Data Engineering on Google Cloud Platform
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- Design and build data processing systems on Google Cloud Platform
- Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
- Derive business insights from extremely large datasets using Google BigQuery
- Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
- Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
- Enable instant insights from streaming data
- Extracting, Loading, Transforming, cleaning, and validating data
- Designing pipelines and architectures for data processing
- Creating and maintaining machine learning and statistical models
- Querying datasets, visualizing query results and creating reports
- Completed Google Cloud Fundamentals: Big Data & Machine Learning course OR have equivalent experience
- 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 Python
- Familiarity with Machine Learning and/or statistics
- Module 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
- Module 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.
- Module 3: 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 4: Running Dataproc Jobs
- Running Pig and Hive jobs
- Separation of storage and computer
- Lab: Running Hadoop and Spark Jobs with Dataproc.
- Lab: Submit and monitor jobs
- Module 5: Integrating Dataproc with Google Cloud Platform
- Customize cluster with initialization actions
- BigQuery Support
- Lab: Leveraging Google Cloud Platform Services
- Module 6: 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 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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