Skip to main content

Building a billion dollar enterprise with Hadoop

Among the major technical trends observed in the year 2013, SQL-on-Hadoop was the most prominent one that caught the market attention and imagination like never before. We saw some major announcements in this space all through the year. Riding on the hype that Hadapt and Cloudera Impala brought in the earlier years, there were more players who brought in technical innovation along with marketing blitz. 

We asked Monte Zweben, co-founder and chief executive officer of Splice Machine on the fuss and excitement around SQL-on-Hadoop. Monte is not a newcomer to technology space. He is a NASA alumni and founded RedPepper software that merged with PeopleSoft in 1996. He later led Blue Martini to a billion dollar market value capitalization on NASDAQ in 2000. His latest venture is Splice Machine which provides transactional SQL-on-Hadoop database designed for real-time Big Data applications. Monte is also currently board member of Rocket Fuel Inc. which was ranked among Hadoopsphere's top Big Data influencers of 2013 in stock performers category. During the interview, we also tried to probe his motivation and vision for the Hadoop technology sphere. 

Here's what he had to say:

Why do we need SQL-on-Hadoop?

- SQL-on-Hadoop solutions have become very popular recently as they address the shortcomings of Hadoop and provide a scale-out alternative for traditional RDBMSs. Because Hadoop requires specialized Java programs to access data, it had become the “roach motel” of Big Data – easy to get data in, but hard to get it out. SQL-on-Hadoop solutions dramatically improve access to data in Hadoop because of most data and business analysts are well trained users of SQL. Existing SQL tools and Business Intelligence (BI) platforms can now connect to Hadoop data through a standard ODBC connection and SQL applications that can now update and act on that data.

For existing databases experiencing scaling issues, SQL-on-Hadoop solutions can provide a full SQL database that can scale out on commodity hardware. With standard SQL, it can eliminate application rewrites to access scale-out technology. With scalability proven in petabytes on inexpensive servers for Hadoop, SQL-on-Hadoop also provides a highly scalable data platform that does not require expensive, specialized hardware.

Is the space already crowded with many vendors pitching in with solutions in SQL-on-Hadoop space?

- Yes, but all SQL-on-Hadoop solutions are not equal. For instance, Splice Machine provides transactional SQL-on-Hadoop database for real-time Big Data applications, whether operational or analytical. Because it was built on proven Hadoop and HBase stacks, Splice Machine takes the best of both SQL and NoSQL database solutions to deliver a massively scalable database that provides robust SQL support, secondary indexes, join optimizations and transactional integrity. 

Do you think Hadoop can help you make another billion $ company?

- Absolutely, given the scaling issues of current databases and dramatic increase of data in many companies. More and more enterprises are beginning to understand the critical nature of big data management and its long-term implications on their application infrastructure and business.


Your top 3 predictions for Hadoop ecosystem in 2014.

- Hadoop will move from being a static repository for data science to a platform that powers real-time, interactive applications.
- Hadoop will complement relational SQL databases, and in some cases be a replacement
- Hadoop-based ETL will be the new norm for large data sets


Popular articles

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…

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…

Hadoop's 10 in LinkedIn's 10

LinkedIn, the pioneering professional social network has turned 10 years old. One of the hallmarks of its journey has been its technical accomplishments and significant contribution to open source, particularly in the last few years. Hadoop occupies a central place in its technical environment powering some of the most used features of desktop and mobile app. As LinkedIn enters the second decade of its existence, here is a look at 10 major projects and products powered by Hadoop in its data ecosystem.
1)      Voldemort: Arguably, the most famous export of LinkedIn engineering, Voldemort is a distributed key-value storage system. Named after an antagonist in Harry Potter series and influenced by Amazon’s Dynamo DB, the wizardry in this database extends to its self healing features. Available in HA configuration, its layered, pluggable architecture implementations are being used for both read and read-write use cases.
2)      Azkaban: A batch job scheduling system with a friendly UI, Azkab…