Getting Started¶

This is the "Getting Started" streamdaq v2 notebook.

In [1]:

!uv pip install --quiet streamdaq
!uv pip show streamdaq

!uv pip install --quiet streamdaq !uv pip show streamdaq

error: No virtual environment found; run `uv venv` to create an environment, or
       pass `--system` to install into a non-virtual environment

Using Python 3.11.16 environment at: /opt/hostedtoolcache/Python/3.11.16/x64
Name: streamdaq
Version: 2.0.0
Location: /opt/hostedtoolcache/Python/3.11.16/x64/lib/python3.11/site-packages
Requires: datasketch, datasketches, dill, fastapi, lmdb, pathway, pydantic, strenum, uvicorn
Required-by:

In [2]:

'''
from streamdaq import StreamDaQ, DaQMeasures as dqm, Windows

# Step 1: Configure your monitoring setup
daq = StreamDaQ().configure(
    window=Windows.tumbling(3),
    instance="user_id",
    time_column="timestamp",
    wait_for_late=1,
    time_format='%Y-%m-%d %H:%M:%S'
)

# Step 2: Define what Data Quality means for you
daq.check(dqm.count('interaction_events'), assess="(5, 15]", name="count") \
   .check(dqm.max('interaction_events'), assess=">5.09", name="max_interact") \

# Step 3: Start monitoring and let Stream DaQ do the work
daq.watch_out()
'''

''' from streamdaq import StreamDaQ, DaQMeasures as dqm, Windows # Step 1: Configure your monitoring setup daq = StreamDaQ().configure( window=Windows.tumbling(3), instance="user_id", time_column="timestamp", wait_for_late=1, time_format='%Y-%m-%d %H:%M:%S' ) # Step 2: Define what Data Quality means for you daq.check(dqm.count('interaction_events'), assess="(5, 15]", name="count") \ .check(dqm.max('interaction_events'), assess=">5.09", name="max_interact") \ # Step 3: Start monitoring and let Stream DaQ do the work daq.watch_out() '''

Out[2]:

'\nfrom streamdaq import StreamDaQ, DaQMeasures as dqm, Windows\n\n# Step 1: Configure your monitoring setup\ndaq = StreamDaQ().configure(\n    window=Windows.tumbling(3),\n    instance="user_id",\n    time_column="timestamp",\n    wait_for_late=1,\n    time_format=\'%Y-%m-%d %H:%M:%S\'\n)\n\n# Step 2: Define what Data Quality means for you\ndaq.check(dqm.count(\'interaction_events\'), assess="(5, 15]", name="count")    .check(dqm.max(\'interaction_events\'), assess=">5.09", name="max_interact") \n# Step 3: Start monitoring and let Stream DaQ do the work\ndaq.watch_out()\n'