Deephaven Python Integration Package is created by Deephaven Data Labs. It allows Python developers, including data scientists, to access data, run queries, and execute Python scripts directly inside Deephaven data servers to achieve maximum performance. By taking advantage of the unique streaming table capability of Deephaven and its many data ingestion facilities (Kafka, Parquet, CSV, SQL, etc.), Python developers can quickly put together a real-time data processing pipeline that is high performing and easy to consume.
If you use a Windows operating system, WSL is not required to run Deephaven via pip.
Because this package depends on the Deephaven server, it comes preinstalled with Deephaven Docker images and is made available at runtime in the Python console in the Deephaven Web UI.
from deephaven import read_csv
from deephaven.stream.kafka.consumer import kafka_consumer, TableType
from deephaven.plot import Figure, PlotStyle
csv_table = read_csv("data1.csv")
kafka_table = kafka_consumer.consume({'bootstrap.servers': 'redpanda:29092'}, topic='realtime_feed', table_type=TableType.Append)
joined_table = kafka_table.join(csv_table, on=["key_col_1", "key_col_2"], joins=["data_col1"])
plot = Figure() \
.axes(plot_style = PlotStyle.STACKED_BAR )\
.plot_cat(series_name="Categories1", t=joined_table, category="Key_col_1", y = "data_col1") \
.show()
FAQs
Deephaven Engine Python Package
We found that deephaven-core demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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