In this lab, you will understand first-hand the difference between some of Google Cloud's Big Data solutions: BigQuery, Dataproc, and Dataflow. Specifically, you will create a BigQuery Dataset and table. You will then upload data to BigQuery directly, using Dataproc and Dataflow.
The following provides a brief high-level comparison between the services to review before starting the lab.
Comparing BigQuery, Dataproc, and Dataflow
BigQuery is a serverless data warehouse that can ingest, store, and query petabyte-scale data. BigQuery provides you with SQL capabilities for querying the data.
Cloud Dataproc is a managed Hadoop platform that includes Apache Spark, Flink, Hive, and other open-source tools and frameworks. Dataproc will provide you with full programming language capabilities in contrast to BigQuery's SQL-only query interface. If you need complex scripts to transform your data, then Dataproc may be a good solution.
Cloud Dataflow is a serverless data processing service fully managed by GCP that provides you with a platform for Apache Beam projects without having to worry about the underlying layer of a cluster, including load balancing and auto-scaling the number of workers for a job. In contrast to Dataproc where your code is tightly coupled to the job runner, i.e. the underlying platform, Cloud Dataflow allows you to focus on your business logic rather than focusing on how the underlying layer works. Cloud Dataflow also offers various ready-made templates from which to choose when establishing a task, making the process even easier.
The pricing models for each service also differ. BigQuery pricing varies based on the amount of data stored, ingested, and amount of data processed while executing queries. Dataproc pricing is based on the number of virtual CPUs in the cluster. Dataflow pricing is based on the CPU, memory, and persistent disk resources used while executing jobs.
Learning Objectives
Upon completion of this lab, you will be able to:
- Choose among different Big Data Services available in GCP
- Create ETL Pipelines to load data from GCS to BigQuery
- Query the Data available in BigQuery
Intended Audience
This lab is intended for:
- Candidates for the Associate Cloud Engineer Certification Exam
- ETL Developers
- Data Engineers
- Cloud Architect
Prerequisites
The following prerequisite is beneficial but not required for completing this lab:
- A basic understanding of SQL and Python
Updates
March 30th, 2024 - Resolved Dataproc cluster creation issue
August 10th, 2023 - Addressed user ban issue and promptly added a warning
July 25th, 2023 - Updated Python source code to resolve an issue preventing the PySpark Dataproc job from finishing
March 9th, 2023 - Updated lab to use Apache Beam 2.45.0
Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, AWS Certified Solutions Architect - Professional, Microsoft Certified Azure Solutions Architect Expert, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Security Specialist (CKS), Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.