Best Data Pipeline Software for Mailchimp Transactional Email (Mandrill)

Find and compare the best Data Pipeline software for Mailchimp Transactional Email (Mandrill) in 2024

Use the comparison tool below to compare the top Data Pipeline software for Mailchimp Transactional Email (Mandrill) on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Panoply Reviews

    Panoply

    SQream

    $299 per month
    Panoply makes it easy to store, sync and access all your business information in the cloud. With built-in integrations to all major CRMs and file systems, building a single source of truth for your data has never been easier. Panoply is quick to set up and requires no ongoing maintenance. It also offers award-winning support, and a plan to fit any need.
  • 2
    Rivery Reviews

    Rivery

    Rivery

    $0.75 Per Credit
    Rivery’s ETL platform consolidates, transforms, and manages all of a company’s internal and external data sources in the cloud. Key Features: Pre-built Data Models: Rivery comes with an extensive library of pre-built data models that enable data teams to instantly create powerful data pipelines. Fully managed: A no-code, auto-scalable, and hassle-free platform. Rivery takes care of the back end, allowing teams to spend time on mission-critical priorities rather than maintenance. Multiple Environments: Rivery enables teams to construct and clone custom environments for specific teams or projects. Reverse ETL: Allows companies to automatically send data from cloud warehouses to business applications, marketing clouds, CPD’s, and more.
  • 3
    Y42 Reviews

    Y42

    Datos-Intelligence GmbH

    Y42 is the first fully managed Modern DataOps Cloud for production-ready data pipelines on top of Google BigQuery and Snowflake.
  • 4
    Data Virtuality Reviews
    Connect and centralize data. Transform your data landscape into a flexible powerhouse. Data Virtuality is a data integration platform that allows for instant data access, data centralization, and data governance. Logical Data Warehouse combines materialization and virtualization to provide the best performance. For high data quality, governance, and speed-to-market, create your single source data truth by adding a virtual layer to your existing data environment. Hosted on-premises or in the cloud. Data Virtuality offers three modules: Pipes Professional, Pipes Professional, or Logical Data Warehouse. You can cut down on development time up to 80% Access any data in seconds and automate data workflows with SQL. Rapid BI Prototyping allows for a significantly faster time to market. Data quality is essential for consistent, accurate, and complete data. Metadata repositories can be used to improve master data management.
  • 5
    Alooma Reviews
    Alooma allows data teams visibility and control. It connects data from all your data silos into BigQuery in real-time. You can set up and flow data in minutes. Or, you can customize, enrich, or transform data before it hits the data warehouse. Never lose an event. Alooma's safety nets make it easy to handle errors without affecting your pipeline. Alooma infrastructure can handle any number of data sources, low or high volume.
  • 6
    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.
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