Urs Hölzle in a post summarizing some of the announcements at Google I/O:

Google Cloud Datastore is a fully managed and schemaless solution for storing non-relational data. Based on the popular App Engine High Replication Datastore, Cloud Datastore is a standalone service that features automatic scalability and high availability while still providing powerful capabilities such as ACID transactions, SQL-like queries, indexes and more.

I’m heading over to the project’s site to read more.

Original title and link: Introducing Google Cloud Datastore (NoSQL database©myNoSQL)

via: http://googlecloudplatform.blogspot.com/2013/05/ushering-in-next-generation-of.html

Interesting speculation by The Register:

AWS already has the AWS Data Pipeline, which helps administrators schedule and shuttle data among various services, AWS Redshift for data warehousing which lets people store large quantities of data in the cloud and run queries on it, its NoSQL SSD-backed DynamoDB, and its Relational Database Service (RDS). So where does MADS fit?

The Reg’s take is that MADS will allow Amazon to build services that can net together the above components and help automate the passing of data among them. It may also become a standalone product in its own right, based on its similarities to the TransLattice and Google Spanner tech.

I almost never bet, but I’d say this could be Amazon’s Spanner.

Original title and link: Amazon Preparing ‘Disruptive’ Big Data AWS Service? (NoSQL database©myNoSQL)

via: http://www.theregister.co.uk/2013/02/19/amazon_new_big_data_aws_service/

Until I learn more about the recently announced Cloudera Impala and Druid from Metamarkets, this article by Jaikumar Vijayan should offer—with some inherent mistakes¹—a good overview of the solutions aiming to offer alternatives to the batch-processing nature of Hadoop:

• Google Dremel (BigQuery)
• Cloudera Impala
• Metamarkets Druid
• Nodeable StreamReduce
• SAP HANA integrated with Hadoop, etc.

Original title and link: Overview of Dremel-Like Solutions: Moving Beyond Hadoop for Big Data Needs (NoSQL database©myNoSQL)

via: http://www.infoworld.com/print/205879

Besides performance and quota changes, Google BigQuery adds support for importing JSON data and nested/repeated fields:

If you’re using App Engine Datastore or other NoSQL databases, it’s likely you’re taking advantage of nested and repeated data in your data model. For example, a customer data entity might have multiple accounts, each storing a list of invoices. Now, instead of having to flatten that data, you can keep your data in a hierarchical format when you import to BigQuery.

Original title and link: Google BigQuery Adds Support for JSON Import and Hierarchical Data (NoSQL database©myNoSQL)

via: http://googleenterprise.blogspot.com/2012/10/google-bigquery-updates-faster-easier.html

Todd Hoff of Highscalability.com:

What struck me most in the paper was a deeply buried section essentially describing Google’s motivation for shifting away from NoSQL and to NewSQL. The money quote:

We believe it is better to have application programmers deal with performance problems due to overuse of transactions as bottlenecks arise, rather than always coding around the lack of transactions.

That’s one piece of the Spanner paper that’s catching everyone’s attention. I’m wondering how much of this reference to transactions refers to:

1. multi-operations transactions
2. synchronous replication
3. data strong consistency

Original title and link: Todd Hoff on Google Spanner’s (NoSQL database©myNoSQL)

via: http://highscalability.com/blog/2012/9/24/google-spanners-most-surprising-revelation-nosql-is-out-and.html

Cloudant’s Mike Miller sharing his thoughts about Google’s Spanner paper:

Spanner’s key innovation is around time. It includes a novel system using GPS and Atomic Clocks to distribute a globally synchronized “proper time.” The previous dogma in distributed systems was that synchronizing time within and between datacenters is insurmountably hard and uncertain. Ergo, serialization of requests is impossible at global scale. Google’s key innovation is to accept uncertainty, keep it small (via atomic clocks and GPS), quantify the uncertainty and operate around it. In retrospect this is obvious, but it doesn’t make it any less brilliant.

Original title and link: Cloudant’s Mike Miller on Google Spanner (NoSQL database©myNoSQL)

via: https://cloudant.com/blog/cloudant-labs-on-google-spanner/

Remember what I was writing in the state of Hadoop market about having a second option for on-demand cloud-based Hadoop services? Benjamin Black compares Google Cloud Platform with Amazon services:

• Cloud Engine is a lot like EC2 & EBS
• Cloud Storage is a lot like S3
• Cloud SQL is a lot like RDS
• Analytics can be used like CloudWatch (and I know of people putting billions of their own data points in Analytics)
• BigQuery has no AWS equivalent, but maybe you could build it with EMR?
• PageSpeed has no AWS equivalent

Hadoop and MapR are already listed as possible use cases for Google Cloud Platform.

I don’t think I could write a better conclusion than Black did in his post:

This is big, planetary scale infrastructure. This is cloud legitimized and super-sized. In the words of the prophet: Shit just got real.

Original title and link: Google Cloud Platform Is the Biggest Deal in IT Since Amazon Launched EC2 (NoSQL database©myNoSQL)

via: http://blog.b3k.us/2012/07/04/cloud-independence-day.html

An ArsTechnica, not very technical, overview of the storage engines developed and used by Google (Google File System, BigTable), Amazon (Dynamo), Microsoft (Azure DFS), plus the Hadoop Distributed File System (HDFS).

Original title and link: How Web giants store big data (NoSQL database©myNoSQL)

via: http://arstechnica.com/business/news/2012/01/the-big-disk-drive-in-the-sky-how-the-giants-of-the-web-store-big-data.ars/1