GCP DATA ENGINEER

Teacher

Pradnya Mane

The Syllabus

Curriculum Designed by Experts

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.

Module 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.

Module 3: Integrating Dataproc with Google Cloud Platform

Customize cluster with initialization actions.

BigQuery Support.

Lab: Leveraging Google Cloud Platform Services.

Module 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.

Module 5: 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.

Module 6: 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.

Module 7: Getting started with Machine Learning

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

Module 8: 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.

Module 9: 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.

Module 10: Feature Engineering

Creating good features.

Transforming inputs.

Synthetic features.

Preprocessing with Cloud ML.

Lab: Feature engineering.

Module 11: Architecture of streaming analytics pipelines

Stream data processing: Challenges.

Handling variable data volumes.

Dealing with unordered/late data.

Lab: Designing streaming pipeline.

Module 12: Ingesting Variable Volumes

What is Cloud Pub/Sub?

How it works: Topics and Subscriptions.

Lab: Simulator.

Module 13: Implementing streaming pipelines

Challenges in stream processing.

Handle late data: watermarks, triggers, accumulation.

Lab: Stream data processing pipeline for live traffic data.

Module 14: 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.

Module 15: High throughput and low-latency with Bigtable

What is Cloud Spanner?

Designing Bigtable schema.

Ingesting into Bigtable.

Lab: streaming into Bigtable.

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