Feature list of ultimate BigData analytics
Volume Scalability => the solution must handle high volumes of data, meaning the cost must scale linearly in the range of 10GB – 10PB. Latency Scalability => the solution must be interactive or...
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Analytics Patterns
Unsatisfied by my previous post‘s Advanced Analytics definition and giving it a thought of what is advanced methods in analytics I realized that analytics industry miss a good analytics pattern...
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CAP equivalent for analytics?
CAP theorem deals with trade-off in transactional system. It doesn’t need an introduction, unless of course you have been busy on the moon for last couple of years. In this case you can easily Google...
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Google Percolator: MapReduce Demise?
Here is my early thoughts after quickly looking into Google Percolator and skimming the paper . Major take-away: massive transactional mutating of tens-petabyte-scale dataset on thousands-node cluster...
View ArticleDebunking common misconceptions in SSD, particularly for analytics
1. SSD is NOT synonymous for flash memory. First of all let’s settle on terms. SSD is best described as a concept of using semiconductor memory as disk. There is two common cases: DRAM-as-disk and...
View ArticleApache Drill Design Meeting
MapR folks invited me to participate in Apache Drill design meeting. Meetup site indicates that 60 people have been participated which sounds about right. Tomer Shiran started the meeting with the...
View ArticleMulti-engine data processing
There is a lot of criticism of HDFS – it is slow, it has SPOF, it is read only, etc. All of the above is true. Systems built on top of a local file system are more efficient than those built on top of...
View ArticleEfficient usage of local drives in the cloud for big data processing
Cloud sounds like a perfect platform for the big data processing – you get as much processing power when you need it and release when you don’t. But why does a lot of big data processing happen outside...
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