GCP DATA ENGINEER

GCP DATA ENGINEER Training in Pune/ Online

Duration of Training  : 32 Hrs

Batch type  :  Weekdays/Weekends

Mode of Training  :  Classroom/Online/Corporate Training

 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.