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Google Cloud Platform: Data Engineering Track
You, This Course and Us
You, This Course and Us (2:02)
Course Materials
Introduction
Theory, Practice and Tests (10:28)
Lab: Setting Up A GCP Account (6:59)
Why Cloud? (9:45)
Hadoop and Distributed Computing (9:03)
On-premise, Colocation or Cloud? (10:07)
Introducing the Google Cloud Platform (13:22)
Lab: Using The Cloud Shell (6:01)
Important! Delete unused GCP projects/instances
Quiz 1 GCP Introduction
Storage
About this section
Storage Options (9:50)
Quick Take (13:43)
Cloud Storage (10:39)
Lab: Working With Cloud Storage Buckets (5:25)
Lab: Bucket And Object Permissions (3:52)
Lab: Life cycle Management On Buckets (5:06)
Lab: Running A Program On a VM Instance And Storing Results on Cloud Storage (7:09)
Transfer Service (5:09)
Cloud SQL, Cloud Spanner ~ OLTP ~ RDBMS
About this section
Cloud SQL (7:42)
Lab: Creating A Cloud SQL Instance (7:54)
Lab: Running Commands On Cloud SQL Instance (6:31)
Lab: Bulk Loading Data Into Cloud SQL Tables (9:09)
Cloud Spanner (7:27)
More Cloud Spanner (9:20)
Lab: Working With Cloud Spanner (6:49)
Important! Delete unused GCP projects/instances
Hadoop Pre-reqs and Context
Hadoop Pre-reqs and Context
BigTable ~ HBase = Columnar Store
About this section
BigTable Intro (7:59)
Columnar Store (8:14)
Denormalised (9:04)
Column Families (8:12)
BigTable Performance (13:21)
Lab: BigTable demo (7:39)
Important! Delete unused GCP projects/instances
Datastore ~ Document Database
About this section
Datastore (14:12)
Lab: Datastore demo (6:42)
Quiz 3 Datastore
BigQuery ~ Hive ~ OLAP
About this section
BigQuery Intro (11:03)
BigQuery Advanced (9:59)
Lab: Loading CSV Data Into Big Query (9:03)
Lab: Running Queries On Big Query (5:26)
Lab: Loading JSON Data With Nested Tables (7:28)
Lab: Public Datasets In Big Query (8:16)
Lab: Using Big Query Via The Command Line (7:45)
Lab: Aggregations And Conditionals In Aggregations (9:51)
Lab: Subqueries And Joins (5:44)
Lab: Regular Expressions In Legacy SQL (5:36)
Lab: Using The With Statement For SubQueries (10:45)
Dataflow ~ Apache Beam
About this section
Data Flow Intro (11:06)
Apache Beam (3:42)
Lab: Running A Python Data flow Program (12:56)
Lab: Running A Java Data flow Program (13:42)
Lab: Implementing Word Count In Dataflow Java (11:17)
Lab: Executing The Word Count Dataflow (4:37)
Lab: Executing MapReduce In Dataflow In Python (9:50)
Lab: Executing MapReduce In Dataflow In Java (6:08)
Lab: Dataflow With Big Query As Source And Side Inputs (15:50)
Lab: Dataflow With Big Query As Source And Side Inputs 2 (6:28)
Dataproc ~ Managed Hadoop
About this section
Data Proc (8:30)
Lab: Creating And Managing A Dataproc Cluster (8:11)
Lab: Creating A Firewall Rule To Access Dataproc (8:25)
Lab: Running A PySpark Job On Dataproc (7:39)
Lab: Running The PySpark REPL Shell And Pig Scripts On Dataproc (8:44)
Lab: Submitting A Spark Jar To Dataproc (2:10)
Lab: Working With Dataproc Using The Gcloud CLI (8:19)
Pub/Sub for Streaming
About this section
Pub Sub (8:25)
Lab: Working With Pubsub On The Command Line (5:35)
Lab: Working With PubSub Using The Web Console (4:39)
Lab: Setting Up A Pubsub Publisher Using The Python Library (5:52)
Lab: Setting Up A Pubsub Subscriber Using The Python Library (4:08)
Lab: Publishing Streaming Data Into Pubsub (8:18)
Lab: Reading Streaming Data From PubSub And Writing To BigQuery (10:14)
Lab: Executing A Pipeline To Read Streaming Data And Write To BigQuery (5:54)
Lab: Pubsub Source BigQuery Sink (10:20)
Datalab ~ Jupyter
About this section
Data Lab (3:01)
Lab: Creating And Working On A Datalab Instance (10:29)
Lab: Importing And Exporting Data Using Datalab (12:14)
Lab: Using The Charting API In Datalab (6:43)
TensorFlow and Machine Learning
About this section
Introducing Machine Learning (8:06)
Representation Learning (10:29)
NN Introduced (7:37)
Introducing TF (7:18)
Lab: Simple Math Operations (8:46)
Computation Graph (10:19)
Tensors (9:04)
Lab: Tensors (5:03)
Linear Regression Intro (9:59)
Placeholders and Variables (8:46)
Lab: Placeholders (6:36)
Lab: Variables (7:49)
Lab: Linear Regression with Made-up Data (4:52)
Image Processing (8:07)
Images As Tensors (8:18)
Lab: Reading and Working with Images (8:05)
Lab: Image Transformations (6:37)
Introducing MNIST (4:15)
K-Nearest Neigbors as Unsupervised Learning (7:44)
One-hot Notation and L1 Distance (7:31)
Steps in the K-Nearest-Neighbors Implementation (9:34)
Lab: K-Nearest-Neighbors (14:14)
Learning Algorithm (11:00)
Individual Neuron (9:54)
Learning Regression (7:53)
Learning XOR (10:29)
XOR Trained (11:13)
Regression in TensorFlow
About this section
Lab: Access Data from Yahoo Finance (2:49)
Non TensorFlow Regression (8:07)
Lab: Linear Regression - Setting Up a Baseline (11:18)
Gradient Descent (9:58)
Lab: Linear Regression (14:42)
Lab: Multiple Regression in TensorFlow (9:15)
Logistic Regression Introduced (10:18)
Linear Classification (5:27)
Lab: Logistic Regression - Setting Up a Baseline (7:33)
Logit (8:35)
Softmax (11:57)
Argmax (12:15)
Lab: Logistic Regression (16:56)
Estimators (4:12)
Lab: Linear Regression using Estimators (7:49)
Lab: Logistic Regression using Estimators (4:54)
Vision, Translate, NLP and Speech: Trained ML APIs
About this section
Lab: Taxicab Prediction - Setting up the dataset (14:38)
Lab: Taxicab Prediction - Training and Running the model (11:22)
Lab: The Vision, Translate, NLP and Speech API (10:53)
Lab: The Vision API for Label and Landmark Detection (7:00)
Appendix: Hadoop Ecosystem
Introducing the Hadoop Ecosystem (1:35)
Hadoop (9:45)
HDFS (10:55)
MapReduce (10:34)
Yarn (5:29)
Hive (7:19)
Hive v RDBMS (7:10)
HQL vs. SQL (7:38)
OLAP in Hive (7:36)
Windowing Hive (8:22)
Pig (8:04)
More Pig (6:38)
Spark (8:56)
More Spark (11:45)
Streams Intro (7:44)
Microbatches (5:42)
Window Types (5:48)
Quiz 6 Hadoop Ecosystem
Lab: Logistic Regression - Setting Up a Baseline
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