Best Data Integration Tools for SurveyMonkey

Dataddo is an enterprise-grade data integration solution engineered to mitigate the operational risks inherent in data movement. Serving as a centralized connectivity backbone, the platform provides a fully managed layer that bridges the gap between any SaaS, database, or file source and your chosen destination—including AI agents. The platform excels by automating the heavy lifting; it proactively manages API updates, schema drift, and the protection of sensitive information. This ensures granular transparency across even the most intricate data flows, whether they reside on-premise, in the cloud, or in hybrid environments. By shifting the perspective of data movement from a "one-off project" to mission-critical infrastructure, Dataddo empowers engineering teams to achieve maximum reliability and redirect their focus toward high-impact AI initiatives rather than tedious pipeline maintenance.

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.

Workato is the operating platform for today's fast-moving businesses. It is the only AI-based middleware platform that allows both IT and business to integrate their apps and automate complex business workflows. Our mission is to help companies automate and integrate their apps and business processes at least 10x faster than traditional tools, and at a tenth the cost of traditional tools. Integration is a mission critical, neutral technology that can be used in heterogeneous IT environments. We are the only technology vendor that is supported by all three of the leading SaaS vendors: Salesforce. Workday. And ServiceNow. We are trusted by the world's most recognizable brands and the fastest-growing innovators. Customers consider us to be one of the best companies to do business.

Datameer is your go-to data tool for exploring, preparing, visualizing, and cataloging Snowflake insights. From exploring raw datasets to driving business decisions – an all-in-one tool.

Airbyte is a data integration platform that operates on an open-source model, aimed at assisting organizations in unifying data from diverse sources into their data lakes, warehouses, or databases. With an extensive library of over 550 ready-made connectors, it allows users to craft custom connectors with minimal coding through low-code or no-code solutions. The platform is specifically designed to facilitate the movement of large volumes of data, thereby improving artificial intelligence processes by efficiently incorporating unstructured data into vector databases such as Pinecone and Weaviate. Furthermore, Airbyte provides adaptable deployment options, which help maintain security, compliance, and governance across various data models, making it a versatile choice for modern data integration needs. This capability is essential for businesses looking to enhance their data-driven decision-making processes.

Unify all your data sources, encompassing both relational and NoSQL databases, SaaS applications, and APIs, allowing you to query them as if they were a single data entity instantly. Process data at its source without delay, enabling you to query, cache, and merge information from various origins seamlessly. Utilize webhooks to bring in real-time streaming data from platforms like Kafka and Segment into the Peaka BI Table, moving away from the traditional nightly batch ingestion in favor of immediate data accessibility. Approach every data source as though it were a relational database, transforming any API into a table that can be integrated and joined with your other datasets. Employ familiar SQL syntax to execute queries in NoSQL environments, allowing you to access data from both SQL and NoSQL databases using the same skill set. Consolidate your data to query and refine it into new sets, which you can then expose through APIs to support other applications and systems. Streamline your data stack setup without becoming overwhelmed by scripts and logs, and remove the complexities associated with building, managing, and maintaining ETL pipelines. This approach not only enhances efficiency but also empowers teams to focus on deriving insights rather than being bogged down by technical hurdles.

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.

Power Query provides a user-friendly solution for connecting, extracting, transforming, and loading data from a variety of sources. Acting as a robust engine for data preparation and transformation, Power Query features a graphical interface that simplifies the data retrieval process and includes a Power Query Editor for implementing necessary changes. The versatility of the engine allows it to be integrated across numerous products and services, meaning the storage location of the data is determined by the specific application of Power Query. This tool enables users to efficiently carry out the extract, transform, and load (ETL) processes for their data needs. With Microsoft’s Data Connectivity and Data Preparation technology, users can easily access and manipulate data from hundreds of sources in a straightforward, no-code environment. Power Query is equipped with support for a multitude of data sources through built-in connectors, generic interfaces like REST APIs, ODBC, OLE, DB, and OData, and even offers a Power Query SDK for creating custom connectors tailored to individual requirements. This flexibility makes Power Query an indispensable asset for data professionals seeking to streamline their workflows.

Meltano offers unparalleled flexibility in how you can deploy your data solutions. Take complete ownership of your data infrastructure from start to finish. With an extensive library of over 300 connectors that have been successfully operating in production for several years, you have a wealth of options at your fingertips. You can execute workflows in separate environments, perform comprehensive end-to-end tests, and maintain version control over all your components. The open-source nature of Meltano empowers you to create the ideal data setup tailored to your needs. By defining your entire project as code, you can work collaboratively with your team with confidence. The Meltano CLI streamlines the project creation process, enabling quick setup for data replication. Specifically optimized for managing transformations, Meltano is the ideal platform for running dbt. Your entire data stack is encapsulated within your project, simplifying the production deployment process. Furthermore, you can validate any changes made in the development phase before progressing to continuous integration, and subsequently to staging, prior to final deployment in production. This structured approach ensures a smooth transition through each stage of your data pipeline.