Elastic MapReduce: All content tagged as Elastic MapReduce in NoSQL databases and polyglot persistence
Over the weekend, Christopher Mims has published an article in which he derives a figure for Amazon Web Services’s annual revenue: $2.4 billions:
Amazon is famously reticent about sales figures, dribbling out clues without revealing actual numbers. But it appears the company has left enough hints to, finally, discern how much revenue it makes on its cloud computing business, known as Amazon Web Services, which provides the backbone for a growing portion of the internet: about $2.4 billion a year.
There’s no way to decompose this number into the revenue of each AWS solution. For the data space I’d be interested into:
S3 revenues. This is the space Basho’s Riak CS competes into.
After writing my first post about Riak CS, I’ve learned that in Japan, the same place where Riak CS is run by Yahoo! new cloud storage, Gemini Mobile Technologies has been offering to local ISPs a similar S3-service built on top of Cassandra.
Redshift is pretty new and while I’m not aware of immediate competitors (what am I missing?), I don’t think it accounts for a significant part of this revenue. Even if some of the early users, like AirBnb, report getting very good performance and costs from it.
Redshift is powered by ParAccell, which, over the weekend, has been acquired by Actian.
Amazon Elastic MapReduce. This is another interesting space from which Microsoft wants a share with its Azure HDInsight developed in collaboration with Hortonworks.
Interestingly Amazon is making money also from some of the competitors of its Amazon Dynamo and RDS services. The advantage of owning the infrastructure.
Original title and link: Amazon Web Services Annual Revenue Estimation ( ©myNoSQL)
Another very interesting news for the Hadoop space, this time coming from Amazon and MapR announcing support for the MapR Hadoop distribution on Amazon Elastic MapReduce:
MapR introduces enterprise-focused features for Hadoop such as high availability, data snapshotting, cluster mirroring across AZs, and NFS mounts. Combined with Amazon Elastic MapReduce’s managed Hadoop environment, seamless integration with other AWS services, and hourly pricing with no upfront fees or long-term commitments, Amazon EMR with the MapR Distribution for Hadoop offers customers a powerful tool for generating insights from their data.
Following the logic of the Amazon Relational Database Services which started with MySQL, the most popular and open source database and then added support for the commercial, but also very popular Oracle and SQL Server, what does this announcement tell us? It’s either that Amazon has got a lot of requests for MapR or that some very big AWS customers have mentioned MapR in their talks with Amazon. I go with the second option.
Original title and link: MapR Hadoop Distribution on Amazon Elastic MapReduce ( ©myNoSQL)
What Are the Pros and Cons of Running Cloudera’s Distribution for Hadoop vs Amazon Elastic MapReduce Service?
Old Quora question, but still very relevant. Top response from Jeff Hammerbacher:
Elastic MapReduce Pros:
- Dynamic MapReduce cluster sizing.
- Ease of use for simple jobs via their proprietary web console.
- Great documentation.
- Integrates nicely with other Amazon Web Services.
Cloudera Distribution for Hadoop:
- CDH is open source; you have access to the source code and can inspect it for debugging purposes and make modifications as required.
- CDH can be run on a number of public or private clouds using an open source framework, Whirr, so you’re not tied to a single cloud provider
- With CDH, you can move your cluster to dedicated hardware with little disruption when the economics make sense. Most non-trivial applications will benefit from this move.
- CDH packages a number of open source projects that are not included with EMR: Sqoop, Flume, HBase, Oozie, ZooKeeper, Avro, and Hue. You have access to the complete platform composed of data collection, storage, and processing tools.
- CDH packages a number of critical bug fixes and features and the most recent stable releases, so you’re usually using a more stable and feature-rich product.
- You can purchase support and management tools for CDH via Cloudera Enterprise.
- CDH uses the open source Oozie framework for workflow management. EMR implemented a proprietary “job flow” system before major Hadoop users standardized on Oozie for workload management.
- CDH uses the open source Hue framework for its user interface. If you require new features from your web interface, you can easily implement them using the Hue SDK.
- CDH includes a number of integrations with other software components of the data management stack, including Talend, Informatica, Netezza, Teradata, Greenplum, Microstrategy, and others. […]
- CDH has been designed and deployed in common Linux environments and you can use standard tools to debug your programs. […]
Make sure you also read Hadoop in the Cloud: Pros and Cons which addresses (almost) the same question.
A Twitter-style answer to this question would be: “Control and customization vs Automated and Managed Service”. 80 characters left to add your own perspective.
Original title and link: What Are the Pros and Cons of Running Cloudera’s Distribution for Hadoop vs Amazon Elastic MapReduce Service? ( ©myNoSQL)
I’ve created the diagram above based on this very brief answer on Quora:
We use python + heavily-modified Django at the application layer. Tornado and (very selectively) node.js as web-servers. Memcached and membase / redis for object- and logical-caching, respectively. RabbitMQ as a message queue. Nginx, HAproxy and Varnish for static-delivery and load-balancing. Persistent data storage using MySQL. MrJob on EMR for map-reduce.
Data from October 2011 showed Pinterest having over 3 million users generating 400+ million pageviews. There are plently of questions to be answered though:
what is node.js used for? what is RabbitMQ used for?
Note: the whole section in the diagram about node.js and RabbitMQ is speculative.
is Amazon Elastic MapReduce used for clickstream analysis only (log based analysis) or more than that?
how is data loaded in the Amazon cloud?
Note: if Amazon Elastic MapReduce is used only for analyzing logs, these are probably uploaded regularly on Amazon S3.
why the need for both Redis and Membase?
Original title and link: Polyglot persistence at Pinterest: Redis, Membase, MySQL ( ©myNoSQL)
My list of 8 most interesting companies for the future of Hadoop didn’t try to include anyone having a product with the Hadoop word in it. But the list from InformationWeek does. To save you 15 clicks, here’s their list:
- Amazon Elastic MapReduce
- EMC (with EMC Greenplum Unified Analytics Platform and EMC Data Computing Appliance)
- IBM (InfoSphere BigInsights)
- Informatica (for HParser)
Original title and link: 12 Hadoop Vendors to Watch in 2012 ( ©myNoSQL)