Table of Contents

Key Takeaways

  • Market Growth: The data integration market is projected to reach $29.1 billion by 2028, driven by enterprises requiring both batch processing and real-time streaming capabilities

  • Data Volume Challenge: Organizations now manage an estimated 147 zettabytes, making efficient data movement between systems critical for competitive advantage

  • Hybrid Requirements: 73% of enterprises operate hybrid cloud environments, requiring ETL tools that handle both batch analytics and streaming operational workloads

  • Cost Predictability: Integrate.io's fixed-fee pricing at $1,999/month eliminates consumption-based surprises while delivering unlimited data volumes, pipelines, and connectors

  • Real-Time Demand: Sub-60-second latency has become essential for operational analytics, with ELT patterns overtaking traditional ETL for cloud data warehouse workloads

Understanding ETL Tools for Batch and Streaming Data

ETL (Extract, Transform, Load) tools serve as the backbone of modern data architectures, moving information from source systems to destinations where it drives business decisions. The distinction between batch and streaming processing fundamentally shapes how organizations approach data integration.

Batch processing handles large data volumes on scheduled intervals—hourly, daily, or weekly—making it ideal for historical analytics, reporting, and data warehousing. Most organizations still rely heavily on batch pipelines for their analytical workloads.

Streaming data processing delivers continuous, real-time data movement with sub-minute latency. This approach powers operational use cases like fraud detection, real-time dashboards, and event-driven applications where delays cost money or create risk.

The most capable modern platforms support both paradigms, enabling organizations to build unified data pipelines that serve analytical and operational needs without maintaining separate toolchains.

15 Best ETL Tools for Batch and Streaming Data

1. Integrate.io – Best Overall for Balanced Batch and Streaming

Best For: Mid-market to enterprise organizations needing operational ETL with near-real-time capabilities

Pricing: $1,999/month flat rate with unlimited data volumes, pipelines, and connectors

Batch/Streaming: Full batch support with 60-second minimum intervals for near-real-time CDC

Integrate.io delivers the optimal balance of batch processing power and near-real-time capabilities through a unified platform covering ETL, ELT, CDC, and Reverse ETL. Founded in 2012 as Xplenty, the platform brings over a decade of market-tested reliability with 200+ connectors serving diverse enterprise use cases.

Key Capabilities:

  • 220+ low-code transformations via drag-and-drop interface

  • Sub-60-second CDC for database replication without lag

  • Bi-directional connectors for operational workflows including Salesforce and HubSpot

  • 30-day white-glove onboarding with dedicated solution engineer

  • SOC 2, GDPR, HIPAA compliance with enterprise-grade encryption

Integrate.io's fixed-fee pricing eliminates the consumption-based surprises common with competitors while delivering strong user satisfaction. The platform serves both technical and non-technical users, reducing dependency on scarce data engineering resources.

2. Fivetran – The fully automated platform

Fivetran is widely viewed as a gold standard for fully automated, zero-maintenance data pipelines. With 500+ managed connectors and automatic schema drift handling, it's built for teams that want reliable data movement without constantly tuning or fixing pipelines.

Key advantages:

  • Fully managed, zero-maintenance pipelines that minimize operational overhead

  • 500+ connectors covering a wide range of SaaS, database, and event sources

  • Automatic schema change handling without manual intervention

  • Native dbt integration for in-warehouse transformations

  • Industry-leading uptime and SLA guarantees for mission-critical workloads

  • Processes 10+ trillion rows monthly, demonstrating proven scalability

Limitations:

  • MAR-based, usage-driven pricing can lead to unpredictable monthly costs as data volumes grow

  • Limited transformation capabilities compared to full ETL platforms

  • No real-time streaming support—batch-focused architecture only

Pricing: Free tier (500K MAR) and MAR-based pricing for paid tiers

Best for: Analytics teams prioritizing automated data replication with minimal engineering overhead who have budget for consumption-based pricing

3. Airbyte – Open-source flexibility champion

Airbyte represents the leading open-source competitor with 400+ connectors spanning managed and community-contributed options. The platform serves 40,000+ data engineers who value transparency and flexibility.

Key advantages:

  • No-code connector builder creates new integrations in 20 minutes

  • Self-hosted or cloud deployment options providing maximum flexibility

  • SOC2, ISO, GDPR, HIPAA compliance certifications

  • Active community contributing new connectors regularly

  • Strong product-market fit for engineering-led organizations prioritizing customization

Limitations:

  • Self-hosted deployments require significant operational expertise

  • Enterprise features locked behind paid tiers

  • Batch-only architecture with 5-minute minimum intervals limits real-time use cases

Pricing: Free (open-source) Core plan; volume-based Standard plan starting at $10/month; and business Pro and Plus plans (talk to sales)

Best for: Engineering teams wanting customization and cost control with technical expertise to manage self-hosted deployments

4. Estuary Flow – Real-time and batch unified

Estuary Flow uniquely combines real-time streaming with batch processing in a single pipeline architecture. The platform has demonstrated 7GB+/sec throughput in production, positioning it among the most scalable options for high-volume workloads.

Key advantages:

  • Sub-second streaming latency for operational analytics

  • Automatic schema evolution without pipeline downtime

  • Multi-destination support from single pipeline

  • Both ETL and ELT capabilities with streaming SQL

  • Hybrid architecture eliminates need to maintain separate batch and streaming pipelines

Limitations:

  • Newer platform with smaller market presence

  • Fewer connectors than established competitors

  • Requires technical expertise for optimal configuration

Pricing: Free tier available; Cloud $0.50/GB + $100/connector/month

Best for: Organizations requiring sub-second streaming with batch processing in one platform without maintaining separate toolchains

5. AWS Glue – AWS-native serverless ETL

AWS Glue provides serverless ETL with automatic scaling and tight integration across the AWS ecosystem. The platform's data catalog and crawler capabilities simplify schema discovery for large-scale batch operations.

Key advantages:

  • Zero infrastructure management with serverless architecture

  • Automatic schema discovery via crawlers

  • Native integration with S3, Redshift, Athena ecosystem

  • Pay-per-use pricing aligned with batch workloads

  • Default choice for AWS-centric organizations

Limitations:

  • Limited connector ecosystem outside AWS—under 20 non-AWS connectors

  • Requires Spark knowledge for advanced use cases

  • Not optimized for real-time streaming

Pricing: Pay-per-use at $0.44 per DPU-hour

Best for: Organizations standardized on AWS needing serverless batch ETL tightly integrated with the AWS ecosystem

6. Azure Data Factory – Microsoft ecosystem integration

Azure Data Factory delivers 100+ connectors with hybrid connectivity through self-hosted integration runtimes. The platform integrates seamlessly with Azure Synapse, Power BI, and the broader Microsoft analytics stack.

Key advantages:

  • Visual pipeline designer plus code-based options

  • Hybrid data integration connecting on-prem and cloud

  • Serverless with automatic scaling

  • Native connectors across enterprise systems

  • Essential for organizations invested in Microsoft technologies

Limitations:

  • Complex pricing model creates cost unpredictability

  • Limited transformation capabilities compared to dedicated ETL tools

  • Streaming requires additional Azure services like Event Hubs

Pricing: Consumption-based pricing for activities, data movement, and pipeline execution

Best for: Microsoft-centric enterprises with hybrid cloud requirements needing batch ETL across on-premise and cloud environments

7. Apache Kafka – Event streaming powerhouse

Apache Kafka powers 80% of Fortune 100 companies with proven capabilities handling 30,000+ messages per second. The platform serves as the foundation for real-time data architectures across major enterprises.

Key advantages:

  • Distributed, horizontally scalable architecture

  • Event streaming with pub/sub model

  • Strong ecosystem with Kafka Connect and Streams

  • Proven at Netflix, Uber, LinkedIn scale

  • Industry standard for event streaming

Limitations:

  • Not a complete ETL solution—requires additional tools for transformations and batch processing

  • Steep learning curve

  • Operational complexity for self-hosted deployments

Pricing: Free and open-source; managed services via Confluent Cloud with usage-based pricing

Best for: Event-driven architectures requiring massive real-time throughput for operational systems and real-time data products

8. Matillion – Cloud warehouse specialist

Matillion specializes in cloud warehouse-native transformations with push-down optimization that leverages Snowflake, Redshift, and BigQuery compute power. The platform's AI-powered Maia assistant represents innovative approaches to pipeline development.

Key advantages:

  • Native integration with major cloud warehouses

  • Push-down optimization reducing data movement

  • Drag-and-drop plus code editor flexibility

  • Expanding real-time capabilities with CDC and streaming features

  • Excels at batch transformations within cloud data warehouses

Limitations:

  • Warehouse-focused scope limits operational ETL use cases

  • Credit-based pricing creates cost variability

  • Organizations needing broader integration capabilities may require supplementary tools

Pricing: Free trial for Developer; Teams and Scale plans available (talk to sales)

Best for: Data teams focused on in-warehouse transformations with cloud data warehouse-centric architectures

9. Informatica PowerCenter – Enterprise complexity champion

Informatica PowerCenter maintains market leadership validation and is a powerful ETL tool for complex transformation requirements.

Key advantages:

  • Comprehensive data transformation capabilities

  • Advanced data governance and quality features

  • Real-time CDC support for operational systems

  • Proven scalability for mission-critical workloads

  • Standard for regulated industries requiring comprehensive governance

Limitations:

  • High total cost of ownership

  • Complex administration requiring specialized skills

  • Steep learning curve for new users

Pricing: Enterprise licensing with custom pricing based on deployment size

Best for: Large enterprises with complex compliance and governance requirements in regulated industries

10. Databricks Delta Live Tables – Lakehouse-native pipelines

Databricks Delta Live Tables provides declarative pipeline design on Apache Spark with built-in data quality expectations that automatically validate and quarantine problematic records.

Key advantages:

  • Unified batch and streaming in single framework

  • Automatic dependency management and optimization

  • Native Databricks Lakehouse integration

  • Real-time data quality validation

  • Modern lakehouse ETL with strong batch and streaming support

Limitations:

  • Requires Databricks platform commitment

  • Not suitable for standalone ETL needs

  • Learning curve for teams unfamiliar with Spark

Pricing: Usage-based as part of Databricks platform

Best for: Organizations standardized on Databricks for unified batch and streaming in lakehouse architectures

11. Talend – Comprehensive data fabric

Talend claims 1,000+ connectors including custom templates, with strong monitoring capabilities spanning the full data lifecycle. The platform's recent acquisition by Qlik positions it for expanded analytics integration.

Key advantages:

  • Comprehensive data fabric beyond just ETL

  • Integrated data quality and governance

  • Open-source roots with enterprise features

  • Both batch and real-time streaming support

  • Unified data management for enterprises

Limitations:

  • Complex implementation requirements

  • Uncertain roadmap following Qlik acquisition

  • Pricing lacks transparency

Pricing: Tiered plans (Starter, Standard, Premium, and Enterprise) with undisclosed prices; contact vendor for quotes

Best for: Enterprises seeking unified data integration, quality, and governance beyond basic ETL functionality

12. Google Cloud Dataflow – GCP unified processing

Google Cloud Dataflow handles both streaming and batch in the same framework using Apache Beam SDKs in Java, Python, and Go. The serverless architecture eliminates infrastructure management.

Key advantages:

  • Apache Beam foundation with multiple SDK options

  • Native GCP integration (BigQuery, Pub/Sub)

  • Auto-scaling serverless architecture

  • Unified framework for both batch and streaming via Apache Beam

  • Optimal choice for GCP-centric organizations

Limitations:

  • Code-based approach requires development expertise

  • Limited connector ecosystem—~50 connectors

  • Primarily beneficial within GCP boundaries

Pricing: Pay-per-use based on processing resources consumed

Best for: Google Cloud organizations needing unified batch and streaming capabilities tightly integrated with GCP services

13. Hevo Data – SMB accessibility focus

Hevo Data serves 2,500+ data teams with 150+ pre-built connectors and a user-friendly interface designed for non-technical users. The platform emphasizes automatic schema detection and 24/7 support.

Key advantages:

  • Low-code interface with Python transformation options

  • Automatic schema detection

  • 24/7 customer support

  • User-friendly design for non-technical users

  • Accessible batch ELT for smaller organizations

Limitations:

  • Limited real-time capabilities—5-minute minimum intervals

  • Primarily suited for basic analytics use cases

  • Scalability concerns for high-volume workloads

Pricing: Free tier available; Starter plan starts at $239/month annually; Professional plan starts at $679/month annually

Best for: Small to mid-size teams prioritizing ease of use without deep technical resources

14. Rivery – Modern ELT orchestrator

Rivery delivers modern ELT with workflow orchestration and pre-built data kits for common use cases. The platform serves 350+ customers with growing adoption, and its recent Boomi acquisition extends enterprise reach.

Key advantages:

  • Python and SQL transformation options

  • Reverse ETL capabilities

  • Workflow orchestration features

  • Strong user satisfaction

  • Modern cloud-native ELT with orchestration

Limitations:

  • Credit-based pricing creates cost variability

  • Batch intervals from 5-60 minutes by tier—streaming limited to extraction only

  • Newer platform with less market validation

Pricing: Credit-based starting at $0.9/credit

Best for: Mid-market teams needing pipeline orchestration with modern ELT workflows

15. Stitch – Basic replication simplicity

Stitch provides simplified ELT based on the Singer framework with 140+ data sources and 11 destinations. Now part of the Talend ecosystem, the platform focuses on basic batch replication without complex transformation requirements.

Key advantages:

  • Simple, extensible Singer framework compatibility

  • 60-day log retention

  • Quick setup for common sources

  • Transparent volume-based pricing

  • Serves teams with straightforward batch replication needs

Limitations:

  • Batch-only with no real-time support—30-minute minimum intervals

  • Limited transformation capabilities

  • Uncertain roadmap within Talend/Qlik structure

Pricing: Row-based pricing for Standard tier starting at $100/month; Advanced plan at $1,250/month annually; and Premium plan at $2,500/month annually

Best for: Small teams with basic data replication needs and limited budgets

Ensuring Data Quality Across Batch and Streaming Pipelines

Regardless of processing paradigm, data observability remains critical. Modern platforms must provide:

  • Real-time alerting for pipeline failures and data anomalies

  • Data freshness monitoring across sources and destinations

  • Lineage tracking for compliance and debugging

  • Quality validation integrated into pipeline execution

Integrate.io's free data observability platform provides customizable alerts without requiring adoption of other platform components—a unique approach to ensuring data reliability across any ETL architecture.

Frequently Asked Questions

What is the main difference between batch and streaming ETL?

Batch ETL processes data in scheduled intervals (hourly, daily, weekly), making it ideal for historical analytics and reporting. Streaming ETL processes data continuously with sub-minute latency, supporting operational use cases like fraud detection and real-time dashboards. Most enterprises need both capabilities—batch for analytical workloads and streaming for operational systems requiring immediate insights.

Can one ETL tool handle both batch and streaming data effectively?

Yes, several modern platforms support both paradigms. Integrate.io provides batch processing with 60-second CDC intervals for near-real-time use cases. Estuary Flow offers true sub-second streaming combined with batch support. Google Cloud Dataflow and Databricks Delta Live Tables also unify both processing types. However, organizations with strict sub-second requirements may still need specialized streaming infrastructure like Apache Kafka.

Why is Change Data Capture (CDC) important for streaming ETL?

CDC captures only changed data from source systems rather than full table scans, dramatically reducing data movement and enabling near-real-time synchronization. Integrate.io's CDC capabilities provide sub-60-second replication intervals, making it possible to power operational analytics and maintain data consistency across systems without the complexity of full streaming architectures.

How does a low-code ETL tool benefit non-technical users?

Low-code platforms like Integrate.io enable business users and analysts to build data pipelines through visual interfaces with 220+ pre-built transformations, reducing dependency on scarce data engineering resources. This accelerates time-to-value while maintaining enterprise governance standards. Technical teams can still use code-based approaches when needed, providing flexibility across skill levels.

What security considerations are crucial for ETL tools handling sensitive data?

Enterprise ETL platforms must provide end-to-end encryption, role-based access controls, and comprehensive audit trails meeting regulatory requirements. Integrate.io maintains compliance with SOC 2, GDPR, HIPAA, and CCPA with encryption in transit and at rest. Critically, Integrate.io acts as a pass-through layer, meaning your data is never stored on their systems—reducing security exposure while maintaining full pipeline functionality.