CData Sync vs Dremio Comparison: Reviews, Features, Pricing & Alternatives in 2026

Detailed side-by-side comparison to help you choose the right solution for your team

Updated Apr 2026 8 min read

CData Sync

0.0 (0 reviews)

CData Sync is a high-performance data replication software that enables you to build and maintain automated data pipelines between hundreds of SaaS, NoSQL, and ERP sources and your preferred database or data warehouse.

Starting at $333/mo
Free Trial 30 days
VS

Dremio

0.0 (0 reviews)

Dremio is a unified data lakehouse platform that enables you to run high-performance SQL analytics directly on your cloud data lake storage without moving or copying your data.

Starting at Free
Free Trial 0 days

Quick Comparison

Feature CData Sync Dremio
Website cdata.com dremio.com
Pricing Model Subscription Freemium
Starting Price $333/month Free
FREE Trial ✓ 30 days free trial ✓ 0 days free trial
Free Plan ✘ No free plan ✓ Has free plan
Product Demo ✓ Request demo here ✓ Request demo here
Deployment saas on-premise desktop cloud on-premise
Integrations Salesforce NetSuite HubSpot SQL Server Snowflake Amazon S3 Google BigQuery PostgreSQL Oracle Microsoft Azure Tableau Power BI Amazon S3 Azure Data Lake Storage Apache Iceberg Apache Hive Snowflake Oracle PostgreSQL Microsoft Teams
Target Users mid-market enterprise mid-market enterprise
Target Industries
Customer Count 0 0
Founded Year 2016 2015
Headquarters Chapel Hill, USA Santa Clara, USA

Overview

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CData Sync

CData Sync is a robust data replication solution that helps you move information from over 300 SaaS, CRM, and ERP applications into any database or data warehouse. You can automate the entire ETL/ELT process, ensuring your analytics tools always have access to the most current data without manual exports or custom coding. It is designed for data engineers and IT teams who need to consolidate siloed information for business intelligence and reporting.

You can deploy the software anywhere—on-premise or in the cloud—giving you full control over your data security and infrastructure. The platform supports incremental loading, which means you only transfer changed data to save time and bandwidth. Whether you are a mid-sized business or a large enterprise, it simplifies the complexity of maintaining reliable data pipelines across your entire organization.

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Dremio

Dremio provides a unified data lakehouse that lets you query your data directly where it lives. Instead of waiting for complex ETL processes to move data into expensive warehouses, you can connect your preferred BI tools like Tableau or Power BI straight to your Amazon S3, Azure Data Lake, or Apache Iceberg tables. This approach reduces data sprawl and gives you immediate access to your information.

You can manage your data with Git-like version control, allowing you to branch, merge, and tag data sets just like code. This makes it easier to experiment with data transformations without affecting your production environment. Whether you are a data engineer or an analyst, the platform simplifies your architecture by providing a single, high-performance layer for all your analytical needs.

Overview

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CData Sync Features

  • Automated Incremental Sync Identify and replicate only the data that has changed since your last sync to reduce processing time and bandwidth.
  • 300+ Native Connectors Connect instantly to hundreds of sources like Salesforce, NetSuite, and HubSpot using pre-built, high-performance drivers.
  • Flexible Deployment Install the software on-premise behind your firewall or deploy it in your preferred cloud environment like AWS or Azure.
  • Point-and-Click Mapping Map your source data to destination tables visually without writing complex SQL queries or integration scripts.
  • Real-time Monitoring Track your job status and data health with detailed logs and automated email alerts for any sync failures.
  • Enterprise Security Keep your data secure by running the integration within your own infrastructure rather than a third-party cloud.
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Dremio Features

  • Reflections. Accelerate your queries automatically using physical data optimizations that make your BI dashboards feel instant and responsive.
  • Data Catalog. Search and discover your data assets easily with a built-in catalog that organizes your tables, views, and metadata.
  • SQL Runner. Run complex SQL queries directly against your data lake storage using a familiar, powerful interface designed for analysts.
  • Data Lineage. Track how your data flows from source to visualization so you can maintain trust and compliance across your organization.
  • Git-for-Data. Manage your data versions with branches and tags to safely test changes before merging them into your production sets.
  • Semantic Layer. Create a consistent view of your data for all users, ensuring everyone uses the same definitions for key business metrics.

Pricing Comparison

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CData Sync Pricing

Standard
$333
  • Up to 5 data sources
  • Unlimited data volume
  • Automated incremental loading
  • Standard email support
  • On-premise or cloud deployment
D

Dremio Pricing

Dremio Discovery
$0
  • Unlimited users
  • Standard SQL engine
  • Community support
  • Basic data catalog
  • Connect to S3 and ADLS

Pros & Cons

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CData Sync

Pros

  • Extensive library of connectors for niche SaaS applications
  • Simple setup process that requires minimal technical training
  • Reliable incremental syncing reduces load on source systems
  • Deployment flexibility ensures compliance with strict security policies

Cons

  • Initial configuration of complex ERP sources can be tricky
  • Documentation can be overly technical for non-engineers
  • Pricing scales quickly when adding many data sources
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Dremio

Pros

  • Significantly reduces the need for complex ETL pipelines
  • Provides fast query performance on large datasets
  • Intuitive interface for both engineers and analysts
  • Easy integration with popular BI tools like Power BI

Cons

  • Initial configuration can be complex for beginners
  • Requires significant memory resources for peak performance
  • Documentation can be sparse for niche data sources
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