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

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…

5 online tools in data visualization playground

While building up an analytics dashboard, one of the major decision points is regarding the type of charts and graphs that would provide better insight into the data. To avoid a lot of re-work later, it makes sense to try the various chart options during the requirement and design phase. It is probably a well known myth that existing tool options in any product can serve all the user requirements with just minor configuration changes. We all know and realize that code needs to be written to serve each customer’s individual needs.
To that effect, here are 5 tools that could empower your technical and business teams to decide on visualization options during the requirement phase. Listed below are online tools for you to add data and use as playground.
1)      Many Eyes: Many Eyes is a data visualization experiment by IBM Researchandthe IBM Cognos software group. This tool provides option to upload data sets and create visualizations including Scatter Plot, Tree Map, Tag/Word cloud and ge…

Deep dive into Actian Vortex architecture

The innovations continue at a rapid pace in SQL on Hadoop solutions with each vendor trying to outsmart the competition. In this second part of interview with Actian’s Emma McGrattan, we try to understand architecture of Actian Vortex’s SQL in Hadoop offering with particular focus on database/SQL layer named Vector. Emma is the Senior Vice President for Engineering at Actian and described the "Marchitecture" (as she likes to term it) in a conversation with Sachin Ghai. As per Emma, Actian Vortex product suite is among the fastest and most mature SQL 'in' Hadoop offering. 

Actian Engineering has definitely put a lot of thought and innovation in the Vortex architecture. It is one of those products where the engineering team exactly knew the nuts and bolts of Hadoop as well as the cranks and shafts of database. It is rare currently to find an SQL offering which relies on HDFS as storage but still achieves enterprise grade resonant with the database category. Utilizing YA…