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
Also,
-          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: freedigitalphotos.net

Comments

Popular posts from this blog

Hadoop's 10 in LinkedIn's 10

LinkedIn, the pioneering professional social network has turned 10 years old. One of the hallmarks of its journey has been its technical accomplishments and significant contribution to open source, particularly in the last few years. Hadoop occupies a central place in its technical environment powering some of the most used features of desktop and mobile app. As LinkedIn enters the second decade of its existence, here is a look at 10 major projects and products powered by Hadoop in its data ecosystem.
1)      Voldemort:Arguably, the most famous export of LinkedIn engineering, Voldemort is a distributed key-value storage system. Named after an antagonist in Harry Potter series and influenced by Amazon’s Dynamo DB, the wizardry in this database extends to its self healing features. Available in HA configuration, its layered, pluggable architecture implementations are being used for both read and read-write use cases.
2)      Azkaban:A batch job scheduling system with a friendly UI, Azkab…

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…