batch Plaid to Parquet 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.
Apache Parquet is an open-source, column-oriented data storage format of the Hadoop ecosystem designed to provide fast querying on large datasets. Parquet is routinely used for creating very highly scaled data lakes that can still be queried. Parquet is similar to other column-storage file formats that are available in Hadoop.
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 then be directed to Parquet using materializations that are also open-source. Connectors have the ability to push data as quicikly as a destination will handle. Parquet likes files that are around 1 GB each. So, if you have high data volumes, Flow can keep your data lake up-to-date in near real-time.
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Estuary helps move data from
Plaid to Parquet in minutes with millisecond latency.
Estuary helps move data fromPlaid to Parquet 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.