Skip to main content

Hadoop engineering support services primer

As the focus has shifted to production deployment of Hadoop based applications, there is an increasing need to have good practices around the production support process. While this may be a BAU transition process for large organizations, the startups and new product orgs. may want to catch up on standard and best implementations. Let’s put across a small primer on what you need while setting up a Hadoop production support system.



People:


            In a typical software product org and/or startup, the first focus and strength is usually skilled people. However, there is a need to segregate your high skilled force among the product/project development and support activities. At best, you need the following skills as part of support org. structure:

-          Hadoop stack Administrator

-          Support professional with MapReduce and essential application knowledge

-          Data scientist (definition of skills may vary specific to your app)

-          Account Manager (multi-variant customer facing role)



Process:


            While process terms may sound slightly offbeat or cliché to ‘hackers’, a service management setup demands it as a basic commandment. To begin with, identify type of issue reported as:

-          Incident, Problem, Service Request

-          Product enhancement

Further, establish how your team can get access to stacks:

-          Logs

-          Remote login rights

At the onset, while taking an issue report, collect basic hygiene information from the customer, including

-          stack details with product versions

-          trigger for issue, if known

-          any new deployment or upgrade info in the cluster

-          data sample, if possible

Further, devise a process on how to:

-          Escalate ticket internally

-          Advise customer on escalation process for:

o       Hierarchical escalation within your organization

o       Cross team escalation for integrated stacks

o       ‘War room’ escalations for major revenue impact incidents



Technology:


            Technology is not limited to just your product and the associated hardware. For a production support system, some must-have include:

-          Voice Call response and recording

-          Ticket management portal for accepting, assigning and transferring tickets

It may be nice to have

-          Self service portal for customers

-          FAQ or answer forum (like MapR) to help customer figures out a few issues themselves



Metrics:


            No support team can be spared on this. You just ought to have a sound metrics system for your team on:

-          Turn Around Time

-          Effort

-          Cost

-          Post production defects

-          Productivity per person



Billing:


            The bread and butter step, this has a few catches when it comes to Hadoop stacks. Your account manager (aka VP) needs to have a clear cut contract on support cost per node or similarly defined unit along with billing milestones. So, you need to keep watching each month for:

-          Any contract deviations (e.g. from inclusions in fixed AMC)

-          Number of supported nodes that your contract stated

-          Any ticket which needs to be billed as Enhancement and not as problem.





While many of these may inspire from a usual service management desk, there are key peculiarities with node based billing combined along with evolutionary nature of MapReduce product ecosystem. Due to the integrated nature of Hadoop technology stack which may have unsupported open source along with evolving NoSQL database combined with enterprise legacy suites, each organization needs to evolve a resilient production support system. It is best advised to review the key constituents holistically and devise a production support organization system.



For any help in setting up a production support setup further, you may drop a query to scale [at] hadoopsphere.com .





 ______________________

 top image source: freedigitalphotos.net

Comments

Popular posts from this blog

Beyond NSA, the intelligence community has a big technology footprint

While all through the past few days the focus has been on NSA activities, the discussion has often veered around the technologies and products used by NSA. At the same time, a side discussion topic has been the larger technical ecosystem of intelligence units. CIA has been one of the more prolific users of Information Technology by its own admission. To that extent, CIA spinned off a venture capital firm In-Q-Tel in 1999 to invest in focused sector companies. Per Helen Coster of Fortune Magazine, In-Q-Tel (IQT) has been named “after the gadget-toting James Bond character Q”.
In-Q-Tel states on its website that “We design our strategic investments to accelerate product development and delivery for this ready-soon innovation, and specifically to help companies add capabilities needed by our customers in the Intelligence Community”. To that effect, it has made over 200 investments in early stage companies for propping up products. Being a not-for-profit group, unlike Private Venture capi…

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…

Top Big Data Influencers of 2015

2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…