10 Best Data Consolidation Tools to Consider in 2025

What happens when your data is scattered across CRMs, spreadsheets, and cloud apps? Fragmented datasets aren’t just an inconvenience; they’re a roadblock to faster decisions and accurate reporting.That’s where data consolidation comes in. By consolidating all your data into a single, unified source of truth, you lay the foundation for a modern data infrastructure, fueling analytics, automation, and more informed strategies.

This blog explores the top 10 data consolidation tools to consider in 2025. We’ll compare them across their key features, use cases, pros, and cons to help you pick the perfect fit for your data consolidation workflows.

First up, let’s dive into the top 3 data consolidation tools we’ve handpicked for you:

Top 10 Data Consolidation Tools

Below, we have curated a list of the top 10 data consolidation tools:

1. Hevo Data

Hevo is a no-code, data consolidation platform designed to combine and integrate data from multiple, disparate sources into a single, unified repository, creating a comprehensive view of data flows. The platform keeps an encrypted backup of data to re-ingest data when pipelines fail.

Hevo extracts data, stages incoming events, and loads them into destinations via configurable loading schedules. It supports manual overrides for controlled ingestion, which benefits data engineers needing low-latency and reliable pipelines.

Hevo offers object-level control over data ingestion, letting teams selectively consolidate only the required datasets. Selective inclusion of pipeline objects ensures efficient consolidation without overloading warehouses with unnecessary historical or incremental data.

Key features:

• Multi-region support: Hevo enables users to operate across various geographic regions from a single account. After an initial cool-off period, you can create additional workspaces in different regions.
• In-flight data formatting: In-flight data formatting allows users to transform data via Python scripts or a drag-and-drop UI. Post-load, it integrates with dbt and SQL for advanced transformations, ensuring data is fully optimized for the destination schema.
• Deduplication: Built-in data reconciliation tools detect and remove duplicate records before loading. This ensures analysts work with a credible source of data without manual cleanup.

Pros:

• User-friendly interface that’s easy to navigate.
• Real-time data replication ensures information stays up to date.
• Comprehensive documentation helps simplify setup and implementation.
• Supports Python scripting and dbt modeling for complex data processing.

Cons:

• Users must rebuild pipelines to make changes.
• Primarily designed for cloud environments, offering limited on-premise support.

Pricing:

• Business: Custom pricing
• Starts as a free tier with limited connectors up to 1 million events
• Starter: $239/month up to 5 million events
• Professional: $679/month up to 20 million events

Customer Review

hevo data is ease to use for building etl pipeline.the main advantage of hevo data is that it requires no code . the implementation of etl piplines is very easy.there are vast no of feature availiable for building pipeline.hevo data provides data integration very easy compared to other vendors . The customer support of hevo data is very i liked the way they reply

Supraja d.

Data Engineer

2. Fivetran

Fivetran is a fully managed ELT platform that consolidates data from 700+ sources into cloud warehouses. Its USP lies in flexible deployment models, SaaS, Hybrid, and Self-Hosted, designed to balance simplicity, security, and control.

Fivetran streamlines data consolidation from various sources into major cloud platforms, like Amazon Redshift, Google BigQuery, and Databricks, using CDC to replicate updates in real time. Data engineers can maintain centralized, analytics-ready AWS data warehouses with minimal operational effort.

Fivetran’s Partner-Built Program lets third-party vendors create and maintain custom connectors, expanding integration options. Combined with its Hybrid Deployment Model, it ensures flexible, secure, and scalable data consolidation for modern enterprises.

Key features:

• REST API: Fivetran REST API enables programmatic management of data pipelines, connectors, users, and groups, facilitating automation and scalability. It supports automation of pipeline orchestration, metadata retrieval, and operational monitoring at scale.
• By Request: Fivetran’s By Request Program allows users to collaborate with the Product team to create Lite connectors for unsupported SaaS apps. These connectors are API-based, rapidly deployable, and added to Fivetran’s catalog for broader use.
• Encryption: The platform secures credentials with customer-managed encryption keys (CMKs) to keep full control over sensitive data. Disabling the key halts data syncs and adds an extra layer of security and compliance.

Pros:

• Connects data sources and destinations within minutes.
• Teams can replicate data from warehouses to operational systems seamlessly.
• Offers robust security, including built-in recovery and error detection mechanisms.

Cons:

• Some partner-built connectors face reliability issues.
• Customer support can be slow or unresponsive at times.
• Costs can rise significantly when handling large volumes of data.

Pricing:

• Pricing is determined by MAR, based on the number of unique rows inserted, updated, or deleted each month. You can try the platform with a 14-day free trial.

3. Stitch

Stitch is a cloud-based ETL platform that automates data consolidation from DBs, SaaS apps, and cloud services, loading it into a centralized warehouse. The tool replicates and unifies data quickly, handling schema changes without heavy engineering effort.

Stitch orchestrates data replication by capturing changes from source systems, enforcing strict data typing, and loading structured and semi-structured data into cloud warehouses for reliable analytics. It helps BI teams quickly access unified, analysis-ready data.

Stitch stands out with its Singer open-source framework, which standardizes how data is extracted and loaded from various sources. Stitch combines this framework with automated schema handling to make consolidation accessible for both technical teams and business users.

Key features:

• Data normalization:  Automatically flattens nested structures (like JSON) and standardizes formats across sources, so different systems can be consolidated into a uniform schema for analysis.
• Incremental replication: Stitch tracks changes at the row level to ensure data warehouses are updated incrementally, reducing duplication and improving synchronization speed. Leverages CDC mechanisms to detect inserts, updates, and deletes.
• Pipeline monitoring: Stitch provides detailed dashboards, logs, and notifications for all pipelines. Teams can quickly identify and resolve errors to protect data reliability without spending hours manually troubleshooting.

Pros:

• Comes with built-in analytics tools for visualizing data and generating BI reports.
• Provides detailed documentation and personalized onboarding support.
• Features a usage-based pricing structure, making costs transparent and predictable.

Cons:

• Real-time data streaming capabilities are limited.
• Customer support may be less responsive in certain situations.
• Transformation options are basic, with more advanced functionality requiring integration with Talend.

Pricing:

• Premium: $2,500 monthly
• Standard: $100 monthly
• Advanced: $1,250 monthly

4. Airbyte

Airbyte is an open-source data integration platform that consolidates data from multiple sources into central destinations. Its Self-Managed Enterprise version ensures all data stays within your infrastructure, offering enhanced security and compliance.

Airbyte’s modular, containerized architecture supports reliable, high-throughput data consolidation by isolating each connector for consistent ingestion across diverse sources. It also scales horizontally to handle growing consolidation needs.

With PyAirbyte, developers can consolidate data directly within Python workflows. They can leverage Airbyte’s connectors programmatically to unify sources without deploying or managing the full platform.

Key features:

• Deployment: Airbyte offers flexible deployment options, including cloud, on-premises, and hybrid environments. It utilizes Kubernetes and Helm charts for scalable and customizable installations tailored to your infrastructure needs.
• Terraform Provider: Airbyte’s Terraform Provider enables infrastructure-as-code management of Airbyte resources, allowing users to define and provision sources, destinations, and connections.
• Customization: The Python CDK simplifies building HTTP API-based connectors through base classes that handle authentication, rate limiting, and caching. This modular approach helps developers create custom connectors with flexibility and control.

Pros:

• Offers a wide range of source and destination connectors.
• Flexible deployment options, including self-hosted, cloud, and hybrid setups.
• Quick and straightforward to get started.

Cons:

• Documentation can be unclear or incomplete.
• Error reporting is limited and not very detailed.
• A few connectors may experience instability or inconsistencies.

Pricing:

• Airbyte offers a free self-hosted plan, a 14-day trial for the cloud version, and flexible Team and Enterprise plans tailored to business needs.

5. AWS Glue

AWS Glue is a fully managed service that consolidates data from diverse sources using automated ETL and schema discovery. It is best suited for organizations with large-scale, cloud-based ecosystems that need unified data integration across multiple AWS services.

AWS Glue workflows consolidate data from multiple AWS sources by orchestrating jobs, crawlers, and triggers in a DAG structure for event-driven data processing. Designed for data engineers for reliable metadata management and transformation across AWS data sources.

AWS Glue provides a rich set of APIs for programmatic access to its features, including job management, crawler operations, and data catalog interactions. These APIs support schema updates and integration with serverless orchestration frameworks like AWS Step Functions.

Key features:

• Job editor: AWS Glue’s job editor provides a visual, no-code interface for building ETL workflows, allowing users to design data pipelines through a graphical representation of job tasks.
• Glue Streaming: AWS Glue Streaming supports real-time ETL processing using Apache Spark Structured Streaming. The support for continuous ingestion from sources like Amazon Kinesis and Apache Kafka facilitates low-latency data pipelines.
• Troubleshooting: Provides detailed logs and error messages for efficient troubleshooting, helping users quickly identify job failures. It also offers automation runbooks to resolve connectivity and configuration issues, minimizing workflow disruptions.

Pros:

• Fast job execution with Apache Spark.
• Serverless architecture with automatic resource provisioning.
• Auto-detects schemas using data crawlers.

Cons:

• Complex transformations may require custom coding.
• Limited support for XML files.
• Users experience frequent connectivity problems.

Pricing:

• AWS Glue offers a serverless data integration service with a pay-as-you-go pricing model.

6. Oracle Data Integrator (ODI)

Oracle Data Integrator (ODI) is an enterprise-grade platform that consolidates data from diverse sources into target systems, pushing transformations downstream for faster execution. Its knowledge modules and reusable templates simplify and automate complex integration tasks across diverse technologies.

ODI uses a declarative design where users define mappings, and knowledge modules generate native SQL scripts for execution. An ODI Agent runs this code directly in the target system, handles scheduling, and logs every step for monitoring and governance. The ELT workflow helps teams manage real-time and heterogeneous data integration at scale.

The ODI Studio stands out by offering four intuitive graphical navigators: Designer, Operator, Topology, and Security. Developers can manage consolidation visually, orchestrate workflows, and enforce governance under one roof.

Key features:

• Application Adapters: These are collections of knowledge modules that enable integration with enterprise applications like Oracle E-Business Suite and SAP. Adapters combine connectivity with prebuilt logic for loading data into ODI.
• Design: ODI uses a declarative flow-based design, where developers specify target data mappings and transformation rules at a metadata level. The tool automatically generates the optimized code needed for execution.
• Monitoring: ODI provides detailed error logs, notifications, and auto-retry mechanisms. It integrates with Oracle Enterprise Manager for centralized monitoring and control across pipelines.

Pros:

• Reusable knowledge modules reduce errors.
• Metadata-driven design enables flexibility and reuse.
• ELT architecture leverages target database processing.

Cons:

• Initial setup and configuration can be complex and time-consuming.
• Pricing can be expensive for small and medium-sized businesses.
• Has a steep learning curve.

Pricing:

ODI pricing is based on deployment model and licensing:

• Oracle Data Integrator Cloud Service: $0.3871 per OCPU per hour.
• Oracle Data Integrator Cloud Service (BYOL): $0.7742 per OCPU per hour.
• Oracle Data Integrator Enterprise Edition: Details are available upon request.

7. Informatica

Informatica is an enterprise-grade platform powered by an AI-driven CLAIRE engine that automates data discovery, lineage, and quality management. It helps organizations consolidate data across cloud, on-premises, and hybrid environments.

Informatica begins with mapping development, specifying source-to-target data flows, transformations, and dependencies to design robust pipelines. Sessions execute these mappings with performance controls, supporting operations teams in maintaining reliable flows.

Informatica provides comprehensive REST APIs to manage data consolidation tasks across the IICS ecosystem. These APIs support resource management, workflow orchestration, metadata access, and integration with CI/CD pipelines for enterprise-grade automation.

Key features:

• ScreenFlow SDK: Informatica ActiveVOS includes a ScreenFlow SDK that enables building custom UIs for business processes. It integrates with external applications and embeds HTML forms for flexible process automation.
• Connectivity: The platform offers thousands of pre-built connectors to databases, applications, files, APIs, and streaming sources. Connectors handle semi-structured and unstructured data, reducing the need for custom coding. 
• Modernization: Informatica’s PowerCenter Modernization facilitates the migration of legacy ETL assets to the cloud through a structured process and bulk metadata updates.

Pros:

• Ready-to-use templates for popular cloud data warehouses.
• AI-driven automation powered by the CLAIRE engine.
• Extensive support for cloud, hybrid, and on-premises environments.

Cons:

• Can be challenging to set up for users without technical expertise.
• Logging and error tracking options are limited.
• May experience slower performance during large-scale data loads.

Pricing:

• Informatica offers a flexible, consumption-based pricing model, which means you pay for what you need.

8. Rivery (Boomi Data Integration)

Rivery (acquired by Boomi) is a cloud-native ELT and orchestration platform that consolidates structured and semi-structured data from multiple sources into a unified warehouse. It offers ready-to-use “Kits” to reduce setup time through prebuilt workflows with connectors, transformations, and orchestration logic.

In Rivery, pipelines run as real-time CDC streams or batch jobs, with orchestration handling dependencies, retries, and error recovery. Engineers and BI teams monitor execution through logs, metrics, and alerts, ensuring data reliability at scale.

Boomi’s Data Connector Agent accelerates the integration of niche or long-tail REST APIs by automatically generating connector blueprints from API documentation. Organizations can ingest complex data sources into their centralized systems, ensuring comprehensive and accurate consolidation across datasets.

Key features:

• API Upgrade Management: Boomi streamlines version upgrades with guided reviews and controlled updates. This ensures smooth API transitions without breaking existing data pipelines.
• Transformation: Rivery offers dual transformation options: SQL for complex, code-driven logic and a no-code visual builder for faster workflows. Transformations execute directly inside cloud data warehouses, leveraging native compute for scalability.
• Blueprint: Provides ready-made reference architectures including design guidance, implementation standards, and deployment best practices. Teams can establish trusted, repeatable data integration frameworks that accelerate rollout and ensure consistency.

Pros:

• Advanced data modeling capabilities.
• Flexible workflow orchestration with conditional steps.
• Strong, responsive customer support.

Cons:

• Sparse documentation for complex setups.
• Occasional bugs in ETL processes.
• Limited data sources and fields.

Pricing:

• Rivery offers customized pricing based on the specific needs and scale of your business. They do not list standard pricing tiers on their website.

9. Integrate.io

Integrate.io is a cloud-based ETL platform that enables advanced data transformations using expressions for dynamic field manipulation and custom calculations. The combination of low-code flexibility and powerful transformation capabilities accelerates data consolidation.

Integrate.io enables users to design ETL workflows by creating clusters, building packages with sources, transformations, and destinations, and running jobs to process data. It supports scheduling recurring tasks and executing multiple jobs simultaneously.

Integrate.io’s uniqueness lies in its powerful ETL API that allows users to create clusters, run jobs, and monitor workflows in real time. It generates secure REST APIs for 20+ native connectors, enabling rapid integration with DBs and big data platforms.

Key features:

• Advanced search: The Advanced Package Search feature enhances package management by supporting a rich set of search modifiers to locate specific components, variables, or expressions within their ETL packages.
• Monitoring: Provides comprehensive job monitoring through its intuitive dashboard for users to track job status, progress, and errors. Additionally, it offers real-time alerts via email, webhooks, or API polling.
• Transformation: In-pipeline transformation allows users to perform SQL, Python, or visual drag-and-drop transformations right within the data flow. It supports complex operations like joins, aggregations, and type casting, eliminating the need for external tools.

Pros:

• Meets key security standards and regulatory requirements.
• Provides over 150 pre-built connectors for batch data integration.
• Includes dedicated support and access to solution engineers.

Cons:

• Real-time data streaming capabilities are limited.
• Sparse documentation can make complex integrations challenging.
• User interface can become cumbersome for managing intricate ETL workflows.

Pricing:

• Priced at $1,999 per month, this plan provides full platform access, 60-second pipeline updates, and unlimited connectors, with options to customize and add extra features.”

10. Matillion

Matillion is a cloud-native platform that enables the design, scheduling, and execution of data pipelines to consolidate data from multiple sources. Its browser-based Designer and modular, component-driven jobs allow teams to visually organize ETL workflows for consistent consolidation.

The platform organizes ETL workflows into modular jobs using a visual, component-based interface and configurable variables. By leveraging SQL within the cloud warehouse and supporting API-driven automation, Matillion allows IT teams to standardize data pipelines.

Matillion stands out with its Universal RPM Installer for reliable Linux deployments and updates. Pushdown transformations accelerate large dataset consolidation, while modular, component-based jobs provide a structured, repeatable framework for integrating multiple sources.

Key features:

• Endpoints: The platform offers pre-built connectors and supports API-based endpoints for seamless integration with SaaS applications, on-premises databases, and third-party services.
• Pushdown transformation: Matillion executes transformations directly in the target cloud data warehouse using SQL, rather than processing data externally. “Pushdown” leverages the warehouse’s native compute power for faster, more efficient processing.
• API-led automation: Matillion supports full automation via REST APIs and integration with orchestration tools. Teams can schedule, trigger, and monitor pipelines programmatically, enabling seamless integration with DevOps and CI/CD workflows.

Pros:

• Offers a visual, no-code interface to simplify ETL workflows.
• Seamlessly integrates with major cloud platforms.
• Version control and collaboration support for teams.

Cons:

• Lacks strong real-time data streaming capabilities.
• Some users find the pricing to be on the higher side.
• UI may struggle to scale with complex ETL workflows.

Pricing:

• The platform offers a pay-as-you-go model.

How to Choose the Right Data Consolidation Software for Your Business?

Key best practices to consider when choosing a data consolidation tool include:

Understand your data sources

• Identify your data landscape: structured, semi-structured, or unstructured. Different tools excel in different data types, affecting data integration techniques and pipeline efficiency.

Assess integration capabilities

• Businesses often require pre-built connectors for SaaS applications, databases, APIs, and file systems to avoid building custom integrations from scratch. Select a tool with a robust library of connectors to reduce development time and minimize errors in data ingestion.

Evaluate scalability

• Performance depends on the architecture of the tool and how it processes transformations. Tools that leverage in-warehouse or pushdown transformations can execute large-scale transformations directly within the cloud warehouse to improve speed.

Workflow management

• Data consolidation tools with scheduling, monitoring, and API-driven automation allow IT teams to reduce manual intervention, enforce consistency, and ensure reliability across pipelines.

Security & compliance

• The selected tool must meet standard security requirements, particularly if handling sensitive or regulated data. Features such as role-based access control, audit logging, encryption in transit and at rest, and compliance with standards like GDPR, HIPAA, or SOC 2 are crucial.

Avoid hidden costs

• Pricing should be clear, predictable, and aligned with usage, whether per row, per API call, per pipeline, or subscription, to avoid hidden costs.

Hevo: No-code Pipelines for Data Consolidation

Hevo stands out with its cloud-native solution for organizations looking to simplify data centralization and accelerate their data consolidation processes. 

By offering over 150 pre-built connectors, real-time data replication, and an intuitive, no-code interface, Hevo enables teams to build reliable pipelines without extensive technical overhead.

The platform complies with industry-standard security certifications, including SOC 2 and GDPR, ensuring data is protected and handled responsibly.

Furthermore, Hevo facilitates the creation of custom connectors, applies tailored transformations, and provides strong pipeline control, all without complex orchestration, making it a perfect fit for SMBs.

For businesses aiming to turn data into actionable insights quickly, Hevo stands out as an efficient and user-friendly choice in 2025.

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FAQs on Data Consolidation Platforms