Learn Data Engineering on Google Cloud Platform and Take Your Career to the Next Level!

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Data Engineering on Google Cloud Platform Course Overview


Google Cloud Platform (GCP) is an enormously powerful cloud platform that allows organizations to build, scale, and secure applications quickly. From its development tools to its robust infrastructure and services, GCP has a lot to offer developers and data engineers alike.
This Google Cloud Platform for Data Engineering course is designed to help developers and data engineers do just that. It starts off with an introduction to the platform and its key components, like Compute Engine and App Engine. It then dives into GCP’s data warehousing technologies and services, followed by an exploration of the data pipeline services available on GCP. After this it moves on to Google BigQuery and its analytics capabilities, as well as how to use GCP’s machine learning tools to build intelligent applications. To wrap up, the course covers best practices for securing and monitoring GCP applications.
By taking this Google Cloud Platform for Data Engineering course, learners will gain an understanding of GCP’s capabilities and architecture, as well as how to use GCP’s Data and Machine Learning Services. This will enable them to design and build applications on the GCP quickly and cost efficiently.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

The 1-on-1 Advantage

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Flexible Dates

  • • Choose Start Date
  • • Reschedule After Booking
  • • Weekend / Evening Option
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4-Hour Sessions

You will learn:

Module 1: Google Cloud Dataproc Overview
  • Creating and managing clusters.
  • Leveraging custom machine types and preemptible worker nodes.
  • Scaling and deleting Clusters
  • Lab: Creating Hadoop Clusters with Google Cloud Dataproc
  • Running Pig and Hive jobs.
  • Separation of storage and compute.
  • Lab: Running Hadoop and Spark Jobs with Dataproc.
  • Lab: Submit and monitor jobs.
  • Customize cluster with initialization actions.
  • BigQuery Support.
  • Lab: Leveraging Google Cloud Platform Services.
  • Google’s Machine Learning APIs
  • Common ML Use Cases
  • Invoking ML APIs
  • Lab: Adding Machine Learning Capabilities to Big Data Analysis
  • What is BigQuery
  • Queries and Functions.
  • Lab: Writing queries in BigQuery.
  • Loading data into BigQuery.
  • Exporting data from BigQuery.
  • Lab: Loading and exporting data.
  • Nested and repeated fields.
  • Querying multiple tables
  • Lab: Complex queries
  • Performance and pricing.
  • The Beam programming model.
  • Data pipelines in Beam Python.
  • Data pipelines in Beam Java.
  • Lab: Writing a Dataflow pipeline
  • Scalable Big Data processing using Beam.
  • Lab: MapReduce in Dataflow.
  • Incorporating additional data.
  • Lab: Side inputs
  • Handling stream data.
  • GCP Reference architecture
  • What is machine learning (ML).
  • Effective ML: concepts, types.
  • ML datasets: generalization
  • Lab: Explore and create ML datasets.
  • Getting started with TensorFlow.
  • Lab: Using tf.learn.
  • TensorFlow graphs and loops + lab.
  • Lab: Using low-level TensorFlow + early stopping.
  • Monitoring ML training.
  • Lab: Charts and graphs of TensorFlow training.
  • Why Cloud ML?
  • Packaging up a TensorFlow model.
  • End-to-end training
  • Lab: Run a ML model locally and on cloud
  • Creating good features.
  • Transforming inputs.
  • Synthetic features
  • Preprocessing with Cloud ML.
  • Lab: Feature engineering.
  • Stream data processing: Challenges.
  • Handling variable data volumes.
  • Dealing with unordered/late data.
  • Lab: Designing streaming pipeline
  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions
  • Lab: Simulator.
  • Challenges in stream processing.
  • Handle late data: watermarks, triggers, accumulation
  • Lab: Stream data processing pipeline for live traffic data.
  • Streaming analytics: from data to decisions
  • Querying streaming data with BigQuery.
  • What is Google Data Studio?
  • Lab: build a real-time dashboard to visualize processed data.
  • What is Cloud Spanner?
  • Designing Bigtable schema
  • Ingesting into Bigtable
  • Lab: streaming into Bigtable.
Live Online Training (Duration : 32 Hours) 1500 + If you accept merging of other students. Per Participant
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
Course Prerequisites

To get the most out of this training course, learners should have the following basic to intermediate experience:
• Understanding of database concepts, schemas, and SQL
• Ability to write basic programs in an object-oriented language like Java, JavaScript, Python, or Go
• Familiarity with using Terminal/shell for navigating file systems, managing processes, and running commands
• Basic working knowledge of Git version control
• Basic networking concepts, such as IP addresses, ports, DNS
• Understanding of Unix/Linux principles and commands
• Knowledge of system design and scalability principles
• Working experience with various cloud, serverless, and NoSQL technologies
• Practical experience with Google Cloud Platform (GCP) services and console will be beneficial

Target Audience


The target audience for a Data Engineering on Google Cloud Platform training program would include data engineers, software developers, IT professionals, data architects, and anyone responsible for designing, developing, and deploying data solutions on the Google Cloud Platform
This course should be well-suited for anyone who wants to build and maintain a GCP data warehouse, data lake, Big Data platform, or any other cloud data solution
It would also be appropriate for IT professionals who require understanding of the underlying architecture of the Google Cloud Platform and its capabilities in data transformation, data analysis, machine learning, and cloud-based analytics

Learning Objectives of Data Engineering on Google Cloud Platform


1. Understand the fundamentals of data engineering and become proficient in the tools used in data engineering.
2. Understand various Big Data components such as Hadoop, Apache Spark, and their interactions on the Google Cloud Platform (GCP).
3. Explore GCP services such as Cloud Storage, BigQuery, and Dataflow to manage and process data at scale.
4. Simplify data based analysis and understanding, by exploring and utilizing Google Cloud Machine Learning APIs.
5. Take advantage of GCP operational aspects, such as cost monitoring and scaling up/down of resources.
6. Leverage both batch and stream based processing and ingestion of data by Engineer for certain use cases.
7. Add a new skill set to the dataset, by developing production grade data applications on GCP.
8. Demonstrate best practices for designing, deploying and managing data engineering applications on GCP.

Student Feedback  (Check Koenig Feedback on Trustpilot)

Q1 Say something about the Trainer? Q2 How is Koenig different from other training Companies? Q3 Will you come back to Koenig for training ?

Student Name Feedback
Abu
United States
A1. Yes, she was very clear & a strong mastery of both English & Azure platforms hence following was easy. She was also patient and was answering every question asked, she also shared material & tips on how to pass the exam, Well done, and keep it up.
Nick Barker
United States
A1. Calm and understanding, she not only kept the course running well but also took time to help those with lots of questions. Very well done.
Robin Johnson
United States
A2. Individual attention during training, excellent customer service and coordination. Neha has been ever so helpful and is definitely an asset to Koenig.
Shivani Prasad
United States
A1. The trainer was great and his teaching methods were incredible. He used different teaching methods to ensure I was learning well. A fun trainer with lots of patience and understanding.
Adam
United States
A1. The trainer was very knowledgeable and well prepared about the presentation/training subject, with a positive attitude towards students and making every effort in answering questions during the training session. Top-notch service!
Suresh
United States
A1. Exceptional... recommend to everyone!
Shyam Vaddakapett
United States
A1. My trainer, Mohit Kakkar was excellent as he focused on hands-on assignments to supplement the concepts of AZ 900. The training coordinator Jasmeet as well as the ops manager Sheshadri are also very professional and customer-centric. I recommend Koenig for training courses, and I will take some more courses with them soon.
Gilles Chirlias
United States
A1. Chavi was a great teacher, I have learned a lot from her. I would like to have her for the next training, which would be the AZ 301 and AZ 500.
Abdulrahman Saad Alshahrani
United States
A1. Excellent trainer. I will take another course with him someday. He deserves a good appreciation from his Manager.
Anonymous
United States
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FAQ's


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You will receive the digital certificate post training completion via learning enhancement tool after registration.
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Yes, we also offer weekend classes.
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You can buy online from the page by clicking on "Buy Now". You can view alternate payment method on payment options page.
Yes you can request your customer experience manager for the same.
1-on-1 Public - Select your start date. Other students can be merged.
1-on-1 Private - Select your start date. You will be the only student in the class.
Yes, course requiring practical include hands-on labs.
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The Fee includes:
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Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1-on-1 you can select your own schedule, other students can be merged but you select the schedule. Choose 1-on-1 if published schedule do not meet your requirement. If you also want a private session, opt for 1-on-1 Public.
Yes.
No, it is not included.

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