Skip to main content

Boom boom data storage

With the enormous increase in data over the last few years, it is an intuitive corollary that the demand for storage solutions must have increased as well. We look at the dynamics of storage space in conjunction with Big Data and Hadoop wave.

The Storage context

To set a context for the storage solutions in Hadoop ecosystem, some of the unique characteristics are listed below.
If you set a replication factor of 3:
-          For each TB (TeraByte) of data, 3 TB of disk storage is required
-          For each block write, there will be 3 trips that the data needs to make across the network
-          Each data block passes through memory bus and RAM before being passed on to storage device and same process is repeated on each DataNode  in the ingest and  replication pipeline
-          The intermediate and final results of the MapReduce process also need to be stored along with the ingested data
Combined with the fact that:
-          Many organizations will use a combination of dedicated Data Centers and outsourced Cloud data storage models
-          There is increasing focus on regulatory compliance for data privacy and retention
Along with variants in technical architecture like:
-          Storing meta data of NameNode on redundant servers to provide custom High Availability (HA) solutions
-          Coupling up warehouse and NoSQL layer like Hive, HBase etc on top of HDFS
And, increasing innovative usage of Hadoop for use cases like:
-          Data archiving
-          Blob storage

Storage market forecast

It is not surprising that the demand for storage has been increasing and IDC predicts that storage market will grow by astounding 184% from 2012 to 2015. The research firm also predicts that file and object based storage will become mainstay in Big Data market. Combined with the emergence of in memory database like SAP HANA, we will also see common deployments of network based flash storage. In the same research report, it notes that 32.7 % of organizations are running open source Hadoop on their data analytics platform while 20.3 % organizations are running commercial Hadoop distributions.

Research the options

As the options grow, so do the queries and pitches. However, beneath the fine print, the following must be taken into account while determining Big Data storage architecture:

- scalable at cost effective prices

- maintains throughput and access speed with volume growth

- eliminate data migration need essentially infeasible with Petabyte data stores

- support automation for script driven data management rather than human driven tasks

- maintain data integrity and availability knowing that hardware failure can occur

- supports replication across WAN and clouds

While the days of proprietary lock-in are moving behind us, it is still advisable to review the options and architecture before committing to a storage buy-in. Storage sprawl and underutilized capacity may have been a common productivity issue but with hybrid and tiered storage architecture, organizations are countering the trend. As attractive the downward trend in per unit storage cost may seem, the volume spike may still cost the buck. For now, make the most of the booming storage options. 

top image source:


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…

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…

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 …