In this first chapter, you will learn about the differences between batch and stream processing, create your first stream, manage its permissions, write to it and read from it.

What is streaming and why does it matter?

Batch vs stream

Producers and destinations

Managing Firehose delivery streams

Getting ready for the first stream

Creating roles

IAM users vs. roles

Permissions practice

S3 bucket creation

Working with the Firehose delivery stream

Create your first Firehose stream

Writing to a Firehose stream

Reading Firehose data

Streaming in the cloud

The next step in your streaming data journey is learning how to use transformational lambda functions to go serverless. Through hands-on exercises, you will add lambda layers and trigger lambda functions on specific conditions.

Serverlos werden

Lambda-Funktionen erstellen und ausführen

Optionen

Großartig für einen Server

Großartig für Serverless

Serverlos vs. Server

Auslöser

Handler

Ebene

Komponenten der Lambda-Funktion

# EDIT ME!
# recordReaderS3/lambda_function.py
# This is lambda handler code that will execute each time an object is added to S3.
import json

def record_created_handler(____, ____):
    # Create a list to store new object keys
    written_objects = []

    # Iterate over each S3 event record
    for record in event['Records']:
       
        # Get the variables to check for
        event_name = record['eventName']
        bucket_name = record['s3']['bucket']['name']
        obj_key = record['s3']['object']['key']

        # Verify that the object was written to S3
        if event_name == 'ObjectCreated:____' and bucket_name == 'sd-vehicle-data':
           
            # If so, then add it to the written_objects list
            written_objects.append(obj_key)
    
    # Return the collected written objects
    return {
        "bucket": 'sd-vehicle-data',
        "new_objects": written_objects
    }

# PLEASE DO NOT EDIT
# run_lambda.py
# This executes a lambda function with a sample event.
from dcs.util.aws_lambda import * 
import json


# Clean up any old lambda functions
clean_lambdas()

# Create a new lambda 
create_lambda("recordReaderS3", "record_created_handler")

# Read the test event JSON file as dictionary
with open("./dcs/sample_events/s3_event_c2l1.json") as json_file:
    payload = json.load(json_file)

# Invoke lambda with test event, print result
result = invoke_function_and_get_message("recordReaderS3", payload)
print(json.dumps(json.loads(result), indent=4))

# EDIT ME!
# recordReaderS3/lambda_function.py
# This is lambda handler code that will execute each time an object is added to S3
import json

def record_created_handler(event, context):
    # Create a list to store new object keys
    written_objects = []

    # Iterate over each S3 event record
    for record in event['Records']:
       
        # Get the variables to check for
        event_name = record['eventName']
        bucket_name = record['s3']['bucket']['name']
        obj_key = record['s3']['object']['key']

        # Verify that the object was written to S3
        if event_name == 'ObjectCreated:Put' and bucket_name == 'sd-vehicle-data':
           
            # If so, then add it to the written_objects list
            written_objects.append(obj_key)
    
    # Return the collected written objects
    return {
        "bucket": 'sd-vehicle-data',
        "new_objects": written_objects
    }                 

# PLEASE DO NOT EDIT
# run_lambda.py
# This executes a lambda function with a sample event
from dcs.util.aws_lambda import * 
import json


# Clean up any old lambda functions
clean_lambdas()

# Create a new lambda 
create_lambda("recordReaderS3", "record_created_handler")

# Read the test event JSON file as dictionary
with open("./dcs/sample_events/s3_event_c2l1.json") as json_file:
    payload = json.load(json_file)

# Invoke lambda with test event, print result
result = invoke_function_and_get_message("recordReaderS3", payload)
print(json.dumps(json.loads(result), indent=4))

Lesen von Daten aus S3 auf der Grundlage eines Ereignisses

Dein erstes Live-Lambda!

Hinzufügen einer Lambda-Ebene

Lambda Flow

Lambda flow

Umgebungsvariablen verwenden

Serverloser Daten-Workflow

Schnell laufende Lambdas

Lambdas, die länger brauchen, um zu laufen

Schnelle vs. langsame Lambdas

Erstellen eines zeitgesteuerten Lambdas

Ein zeitgesteuertes Lambda

Zeitgesteuerter Lambda-Code

Serverlose APIs

API-gesteuerte Lambdas erstellen

Ein API-Lambda erstellen

import boto3

lambda_client = boto3.client('lambda', aws_access_key_id="12345",
    aws_secret_access_key="12345",
    region_name='us-east-1',
    endpoint_url="http://localhost:4574")

def trigger_full_report_builder(event, context):
    async = event['____']['async']
    if async == 'true':
        exec_type = '____'
    else:
        exec_type = 'RequestResponse'
        
    result = lambda_client.____(
      FunctionName='fullReportBuilder',
      InvocationType=exec_type)
    
    return {
        'statusCode': 200,
        'headers': {
            "content-type" : "application/json"
        },
        'body': "Successfully Invoked"
    }

import awswrangler_c as wr
import boto3
# Initialize boto3 session
session = boto3.Session(aws_access_key_id = "12345", 
  aws_secret_access_key = "12345", 
  region_name="us-east-1")

def full_report_builder(event, context):
  # Read all records in the speeders folder
  speeders_total = wr.s3.read_csv('s3://sd-vehicle-data/speeders', 
                                  boto3_session = session, 
                                  delimiter=" ")

  # Write aggregated speeders file
  return wr.s3.to_csv(df = speeders_total, 
               boto3_session=session, 
               path="s3://sd-vehicle-data/speeders-full/full.csv")
               

# PLEASE DO NOT EDIT
# aws_wrangler_c.py
def read_csv(path, boto3_session, delimiter):
  bucket = path.split("//")[1].split("/")[0]
  prefix = path.split("//")[1].replace(f"{bucket}/", "")
  objects = s3.list_objects_v2(Bucket=bucket, Prefix=prefix)['Contents']
  dfs = []
  for o in objects:
      key = o['Key']
      data = s3.get_object(Bucket='sd-vehicle-data', Key=key)
      dfs.append(pd.read_csv(data['Body'], delimiter=delimiter))
  return pd.concat(dfs)
  
def to_csv(df, path, boto3_session, separator=","):
  bucket = path.split("//")[1].split("/")[0]
  key = path.split("//")[1].replace(f"{bucket}/", "")
  s3.put_object(Bucket=bucket, Key=key, Body=df.to_csv(sep=separator, index=False))
  return True

s3 = {
    "to_csv": to_csv,
    "read_csv": read_csv
}
                                                  

# PLEASE DO NOT EDIT
# run_lambda.py
# This executes a lambda function with a sample event.
from dcs.util.aws_lambda import * 
import json

# Clean up any old lambda functions
clean_lambdas()

# Create a new lambda 
create_lambda("fullReportBuilder", "full_report_builder")
create_lambda("triggerFullReportBuilder", "trigger_full_report_builder")


# Invoke lambda with test event, print result.
result = invoke_function_and_get_message("triggerFullReportBuilder", {
  "queryStringParameters": {
    "async": "true"
  },
  "isBase64Encoded": "false"
})
print(result)

import boto3

lambda_client = boto3.client('lambda', aws_access_key_id="12345",
    aws_secret_access_key="12345",
    region_name='us-east-1',
    endpoint_url="http://localhost:4574")

def trigger_full_report_builder(event, context):
    async = event['queryStringParameters']['async']
    if async == 'true':
        exec_type = 'Event'
    else:
        exec_type = 'RequestResponse'
        
    result = lambda_client.invoke(
      FunctionName='fullReportBuilder',
      InvocationType=exec_type)
    
    return {
        'statusCode': 200,
        'headers': {
            "content-type" : "application/json"
        },
        'body': "Successfully Invoked"
    }

    

import awswrangler_c as wr
import boto3
# Initialize boto3 session
session = boto3.Session(aws_access_key_id = "12345", 
  aws_secret_access_key = "12345", 
  region_name="us-east-1")

def full_report_builder(event, context):
  # Read all records in the speeders folder
  speeders_total = wr.s3.read_csv('s3://sd-vehicle-data/speeders', 
                                  boto3_session = session, 
                                  delimiter=" ")

  # Write aggregated speeders file
  return wr.s3.to_csv(df = speeders_total, 
               boto3_session=session, 
               path="s3://sd-vehicle-data/speeders-full/full.csv")
               
  

# PLEASE DO NOT EDIT
# run_lambda.py
# This executes a lambda function with a sample event.
from dcs.util.aws_lambda import * 
import json

# Clean up any old lambda functions
clean_lambdas()

# Create a new lambda 
create_lambda("fullReportBuilder", "full_report_builder")
create_lambda("triggerFullReportBuilder", "trigger_full_report_builder")

# Invoke lambda with test event, print result.
result = invoke_function_and_get_message("triggerFullReportBuilder", {
  "queryStringParameters": {
    "async": "true"
  },
  "isBase64Encoded": "false"
})
print(result)

Eine serverlose API

Going serverless

You're now ready to encode and decode streaming data and analyze data directly in the stream. You will even use multiple streams to get daily vehicle top speeds.

A transformational Lambda

Transforming data inside a stream

Encoding and decoding base64

Create a transformational lambda

A barebones transformational lambda

Analyzing data in the stream

Creating a Kinesis data analytics application

Kinesis data analytics vs transformational Lambda

Building a Kinesis data analytics application

Kinesis data analytics SQL components

Using multiple streams

Delivering data from Kinesis Analytics

Get the daily top speed

Using Kinesis data analytics for alerts

Analyzing streaming data

In this final chapter, you will discover how to monitor your stream's performance using logs, metrics, alarms and dashboards. You will use Elasticsearch and build your own Kibana dashboard.

Streaming data case study

Creating an Elasticsearch cluster

RedShift vs Elasticsearch

Monitoring performance

Cloudwatch dasbhoards and alarms

Cloudwatch components

Cloudwatch monitoring flow

Visualizing streaming data

Working with ElasticSearch using Kibana

Cloudwatch vs Elasticsearch

Elasticsearch visualization flow

An alternative approach

Another alternative approach

Monitoring and visualizing streaming data

Was treibt die Systeme an, die wir jeden Tag benutzen, ohne es zu merken, wie z.B. die Betrugserkennung, die unsere Transaktionen sicher macht, oder die Verkehrssignale, die für einen reibungslosen Verkehrsfluss sorgen? Die Antwort sind Streaming-Daten (Daten, die kontinuierlich von verschiedenen Quellen erzeugt werden) und serverlose Technologien wie Amazon Kinesis und AWS Lambda.
<br /> <br />
In diesem Kurs lernst du, wie du diese leistungsstarken Technologien nutzen kannst, indem du einem fiktiven Dateningenieur namens Cody hilfst. Dein Ziel ist es, ihr dabei zu helfen, Echtzeit-Datenströme von stadteigenen Fahrzeugen zu sammeln, die Daten zu analysieren und relevante Warnungen wie Geschwindigkeitswarnungen an die Fahrer zu senden. Mit Amazon Kinesis und Firehose lernst du, wie du Daten aus Millionen von Quellen einlesen kannst, bevor du Kinesis Analytics nutzt, um die Daten zu analysieren, während sie sich durch den Stream bewegen. Du wirst auch serverlose Funktionen in AWS Lambda erstellen, die basierend auf den empfangenen Daten bedingte Aktionen auslösen. Am Ende dieser Schulung wirst du wissen, wie du mit AWS QuickSight und CloudWatch Live-Dashboards mit ElasticSearch erstellst - und hoffentlich Cody geholfen haben, ihr ehrgeiziges Projekt zu vollenden.


Introduction to AWS Boto in Python

Introduction to Shell

Streaming Concepts

Erfahren Sie, wie Sie mit serverlosen Technologien auf AWS Streaming-Daten verarbeiten.

Datenstreaming mit AWS Kinesis und Lambda

In diesem Kurs lernst du den Umgang mit kontinuierlichen Datenströmen mithilfe von serverlosen Technologien basierend auf AWS.

Firehose schreibt eingehende Datensätze in den S3 Bucket.

Das von S3 ausgelöste PutObject-Ereignis triggert unsere Lambda-Funktion

Lambda Layer ermöglicht die Verwendung von Pandas für den Lambda Handler

Lambda-Funktionshandler führt aus und löst SNS Benachrichtigung aus

Lambda flow

Interaktive praktische Übung