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

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…

In-memory data model with Apache Gora

Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB. Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive.
Introducing Apache Gora Although there are var…

Large scale graph processing with Apache Hama

Recently Apache Hama team released official 0.7.0 version. According to the release announcement, there were big improvements in Graph package. In this article, we provide an overview of the newly improved Graph package of Apache Hama, and the benchmark results that performed by cloud platform team at Samsung Electronics.

Large scale datasets are being increasingly used in many fields. Graph algorithms are becoming important for analyzing big data. Data scientists are able to predict the behavior of the customer, the trends of the market, and make a decision by analyzing the graph structure and characteristics. Currently there are a variety of open source graph analytic frameworks, such as Google’s Pregel[1], Apache Giraph[2], GraphLab[3] and GraphX[4]. These frameworks are aimed at computations varying from classical graph traversal algorithms to graph statistics calculations such as triangle counting to complex machine learning algorithms. However these frameworks have been developed…