In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
Pelajari lebih lanjut
Selesaikan badge keahlian pengantar Mendapatkan Insight dari Data BigQuery untuk menunjukkan keterampilan dalam hal berikut: menulis kueri SQL, membuat kueri tabel publik, memuat sampel data ke dalam BigQuery, memecahkan masalah error sintaksis umum dengan validator kueri di BigQuery, dan membuat laporan di Data Studio dengan menghubungkannya ke data BigQuery.
Pelajari lebih lanjut
Selesaikan badge keahlian tingkat menengah Membangun Data Warehouse dengan BigQuery untuk menunjukkan keterampilan Anda dalam hal berikut: menggabungkan data untuk membuat tabel baru, memecahkan masalah penggabungan, menambahkan data dengan union, membuat tabel berpartisi tanggal, serta menggunakan JSON, array, dan struct di BigQuery.
Pelajari lebih lanjut
Selesaikan badge keahlian tingkat menengah Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML untuk menunjukkan keterampilan Anda dalam hal berikut: membangun pipeline transformasi data ke BigQuery dengan Dataprep by Trifacta; menggunakan Cloud Storage, Dataflow, dan BigQuery untuk membangun alur kerja ekstrak, transformasi, dan pemuatan (ETL); serta membangun model machine learning menggunakan BigQuery ML.
Pelajari lebih lanjut
Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
Pelajari lebih lanjut
In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
Pelajari lebih lanjut
While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
Pelajari lebih lanjut
Build Batch Data Pipelines on Google Cloud
Earned Agu 12, 2023 EDT
Mendapatkan Insight dari Data BigQuery
Earned Jul 20, 2023 EDT
Membangun Data Warehouse dengan BigQuery
Earned Jul 16, 2023 EDT
Rekayasa Data untuk Pembuatan Model Prediktif dengan BigQuery ML
Earned Jul 15, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jul 8, 2023 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Jul 6, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jun 20, 2023 EDT