Pada bab pertama ini, Anda akan mempelajari perbedaan antara pemrosesan batch dan stream, membuat stream pertama Anda, mengelola izinnya, menulis ke stream, dan membacanya.

Apa itu streaming dan mengapa hal itu penting?

Options

Batch

Stream

Batch vs stream

Opsi

Data Producers

..memasukkan data ke Firehose Stream

...yang mengirimkannya ke Destination

Produser dan destination

Mengelola Firehose delivery stream

Bersiap untuk stream pertama

Membuat role

IAM User

Role

Baik IAM Users maupun Roles

Perbandingan IAM users vs. roles

Pilihan

Permissions for User only

Permissions for User and Role

Latihan izin akses

Pembuatan bucket S3

Bekerja dengan delivery stream Firehose

# _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']))


Buat Firehose stream pertama Anda

# _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']))


Menulis ke stream Firehose

# _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())


Membaca data Firehose

Streaming di cloud

Langkah berikutnya dalam perjalanan data streaming Anda adalah mempelajari cara menggunakan fungsi lambda transformasional untuk beralih ke arsitektur tanpa server. Melalui latihan praktik, Anda akan menambahkan lapisan lambda dan memicu fungsi lambda berdasarkan kondisi tertentu.

Menuju serverless

Membuat dan menjalankan fungsi Lambda

Serverless vs server

Komponen fungsi Lambda

Membaca data dari S3 berdasarkan sebuah peristiwa

Lambda live pertama Anda!

Menambahkan lapisan lambda

Alur Lambda

Menggunakan variabel lingkungan

Alur kerja data tanpa server

Lambda cepat vs lambat

Lambda yang dipicu oleh waktu

Kode lambda berbasis jadwal waktu

API tanpa server

Buat Lambda API

Menuju tanpa server

Sekarang Anda siap untuk melakukan encoding dan decoding data streaming serta menganalisis data langsung di dalam stream. Anda bahkan akan menggunakan beberapa stream untuk mendapatkan kecepatan puncak harian kendaraan.

Lambda yang transformatif

Mentransformasikan data di dalam sebuah stream

Menyandi dan mengurai base64

Buat Lambda transformasional

Sebuah lambda transformasi sederhana

Menganalisis data dalam stream

Membuat aplikasi analitik data Kinesis

Kinesis Data Analytics vs Transformational Lambda

Membangun aplikasi analitik data Kinesis

Komponen SQL Kinesis Data Analytics

Menggunakan beberapa stream

Menyampaikan data dari Kinesis Analytics

Dapatkan kecepatan puncak harian

Menggunakan Kinesis Data Analytics untuk peringatan

Menganalisis data streaming

Di bab terakhir ini, Anda akan mempelajari cara memantau kinerja stream menggunakan log, metrik, alarm, dan dasbor. Anda akan menggunakan Elasticsearch dan membangun dasbor Kibana Anda sendiri.

Studi kasus data streaming

Membuat klaster Elasticsearch

RedShift vs Elasticsearch

Memantau kinerja

Dasbor dan alarm CloudWatch

Komponen CloudWatch

Alur pemantauan CloudWatch

Memvisualisasikan data streaming

Bekerja dengan ElasticSearch menggunakan Kibana

CloudWatch vs Elasticsearch

Alur visualisasi Elasticsearch

Pendekatan alternatif

Pendekatan alternatif lainnya

Memantau dan memvisualisasikan data streaming

Apa yang menggerakkan sistem yang kita gunakan setiap hari tanpa kita sadari, seperti deteksi penipuan untuk menjaga keamanan transaksi atau lampu lalu lintas yang membuat arus kendaraan tetap lancar? Jawabannya adalah data streaming (data yang dihasilkan terus-menerus dari berbagai sumber) dan teknologi tanpa server—seperti Amazon Kinesis dan AWS Lambda.
<br /> <br />
Dalam kursus ini, Anda akan mempelajari cara memanfaatkan teknologi kuat tersebut dengan membantu seorang data engineer fiksi bernama Cody. Tujuan Anda adalah membantunya mengumpulkan data streaming waktu nyata dari kendaraan milik kota, menganalisis data tersebut, dan mengirim peringatan yang relevan seperti peringatan kecepatan kepada pengemudi. Dengan Amazon Kinesis dan Firehose, Anda akan belajar cara mengumpulkan data dari jutaan sumber sebelum menggunakan Kinesis Analytics untuk menganalisis data saat data mengalir dalam stream. Anda juga akan membuat fungsi tanpa server di AWS Lambda yang akan memicu tindakan secara kondisional berdasarkan data yang diterima. Pada akhir pelatihan ini Anda akan mengetahui cara membuat dasbor ElasticSearch langsung dengan AWS QuickSight dan CloudWatch—dan semoga membantu Cody menyelesaikan proyek ambisiusnya.


Introduction to AWS Boto in Python

Introduction to Shell

Streaming Concepts

Pelajari cara bekerja dengan data streaming menggunakan teknologi serverless di AWS.

Streaming Data dengan AWS Kinesis dan Lambda

Perangkat pengukur yang terpasang pada kendaraan.

Produser dan destination

Streaming Data dengan AWS Kinesis dan Lambda

Latihan interaktif langsung