Skip to main content

10 parameters for Big Data networks

Big Data and Hadoop clusters involve heavy volume of data and in many instances high velocity in bursty traffic patterns. With these clusters finding in-roads in enterprise data centers, the network designers have a few more requirements to take care. Listed below are 10 parameters to evaluate while designing a network for Big Data and Hadoop cluster.



10) Available and resilient


- Allows network designs with multiple redundant paths between the data nodes than having one or two points of failure.
- Supports upgrades without any disruption to the data nodes


9) Predictable

- Right sizing the network configuration (1GbE/10GbE/100GbE switch capacity) to achieve predictable latency in network
- real time latency may not be required for batch processing


8) Holistic network

- one network can support all workloads : Hadoop, NoSQL, Warehouse, ETL, Web
- support Hadoop and existing storage systems like DAS, SAN, or NAS

7) Multitenant

- be able to consolidate and centralize Big Data projects
- have capability to leverage the fabric across multiple use cases

6) Network partitioning

- support separate Big Data infrastructure from other IT resources on the network
- support privacy and regulatory norms

5) Scale Out

- provide seamless transition as projects increase in size and number
- accommodate new traffic patterns and larger, more complex workloads

4) Converged/ unified fabric network

-  target a flatter and converged network with Big Data as an additional configurable workload
- provide virtual chassis architecture with provision to logically manage access to multiple switches as a single device


3) Network intelligence

- carry any-to-any traffic flows of a Big Data as well as traditional cluster over an Ethernet connection
-  manage single network fabric irrespective of data requirements or storage design



2) Enough bandwidth for data node network

- provision data nodes with enough bandwidth for efficient job completion
- do cost/benefit trade-off on increasing data node uplinks

1) Support bursty traffic

- support loading files into HDFS which triggers replication of data blocks or writing mapper output files and lead to higher network use in a short period of time causing bursts of traffic in the network.
- provide optimal buffering in network devices to absorb bursts



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…