Data Engineering on Google Cloud Platform (4 days) 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. A personal laptop is required for all workshops and will not be
provided. Objectives This course teaches participants the following
skills: 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 Audience This class is
intended 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 Creating and maintaining machine learning and
statistical models Querying datasets, visualizing query results and
creating reports Prerequisites To get the most of out of this course,
participants should have: Completed Google Cloud Fundamentals- Big
Data and 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 Course Outline
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. **
Notice: Cancellations will be charged an administrative fee through
Eventbrite.
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29/01/2020 Last update