Skip to main content

Apache Hadoop ecosystem - March 2013

Apache Hadoop ecosystem continues to evolve at a rapid pace with newer projects that are being added as incubators while those currently under incubation are getting ready to graduate out. Let’s visit the current state of open source Apache Hadoop ecosystem.


(Slides may render differently on browsers - in certain cases font, links and curves may not appear as intended. Use contact option to receive original presentation)


About the categorization:
(1)   Core Layers – this category comprises the core components which are required in part or as a whole to optimally leverage Apache Hadoop
(2)  Atmospheric Layers – this category comprises the additional components which provide advanced capabilities and insights for the composite use cases.

The nomenclature adopted for categories (core/atmospheric) and layers does not constitute official lingo and are being introduced to demarcate the basic use case versus advanced use cases of Apache Hadoop. As Hadoop distribution matures out along with technology stacks built up by organizations, we expect components and probably even layers to move around from one category to other.

Other open source projects/components like Cloudera Impala, Kerberos, Protocol Buffer etc. are not included in this ecosystem diagram since they are not Apache Software Foundation (ASF) projects.



Comments and inputs are welcome.


You may also download the full size image.



If you would like to contribute your content to hadoopsphere.com, click here.


Comments

Popular posts from this blog

Technical deep dive into Apache Tajo

Over the past few months, a new Hadoop based warehouse called Apache Tajo has been making the buzz. We tried to do a technical deep dive in the topic and reached out to PMC chair Hyunsik Choi. Apache Tajo is an Apache top level project since March 2014 and supports SQL standards. It has a powerful distributed processing architecture which is not based on MapReduce. To get more sense on Tajo's claims for providing a distributed, fault-tolerant, low-latency and high throughput SQL engine, we asked a few questions to Choi. This Q&A is published in a two part article series on hadoopsphere.com. Read below to get a better idea on Apache Tajo.
How does Apache Tajo work? As you may already know, Tajo does not use MapReduce and has its own distributed processing framework which is flexible and specialized to relational processing. Since its storage manager is pluggable, Tajo can access data sets stored in various storages , such as HDFS, Amazon S3, Openstack Swift or local file system. …

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…

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…