batch Facebook Marketing to Parquet in minutes

Facebook Marketing

Facebook supports both a core marketing API and a reporting API, both of which this connector uses to pull marketing data. It pulls entity information about Campaigns, AdSets, Ads and AdCreactives, as well as insights from the reporting API.

Parquet

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 Facebook with low latency, allowing you to replicate that data to various systems for both analytic and operational purposes. Facebook 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

Facebook Marketing to Parquet in minutes with millisecond latency.

Estuary helps move data fromFacebook Marketing to Parquet in minutes with millisecond latency.

About Estuary

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.