Best Data Replication Software for Azure Synapse Analytics

Find and compare the best Data Replication software for Azure Synapse Analytics in 2025

Use the comparison tool below to compare the top Data Replication software for Azure Synapse Analytics on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Lyftrondata Reviews
    Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.
  • 2
    TROCCO Reviews

    TROCCO

    primeNumber Inc

    TROCCO is an automation and data integration platform that streamlines the data engineering workflow by combining multiple aspects into a single solution. This reduces the time and effort needed to build data pipelines using different tools. It has a wide range of features including ETL/ELT and orchestration, transformation and reverse ETL. This allows for seamless data movement from and to cloud warehouses, allowing downstream analytics, AI and ML applications. TROCCO is a SaaS platform that manages infrastructure and scaling issues, allowing users the freedom to focus on extracting maximum value from their data, rather than managing pipelines. It supports batch and near-real-time data synchronization via HTTP, custom integrations and connectivity to on-premise data sources. Users can transform data with Python or no-code template, model it using SQL or dbt and orchestrate pipelines via an integrated workflow engine.
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