{"id":4102,"date":"2020-04-09T11:00:11","date_gmt":"2020-04-09T05:30:11","guid":{"rendered":"https:\/\/www.testpreptraining.com\/blog\/?p=4102"},"modified":"2024-06-12T11:07:59","modified_gmt":"2024-06-12T05:37:59","slug":"gcp-data-engineer-online-course-launched","status":"publish","type":"post","link":"https:\/\/www.testpreptraining.ai\/blog\/gcp-data-engineer-online-course-launched\/","title":{"rendered":"GCP Data Engineer Online Course Launched"},"content":{"rendered":"\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p><em>When it comes to preparing for any certification exam, there are no better learning resources other than Online Courses.&nbsp;<\/em><\/p>\n<\/blockquote>\n\n\n\n<p>The demand for Google Cloud Platform certifications is largely due to the higher salary estimates for GCP-certified professionals. So, if you desire to become a GCP Data Engineer, then the new Testpreptraining course is ideal for you. Our experts have curated this online course, keeping in mind the need for the GCP Data Engineer Certification Exam. The following discussion will point out the quintessential aspects of the GCP Data Engineer certificate exam.<\/p>\n\n\n\n<p>In addition, we\u2019ll also be discussing all the highlights of our GCP Data Engineer Online Course. The central purpose of the following article focuses on assisting you to understand the prospects with the job role of a <a href=\"https:\/\/www.testpreptraining.ai\/blog\/how-to-prepare-for-google-professional-data-engineer\/\">GCP Data Engineer<\/a>. Not to mention, you\u2019ll be able to observe the ways in which Testpreptraining GCP Data Engineer Online Course will be very helpful. So, without a further due, let\u2019s begin.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>GCP Data Engineer Certification Exam Overview<\/strong><\/h3>\n\n\n\n<p>The Testpreptraining GCP Data Engineer training course is suitable for candidates with interest in data investigation. Candidates for the <a href=\"https:\/\/www.testpreptraining.ai\/blog\/how-to-prepare-for-google-professional-data-engineer\/\">GCP Data Engineer certification exam<\/a> assume roles for data-based decision making. The objective of the certification is the validation of the abilities of an individual for the collection, transformation, and publishing of data.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Prerequisites<\/strong><\/h4>\n\n\n\n<p>The prerequisites for the GCP Data Engineer certification exam is vital for every aspiring Data Engineer. And, the most prominent highlight for the GCP Data Engineer is that it doesn\u2019t require any prerequisites. However, candidates need to fulfil the recommended experience required for the GCP Data Engineer certification exam.<\/p>\n\n\n\n<p>So, one needs a minimum of three years of industry experience in data-based roles along with more than one year of practical experience in the design and management of solutions using GCP can be helpful. Also, another crucial prerequisite that candidates must fulfil for authenticating their eligibility is candidate\u2019s age. Candidates should be at least 18 years of age or more to appear for the examination.&nbsp;<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter is-resized\"><img decoding=\"async\" src=\"https:\/\/miro.medium.com\/max\/3000\/1*T0_akZfhC_BmZNc0znAtdQ.png\" alt=\"GCP Data Engineer Exam\" style=\"width:305px;height:305px\"\/><\/figure>\n<\/div>\n\n\n<h4 class=\"wp-block-heading\"><strong>GCP Data Engineer Exam Details<\/strong><\/h4>\n\n\n\n<p>When it comes to preparing for any examination, basic exam details must be acknowledged by the candidate. Therefore, the Testpreptraining GCP Data Engineer online course outlines out the comprehensive course that can help qualify the examination. This course follows the same format of the actual certification exam as a reference. Knowledge about the basic information for the GCP professional data engineer certification exam can help in improving your confidence.<\/p>\n\n\n\n<p>The Testpreptraining GCP Data Engineer training course aims at helping candidates find the right path for success.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Basic Exam Details<\/strong><\/h4>\n\n\n\n<p>The GCP Data Engineer certification exam comprises of multiple-choice and multiple-select format for the questions. The total duration of the exam is 2 hours, and candidates can choose the test centre while scheduling the exam.<\/p>\n\n\n\n<p>The registration fee for the exam is USD 200, along with applicable taxes. The GCP Data Engineer certification exam is available in only four languages i.e., English, Portuguese, Japanese, and Spanish. So, make sure you are familiar with at least one of the language.&nbsp;<\/p>\n\n\n\n<p>For a better understanding of the examination, it\u2019s important for you to understand all the exam objectives. Therefore, it\u2019s important to get acquainted with the course outline.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Course Outline<\/strong><\/h3>\n\n\n\n<p>The next important highlight of the Testpreptraining GCP Data Engineer online course is its alignment with exam objectives. The GCP Data Engineer certification exam verifies the candidate\u2019s abilities for designing, building, operationalizing, securing, and monitoring data processing systems. So, the exam tests the following abilities of candidates.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Design data processing systems<\/li>\n\n\n\n<li>Ingest and process the data<\/li>\n\n\n\n<li>Store the data<\/li>\n\n\n\n<li>Prepare and use data for analysis<\/li>\n\n\n\n<li>Maintain and automate data workloads<\/li>\n<\/ol>\n\n\n\n<p>For the above mentioned, you can depend on the Testpreptraining GCP Data Engineer online course. Not only will this be helpful for covering all underlying topics in these exam domains but also help candidates have a better understanding of the exam. In the same vein, let us take a closer look at the subtopics in each exam domain.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Section 1: Designing data processing systems (22%)<\/h4>\n\n\n\n<p>1.1 Designing for security and compliance. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Identity and Access Management (e.g., Cloud IAM and organization policies)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/iam\/docs\" target=\"_blank\" rel=\"noreferrer noopener\">Identity and Access Management<\/a>)<\/li>\n\n\n\n<li>Data security (encryption and key management)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/docs\/security\/encryption\/default-encryption\" target=\"_blank\" rel=\"noreferrer noopener\">Default encryption at rest<\/a>)<\/li>\n\n\n\n<li>Privacy (e.g., personally identifiable information, and Cloud Data Loss Prevention API)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/sensitive-data-protection\/docs\" target=\"_blank\" rel=\"noreferrer noopener\">Sensitive Data Protection<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/security\/products\/dlp?hl=en\" target=\"_blank\" rel=\"noreferrer noopener\">Cloud Data Loss Prevention<\/a>)<\/li>\n\n\n\n<li>Regional considerations (data sovereignty) for data access and storage&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/architecture\/framework\/security\/data-residency-sovereignty\" target=\"_blank\" rel=\"noreferrer noopener\">Implement data residency and sovereignty requirements<\/a>)<\/li>\n\n\n\n<li>Legal and regulatory compliance<\/li>\n<\/ul>\n\n\n\n<p>1.2 Designing for reliability and fidelity. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Preparing and cleaning data (e.g., Dataprep, Dataflow, and Cloud Data Fusion)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/data-fusion\/docs\/concepts\/overview\" target=\"_blank\" rel=\"noreferrer noopener\">Cloud Data Fusion overview<\/a>)<\/li>\n\n\n\n<li>Monitoring and orchestration of data pipelines&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/blog\/topics\/developers-practitioners\/orchestrating-your-data-workloads-google-cloud\" target=\"_blank\" rel=\"noreferrer noopener\">Orchestrating your data workloads in Google Cloud<\/a>)<\/li>\n\n\n\n<li>Disaster recovery and fault tolerance&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/learn\/what-is-disaster-recovery\" target=\"_blank\" rel=\"noreferrer noopener\">What is a Disaster Recovery Plan?<\/a>)<\/li>\n\n\n\n<li>Making decisions related to ACID (atomicity, consistency, isolation, and durability) compliance and availability<\/li>\n\n\n\n<li>Data validation<\/li>\n<\/ul>\n\n\n\n<p>1.3 Designing for flexibility and portability. Considerations include<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Mapping current and future business requirements to the architecture<\/li>\n\n\n\n<li>Designing for data and application portability (e.g., multi-cloud and data residency requirements)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/architecture\/framework\/security\/data-residency-sovereignty\" target=\"_blank\" rel=\"noreferrer noopener\">Implement data residency and sovereignty requirements<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/architecture\/multi-cloud-database-management\" target=\"_blank\" rel=\"noreferrer noopener\">Multicloud database management: Architectures, use cases, and best practices<\/a>)<\/li>\n\n\n\n<li>Data staging, cataloging, and discovery (data governance)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/data-catalog\/docs\/concepts\/overview\" target=\"_blank\" rel=\"noreferrer noopener\">Data Catalog overview<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>1.4 Designing data migrations. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Analyzing current stakeholder needs, users, processes, and technologies and creating a plan to get to desired state<\/li>\n\n\n\n<li>Planning migration to Google Cloud (e.g., BigQuery Data Transfer Service, Database Migration Service, Transfer Appliance, Google Cloud networking, Datastream)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/architecture\/migration-to-google-cloud-transferring-your-large-datasets\" target=\"_blank\" rel=\"noreferrer noopener\">Migrate to Google Cloud: Transfer your large datasets<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/database-migration?hl=en\" target=\"_blank\" rel=\"noreferrer noopener\">Database Migration Service<\/a>)<\/li>\n\n\n\n<li>Designing the migration validation strategy&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/architecture\/migration-to-google-cloud-best-practices\" target=\"_blank\" rel=\"noreferrer noopener\">Migrate to Google Cloud: Best practices for validating a migration plan<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/migration-center\/docs\/migration-planning-overview\" target=\"_blank\" rel=\"noreferrer noopener\">About migration planning<\/a>)<\/li>\n\n\n\n<li>Designing the project, dataset, and table architecture to ensure proper data governance&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/data-governance\" target=\"_blank\" rel=\"noreferrer noopener\">Introduction to data governance in BigQuery<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/datasets\" target=\"_blank\" rel=\"noreferrer noopener\">Create datasets<\/a>)<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Section 2: Ingesting and processing the data (25%)<\/h4>\n\n\n\n<p>2.1 Planning the data pipelines. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Defining data sources and sinks&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/storage-transfer\/docs\/sources-and-sinks\" target=\"_blank\" rel=\"noreferrer noopener\">Sources and sinks<\/a>)<\/li>\n\n\n\n<li>Defining data transformation logic&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/transform-intro\" target=\"_blank\" rel=\"noreferrer noopener\">Introduction to data transformation<\/a>)<\/li>\n\n\n\n<li>Networking fundamentals<\/li>\n\n\n\n<li>Data encryption&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/storage\/docs\/encryption\" target=\"_blank\" rel=\"noreferrer noopener\">Data encryption options<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>2.2 Building the pipelines. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Data cleansing<\/li>\n\n\n\n<li>Identifying the services (e.g., Dataflow, Apache Beam, Dataproc, Cloud Data Fusion, BigQuery, Pub\/Sub, Apache Spark, Hadoop ecosystem, and Apache Kafka)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/dataflow\/docs\/overview\" target=\"_blank\" rel=\"noreferrer noopener\">Dataflow overview<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/dataflow\/docs\/concepts\/beam-programming-model\" target=\"_blank\" rel=\"noreferrer noopener\">Programming model for Apache Beam<\/a>)<\/li>\n\n\n\n<li>Transformation:\n<ul class=\"wp-block-list\">\n<li>Batch&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/batch\/docs\/get-started\" target=\"_blank\" rel=\"noreferrer noopener\">Get started with Batch<\/a>)<\/li>\n\n\n\n<li>Streaming (e.g., windowing, late arriving data)<\/li>\n\n\n\n<li>Language<\/li>\n\n\n\n<li>Ad hoc data ingestion (one-time or automated pipeline)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/dataflow\/docs\/guides\/pipeline-workflows\" target=\"_blank\" rel=\"noreferrer noopener\">Design Dataflow pipeline workflows<\/a>)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Data acquisition and import&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/datastore\/docs\/export-import-entities\" target=\"_blank\" rel=\"noreferrer noopener\">Exporting and Importing Entities<\/a>)<\/li>\n\n\n\n<li>Integrating with new data sources&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/data-catalog\/docs\/integrate-data-sources\" target=\"_blank\" rel=\"noreferrer noopener\">Integrate your data sources with Data Catalog<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>2.3 Deploying and operationalizing the pipelines. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Job automation and orchestration (e.g., Cloud Composer and Workflows)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/workflows\/docs\/choose-orchestration\" target=\"_blank\" rel=\"noreferrer noopener\">Choose Workflows or Cloud Composer for service orchestration<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/composer\/docs\/concepts\/overview\" target=\"_blank\" rel=\"noreferrer noopener\">Cloud Composer overview<\/a>)<\/li>\n\n\n\n<li>CI\/CD (Continuous Integration and Continuous Deployment)<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Section 3: Storing the data (20%)<\/h4>\n\n\n\n<p>3.1 Selecting storage systems. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Analyzing data access patterns&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/docs\/data\" target=\"_blank\" rel=\"noreferrer noopener\">Data analytics and pipelines overview<\/a>)<\/li>\n\n\n\n<li>Choosing managed services (e.g., Bigtable, Cloud Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/blog\/topics\/developers-practitioners\/your-google-cloud-database-options-explained\" target=\"_blank\" rel=\"noreferrer noopener\">Google Cloud database options<\/a>)<\/li>\n\n\n\n<li>Planning for storage costs and performance&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/architecture\/framework\/cost-optimization\/storage\" target=\"_blank\" rel=\"noreferrer noopener\">Optimize cost: Storage<\/a>)<\/li>\n\n\n\n<li>Lifecycle management of data&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/storage\/docs\/control-data-lifecycles\" target=\"_blank\" rel=\"noreferrer noopener\">Options for controlling data lifecycles<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>3.2 Planning for using a data warehouse. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Designing the data model&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/firestore\/docs\/data-model\" target=\"_blank\" rel=\"noreferrer noopener\">Data model<\/a>)<\/li>\n\n\n\n<li>Deciding the degree of data normalization&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/document-ai\/docs\/normalization\" target=\"_blank\" rel=\"noreferrer noopener\">Normalization<\/a>)<\/li>\n\n\n\n<li>Mapping business requirements<\/li>\n\n\n\n<li>Defining architecture to support data access patterns&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/architecture\/reference-patterns\/overview\" target=\"_blank\" rel=\"noreferrer noopener\">Data analytics design patterns<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>3.3 Using a data lake. Considerations include<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Managing the lake (configuring data discovery, access, and cost controls)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/dataplex\/docs\/manage-lake\" target=\"_blank\" rel=\"noreferrer noopener\">Manage a lake<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/dataplex\/docs\/lake-security\" target=\"_blank\" rel=\"noreferrer noopener\">Secure your lake<\/a>)<\/li>\n\n\n\n<li>Processing data&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/stackdriver\/docs\/solutions\/slo-monitoring\/sli-metrics\/data-proc-metrics\" target=\"_blank\" rel=\"noreferrer noopener\">Data processing services<\/a>)<\/li>\n\n\n\n<li>Monitoring the data lake&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/learn\/what-is-a-data-lake\" target=\"_blank\" rel=\"noreferrer noopener\">What is a Data Lake?<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>3.4 Designing for a data mesh. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Building a data mesh based on requirements by using Google Cloud tools (e.g., Dataplex, Data Catalog, BigQuery, Cloud Storage)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/dataplex\/docs\/build-a-data-mesh\" target=\"_blank\" rel=\"noreferrer noopener\">Build a data mesh<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/blog\/products\/data-analytics\/building-a-data-mesh-on-google-cloud-using-bigquery-and-dataplex\" target=\"_blank\" rel=\"noreferrer noopener\">Build a modern, distributed Data Mesh with Google Cloud<\/a>)<\/li>\n\n\n\n<li>Segmenting data for distributed team usage&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/architecture\/ccn-distributed-apps-design\/connectivity\" target=\"_blank\" rel=\"noreferrer noopener\">Network segmentation and connectivity for distributed applications in Cross-Cloud Network<\/a>)<\/li>\n\n\n\n<li>Building a federated governance model for distributed data systems<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Section 4: Preparing and using data for analysis (15%)<\/h4>\n\n\n\n<p>4.1 Preparing data for visualization. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Connecting to tools<\/li>\n\n\n\n<li>Precalculating fields&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/materialized-views-intro\" target=\"_blank\" rel=\"noreferrer noopener\">Introduction to materialized views<\/a>)<\/li>\n\n\n\n<li>BigQuery materialized views (view logic)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/materialized-views-create#:~:text=To%20create%20materialized%20views%20over,queries%20as%20other%20materialized%20views.\" target=\"_blank\" rel=\"noreferrer noopener\">Create materialized views<\/a>)<\/li>\n\n\n\n<li>Determining granularity of time data&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/monitoring\/api\/v3\/aggregation\" target=\"_blank\" rel=\"noreferrer noopener\">Filtering and aggregation: manipulating time series<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/billing\/docs\/how-to\/export-data-bigquery-tables\/detailed-usage\" target=\"_blank\" rel=\"noreferrer noopener\">Structure of Detailed data export<\/a>)<\/li>\n\n\n\n<li>Troubleshooting poor performing queries&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/sql\/docs\/postgres\/diagnose-issues\" target=\"_blank\" rel=\"noreferrer noopener\">Diagnose issues<\/a>)<\/li>\n\n\n\n<li>Identity and Access Management (IAM) and Cloud Data Loss Prevention (Cloud DLP)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/sensitive-data-protection\/docs\/iam-roles\" target=\"_blank\" rel=\"noreferrer noopener\">IAM roles<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>4.2 Sharing data. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Defining rules to share data&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/vpc-service-controls\/docs\/secure-data-exchange\" target=\"_blank\" rel=\"noreferrer noopener\">Secure data exchange with ingress and egress rules<\/a>)<\/li>\n\n\n\n<li>Publishing datasets&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/public-data\" target=\"_blank\" rel=\"noreferrer noopener\">BigQuery public datasets<\/a>)<\/li>\n\n\n\n<li>Publishing reports and visualizations<\/li>\n\n\n\n<li>Analytics Hub&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/analytics-hub-introduction\" target=\"_blank\" rel=\"noreferrer noopener\">Introduction to Analytics Hub<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>4.3 Exploring and analyzing data. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Preparing data for feature engineering (training and serving machine learning models)<\/li>\n\n\n\n<li>Conducting data discovery&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/dataplex\/docs\/discover-data\" target=\"_blank\" rel=\"noreferrer noopener\">Discover data<\/a>)<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Section 5: Maintaining and automating data workloads (18%)<\/h4>\n\n\n\n<p>5.1 Optimizing resources. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Minimizing costs per required business need for data&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/architecture\/migration-to-google-cloud-minimize-costs#:~:text=Configure%20automatic%20scaling.,to%20match%20your%20current%20demand.\" target=\"_blank\" rel=\"noreferrer noopener\">Migrate to Google Cloud: Minimize costs<\/a>)<\/li>\n\n\n\n<li>Ensuring that enough resources are available for business-critical data processes&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/architecture\/dr-scenarios-planning-guide\" target=\"_blank\" rel=\"noreferrer noopener\">Disaster recovery planning guide<\/a>)<\/li>\n\n\n\n<li>Deciding between persistent or job-based data clusters (e.g., Dataproc)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/dataproc\/docs\/concepts\/overview\" target=\"_blank\" rel=\"noreferrer noopener\">Dataproc overview<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>5.2 Designing automation and repeatability. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Creating directed acyclic graphs (DAGs) for Cloud Composer&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/composer\/docs\/how-to\/using\/writing-dags\" target=\"_blank\" rel=\"noreferrer noopener\">Write Airflow DAGs<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/composer\/docs\/how-to\/using\/managing-dags\" target=\"_blank\" rel=\"noreferrer noopener\">Add and update DAGs<\/a>)<\/li>\n\n\n\n<li>Scheduling jobs in a repeatable way&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/scheduler\/docs\/schedule-run-cron-job#:~:text=topic%20cron%2Dtopic-,Create%20a%20cron%20job%20using%20Cloud%20Scheduler,to%20the%20Cloud%20Scheduler%20page.&amp;text=Click%20Create%20job.,Give%20your%20job%20a%20name.\" target=\"_blank\" rel=\"noreferrer noopener\">Schedule and run a cron job<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>5.3 Organizing workloads based on business requirements. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Flex, on-demand, and flat rate slot pricing (index on flexibility or fixed capacity)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/reservations-intro\" target=\"_blank\" rel=\"noreferrer noopener\">Introduction to workload management<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/reservations-intro-legacy\" target=\"_blank\" rel=\"noreferrer noopener\">Introduction to legacy reservations<\/a>)<\/li>\n\n\n\n<li>Interactive or batch query jobs&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/running-queries#:~:text=a%20dry%20run.-,Interactive%20versus%20batch%20queries,idle%20compute%20resources%20are%20available.\" target=\"_blank\" rel=\"noreferrer noopener\">Run a query<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>5.4 Monitoring and troubleshooting processes. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Observability of data processes (e.g., Cloud Monitoring, Cloud Logging, BigQuery admin panel)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/stackdriver\/docs\" target=\"_blank\" rel=\"noreferrer noopener\">Observability in Google Cloud<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/monitoring\" target=\"_blank\" rel=\"noreferrer noopener\">Introduction to BigQuery monitoring<\/a>)<\/li>\n\n\n\n<li>Monitoring planned usage<\/li>\n\n\n\n<li>Troubleshooting error messages, billing issues, and quotas&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/docs\/quotas\/troubleshoot\" target=\"_blank\" rel=\"noreferrer noopener\">Troubleshoot quota errors<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/troubleshoot-quotas\" target=\"_blank\" rel=\"noreferrer noopener\">Troubleshoot quota and limit errors<\/a>)<\/li>\n\n\n\n<li>Manage workloads, such as jobs, queries, and compute capacity (reservations)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/reservations-workload-management\" target=\"_blank\" rel=\"noreferrer noopener\">Workload management using Reservations<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>5.5 Maintaining awareness of failures and mitigating impact. Considerations include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Designing system for fault tolerance and managing restarts&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/compute\/docs\/tutorials\/robustsystems\" target=\"_blank\" rel=\"noreferrer noopener\">Designing resilient systems<\/a>)<\/li>\n\n\n\n<li>Running jobs in multiple regions or zones&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/run\/docs\/multiple-regions\" target=\"_blank\" rel=\"noreferrer noopener\">Serve traffic from multiple regions<\/a>,&nbsp;<a href=\"https:\/\/cloud.google.com\/compute\/docs\/regions-zones\" target=\"_blank\" rel=\"noreferrer noopener\">Regions and zones<\/a>)<\/li>\n\n\n\n<li>Preparing for data corruption and missing data&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/kms\/docs\/data-integrity-guidelines\" target=\"_blank\" rel=\"noreferrer noopener\">Verifying end-to-end data integrity<\/a>)<\/li>\n\n\n\n<li>Data replication and failover (e.g., Cloud SQL, Redis clusters)&nbsp;<strong>(Google Documentation:<\/strong>&nbsp;<a href=\"https:\/\/cloud.google.com\/memorystore\/docs\/cluster\/ha-and-replicas\" target=\"_blank\" rel=\"noreferrer noopener\">High availability and replicas<\/a>)<\/li>\n<\/ul>\n\n\n\n<p>The Testpreptraining online course for GCP Data Engineer certification exam could thus ensure detailed coverage of the exam\u2019s course outline.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>The all NEW<\/strong> <strong>Testpreptraining GCP Data Engineer Online Course<\/strong> <img decoding=\"async\" width=\"40\" height=\"40\" class=\"wp-image-3765\" style=\"width: 40px;\" src=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2020\/03\/icons8-training-64.png\" alt=\"\"><\/h3>\n\n\n\n<p>The Testpreptraining GCP Data Engineer video course is the best learning resource for aspiring GCP Data Engineers. <\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Course Overview<\/strong><\/h4>\n\n\n\n<p>This particular online course will act as a bridge to qualify your GCP Data Engineer certification exam. So, below we are listing different features of our GCP Data Engineer certification online training course.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Over 20 hours of comprehensive training videos.<\/li>\n\n\n\n<li>Covers all domains of the course outline, to get past the GCP Data Engineer exam.&nbsp;<\/li>\n\n\n\n<li>Unlimited access to the course.<\/li>\n\n\n\n<li>Flexibility for accessing the course on various devices.<\/li>\n<\/ul>\n\n\n\n<p>Now, the most valuable highlight of the Testpreptraining GCP Data Engineer online course is its clarity. The course adopts a flexible approach in which you can find seventeen different modules. Seventeen may seem too long. But, when you understand that the course builds your foundation with every step and that each plays an important role.&nbsp;<\/p>\n\n\n\n<p>Moreover, all the training videos for each subtopic in different course module covers all the necessary information required for you to qualify the examination. As a result, this online course will only prove to be in your favour. By devoting only a few hours every day, the result can astonish you.&nbsp;<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Table of Content<\/strong><\/h5>\n\n\n\n<p>So, below we are providing you with a list of all the modules presented in the Testpreptraining Online course.&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Compute Choices<\/strong><\/li>\n\n\n\n<li><strong>Storage<\/strong><\/li>\n\n\n\n<li><strong>Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS<\/strong><\/li>\n\n\n\n<li><strong>Datastore ~ Document Database<\/strong><\/li>\n\n\n\n<li><strong>BigTable ~ HBase = Columnar Store<\/strong><\/li>\n\n\n\n<li><strong>BigQuery ~ Hive ~ OLAP<\/strong><\/li>\n\n\n\n<li><strong>Dataflow ~ Apache Beam<\/strong><\/li>\n\n\n\n<li><strong>Dataproc ~ Managed Hadoop<\/strong><\/li>\n\n\n\n<li><strong>Pub\/Sub for Streaming<\/strong><\/li>\n\n\n\n<li><strong>Datalab ~ Jupyter<\/strong><\/li>\n\n\n\n<li><strong>TensorFlow and Machine Learning<\/strong><\/li>\n\n\n\n<li><strong>Regression in TensorFlow<\/strong><\/li>\n\n\n\n<li><strong>Vision, Translate, NLP and Speech: Trained ML APIs<\/strong><\/li>\n\n\n\n<li><strong>Networking<\/strong><\/li>\n\n\n\n<li><strong>Ops and Security<\/strong><\/li>\n\n\n\n<li><strong>Appendix: Hadoop Ecosystem<\/strong><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Benefits of Testpreptraining GCP Professional Data Engineer Online Course <\/strong><\/h3>\n\n\n\n<p>Candidates should know that the online training course for GCP Data Engineer certification by Testpreptraining is a quality product. Various subject matter experts and certified professionals have designed the course with their years of experience and skills. Here are the noticeable ways in which the Testpreptraining GCP professional data engineer certification online course can help you.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Candidates get an assured approach to gear up for the certification exam. How? The Testpreptraining online course aligns perfectly with the exam objectives and focuses only on the important ones. So, you only study for exactly what would form the basis of questions in the actual exam!<\/li>\n\n\n\n<li>The money-back-guarantee topped with an appealing price makes the Testpreptraining course more reasonable. Candidates could verify that their investment would find the right place at Testpreptraining.<\/li>\n\n\n\n<li>Testpreptraining also provides the assurance of promising customer support. The availability of customer support at Testpreptraining round the clock could also provide essential support to candidates. You don\u2019t have to worry about your doubts about online training courses anymore.&nbsp;<\/li>\n\n\n\n<li>Comprehensive coverage of all exam topics helps you in preparing thoroughly for the exam. In addition, candidates also have the option of practice tests to improve their preparations.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Last words<\/strong> <img decoding=\"async\" width=\"37\" height=\"37\" class=\"wp-image-3634\" style=\"width: 37px;\" src=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2020\/03\/icons8-google-classroom-64.png\" alt=\"\"><\/h3>\n\n\n\n<p>Lastly, we would like to remind you, it\u2019s time to take that plunge. Therefore, make sure, to begin with, the right pathway. In the same light, this online course would be your stepping stone. Not will it build a solid foundation but will also polish your skills, talents and achievements. Since showcasing your potential to the employers will enhance your resume. So, make sure to get certified with an excellent remark. As it\u2019s not just a credential on the parer, but the knowledge you possess.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.youtube.com\/channel\/UCfOBMdIF1rAGY4VnFDsNHcA?view_as=subscriber\"><img decoding=\"async\" width=\"1024\" height=\"174\" src=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2020\/03\/Copy-of-Untitled-Design-1024x174.png\" alt=\"Testprep Training Youtube Page\" class=\"wp-image-3838\" srcset=\"https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2020\/03\/Copy-of-Untitled-Design-1024x174.png 1024w, https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2020\/03\/Copy-of-Untitled-Design-300x51.png 300w, https:\/\/www.testpreptraining.ai\/blog\/wp-content\/uploads\/2020\/03\/Copy-of-Untitled-Design.png 1128w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n\n\n<p><strong>Make your resume stand out and become a Certified GCP Data Engineer. Try <a href=\"https:\/\/www.testpreptraining.ai\/google-cloud-certified-professional-data-engineer-free-practice-test\">free practice<\/a> tests here!<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>When it comes to preparing for any certification exam, there are no better learning resources other than Online Courses.&nbsp; The demand for Google Cloud Platform certifications is largely due to the higher salary estimates for GCP-certified professionals. So, if you desire to become a GCP Data Engineer, then the new Testpreptraining course is ideal for&#8230;<\/p>\n","protected":false},"author":1,"featured_media":4103,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[244],"tags":[460,458,459],"class_list":["post-4102","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-google","tag-gcp-data-engineer","tag-gcp-data-engineer-online-course","tag-gcp-data-engineer-online-course-launched"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>GCP Data Engineer Online Course Launched - Blog<\/title>\n<meta name=\"description\" content=\"Enrich your profile with advanced learning skills and expert tutorials on GCP Data Engineer Online Course. Become a certified Data Engineer Now!\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.testpreptraining.ai\/blog\/gcp-data-engineer-online-course-launched\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GCP Data Engineer Online Course Launched - Blog\" \/>\n<meta property=\"og:description\" content=\"Enrich your profile with advanced learning skills and expert tutorials on GCP Data Engineer Online Course. 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