Google Cloud - Revision history

Admin: /* Data Analytics */

2017-10-24T07:44:02Z

Data Analytics

← Older revision		Revision as of 07:44, 24 October 2017
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	* Hive		* Hive
	* Hadoop		* Hadoop

			===Data Storage===

	Choosing a storage option: https://cloud.google.com/storage-options/		Choosing a storage option: https://cloud.google.com/storage-options/

2017-10-24T07:43:48Z

Data Processing Architectures

2017-10-24T07:29:51Z

Data Analytics

← Older revision		Revision as of 07:29, 24 October 2017
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	Dataproc for machine learning		Dataproc for machine learning
	* Bigtop ecosystem:
			BigTop ecosystem:
	* Pig		* Pig
	* Spark		* Spark
	* Hive		* Hive
	* Hadoop		* Hadoop

			Choosing a storage option: https://cloud.google.com/storage-options/

			Data warehousing:
			* Bigtable - low-latency and updatable data warehouse solution, data is not highly structurable, no need to support ACID transactions
			* BigQuery - petabyte-scale, structured, column-major, SQL-queryable data warehouse solution

			Data storage:
			* Cloud Storage - unstructured data (documents, sound files, PDFs, etc etc)
			* Cloud Datastore - non-relational (NoSQL), highly scalable storage solution; SQL-like query language; more restrictive queries (b/c optimized to be faster); supports ACID transactions
			* Cloud SQL - full SQL support and online transaction processing (OLTP) system
			* Cloud Spanner - (horizontally sharded SQL) fully managed mission-critical relational OLTP database that can scale horizontally to hundreds or thousands of servers to handle high workload transactions; supports ACID transactions

			Writeup of Spanner: https://quizlet.com/blog/quizlet-cloud-spanner

	===Scaling Data Analysis===		===Scaling Data Analysis===

2017-09-24T02:51:37Z

Google Cloud Services

2017-09-15T17:49:50Z

Data Processing Architectures

← Older revision		Revision as of 17:49, 15 September 2017
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	===Data Processing Architectures===		===Data Processing Architectures===

	PubSub		PubSub (messaging architecture)

	Dataflow		Dataflow (way to execute code that processes streaming and batch data in similar ways)



	[[Category:Google Cloud]]		[[Category:Google Cloud]]

2017-09-15T17:49:03Z

Scaling Data Analysis

← Older revision		Revision as of 17:49, 15 September 2017
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	===Scaling Data Analysis===		===Scaling Data Analysis===

			(Transformational use cases)

	Datalab		Datalab
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	Datastore		Datastore

	BigTable		BigTable (fast random access, tradeoffs between consistency and availability)

	BigQuery (query petabytes in seconds)		BigQuery (query petabytes in seconds)

	TensorFlow		TensorFlow (distributed in the cloud over very large data sets)

	Demand forecasting with machine learning		Demand forecasting with machine learning

2017-09-15T17:48:03Z

Scaling Data Analysis

← Older revision		Revision as of 17:48, 15 September 2017
Line 58:		Line 58:
	BigTable		BigTable

	BigQuery		BigQuery (query petabytes in seconds)

	TensorFlow		TensorFlow

2017-09-15T17:47:14Z

Data Analytics

← Older revision		Revision as of 17:47, 15 September 2017
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	* Hadoop		* Hadoop

			===Scaling Data Analysis===

			Datalab

			Datastore

			BigTable

			BigQuery

			TensorFlow

			Demand forecasting with machine learning

			===Data Processing Architectures===

			PubSub

			Dataflow



	[[Category:Google Cloud]]		[[Category:Google Cloud]]

2017-09-15T17:46:02Z

Data Analytics

← Older revision		Revision as of 17:46, 15 September 2017
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	===Data Analytics===		===Data Analytics===

			Cloud SQL - relational database

			Dataproc for machine learning
			* Bigtop ecosystem:
			* Pig
			* Spark
			* Hive
			* Hadoop

	~~Cloud SQL~~

	~~Dataproc for machine learning~~


	[[Category:Google Cloud]]		[[Category:Google Cloud]]

2017-09-15T17:44:22Z

Case Study

← Older revision		Revision as of 17:44, 15 September 2017
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	==Case Study==		==Case Study==

	The [[GCDEC]] page gives an example of a case study that can be used to see how different parts of the Google Cloud platform come together in the kind of scenario a real company might face:		The [[GCDEC]] page gives an example of a case study that can be used to see how different parts of the Google Cloud platform come together in the kind of scenario a real company might face. The case study focuses on a logistics company that delivers packages and tracks the deliveries with servers, software, and other infrastructure already in-place. The company's goal is to improve their computational infrastructure by moving parts of it to the cloud, and implement the ability to predict late shipments.

	[[Google Cloud/Case Study]]		[[Google Cloud/Case Study]]


	==Google Cloud Services==		==Google Cloud Services==