Skip to main content

So, what's brewing with HCatalog

Apache HCatalog announced release of version 0.5.0 in the past week. Along with that, it has initiated steps to graduate from an incubator project to be an Apache Top Level project or sub-project. Let's look at the current state of HCatalog, its increasing relevance and where it is heading. 
 
HCatalog  for a small introduction, is a “table management and storage management layer for Apache Hadoop” which:
-         enables Pig, MapReduce, and Hive users to easily share data on the grid.
-         provides a table abstraction for a relational view of data in HDFS
-         ensures format indifference (viz RCFile format, text files, sequence files)
-         provides a notification service when new data becomes available 

The following presentation explains the role of HCatalog in Hadoop ecosystem. (jump to slide 12)



 
What's new with 0.5.0 version?
  - HCatalog is now published in the Apache Maven repository.
  - New web services API to HCatalog: webhcat
  - Major notifications update
  - Build has been updated to provide per-submodule artifacts.
  - Improved pig adapter support.
  - Updated to Hive 0.10.0.
  - Many improvements and bug fixes.
 
 

The increasing relevance of HCatalog

If we look at Hortonworks Data Platform (HDP) architecture, we see HCatalog as a key enabler for Data Services layer.  The key drivers for this relevant position are:
-         increased demand for SQL like interface to query HDFS
-         better performance for analytical queries
-         consistent access method to HDFS data regardless of tools
-         flexibility and simplification of data access
-         decoupling data access with data storage

HCatalog, today, is being utilized for use cases which include the following, but are not limited to:
-         Complex Data processing:
o        Teams where a combination of tools may be employed. e.g for Hive for analytic queries and Pig for ETL
-         Data Discovery:
o        Using REST API to access data and discover the patterns before refining the model for further MapReduce operations
-         Integration:
o        Utilizing API to invoke data (< 1 MB) from HDFS


Where is HCatalog heading

While HCatalog cements its position in the enterprise Hadoop stack, it has also been leveraged by organizations like Teradata in their SQL like offering for Hadoop. At the same time, due to the rapid and consistent progress that the project has made, it is now being voted across to become a sub project for Apache Hive. Future work for HCatalog may include stable integration with HBase, fixing bugs in the API/public interface, and single connection to Hive-HCatalog with more Hive metadata access if adopted as part of Hive project.

Comments

Popular posts from this blog

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…

Pricing models for Hadoop products

A look at the various pricing models adopted by the vendors in the Hadoop ecosystem. While the pricing models are evolving in this rapid and dynamic market, listed below are some of the major variations utilized by companies in the sphere.
1) Per Node:Among the most common model, the node based pricing mechanism utilizes customized rules for determining pricing per node. This may be as straight forward as pricing per name node and data node or could have complex variants of pricing based on number of core processors utilized by the nodes in the cluster or per user license in case of applications.
2) Per TB:The data based pricing mechanism charges customer for license cost per TB of data. This model usually accounts non replicated data for computation of cost.
3) Subscription Support cost only:In this model, the vendor prefers to give away software for free but charges the customer for subscription support on a specified number of nodes. The support timings and level of support further …