Data Engineering on Google Cloud

Kursus ini mempersiapkan para peserta untuk mendesain dan membangun data pipelines di Google Cloud Platform. Di kursus ini, peserta akan diajarkan cara mendesain data processing system, membuat end-to-end data pipelines, menganalisa data dan membangun insight.


Kursus berdurasi 32 jam yang dipandu oleh instruktur ini memberi peserta pengantar langsung untuk merancang dan membuat pipeline data di Google Cloud Platform. Melalui kombinasi presentasi, demo, dan praktik langsung, peserta akan mempelajari cara merancang sistem pemrosesan data, membangun pipeline data ujung ke ujung, menganalisis data, dan memperoleh wawasan. Kursus ini mencakup data terstruktur, tidak terstruktur, dan streaming.

Setelah mengikuti kursus ini, peserta diharapkan akan mampu:

  • Mendesain dan membangun sistem pemrosesan data di Google Cloud Platform Proses batch dan streaming data dengan mengimplementasikan pipeline data penskalaan otomatis di Cloud Dataflow.
  • Mendapatkan wawasan bisnis dari kumpulan data yang sangat besar menggunakan Google BigQuery Train.
  • Mengevaluasi dan memprediksi menggunakan model pembelajaran mesin menggunakan Tensorflow dan Cloud ML.
  • Memanfaatkan data tidak terstruktur menggunakan Spark dan ML API di Cloud Dataproc.
  • Megaktifkan insight instan dari data streaming.

Siapakah yang bisa mengikuti kursus ini?

Kelas ini ditujukan bagi engineer yang bertanggung jawab untuk:

  • Mengekstrak, memuat, mengubah, membersihkan, dan memvalidasi data.
  • Merancang pipeline dan arsitektur untuk pemrosesan data.
  • Mengintegrasikan kemampuan analitik dan pembelajaran mesin ke dalam pipeline data.
  • Membuat kueri set data, memvisualisasikan hasil kueri, dan membuat laporan.

Prasyarat

Untuk mendapatkan hasil yang maksimal dari kursus ini, peserta harus sudah menyelesaikan dan memahami dasar-dasar Google Cloud: Course Big Data & Machine Learning atau memiliki pengalaman yang setara dengan kemahiran dasar dengan bahasa kueri umum seperti SQL.

Selain itu calon peserta juga sudah harus memiliki pengalaman dengan pemodelan data, mengekstrak, mengubah, memuat aktivitas serta bisa mengembangkan aplikasi yang menggunakan bahasa pemrograman umum seperti Python.

Calon peserta juga harus sudah familiar dengan Machine Learning dan/atau statistik.

Course Outline

  • Module 1: Introduction to Data Engineering
    • Explore the role of a data engineer.
    • Analyze data engineering challenges.
    • Intro to BigQuery.
    • Data Lakes and Data Warehouses.
    • Demo: Federated Queries with BigQuery.
    • Transactional Databases vs Data Warehouses.
    • Website Demo: Finding PII in your dataset with DLP API.
    • Partner effectively with other data teams.
    • Manage data access and governance.
    • Build production-ready pipelines.
    • Review GCP customer case study.
    • Lab: Analyzing Data with BigQuery.
  • Module 2: Building a Data Lake
    • Introduction to Data Lakes.
    • Data Storage and ETL options on GCP.
    • Building a Data Lake using Cloud Storage.
    • Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
    • Securing Cloud Storage.
    • Storing All Sorts of Data Types.
    • Video Demo: Running federated queries on Parquet and ORC files in BigQuery.
    • Cloud SQL as a relational Data Lake.
    • Lab: Loading Taxi Data into Cloud SQL.
  • Module 3: Building a Data Warehouse
    • The modern data warehouse.
    • Intro to BigQuery.
    • Demo: Query TB+ of data in seconds.
    • Getting Started.
    • Loading Data.
    • Video Demo: Querying Cloud SQL from BigQuery.
    • Lab: Loading Data into BigQuery.
    • Exploring Schemas.
    • Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
    • Schema Design.
    • Nested and Repeated Fields.
    • Demo: Nested and repeated fields in BigQuery.
    • Lab: Working with JSON and Array data in BigQuery.
    • Optimizing with Partitioning and Clustering.
    • Demo: Partitioned and Clustered Tables in BigQuery.
    • Preview: Transforming Batch and Streaming Data.
  • Module 4: Introduction to Building Batch Data Pipelines
    • EL, ELT, ETL.
    • Quality considerations.
    • How to carry out operations in BigQuery.
    • Demo: ELT to improve data quality in BigQuery.
    • Shortcomings.
    • ETL to solve data quality issues.
  • Module 5: Executing Spark on Cloud Dataproc
    • The Hadoop ecosystem.
    • Running Hadoop on Cloud Dataproc.
    • GCS instead of HDFS.
    • Optimizing Dataproc.
    • Lab: Running Apache Spark jobs on Cloud Dataproc.
  • Module 6: Serverless Data Processing with Cloud Dataflow
    • Cloud Dataflow.
    • Why customers value Dataflow.
    • Dataflow Pipelines.
    • Lab: A Simple Dataflow Pipeline (Python/Java).
    • Lab: MapReduce in Dataflow (Python/Java).
    • Lab: Side Inputs (Python/Java).
    • Dataflow Templates.
    • Dataflow SQL.
  • Module 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
    • Building Batch Data Pipelines visually with Cloud Data Fusion.
    • Components.
    • UI Overview.
    • Building a Pipeline.
    • Exploring Data using Wrangler.
    • Lab: Building and executing a pipeline graph in Cloud Data Fusion.
    • Orchestrating work between GCP services with Cloud Composer.
    • Apache Airflow Environment.
    • DAGs and Operators.
    • Workflow Scheduling.
    • Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
    • Monitoring and Logging.
    • Lab: An Introduction to Cloud Composer.
  • Module 8: Introduction to Processing Streaming Data
    • Processing Streaming Data.
  • Module 9: Serverless Messaging with Cloud Pub/Sub
    • Cloud Pub/Sub.
    • Lab: Publish Streaming Data into Pub/Sub.
  • Module 10: Cloud Dataflow Streaming Features
    • Cloud Dataflow Streaming Features.
    • Lab: Streaming Data Pipelines.
  • Module 11: High-Throughput BigQuery and Bigtable Streaming Features
    • BigQuery Streaming Features.
    • Lab: Streaming Analytics and Dashboards.
    • Cloud Bigtable.
    • Lab: Streaming Data Pipelines into Bigtable.
  • Module 12: Advanced BigQuery Functionality and Performance
    • Analytic Window Functions.
    • Using With Clauses.
    • GIS Functions.
    • Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
    • Performance Considerations.
    • Lab: Optimizing your BigQuery Queries for Performance.
    • Optional Lab: Creating Date-Partitioned Tables in BigQuery.
  • Module 13: Introduction to Analytics and AI
    • What is AI?.
    • From Ad-hoc Data Analysis to Data Driven Decisions.
    • Options for ML models on GCP.
  • Module 14: Prebuilt ML model APIs for Unstructured Data
    • Unstructured Data is Hard.
    • ML APIs for Enriching Data.
    • Lab: Using the Natural Language API to Classify Unstructured Text.
  • Module 15: Big Data Analytics with Cloud AI Platform Notebooks
    • Whats a Notebook.
    • BigQuery Magic and Ties to Pandas.
    • Lab: BigQuery in Jupyter Labs on AI Platform.
  • Module 16: Production ML Pipelines with Kubeflow
    • Ways to do ML on GCP.
    • Kubeflow.
    • AI Hub.
    • Lab: Running AI models on Kubeflow.
  • Module 17: Custom Model building with SQL in BigQuery ML
    • BigQuery ML for Quick Model Building.
    • Demo: Train a model with BigQuery ML to predict NYC taxi fares.
    • Supported Models.
    • Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
    • Lab Option 2: Movie Recommendations in BigQuery ML.
  • Module 18: Custom Model building with Cloud AutoML
    • Why Auto ML?
    • Auto ML Vision.
    • Auto ML NLP.
    • Auto ML Tables.

Jadwal

TanggalPukulBiaya (per pax;
belum termasuk
VAT 10%)
TrainerVenueDaftar
21 Jun 2021
22 Jun 2021
23 Jun 2021
24 Jun 2021
9:00 – 17:00 WIBRp 14,5 jutaRocky van SchuylenburchOnlineDAFTAR
5 Jul 2021
6 Jul 2021
7 Jul 2021
8 Jul 2021
9:00 – 17:00 WIBRp 14,5 jutaElkana PariwonoOnlineDAFTAR
2 Aug 2021
3 Aug 2021
4 Aug 2021
5 Aug 2021
9:00 – 17:00 WIBRp 14,5 jutaRocky van SchuylenburchOnlineDAFTAR
TBA: To be announced

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