aws kinesis stream

Making data available and accessible

Zero administration, pre-built AWS Kinesis webhooks

The AWS Kinesis webhook is a data pipeline API that allows you to securely transfer, process and load events from a variety of data sources. The API automatically cleans, converts and routes your event data to target data lake or warehouses. Each API pipeline includes automated schema, table and views creation/versioning as well as de-duplication routines.

All your AWS Kinesis webhook data is ready to be used in your favorite analytics tools like Grow, Tableau, Microsoft Power BI, or Looker.

Making data available and accessible

Connect your cloud apps for automated AWS Kinesis data pipelines

Event data is automatically processed and loaded from your source applications into target warehouses. Data can be loaded via simple curl POST commands, or you can use the API to stream events from 100s of cloud applications like Google Sheets, Google Tag Manager, Stripe, Mailchimp and many others.

data lake cloud

Making data available and accessible

AWS Kinesis webhooks data pipelines

Our automated Amazon Kinesis streams send data to target private data lakes or cloud data warehouses like BigQuery, AWS Athena, AWS Redshift, or Redshift Spectrum, Azure Data Lake Storage Gen2, and Snowflake. We provision the AWS Kinesis service, process data sent to your private webhook, and load it to one or more data destinations. You can send real time data directly or send batch files as streaming data.

Stream your application or cloud service events to a pre-built AWS Kinesis webhook

The flexibility and simplicity to stream events from the cloud applications you use every day directly to a data lake or cloud warehouse.

Keeping track with automated data catalog

If upstream data changes, we automatically version tables and views with a data catalog. Data is analyzed, and the system trained. Data governance rules trigger the automated creation of databases, views, and tables in a destination warehouse or data lake for transformed data.

Data integrity, consistency, and accuracy

We de-duplicate data assets from real-time or batch source systems to improve data accuracy. We use machine learning algorithms behind the scenes to learn how to identify duplicate records prior to loading data into a target system.

Automatic, efficient data partitioning

Data partitioning is part of our data lake and pipeline processing, this optimization ensures the minimal data scanning for a query. Our optimization approach improves performance by reducing the cost of data stored in your lake or clouse warehouse.

Open-source, optimized Apache Parquet

We convert data into an efficient and optimized open-source columnar format, Apache Parquet. Using Parquet lowers costs when you execute queries as the files columnar format optimizes for interactive query services like Amazon Athena, Redshift Spectrum.

Data routing to preferred destinations

Data routing allows you to easily map a data source to a target data destination. Route data to different regions or you can choose to route some data sourcs to a data lake and others to a cloud warehouse. This allows you to easily partition according to preferred data governance strategies.

Improved data literacy with metadata generation

We append additional metadata unique to information resident in a record. Your tables and views will include a series of system generated fields that provide users with vital information about the meaning of the data we collected on your behalf.

Faster innovation, flexibility, and freedom from vendor lock-in. We help customers stay nimble so you can meet whatever your priority the business demands, now or in the future.

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