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.

Akış nedir ve neden önemlidir?

Options

Batch

Stream

Batch vs stream

Seçenekler

Veri Üreticileri

..veriyi Firehose Stream'e koyar

...ve Firehose bunu Hedefe iletir

Üreticiler ve hedefler

Firehose delivery stream'lerini yönetme

İlk akışa hazırlanma

Roller oluşturma

IAM Kullanıcısı

Hem IAM Kullanıcıları hem de Roller

IAM kullanıcıları ve roller

Yalnızca Kullanıcı için İzinler

Kullanıcı ve Rol için İzinler

İzin pratiği

S3 bucket oluşturma

Firehose teslim akışıyla çalışma

# _setup.py: Create clients, load data, etc. No need to edit.

import boto3

# Create firehose client
firehose = boto3.client('firehose', 
    aws_access_key_id="None", 
    aws_secret_access_key="None", 
    region_name='us-east-1', 
    endpoint_url="http://localhost:4573")

# Create s3 client
s3 = boto3.client('s3', 
    aws_access_key_id="None", 
    aws_secret_access_key="None", 
    region_name='us-east-1', 
    endpoint_url="http://localhost:4572")

# Prep variables for export
ex_vars = [firehose, s3]


# create_firehose.py: Create firehose stream. No need to edit.
import _setup
firehose, s3 = _setup.ex_vars

# Create s3 bucket
s3.create_bucket(Bucket='sd-vehicle-data')

# Create Firehose delivery stream
res = firehose.____(
    DeliveryStreamName="gps-delivery-stream",
    DeliveryStreamType="DirectPut",
    # Specify configuration of the destination S3 bucket
    ____ = {
        "BucketARN": "arn:aws:s3:::sd-vehicle-data",
        "RoleARN": "arn:aws:iam::0000000:role/firehoseDeliveryRole"
    })
    
# Print the stream ARN
print("Created Firehose Stream ARN: {}".format(res['DeliveryStreamARN']))


# _setup.py: Create clients, load data, etc. No need to edit.
import boto3

# Create firehose client
firehose = boto3.client('firehose', 
    aws_access_key_id="None", 
    aws_secret_access_key="None", 
    region_name='us-east-1', 
    endpoint_url="http://localhost:4573")

# Create s3 client
s3 = boto3.client('s3', 
    aws_access_key_id="None", 
    aws_secret_access_key="None", 
    region_name='us-east-1', 
    endpoint_url="http://localhost:4572")

# Prep variables for export
ex_vars = [firehose, s3]


# create_firehose.py: Create firehose stream. No need to edit.
import _setup
firehose, s3 = _setup.ex_vars

# Create s3 bucket
s3.create_bucket(Bucket='sd-vehicle-data')

# Create Firehose delivery stream
res = firehose.create_delivery_stream(
    DeliveryStreamName =  "gps-delivery-stream",
    DeliveryStreamType = "DirectPut",
    
    # Specify configuration of the destination S3 bucket
    S3DestinationConfiguration = {
        "BucketARN": "arn:aws:s3:::sd-vehicle-data",
        "RoleARN": "arn:aws:iam::0000000:role/firehoseDeliveryRole"
    })
    
# Print the stream ARN
print("Created Firehose Stream ARN: {}".format(res['DeliveryStreamARN']))


İlk Firehose akışını oluştur

# _setup.py: Create clients, load data, etc. No need to edit.
import boto3, pandas as pd

# Load Data
data_url = "https://assets.datacamp.com/production/repositories/5668/datasets/6bba555e0e42ae31d1d634256679db718cfb8d76/vehicles.csv"
records = pd.read_csv(data_url).head(100)

# Create firehose client
firehose = boto3.client('firehose', 
    aws_access_key_id="None", 
    aws_secret_access_key="None", 
    region_name='us-east-1', 
    endpoint_url="http://localhost:4573")

# Create s3 client
s3 = boto3.client('s3', 
    aws_access_key_id="None", 
    aws_secret_access_key="None", 
    region_name='us-east-1', 
    endpoint_url="http://localhost:4572")
    
# Prep variables for export
ex_vars = [firehose, s3, records]
    


# create_firehose.py: Create firehose stream. No need to edit.
import _setup
firehose, s3, records = _setup.ex_vars

# Create s3 bucket
s3.create_bucket(Bucket='sd-vehicle-data')

# Create Firehose delivery stream
res = firehose.create_delivery_stream(
    DeliveryStreamName =  "gps-delivery-stream",
    DeliveryStreamType = "DirectPut",
    
    # Specify configuration of the destination S3 bucket
    S3DestinationConfiguration = {
        "BucketARN": "arn:aws:s3:::sd-vehicle-data",
        "RoleARN": "arn:aws:iam::0000000:role/firehoseDeliveryRole"
    })
    


# OBD2_sensors.py: Write to Firehose stream. EDIT HERE.
import _setup, create_firehose
firehose, s3, records = _setup.ex_vars
for idx, row in records.iterrows(): 

    # Create a payload string that ends with a newline
    payload = ' '.join(str(value) for value in row) 
    payload = payload + "\n"
    print("Sending payload: {}".format(payload))

    # Send the payload string to Firehose stream
    res = firehose.____(
        DeliveryStreamName = 'gps-delivery-stream',
        Record = {'____': payload})

    # Print the written RecordId
    print("Wrote to RecordId: {}".format(res['RecordId']))


# OBD2_sensors.py: Write to Firehose stream. EDIT HERE.
import _setup, create_firehose
firehose, s3, records = _setup.ex_vars
for idx, row in records.iterrows(): 

    # Create a payload string that ends with a newline
    payload = ' '.join(str(value) for value in row) 
    payload = payload + "\n"
    print("Sending payload: {}".format(payload))

    # Send the payload string to Firehose stream
    res = firehose.put_record(
        DeliveryStreamName = 'gps-delivery-stream',
        Record = {'Data': payload})

    # Print the written RecordId
    print("Wrote to RecordId: {}".format(res['RecordId']))


Bir Firehose akışına yazma

# _setup.py: Create clients, load data, etc. No need to edit.
import boto3, pandas as pd

# Load Data
data_url = "https://assets.datacamp.com/production/repositories/5668/datasets/6bba555e0e42ae31d1d634256679db718cfb8d76/vehicles.csv"
records = pd.read_csv(data_url).sample(100)

# Create firehose client
firehose = boto3.client('firehose', 
    aws_access_key_id="None", 
    aws_secret_access_key="None", 
    region_name='us-east-1', 
    endpoint_url="http://localhost:4573")

# Create s3 client
s3 = boto3.client('s3', 
    aws_access_key_id="None", 
    aws_secret_access_key="None", 
    region_name='us-east-1', 
    endpoint_url="http://localhost:4572")
    
# Prep variables for export
ex_vars = [firehose, s3, records]
    


# _run_deps.py: Execute dependencies. No need to edit.
import create_firehose
import OBD2_sensors


# create_firehose.py: Create firehose stream and s3 bucket. No need to edit.
import _setup
firehose, s3, records = _setup.ex_vars

# Create s3 bucket
s3.create_bucket(Bucket='sd-vehicle-data')

# Create Firehose delivery stream
res = firehose.create_delivery_stream(
    DeliveryStreamName =  "gps-delivery-stream",
    DeliveryStreamType = "DirectPut",
    
    # Specify configuration of the destination S3 bucket
    S3DestinationConfiguration = {
        "BucketARN": "arn:aws:s3:::sd-vehicle-data",
        "RoleARN": "arn:aws:iam::0000000:role/firehoseDeliveryRole"
    })
    


# OBD2_sensors.py: Write OBD2 sensor data. No need to edit.
import _setup
firehose, s3, records = _setup.ex_vars
for idx, row in records.iterrows(): 

    # Create a payload string that ends with a newline
    payload = ' '.join(str(value) for value in row) 
    payload = payload + "\n"

    # Send the payload string to Firehose stream
    res = firehose.put_record(
        DeliveryStreamName = 'gps-delivery-stream',
        Record = {'Data': payload})
    


# analyze_data.py: Analyze written sensor data. EDIT HERE.
import _setup, _run_deps, pandas as pd
firehose, s3, records = _setup.ex_vars

# List the objects that have been written to the S3 bucket
objects = s3.____(Bucket='sd-vehicle-data')['Contents']

# Create list for collecting dataframes from read files.
dfs = []

# For every object, load it from S3
for obj in objects:
    data_file = s3.____(Bucket='sd-vehicle-data', ____=obj['Key'])

    # Load it into a dataframe, specifying a delimiter and column names
    dfs.append(pd.read_csv(data_file['Body'], 
                           delimiter = " ", 
                           names=["record_id", "timestamp", "vin", "lon", "lat", "speed"]))

# Concatenate the resulting dataframes.
data = pd.concat(dfs)
print(data.groupby(['vin'])['speed'].max())

# OBD2_sensors.py: Analyze written sensor data. EDIT HERE.
import _setup, _run_deps, pandas as pd
firehose, s3, records = _setup.ex_vars

# List the objects that have been written to the S3 bucket
objects = s3.list_objects(Bucket='sd-vehicle-data')['Contents']

# Create list for collecting dataframes from read files.
dfs = []

# For every object, load it from S3
for obj in objects:
    data_file = s3.get_object(Bucket='sd-vehicle-data', Key=obj['Key'])

    # Load it into a dataframe, specifying a delimiter and column names
    dfs.append(pd.read_csv(data_file['Body'], 
                           delimiter = " ", 
                           names=["record_id", "timestamp", "vin", "lon", "lat", "speed"]))

# Concatenate the resulting dataframes.
data = pd.concat(dfs)
print(data.groupby(['vin'])['speed'].max())


Firehose verilerini okuma

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.

Going serverless

Creating and running Lambda functions

Serverless vs servers

Lambda function components

Reading data from S3 based on an event

Your first live lambda!

Adding a lambda layer

Lambda flow

Using environment variables

Serverless data workflow

Fast vs slow lambdas

A time-triggered lambda

Time-triggered lambda code

Serverless APIs

Create an API lambda

A serverless API

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

Günlük hayatta fark etmeden kullandığımız sistemleri — örneğin işlemlerimizi güvende tutan dolandırıcılık tespiti ya da trafiğin akışını düzenleyen trafik ışıkları — hangi teknoloji çalıştırıyor? Cevap: akış verileri (farklı kaynaklar tarafından sürekli üretilen veriler) ve Amazon Kinesis ile AWS Lambda gibi sunucusuz teknolojiler.
<br /> <br />
Bu derste, bu güçlü teknolojilerden nasıl yararlanacağını, kurgusal bir veri mühendisi olan Cody’e yardım ederek öğreneceksin. Amacın, şehir araçlarından gerçek zamanlı akış verisi toplamasına, bu veriyi analiz etmesine ve sürücülere hız uyarıları gibi ilgili bildirimler göndermesine yardımcı olmak. Amazon Kinesis ve Firehose kullanarak milyonlarca kaynaktan veri almayı; ardından veri akış içindeyken Kinesis Analytics ile analiz etmeyi öğreneceksin. AWS Lambda’da, gelen veriye göre koşullu olarak eylem tetikleyen sunucusuz fonksiyonlar da çalıştıracaksın. Eğitimin sonunda AWS QuickSight ve CloudWatch ile canlı ElasticSearch panoları oluşturmayı bilecek — ve umarız Cody’nin iddialı projesini tamamlamasına yardım etmiş olacaksın.


Introduction to AWS Boto in Python

Introduction to Shell

Streaming Concepts

AWS üzerinde sunucusuz teknolojilerle akış verileriyle çalışmayı öğrenin.

AWS Kinesis ve Lambda ile Akış Verileri

AWS'de sunucusuz teknolojileri kullanarak akış verileriyle nasıl çalışılacağını öğrenin.

Konsola giriş yapabilir (izinler verildiyse)

Diğer AWS hizmetleri (güvenilen varlıklar), ek izinler almak için bir şapka gibi takabilir

Asla bir AWS ACCESS KEY ve SECRET'e sahip OLAMAZ

IAM kullanılarak kendilerine izin ilkeleri atanır

IAM kullanıcıları ve roller

AWS Kinesis ve Lambda ile Akış Verileri

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