मुख्य सामग्री पर जाएं

होम Google Cloud

कोर्स

Build Data Lakes and Data Warehouses on Google Cloud

मध्यमकौशल स्तर

अपडेट किया गया 06/2026

Build modern data lakehouses on Google Cloud using BigQuery, Cloud Storage, Apache Iceberg, BigLake, federated queries, and data governance tools.

मुफ़्त में पाठ्यक्रम शुरू करें

Google CloudCloud

3 घंटे 48 मिनट

52 अभ्यास

2,800 XP

उपलब्धि का प्रमाण पत्र

अपना मुफ़्त खाता बनाएं

Google के साथ जारी रखें अधिक विकल्प दिखाएँ

या

जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।

हजारों कंपनियों के शिक्षार्थियों द्वारा पसंद किया गया

टीम को ट्रेनिंग देना चाहते हैं?

व्यवसाय के लिए आज़माएँ

पाठ्यक्रम विवरण

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.

पूर्व आवश्यकताएं

इस पाठ्यक्रम के लिए कोई पूर्वापेक्षाएं नहीं हैं

1

Course introduction

Introduce the learner to the topics that will be covered in the course and the skills they will learn.

Course Introduction

अध्याय शुरू करें

2

Introduction to Modern Data Engineering on Google Cloud

This module introduces the foundational concepts of data lakes and data warehouses, setting the stage for modern architectures on Google Cloud.

Module 1: Introduction to modern data engineering on Google Cloud

The classics: Data lakes and data warehouses

The modern approach: Data lakehouse

Choosing the right architecture

Data warehouses, lakes, and lakehouses concepts — Question 1

Data warehouses, lakes, and lakehouses concepts — Question 2

Data warehouses, lakes, and lakehouses concepts — Question 3

Data warehouses, lakes, and lakehouses concepts — Question 4

अध्याय शुरू करें

3

Building a Data Lakehouse with Cloud Storage, Open Formats, and BigQuery

This module details the concept of a lakehouse and introduces the Google Cloud products most commonly used to build a modern data lakehouse using open-source formats.

Module 2: Building a data lakehouse with Cloud Storage, open formats, and BigQuery

Building a data lake foundation

Introduction to Apache Iceberg open table format

BigQuery as the central processing engine

Combining operational data in AlloyDB

Combining operational and analytical data with federated queries

Real world use case

Data lakehouse features and benefits — Question 1

Data lakehouse features and benefits — Question 2

Data lakehouse features and benefits — Question 3

Data lakehouse features and benefits — Question 4

Federated query with BigQuery

अध्याय शुरू करें

4

Modernizing Data Warehouses with BigQuery and Lakehouse

This module explores BigQuery as the cornerstone of a modern data warehouse and introduces Lakehouse for unifying access across the data lake and warehouse.

Module 3: Modernizing data warehouses with BigQuery and Lakehouse

BigQuery fundamentals

Partitioning and clustering in BigQuery

Introducing Lakehouse and external tables

Querying external data and Iceberg tables

BigQuery and Lakehouse — Question 1

BigQuery and Lakehouse — Question 2

BigQuery and Lakehouse — Question 3

BigQuery and Lakehouse — Question 4

BigQuery and Lakehouse — Question 5

BigQuery and Lakehouse — Question 6

BigQuery and Lakehouse — Question 7

BigQuery and Lakehouse — Question 8

अध्याय शुरू करें

5

Advanced Lakehouse Patterns and Data Governance

This module focuses on advanced architectural patterns for the lakehouse, including data processing, orchestration, and comprehensive data governance across BigQuery, Cloud Storage, and Lakehouse.

Module 4: Advanced lakehouse patterns and data governance

Data governance and security in a unified platform

Demo: Data Loss Prevention

Analytics and machine learning on the lakehouse

Real-world lakehouse architectures and migration strategies

Security and ML in the lakehouse — Question 1

Security and ML in the lakehouse — Question 2

Security and ML in the lakehouse — Question 3

Security and ML in the lakehouse — Question 4

Security and ML in the lakehouse — Question 5

Security and ML in the lakehouse — Question 6

Security and ML in the lakehouse — Question 7

Security and ML in the lakehouse — Question 8

अध्याय शुरू करें

6

Labs and Best Practices

This module provides labs to deepen skills in the tools and technologies used by a lakehouse on Google Cloud and an overview of best practices, common mistakes, and future trends

Module 5: Labs and best practices

Getting Started with BigQuery ML

Vector search with BigQuery

Review and best practices

अध्याय शुरू करें

7

Course Summary

Summarize the architectural and operational capabilities of the BigQuery-centric data lakehouse, covering governance, advanced analytics, and machine learning

Summary and next steps

अध्याय शुरू करें

Build Data Lakes and Data Warehouses on Google Cloud

पाठ्यक्रम
पूर्ण

उपलब्धि का प्रमाण पत्र अर्जित करें

इस प्रमाण पत्र को अपनी LinkedIn प्रोफ़ाइल, रिज्यूमे या CV में जोड़ें
इसे सोशल मीडिया पर और अपनी प्रदर्शन समीक्षा में साझा करेंअभी नामांकन करें

19 मिलियन से अधिक शिक्षार्थियों के साथ जुड़ें और आज ही Build Data Lakes and Data Warehouses on Google Cloud शुरू करें!

अपना मुफ़्त खाता बनाएं

Google के साथ जारी रखें अधिक विकल्प दिखाएँ

या

जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।

मोबाइल के लिए DataCamp के साथ अपने डेटा कौशल को बढ़ाएं

हमारे मोबाइल कोर्स और दैनिक 5 मिनट की कोडिंग चुनौतियों के साथ चलते-फिरते प्रगति करें।