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Open Table Format Lake Loader

The Lake Loader is an application that loads Snowplow events to a cloud storage bucket using Open Table Formats.

Open Table Formats

The Lake Loader supports the two major Open Table Formats: Delta and Iceberg.

For Iceberg tables, the loader supports AWS Glue and Iceberg REST as catalogs. The REST catalog integration has been tested with Snowflake Open Catalog.

On AWS the Lake Loader continually pulls events from Kinesis and writes to S3.

flowchart LR stream[["<b>Enriched Events</b> (Kinesis stream)"]] loader{{"<b>Lake Loader</b>"}} subgraph bucket ["S3"] table[("<b>Events table</b>")] end stream-->loader-->bucket

The Lake Loader is published as a Docker image which you can run on any AWS VM. You do not need a Spark cluster to run this loader.

bash
docker pull snowplow/lake-loader-aws:0.14.0

To run the loader, mount your config files into the docker image, and then provide the file paths on the command line:

bash
docker run \
--mount=type=bind,source=/path/to/myconfig,destination=/myconfig \
snowplow/lake-loader-aws:0.14.0 \
--config=/myconfig/loader.hocon \
--iglu-config=/myconfig/iglu.hocon

For some output formats, you need to pull a slightly different tag to get a compatible docker image. The configuration reference page explains when this is needed.

Configuring the loader​

The loader config file is in HOCON format, and it allows configuring many different properties of how the loader runs.

The simplest possible config file just needs a description of your pipeline inputs and outputs:

jsonconfig/config.aws.minimal.hocon
loading...

See the configuration reference for all possible configuration parameters.

Windowing​

"Windowing" is an important config setting, which controls how often the Lake Loader commits a batch of events to the data lake. If you adjust this config setting, you should be aware that data lake queries are most efficient when the size of the parquet files in the lake are relatively large.

  • If you set this to a low value, the loader will write events to the lake more frequently, reducing latency. However, the output parquet files will be smaller, which will make querying the data less efficient.
  • Conversely, if you set this to a high value, the loader will generate bigger output parquet files, which are efficient for queries — at the cost of events arriving to the lake with more delay.

The default setting is 5 minutes. For moderate to high volumes, this value strikes a nice balance between the need for large output parquet files and the need for reasonably low latency data.

{
  "windowing": "5 minutes"
}

If you tune this setting correctly, then your lake can support efficient analytic queries without the need to run an OPTIMIZE job on the files.

Iglu​

The Lake Loader requires an Iglu resolver file which describes the Iglu repositories that host your schemas. This should be the same Iglu configuration file that you used in the Enrichment process.

Monitor the loader​

The Lake Loader reports metrics covering event counts, latency, the state of the table after each commit, and the disk and memory used by its internal Spark instance. It can send them to StatsD, to Prometheus, or to both.

See Monitoring the Lake Loader for the full list of metrics and for how to configure each protocol.

Telemetry notice

By default, Snowplow collects telemetry data for Lake Loader (since version 0.1.0). Telemetry allows us to understand how our applications are used and helps us build a better product for our users (including you!).

This data is anonymous and minimal, and since our code is open source, you can inspect what’s collected.

If you wish to help us further, you can optionally provide your email (or just a UUID) in the telemetry.userProvidedId configuration setting.

If you wish to disable telemetry, you can do so by setting telemetry.disable to true.

See our telemetry principles for more information.

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