Skip to main content

Delivery Methodology for Hadoop projects


As the enterprise projects for Hadoop have started picking up steam, we need to come up with an effective delivery methodology for Hadoop and machine learning projects. It is evident to any industry player that the traditional waterfall methodology won’t work for such projects. Also, a typical hackathon development attitude from a startup culture may not fit in the CMMi criteria of multi million enterprise bids.

Hadoopsphere tries to present a draft delivery methodology for Hadoop development projects. The draft still needs to go through a few reviews and comments are welcome to make this a robust methodology.



Some notes on this delivery methodology:
1)      Agile delivery techniques are proposed – which may include Scrum or Kanban based delivery techniques. While Scrum may rely on time bound iterations/sprints, Kanban relies on exploratory task/event based assignment and may not be time bound. Kanban is well suited for initial product or application development while Scrum may be recommended for subsequent releases.
2)      The methodology has been tried to accustom to both product and services project development – further inputs are welcome.1
3)      The methodology has been tailored with Big Data Analytics Process proposed in Gartner report. 2
4)      The artifacts which play a key role in Hadoop projects include but are not limited to Hardware & Software Plan along with Data Management Plan. The other proposed artifacts are mentioned in figure above.

[1] Key inputs from Sachin Ghai.
[2] Use Kanban to Manage Big DataDevelopment, Nathan Wilson, Gartner, August 2012
Labels:

Comments

  1. I assume the arrows at top indicate the repetitive agile iterations

    ReplyDelete

Post a Comment

Popular posts from this blog

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…
Post a Comment
Read more

5 online tools in data visualization playground

While building up an analytics dashboard, one of the major decision points is regarding the type of charts and graphs that would provide better insight into the data. To avoid a lot of re-work later, it makes sense to try the various chart options during the requirement and design phase. It is probably a well known myth that existing tool options in any product can serve all the user requirements with just minor configuration changes. We all know and realize that code needs to be written to serve each customer’s individual needs.
To that effect, here are 5 tools that could empower your technical and business teams to decide on visualization options during the requirement phase. Listed below are online tools for you to add data and use as playground.
1)      Many Eyes: Many Eyes is a data visualization experiment by IBM Researchandthe IBM Cognos software group. This tool provides option to upload data sets and create visualizations including Scatter Plot, Tree Map, Tag/Word cloud and ge…
5 comments
Read more

Pricing models for Hadoop products

A look at the various pricing models adopted by the vendors in the Hadoop ecosystem. While the pricing models are evolving in this rapid and dynamic market, listed below are some of the major variations utilized by companies in the sphere.
1) Per Node:Among the most common model, the node based pricing mechanism utilizes customized rules for determining pricing per node. This may be as straight forward as pricing per name node and data node or could have complex variants of pricing based on number of core processors utilized by the nodes in the cluster or per user license in case of applications.
2) Per TB:The data based pricing mechanism charges customer for license cost per TB of data. This model usually accounts non replicated data for computation of cost.
3) Subscription Support cost only:In this model, the vendor prefers to give away software for free but charges the customer for subscription support on a specified number of nodes. The support timings and level of support further …
Post a Comment
Read more