batch Plaid to Snowflake in minutes
Plaid is a financial services company that allows companies to build secure fintech solutions. It provides an intermediate link between customers' sensitive information and the company's application or website. This connector allows you to sync Plaid balance data using the Plaid API.
Snowflake is a cloud-based data warehouse that offers highly scalable and distributed SQL querying over large datasets.
Using OLAP (Online Analytical Processing), Snowflake offers the ability to rapidly answer multi-dimensional analytic database queries with potentially large reporting views by breaking each query up between many worker nodes and reassembling the finalized answer.
Estuary integrates with an ecosystem of free, open-source connectors to extract data from Plaid with low latency, allowing you to replicate that data to various systems for both analytic and operational purposes. The Plaid data can be organized into a data lake or loaded into other data warehouses or streaming systems.
Data can be directed to Snowflake using open-source materialization connectors. Connectors have the ability to keep warehouses as up-to-date as the warehouse can handle. This allows Snowflake to receive data with 30 seconds to a minute of latency.
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Estuary helps move data from
Plaid to Snowflake in minutes with millisecond latency.
Estuary helps move data fromPlaid to Snowflake in minutes with millisecond latency.
Estuary enables the first fully managed ELT service that combines both millisecond-latency and point-and-click simplicity. Flow empowers customers to analyze and act on both historical and real-time data across their analytics and operational systems for a truly unified and up-to-date view.
Flow is developed in the open and utilizes open source connectors that are compatible with a community standard. By making connectors interchangeable with other systems, the Estuary team hopes to expand the ecosystem for everyone’s benefit, empowering organizations of all sizes to build frictionless data pipelines, regardless of their existing data stack.