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Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

Reference: https://cloud.google.com/certification/data-engineer

Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

• Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
• Hire BigQuery and Cloud SQL for interactive data analysis
• Create ML models using BigQuery ML, APIs, and AutoML.
• Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud

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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

• Building a Data Warehouse
• Big Data Analytics with Cloud Al Platform Notebook
• Production ML Pipelines and use of Kubeflow
• Introduction to Data Engineering
• Performing Spark on Cloud Dataproc
• Cloud Dataflow Streaming Features
• Introduction to Processing Streaming Data
• Prebuilt ML Models APIs for Unsaturated Data
• Advanced BigQuery Performance and Functionality
• Serverless Messaging Using Cloud Sub/Pub
• Custom Model building Utilizing Cloud AutoML
• Serverless Data Processing with Cloud Dataflow
• Custom Model building Using SQL in BigQuery ML
• Bigtable Streaming Features and High-Throughput BigQuery
• Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
• Creating a Data Lake
• Introduction to Building Batch Data Pipelines

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.

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