Pemrosesan Waktu Nyata dengan Kinesis
Gunakan Amazon Kinesis Data Streams dan Kinesis Firehose untuk menerima, memproses, dan mengirimkan aliran data berukuran besar secara waktu nyata menggunakan fungsi Lambda.
Pemrosesan Waktu Nyata dengan Kinesis adalah pelajaran Serverless AWS Lambda Development gratis di CoddyKit. Ini adalah pelajaran 3 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Serverless AWS Lambda Development, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Serverless AWS Lambda Development mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
Why Real-time Data Matters
In today's fast-paced world, many applications need to react to data instantly. This is known as real-time data processing.
- Immediate insights: Analyze data as it arrives.
- Quick responses: Trigger actions without delay.
- Enhanced user experience: Personalize content on the fly.
Think of fraud detection, live dashboards, or IoT device monitoring.
Introducing Amazon Kinesis
Amazon Kinesis is a powerful suite of services designed for collecting, processing, and analyzing streaming data in real-time. It helps you handle massive volumes of data efficiently.
Key Kinesis services include:
- Kinesis Data Streams (KDS): For custom applications needing granular control.
- Kinesis Firehose: For easy delivery to data stores like S3 or Redshift.
Kinesis Data Streams (KDS)
Kinesis Data Streams (KDS) is like a continuous pipeline for high-throughput data. It captures data from various sources and makes it available for processing by different applications.
KDS organizes data into shards. A shard is a base unit of throughput. Data producers write to shards, and data consumers read from them.
Producing Data to KDS
Applications send data to KDS using operations like PutRecord or PutRecords. Each record includes a partition key, which KDS uses to group data and route it to a specific shard.
A good partition key ensures even distribution of data across shards, preventing hot spots and maximizing throughput.
Lambda as a KDS Consumer
AWS Lambda functions are excellent consumers for Kinesis Data Streams. You can configure a Lambda event source mapping to automatically invoke your function whenever new data records are available in a KDS stream.
Lambda polls the stream, reads records in batches, and passes them to your function for processing. This makes building real-time processors very efficient.
Processing Kinesis Records
Here's a simple Python Lambda function that processes records from a Kinesis Data Stream. It iterates through the records in the event and prints their data.
Try running this example:
import base64
import json
def lambda_handler(event, context):
for record in event['Records']:
# Kinesis data is base64 encoded
payload = base64.b64decode(record['kinesis']['data']).decode('utf-8')
print(f"Processed record: {payload}")
return {'statusCode': 200}Kinesis Firehose for Delivery
Kinesis Firehose is a fully managed service for delivering real-time streaming data to destinations like Amazon S3, Amazon Redshift, Amazon OpenSearch Service, or HTTP endpoints.
Unlike KDS, Firehose requires almost no administration. You simply create a delivery stream, specify your source and destination, and Firehose handles all the scaling, buffering, and delivery.
Firehose Destinations
Firehose is designed for simplified data delivery. It automatically batches, compresses, and encrypts data before sending it to your chosen destination.
Common destinations include:
- Amazon S3: For long-term storage and data lakes.
- Amazon Redshift: For data warehousing and analytics.
- Amazon OpenSearch Service: For logging and search.
- HTTP endpoints: For custom integrations.
Transform Data with Firehose & Lambda
Kinesis Firehose can integrate with Lambda to transform incoming data before it's delivered to its final destination. This is incredibly useful for cleaning, enriching, or reformatting data on the fly.
You configure a Lambda function within your Firehose delivery stream, and Firehose invokes it for each batch of records, expecting transformed records in return.
Kinesis Service Comparison
Which statements accurately describe the differences or uses of Kinesis Data Streams (KDS) and Kinesis Firehose?
Recap & Next Steps
You've learned about Amazon Kinesis, a key service for real-time data processing.
- Kinesis Data Streams (KDS): Offers flexible, shard-based streaming for custom applications, often consumed by Lambda.
- Kinesis Firehose: Provides a managed solution for delivering streaming data to various destinations, with optional Lambda transformation.
These services, combined with Lambda, enable powerful event-driven architectures for handling massive data streams in real-time.
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Pemrosesan Waktu Nyata dengan Kinesis” gratis?
Ya — teks lengkap “Pemrosesan Waktu Nyata dengan Kinesis” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Serverless AWS Lambda Development, upgrade ke CoddyKit PRO. Kursus Serverless AWS Lambda Development mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Pemrosesan Waktu Nyata dengan Kinesis”?
Gunakan Amazon Kinesis Data Streams dan Kinesis Firehose untuk menerima, memproses, dan mengirimkan aliran data berukuran besar secara waktu nyata menggunakan fungsi Lambda. Kamu berlatih Serverless AWS Lambda Development dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
Apakah aku perlu pengalaman untuk memulai Serverless AWS Lambda Development?
Tidak diperlukan pengalaman sebelumnya. Serverless AWS Lambda Development di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 3 dari 4.
Berapa lama pelajaran “Pemrosesan Waktu Nyata dengan Kinesis” memakan waktu?
Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.
Bisakah aku menulis dan menjalankan kode dalam pelajaran Serverless AWS Lambda Development ini?
Ya. Setiap pelajaran Serverless AWS Lambda Development menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.
Semua pelajaran dalam kursus ini
- Membangun Layanan Mikro Berbasis Peristiwa
- Mengintegrasikan dengan Amazon EventBridge
- Pemrosesan Waktu Nyata dengan Kinesis
- Pola Saga untuk Transaksi Terdistribusi