Ingest data using the feature store#

Define the source and material targets, and start the ingestion process (as local process, using an MLRun job, real-time ingestion, or incremental ingestion).

Data can be ingested as a batch process either by running the ingest command on demand or as a scheduled job. Batch ingestion can be done locally (i.e. running as a python process in the Jupyter pod) or as an MLRun job.

The data source can be a DataFrame or files (e.g. csv, parquet). Files can be either local files residing on a volume (e.g. v3io), or remote (e.g. S3, Azure blob). MLRun also supports Google BigQuery as a data source. If you define a transformation graph, then the ingestion process runs the graph transformations, infers metadata and stats, and writes the results to a target data store.

When targets are not specified, data is stored in the configured default targets (i.e. NoSQL for real-time and Parquet for offline).


  • Do not name columns starting with either _ or aggr_. They are reserved for internal use. See also general limitations in Attribute name restrictions.

  • When using the pandas engine, do not use spaces ( ) or periods (.) in the column names. These cause errors in the ingestion.

In this section

Inferring data#

There are two types of inferring:

  • Metadata/schema: This is responsible for describing the dataset and generating its meta-data, such as deducing the data-types of the features and listing the entities that are involved. Options belonging to this type are Entities, Features and Index. The InferOptions class has the InferOptions.schema() function which returns a value containing all the options of this type.

  • Stats/preview: Ths related to calculating statistics and generating a preview of the actual data in the dataset. Options of this type are Stats, Histogram and Preview.

The InferOptions class has the following values:
class InferOptions:
Null = 0
Entities = 1
Features = 2
Index = 4
Stats = 8
Histogram = 16
Preview = 32

The InferOptions class basically translates to a value that can be a combination of the above values. For example, passing a value of 24 means Stats + Histogram.

When simultanesouly ingesting data and requesting infer options, part of the data might be ingested twice: once for inferring metadata/stats and once for the actual ingest. This is normal behavior.

Ingest data locally#

Use a Feature Set to create the basic feature-set definition and then an ingest method to run a simple ingestion “locally” in the Jupyter Notebook pod.

# Simple feature set that reads a csv file as a dataframe and ingests it as is 
stocks_set = FeatureSet("stocks", entities=[Entity("ticker")])
stocks = pd.read_csv("stocks.csv")
df = ingest(stocks_set, stocks)

# Specify a csv file as source, specify a custom CSV target 
source = CSVSource("mycsv", path="stocks.csv")
targets = [CSVTarget("mycsv", path="./new_stocks.csv")]
ingest(measurements, source, targets)

To learn more about ingest go to ingest.

Ingest data using an MLRun job#

Use the ingest method with the run_config parameter for running the ingestion process using a serverless MLRun job.
By doing that, the ingestion process runs on its own pod or service on the kubernetes cluster.
This option is more robust since it can leverage the cluster resources, as opposed to running within the Jupyter Notebook.
It also enables you to schedule the job or use bigger/faster resources.

# Running as remote job
stocks_set = FeatureSet("stocks", entities=[Entity("ticker")])
config = RunConfig(image='mlrun/mlrun')
df = ingest(stocks_set, stocks, run_config=config)

Real-time ingestion#

Real-time use cases (e.g. real time fraud detection) require feature engineering on live data (e.g. z-score calculation) while the data is coming from a streaming engine (e.g. kafka) or a live http endpoint.
The feature store enables you to start real-time ingestion service.
When running the deploy_ingestion_service the feature store creates an elastic real-time serverless function (the nuclio function) that runs the pipeline and stores the data results in the “offline” and “online” feature store by default.
There are multiple data source options including http, kafka, kinesis, v3io stream, etc.
Due to the asynchronous nature of feature store’s execution engine, errors are not returned, but rather logged and pushed to the defined error stream.

# Create a real time function that receives http requests
# the "ingest" function runs the feature engineering logic on live events
source = HTTPSource()
func = mlrun.code_to_function("ingest", kind="serving").apply(mount_v3io())
config = RunConfig(function=func)
fstore.deploy_ingestion_service(my_set, source, run_config=config)

To learn more about deploy_ingestion_service go to deploy_ingestion_service.

Incremental ingestion#

You can schedule an ingestion job for a feature set on an ongoing basis. The first scheduled job runs on all the data in the source and the subsequent jobs ingest only the deltas since the previous run (from the last timestamp of the previous run until Example:

cron_trigger = "* */1 * * *" #will run every hour
source = ParquetSource("myparquet", path=path, time_field="time", schedule=cron_trigger)
feature_set = fs.FeatureSet(name=name, entities=[fs.Entity("first_name")], timestamp_key="time",)
fs.ingest(feature_set, source, run_config=fs.RunConfig())

The default value for the overwrite parameter in the ingest function for scheduled ingest is False, meaning that the target from the previous ingest is not deleted. For the storey engine, the feature is currently implemented for ParquetSource only. (CsvSource will be supported in a future release). For Spark engine, other sources are also supported.

Data sources#

For batch ingestion the feature store supports dataframes and files (i.e. csv & parquet).
The files can reside on S3, NFS, Azure blob storage, or the Iguazio platform. MLRun also supports Google BigQuery as a data source. When working with S3/Azure, there are additional requirements. Use pip install mlrun[s3] or pip install mlrun[azure-blob-storage] to install them.

  • Azure: define the environment variable AZURE_STORAGE_CONNECTION_STRING.


For real time ingestion the source can be http, kafka or v3io stream, etc. When defining a source, it maps to nuclio event triggers.

You can also create a custom source to access various databases or data sources.

Target stores#

By default, the feature sets are saved in parquet and the Iguazio NoSQL DB (NoSqlTarget).
The parquet file is ideal for fetching large set of data for training while the key value is ideal for an online application since it supports low latency data retrieval based on key access.


When working with the Iguazio MLOps platform the default feature set storage location is under the “Projects” container: <project name>/fs/.. folder. The default location can be modified in mlrun config or specified per ingest operation. The parquet/csv files can be stored in NFS, S3, Azure blob storage, Redis, and on Iguazio DB/FS.

Redis target store (Tech preview)#

The Redis online target is called, in MLRun, RedisNoSqlTarget. The functionality of the RedisNoSqlTarget is identical to the NoSqlTarget except for:

  • The RedisNoSqlTarget does not support the spark engine, (only supports the storey engine).

  • The RedisNoSqlTarget accepts path parameter in the form <redis|rediss>://[<username>]:[<password>]@<host>[:port]
    For example: rediss://:abcde@localhost:6379 creates a redis target, where:

    • The client/server protocol (rediss) is TLS protected (vs. “redis” if no TLS is established)

    • The server is password protected (password=“abcde”)

    • The server location is localhost port 6379.

  • A default path can be configured in redis.url config (mlrun client has priority over mlrun server), and can be overwritten by MLRUN_REDIS__URL env var.

  • Two types of Redis servers are supported: StandAlone and Cluster (no need to specify the server type in the config).

  • A feature set supports one online target only. Therefore RedisNoSqlTarget and NoSqlTarget cannot be used as two targets of the same feature set.

To use the Redis online target store, you can either change the default to be parquet and Redis, or you can specify the Redis target explicitly each time with the path parameter, for example:
RedisNoSqlTarget(path ="redis://")