Skip to main content

Tez - The race for secure low latency Hadoop picks up more speed



We had been expecting this… honestly, yes. Hortonworks, a Yahoo spin-off for Hadoop, has made the headlines again. So while Marissa Mayer was busy revealing a new version of Yahoo.com home page in a purple Facebook avatar, the erudite guys at Hortonworks revealed the open source Apache Hadoop immediate roadmap.

Hortonwoks, the Palo Alto based company revealed its 3 pronged strategy to stay ahead of the sharp competition in the Hadoop ecosystem. The key drivers of its strategy include:
(1)   Accelerate enterprise adoption of Hadoop
(2)  Providing a low latency based SQL type query interface for Hadoop
(3)  Making Hadoop cluster more secure

With these in mind, the company revealed 3 components of its strategy:
(1)   Stinger initiative
(2)  Tez framework
(3)  Knox Gateway

Stinger seems to be a strategic move to keep Hive as the central interface for Hadoop querying. Just this week, hadoopsphere.com published a post citing HCatalog could become partof Hive project. With Stinger, the company plans to make use of community driven contributions to add more SQL like querying clause to HQL (Hive Query language). Also, it claims to have achieved 90% reduction in Hive query result time. Another significant addition is the introduction of ORCFile in direct competition to Trevni and to tide over RC File format limitations. Tez framework described in next paragraph also constitutes part of Stinger initiative. Future additions include Buffer Caching, Vector Querying engine and Query Planner.



Tez is one of the most exciting revelations of the day. It is a “general-purpose, highly customizable framework that creates simplifies data-processing tasks across both small scale (low-latency) and large-scale (high throughput) workloads in Hadoop.”
(Tez in Hindi language means fast speed) 
Tez apparently throws a challenge to Cloudera Impala. Currently, proposed as an Apache incubator project with seed work already done, the project already has 22 committers which tells us something about the exciting race here. Tez aims to optimize the latency by running the query in a single job rather than multiple MapReduce jobs. Further it aims to leverage YARN to share data processing primitives across Apache Pig, Apache Hive, Cascading and others.


Knox is the other significant project which has been proposed for Apache incubation. Since security is one of the key focus areas, it “provides a single point of authentication and access for Apache Hadoop services in a cluster”. Earlier, hadoopsphere.com had proposed comprehensive security architecture forApache Hadoop cluster which could be implemented with custom built utilities or custom off the shelf tools. Knox fills in the vital authentication layer of the security architecture instead of just relying on Kerberos. However, it still needs to do a bit of work on cloud integration and web interface for the Hadoop cluster.

Overall, Hortonworks has shown its commitment to open source once again and driving upon the fact that community based contributions can be innovative, exciting and ‘tez’ (fast).




ps: Arun Murthy/folks at Hortonworks, please excuse the discretion of using Arun’s image in Speed movie look-alike poster. And of course, kudos to many more heroes in the team.

All other images in this post taken from Hortonworks blog

Comments

Popular posts from this blog

Offloading legacy with Hadoop

With most Fortune 500 organizations having invested in mainframes and other workload systems in the past, the rise of Big Data platforms poses newer integration challenges. The data integration and ETL players are finding fresh opportunities to solve business and IT problems within the Hadoop ecosystem.
To understand the context, challenges and opportunities, we asked a few questions to Syncsort CEO Lonne Jaffe. Syncsort provides fast, secure, enterprise-grade software spanning Big Data in Apache Hadoop to Big Iron on mainframes. At Syncsort, Lonne Jaffe is focusing on accelerating the growth of the company's high-performance Big Data offerings, both organically and through acquisition.
From mainframes to Hadoop and other platforms, Syncsort seems to have been evolving itself continuously. Where do you see Syncsort heading further?Lonne Jaffe: Syncsort is extraordinary in its ability to continuously reinvent itself. Today, we’re innovating around Apache Hadoop and other Big Data pla…

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…