Comments for cloudeventprocessing.com http://blog.cloudeventprocessing.com Sun, 12 Feb 2012 11:32:42 +0000 hourly 1 http://wordpress.org/?v=3.3.1 Comment on It’s Time to Kill the Elephant by Link Collection (weekly) : Elemental Links http://blog.cloudeventprocessing.com/2011/07/03/its-time-to-kill-the-elephant/#comment-646 Link Collection (weekly) : Elemental Links Sun, 12 Feb 2012 11:32:42 +0000 http://cloudeventprocessing.wordpress.com/?p=1374#comment-646 [...] It’s Time to Kill the Elephant | cloudeventprocessing.com [...] [...] It’s Time to Kill the Elephant | cloudeventprocessing.com [...]

]]> Comment on Cassandra’s Data Model by William Sharp http://blog.cloudeventprocessing.com/2011/01/27/cassandras-data-model/#comment-643 William Sharp Sat, 21 Jan 2012 14:29:22 +0000 http://cloudeventprocessing.com/?p=922#comment-643 Well written post. It was clear, concise and had good flow. It was informative, while not being overwhelming. This is a good post for those who are starting out with Cassandra/NoSQL. Well written post. It was clear, concise and had good flow. It was informative, while not being overwhelming. This is a good post for those who are starting out with Cassandra/NoSQL.

]]> Comment on Hope, FPGA’s, High Frequency Trading and the New Market Access Rules by 10 Tuesday AM Reads | Financial Feeder http://blog.cloudeventprocessing.com/2011/02/03/hope-fpgas-high-frequency-trading-market-access-rules/#comment-640 10 Tuesday AM Reads | Financial Feeder Tue, 17 Jan 2012 15:09:44 +0000 http://cloudeventprocessing.com/?p=961#comment-640 [...] economists (Noahpinion) • Hope, FPGA’s, High Frequency Trading and the New Market Access Rules (Dark Star) see also ‘Bloated’ London Banks Shrink in the City (Bloomberg) • NCSE Picks Fight Against [...] [...] economists (Noahpinion) • Hope, FPGA’s, High Frequency Trading and the New Market Access Rules (Dark Star) see also ‘Bloated’ London Banks Shrink in the City (Bloomberg) • NCSE Picks Fight Against [...]

]]> Comment on Hope, FPGA’s, High Frequency Trading and the New Market Access Rules by 10 Tuesday AM Reads | The Big Picture http://blog.cloudeventprocessing.com/2011/02/03/hope-fpgas-high-frequency-trading-market-access-rules/#comment-639 10 Tuesday AM Reads | The Big Picture Tue, 17 Jan 2012 15:00:58 +0000 http://cloudeventprocessing.com/?p=961#comment-639 [...] economists (Noahpinion) • Hope, FPGA’s, High Frequency Trading and the New Market Access Rules (Dark Star) see also ‘Bloated’ London Banks Shrink in the City (Bloomberg) • NCSE Picks Fight Against [...] [...] economists (Noahpinion) • Hope, FPGA’s, High Frequency Trading and the New Market Access Rules (Dark Star) see also ‘Bloated’ London Banks Shrink in the City (Bloomberg) • NCSE Picks Fight Against [...]

]]> Comment on It’s Time to Kill the Elephant by Amit Piplani http://blog.cloudeventprocessing.com/2011/07/03/its-time-to-kill-the-elephant/#comment-635 Amit Piplani Mon, 16 Jan 2012 10:13:34 +0000 http://cloudeventprocessing.wordpress.com/?p=1374#comment-635 Good Article. http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-136.pdf is the link to the paper on "Map Reduce Online". They presented a modi?ed version of the Hadoop MapReduce framework that supports online aggregation, which allows users to see “early returns” from a job as it is being computed. Their Hadoop Online Prototype (HOP) also supported continuous queries, which enabled MapReduce programs to be written for applications such as event monitoring and stream processing. Good Article. http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-136.pdf is the link to the paper on “Map Reduce Online”. They
presented a modi?ed version of the Hadoop MapReduce
framework that supports online aggregation, which allows users to see “early returns” from a job as it is being
computed. Their Hadoop Online Prototype (HOP) also
supported continuous queries, which enabled MapReduce
programs to be written for applications such as event
monitoring and stream processing.

]]> Comment on Cassandra’s Data Model by Hector http://blog.cloudeventprocessing.com/2011/01/27/cassandras-data-model/#comment-625 Hector Tue, 10 Jan 2012 01:02:55 +0000 http://cloudeventprocessing.com/?p=922#comment-625 Have you considered using the OrderPreservingPartitioner to aid in range queries? Also, couldn't you concatenate the sequence number to the column family key in order to avoid creating a super column family? Doesn't using a super column family add additional overhead? Have you considered using the OrderPreservingPartitioner to aid in range queries? Also, couldn’t you concatenate the sequence number to the column family key in order to avoid creating a super column family? Doesn’t using a super column family add additional overhead?

]]> Comment on Big Data Isn’t About the Data by Partha Dutta http://blog.cloudeventprocessing.com/2011/12/23/big-data-isnt-about-the-data/#comment-596 Partha Dutta Wed, 28 Dec 2011 11:20:31 +0000 http://blog.cloudeventprocessing.com/?p=1443#comment-596 I disagree with you on couple of points : 1. "There are a couple of working definitions in progress in the group, but the one I like that seems to be emerging really doesn’t have that much to do with data at all. It’s about architecture." Big Data has always been about huge amount of data , specially unstructured or semistructured by nature. Challenge till date was how to consume that huge data as most of the enterprise valuable content lies in that basket (almost 80-90%). Big Data Processing is about the architectural nuances and how to apply the different architectural options to process that in small amount of time. 2. "How about scaling to handle all this data. I think one of the core tenants of big data is that it doesn’t fit on one machine." Mainframes , Supercomputers were always there. The term got coined recently. I can have a mainframe systems to process some tera byte of data. I need not scale unless I am short on dollars. Big Data processing is all about processing huge amount of data using commodity hardware. Grid computing, as you mentioned , was the first step towards that and with the advent of cloud (infra elasticity), we talk more about processing data on cloud. Its cheap. Cloud basically have helped us innovate and find not-so-traditional approach to some classic problem that we have been facing traditionally. Cloud based map reduce infrastructure using a distributed framework like Hadoop or GFS is just one of the solution to it. I disagree with you on couple of points :

1. “There are a couple of working definitions in progress in the group, but the one I like that seems to be emerging really doesn’t have that much to do with data at all. It’s about architecture.”
Big Data has always been about huge amount of data , specially unstructured or semistructured by nature. Challenge till date was how to consume that huge data as most of the enterprise valuable content lies in that basket (almost 80-90%). Big Data Processing is about the architectural nuances and how to apply the different architectural options to process that in small amount of time.

2. “How about scaling to handle all this data. I think one of the core tenants of big data is that it doesn’t fit on one machine.”
Mainframes , Supercomputers were always there. The term got coined recently. I can have a mainframe systems to process some tera byte of data. I need not scale unless I am short on dollars. Big Data processing is all about processing huge amount of data using commodity hardware. Grid computing, as you mentioned , was the first step towards that and with the advent of cloud (infra elasticity), we talk more about processing data on cloud. Its cheap.

Cloud basically have helped us innovate and find not-so-traditional approach to some classic problem that we have been facing traditionally. Cloud based map reduce infrastructure using a distributed framework like Hadoop or GFS is just one of the solution to it.

]]> Comment on It’s Time to Kill the Elephant by Brian Hopkins http://blog.cloudeventprocessing.com/2011/07/03/its-time-to-kill-the-elephant/#comment-587 Brian Hopkins Sun, 25 Dec 2011 06:39:05 +0000 http://cloudeventprocessing.wordpress.com/?p=1374#comment-587 Couldn't agree more...things are moving to real time, which is why I put stream processing in our Big Data reference architecture. Thing is, Horton Works knows it which is why even Hadoop is going that way. They are opening up Hadoop to allow it to work with other computational frameworks like BSP...check out Hama. Couldn’t agree more…things are moving to real time, which is why I put stream processing in our Big Data reference architecture. Thing is, Horton Works knows it which is why even Hadoop is going that way. They are opening up Hadoop to allow it to work with other computational frameworks like BSP…check out Hama.

]]> Comment on It’s Time to Kill the Elephant by No, Hadoop Doesn’t Own Big Data Analytics! « Big Data Big Analytics http://blog.cloudeventprocessing.com/2011/07/03/its-time-to-kill-the-elephant/#comment-584 No, Hadoop Doesn’t Own Big Data Analytics! « Big Data Big Analytics Mon, 12 Dec 2011 21:41:45 +0000 http://cloudeventprocessing.wordpress.com/?p=1374#comment-584 [...] In summary:  Hadoop is a great batch-focused distributing processing engine and I am glad that the work of that community is paying off for them, but they are not an enterprise analytics system! BTW, if this very mild piece gets any Hadoop loyalists screaming hatchet job go read my Twitter buddy Colin Clark’s piece on killing the elephant. [...] [...] In summary:  Hadoop is a great batch-focused distributing processing engine and I am glad that the work of that community is paying off for them, but they are not an enterprise analytics system! BTW, if this very mild piece gets any Hadoop loyalists screaming hatchet job go read my Twitter buddy Colin Clark’s piece on killing the elephant. [...]

]]> Comment on Hope, FPGA’s, High Frequency Trading and the New Market Access Rules by Monex http://blog.cloudeventprocessing.com/2011/02/03/hope-fpgas-high-frequency-trading-market-access-rules/#comment-113 Monex Wed, 02 Mar 2011 09:17:37 +0000 http://cloudeventprocessing.com/?p=961#comment-113 ..........Introduction.This policy sets out the basis on which ITG will provide best execution as described in the Markets in Financial Instruments Directive MiFID . ITG s approach is to employ technology to help achieve best execution and some examples of this approach are referred to in this Policy..Factors ITG will take into account to achieve best execution.The factors which ITG regards as most important in determining the best way to execute a client order are . a price the current price available on execution venues . b cost the costs associated with an execution venue including settlement costs will be taken into account in making the decision where to execute an order . c size the routing of orders to markets that provide the greatest liquidity and potential for execution . d speed and certainty the routing of orders to venues which provide speed and certainty of execution and settlement and. e overall execution quality.Other considerations relevant to the execution of the order such as the nature of the order the characteristics of the client client priorities and the characteristics of the instrument and market may also be taken into account. ……….Introduction.This policy sets out the basis on which ITG will provide best execution as described in the Markets in Financial Instruments Directive MiFID . ITG s approach is to employ technology to help achieve best execution and some examples of this approach are referred to in this Policy..Factors ITG will take into account to achieve best execution.The factors which ITG regards as most important in determining the best way to execute a client order are . a price the current price available on execution venues . b cost the costs associated with an execution venue including settlement costs will be taken into account in making the decision where to execute an order . c size the routing of orders to markets that provide the greatest liquidity and potential for execution . d speed and certainty the routing of orders to venues which provide speed and certainty of execution and settlement and. e overall execution quality.Other considerations relevant to the execution of the order such as the nature of the order the characteristics of the client client priorities and the characteristics of the instrument and market may also be taken into account.

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