Serverless data workflow

Datenstreaming mit AWS Kinesis und Lambda

Maksim Pecherskiy

Data Engineer

A look back

Current partitioning

Filtering out speeders

Aggregating by day

Open recordReaderS3

Editing recordReaderS3

...
import pytz
from datetime import datetime
tz = pytz.timezone('America/Los_Angeles')

Editing recordReaderS3

def record_created_handler(event, context):

    ...
    ## Get top speeds that exceed the limit of 45
    too_fast = top_speeds.loc[top_speeds.speed > SPEED_ALERT_THRESHOLD :]

    ## Generate object key
    fdate = datetime.now(tz).strftime("%Y%m%d/%HH%MM%SS")
    obj_key = f"speeders/{fdate}.csv" # filename in speeders folder


    ## Write the object to S3
    s3.put_object(Bucket='sd-vehicle-data', 
                  Key=obj_key,
                  Body=too_fast.to_csv(sep=" ", index=False)
    )

Writing speeders by date and time

speederAggregator

Create speederAggregator

Add AWS data wrangler layer

speederAggregator Resources

Create the timed trigger

cron(50 6 * * ? *)

On Minute 50
Of the 6th hour (UTC)
Of Every Day of the Month
Of Every Month
Of Every Day of the Week
Of Every Year

speederAggregator callback

import boto3, pytz, pandas as pd

s3 = boto3.client("s3" ...)
...

def speeder_aggregator(event, context):
    tz = pytz.timezone('America/Los_Angeles')
    filter_date = datetime.now(tz).strftime("%Y%m%d")

speederAggregator callback

def speeder_aggregator(event, context):
  ...
  objects = s3.list_objects_v2(
      Bucket='sd-vehicle-data', Prefix=f'speeders/{filter_date}')
  day_data = []


  for obj in objects['Contents']:
      print(obj['Key'])
      day_record = s3.get_object(Bucket='sd-vehicle-data', Key = obj['Key'])
      day_data.append(
          pd.read_csv(day_record['Body'], delimiter = " "))

  # Concatenate new records into a single dataframe.
  data = pd.concat(day_data)
  data.columns = ["record_id", "timestamp", "vin", "lon", "lat", "speed"]

awswrangler package

import awswrangler as wr

session = boto3.Session(aws_access_key_id = AWS_KEY, 
                        aws_secret_access_key = AWS_SECRET, region_name="us-east-1")

df = wr.s3.read_csv(f"s3://sd-vehicle-data/speeders/{filter_date}")      
df.head()

Old way vs awswrangler

def speeder_aggregator(event, context):
  ...
  objects = s3.list_objects_v2(
      Bucket='sd-vehicle-data', Prefix=f'speeders/{filter_date}')
  day_data = []

  for obj in objects['Contents']:
      print(obj['Key'])
      day_record = s3.get_object(Bucket='sd-vehicle-data', Key = obj['Key'])
      day_data.append(
          pd.read_csv(day_record['Body'], delimiter = " "))

  # Concatenate new records into a single dataframe.
  data = pd.concat(day_data)
  data.columns = ["record_id", "timestamp", "vin", "lon", "lat", "speed"]

Old way vs awswrangler

import awswrangler as wr

session = boto3.Session(aws_access_key_id = AWS_KEY, 
                        aws_secret_access_key = AWS_SECRET, region_name="us-east-1")
def speeder_aggregator(event, context):
    df = wr.s3.read_csv(f"s3://sd-vehicle-data/speeders/{filter_date}")      
    df.head()

Writing aggregate speeders file

wr.s3.to_csv(df, 
             f"s3://sd-vehicle-data/speeders_daily/{filter_date}.csv", 
             boto3_session=session, 
             sep=" ", index=False)

Serverless data workflow

A look back

Current partitioning

Filtering out speeders

Aggregating by day

Open recordReaderS3

Editing recordReaderS3

Editing recordReaderS3

Writing speeders by date and time

speederAggregator

Create speederAggregator

Add AWS data wrangler layer

speederAggregator Resources

Create the timed trigger

speederAggregator callback

speederAggregator callback

awswrangler package

Old way vs awswrangler

Old way vs awswrangler

Writing aggregate speeders file

Save and test

Enable the trigger

Review

Review

Review

Let's practice!