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  • PRIVATE BATCH
  • PUBLIC PROGRAM
  • ON DEMAND
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Course Details

  • Course Overview
  • Skills Gained
  • Who Can Benefit
  • Prerequisite
  • Syllabus

This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.

  • Design and build data processing systems on Google Cloud Platform
  • Process batch and streaming data by successfully implementing autoscaling data pipelines on Cloud Dataflow
  • Derive business insights from intensely large datasets using Google BigQuery
  • Train, evaluate and anticipate using machine learning models using Tensorflow and Cloud ML
  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
  • Efficiently enable instant insights from streaming data

This course is designed for experienced developers who are responsible for managing big data transformations including:

  • Extracting, Loading, Transforming, cleaning, and validating data
  • Designing pipelines and architectures for data processing
  • Querying datasets, anticipating query results and creating reports
  • Creating and maintaining machine learning and statistical models


To get the most of out of this course, participants should have:

  • Completed Google Cloud Fundamentals: Big Data & Machine Learning course OR have equivalent practice
  • knowledge of basic proficiency with common query language such as SQL
  • Experience with data modeling, extract, transform, load activities
  • Developing applications using a common programming language such as Python
  • Best practice with Machine Learning and/or statistics
Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform

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. 

2.Running Dataproc Jobs

  • Running Pig and Hive jobs.
  • Separation of storage and compute.
  • Lab: Running Hadoop and Spark Jobs with Dataproc.
  • Lab: Submit and monitor jobs.

3.Integrating Dataproc with Google Cloud Platform

  • Customize cluster with initialization actions.
  • BigQuery Support.
  • Lab: Leveraging Google Cloud Platform Services.

4.Making Sense of Unstructured Data with Google’s Machine Learning APIs

  • Google’s Machine Learning APIs.
  • Common ML Use Cases.
  • Invoking ML APIs.
  • Lab: Adding Machine Learning Capabilities to Big Data Analysis.

 
Serverless Data Analysis with Google BigQuery and Cloud Dataflow

1.Serverless data analysis with BigQuery

  • 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.

2.Serverless, autoscaling data pipelines with Dataflow

  • 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.

Serverless Machine Learning with TensorFlow on Google Cloud Platform

1.Getting started with Machine Learning

  • What is machine learning (ML).
  • Effective ML: concepts, types.
  • ML datasets: generalization.
  • Lab: Explore and create ML datasets.

2.Building ML models with Tensorflow

  • 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.

3.Scaling ML models with CloudML

  • Why Cloud ML?
  • Packaging up a TensorFlow model.
  • End-to-end training.
  • Lab: Run a ML model locally and on cloud.

4.Feature Engineering

  • Creating good features.
  • Transforming inputs.
  • Synthetic features.
  • Preprocessing with Cloud ML.
  • Lab: Feature engineering.

Building Resilient Streaming Systems on Google Cloud Platform

5.Architecture of streaming analytics pipelines

  • Stream data processing: Challenges.
  • Handling variable data volumes.
  • Dealing with unordered/late data.
  • Lab: Designing streaming pipeline.

6.Ingesting Variable Volumes

  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions.
  • Lab: Simulator.

7.Implementing streaming pipelines

  • Challenges in stream processing.
  • Handle late data: watermarks, triggers, accumulation.
  • Lab: Stream data processing pipeline for live traffic data.

8.Streaming analytics and dashboards

  •  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.

9.High throughput and low-latency with Bigtable

  • What is Cloud Spanner?
  •  Designing Bigtable schema.
  • Ingesting into Bigtable.
  • Lab: streaming into Bigtable.


Audience

  • Developer

Public Program Schedule

Course Name Duration Brochure Location Schedule Enroll
There is no upcoming Public Batch Schedule, you can ask for Private Batch or for On-Demand Learning

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FAQ

  • What is a Google Cloud certification?
  • Why should I get a Google Cloud certification?
  • How long is a Google Cloud certification valid?
  • What is the recertification policy?
  • Why do I need to recertify?
  • How do I pay?
  • What are your cancellation & refund policy?

Google Cloud's certification program gives Google Cloud users, customers and partners a way to demonstrate their technical skills in a particular job role or technology. Individuals are assessed using a variety of rigorously developed industry standard methods to determine whether they meet Google Cloud's proficiency standards.

Google Cloud certifications give you benchmarks for your professional development. They help you gauge your skill set against your peers and demonstrate your value to hiring managers.

Unless explicitly stated in the detailed exam descriptions, all Google Cloud certifications are valid for two years from the date certified. Candidates must recertify in order to maintain their certification status.

Unless explicitly stated in the detailed exam descriptions, all Google cloud certifications are valid for two years from the date certified. Candidates must recertify in order to maintain their certification status and certificate number.

Google technology changes frequently. We require recertification to ensure you're maintaining your skills.

We accept all modes of payment. If you are being nominated by your organization, your organization need to release PO before the course start date. If you are an individual you can pay through credit / debit cards, online transfer (RTGS/NEFT) to our account 7 days prior to the course start date.

  • In a highly unlikely event of cancellation of batch from our end, we shall refund 100% that is paid by you. If client choose to cancel for any reasons, below is the terms.
  • If you cancel or reschedule your registration 5 or more calendar days before the scheduled start date of the class – No cancellation charges
  • If you cancel or reschedule your registration less than 5 calendar days before the scheduled start date of the class – cancellation charges 100% of the course fee
  • If you do not show up for the event, or cancel on the day of the event - cancellation charges 100% of the course fee

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