Apache Hadoop ecosystem

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.

Subscribe to hadoopsphere.com and receive free Apache Hadoop ecosystem analyst report in pdf format.

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

Comments

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…

HadoopSphere

Search This Blog