Azure Data Lake Storage (ADLS))

• Processing: Apache Flink®, Apache Spark™, Trino, Snowflake, BigQuery, Amazon Athena, etc.

Batch ELT Pipelines vs Streaming-First Data Integration Architectures

Let’s quickly compare traditional batch-led ETL/ELT pipelines with streaming-first architecture and show how Confluent helps.

In practice, very few organizations want to assemble and operate end-to-end data platforms themselves. This is where a fully managed data streaming platform becomes essential. Apache Kafka provides the foundation for streaming data pipelines, but Confluent turns this foundation into a fully managed, enterprise-grade data integration platform—spanning ingestion, streaming, governance, and analytics-ready data delivery. 

Driving Innovation and Growth: Beyond the Tech

These shifts in architecture are more than just technical advantages. They’re powerful catalysts for financial and operational transformation. By moving beyond legacy constraints, organizations can unlock significant value across both top and bottom lines.

• Lower TCO: By rationalizing tools and eliminating redundant processing through "write once, query anywhere" architectures, you strip away the "data tax" of inefficient systems. This isn't just about saving on cloud spend; it’s about redirecting engineering hours from maintenance to high-value projects.

• Accelerated Innovation: Teams no longer have to wait weeks for data pipelines to be built; instead, they can experiment, iterate, and launch new products at the speed of their ideas. This accessibility is the foundation of long-term organic growth.

• From Reactive to Proactive Operations: Batch systems belong to a slower era. Today’s market demands immediate action. A real-time data paradigm allows you to seize opportunities that disappear in minutes, such as:

  • Customer Experience – Delivering hyper-personalized offers and contextual support exactly when the customer needs it

  • Operational Agility – Using predictive maintenance and supply chain optimization to prevent bottlenecks before they occur

  • Risk Mitigation – Identifying fraud and inventory gaps the moment they happen, not hours after the fact

• AI Readiness: You can’t build a real-time AI strategy on yesterday’s data. To be truly AI-ready, your models require high-quality, fresh context. Moving to a modern data paradigm ensures that your AI initiatives are fed the real-time insights they need to provide a genuine competitive advantage.

A New Standard for Data Engineering

While the buzz often centers on AI and real-time responsiveness, the transition to a streaming-first architecture is ultimately about solving the structural failures of traditional batch ELT. As data scales in volume and complexity, the "load now, fix later" model becomes a liability, burdened by unpredictable costs, black box vendor lock-in, and fragmented data estates.

By adopting a unified foundation—Kafka + Schema Registry + Iceberg/Delta Lake + any compatible query engine—organizations can move away from brittle, point-to-point pipelines toward a reusable, high-scale integration layer. This shift delivers more than just technical speed. It also provides:

• Financial Predictability – Moving from per-row change pricing to consumption-based throughput, significantly lowering TCO

• Architectural Freedom – Eliminating vendor lock-in through open standards, allowing you to use the best query engine for the task at hand

• Shift-Left Quality – Catching and governing data issues at the point of ingestion rather than burning expensive compute credits to fix them in the warehouse

• Operational Unity – Bridging the gap between operational and analytical workloads, ensuring that the same high-quality data powers everything from basic dashboards to agentic AI

Ultimately, streaming data integration isn't just a niche requirement for fraud detection or personalization. It’s the most efficient, scalable way to move and reuse data in the modern enterprise. By modernizing your data movement layer today, you aren't just fixing your pipelines; you’re building the foundation for top-line growth and long-term innovation.

Join a hands-on workshop to learn how to build end-to-end Kafka to AI data pipelines with Confluent, Databricks, and AWS.

FAQs – Common Objections Against Streaming Data Integration

Does anybody need real-time data in analytics?

This is perhaps one of the most commonly heard objections. Hopefully, this blog post has addressed why it’s worth considering a data streaming platform for all data movement needs (not just real time)—a streamlined decoupled data architecture, higher-quality data, fewer tools to manage, lower TCO, and more.

And while we haven’t dived into real time, in a world where autonomous AI-driven systems are on the rise, it’s only a matter of time before what was a nice-to-have becomes a must-have. The question is: Do we want to be ahead of the curve or ride behind it?

Beyond the technical and business benefits, we often hear from customers that once the data is there and available as reusable data products, it opens the door for ideas and innovations that simply couldn’t happen before.

Shouldn’t I just use microbatching?

Microbatching may get your data faster, but it’s still batching. Microbatching is essentially polling harder: Instead of a job running every few hours, it runs every few minutes or seconds. It hammers your source APIs and database CPUs by asking "Are we there yet?" every few seconds. The method is still going to the mailbox to get the data every few minutes versus twice a day. All the problems that exist with batch processing exist here, too, and are perhaps exacerbated due to the frequency of jobs.

True streaming is event-driven. It’s more efficient for the source system, it scales better, and it doesn't waste compute cycles checking for updates that haven't happened.

Isn’t Kafka overkill or way too complicated for data  pipelines?

Confluent Cloud was designed from the ground up to make it easy to get all the benefits of Kafka without the overhead. If pricing is your concern, make sure to check out Confluent’s price guarantee.

Aren’t batch pipelines easier to debug than streaming pipelines?

This is a common myth. In batch ELT, when a number looks wrong, you have to traverse back through layers of SQL logic and parse static logs to guess what the source state was hours ago.

With Confluent, you can point to exactly where the error occurred, tap into the dead letter queue (DLQ) to inspect the specific bad records, and replay only the failed data without rerunning the entire batch. It turns debugging from a ”search and rescue” mission into a surgical fix.

Do legacy systems support streaming data?

Legacy systems don’t have to. This is exactly where change data capture (CDC) shines. Confluent’s CDC connectors read directly from database transaction logs, streaming every insert, update, and delete in near–real time with minimal overhead.

ELT tools, such as Fivetran, rely on polling and microbatching rather than true CDC. Without log-based CDC, ELT tools introduce an unavoidable latency floor, making them unsuitable for real-time use cases.

Does Kafka Connect work well with software-as-a-service (SaaS) apps?

The ecosystem has evolved. Confluent now offers fully managed source connectors for the major SaaS players, such as Salesforce and ServiceNow. While traditional ELT players have more SaaS app connectors, data streaming platforms are catching up with new connectors.

Apache®, Apache Kafka®, Kafka®, Apache Flink®, Flink®, Apache Iceberg™, Iceberg™, Apache Spark™, Spark™, and their respective logos are either trademarks or registered trademarks of the Apache Software Foundation. No endorsement by the Apache Software Foundation is implied by the use of these marks.