Apache Spark to Redefine Data Analytics for Advertising Industry

By CIOReview | Friday, July 29, 2016
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An Overview

Developed in Berkeley's AMPLab of the University of California, ‘Apache Spark’ has set the bar of analytics on fire. Technology experts are admiring ‘Spark’ for its greater compatibility with various technical platforms such as Java, Python, SQL, and others. CIOs and CTOs find the tool very handy to formulate business decisions. They are actually keeping the information at their fingertips and controlling the contents as per the needs. Apache Spark is not only limited to IT environments but the technology is expanding its wings in the advertising industries as well.

Apache Spark and Advertising Optimization

Often, advertising companies deals with the pile of data in the form of video, text, or audio and sometimes with all three. Using the right data at right time and at right place is the principle advertising companies follow to educate targeted audience through info-graphical means. They therefore need to have accurate information of data using appropriate tools. In the same way, BlackArrow Inc. was in search of genteel analytical solution to grab larger audience with its video advertising expertise.  

Joe Matarese, CTO of BlackArrow Inc., was astonished by scalability of ‘Spark’ and viewed it as a must solution for its firm which deals with big data. BlackArrow assist cable TV operators by broadcasting ads meticulously for targeted audiences across TV services or online platforms. The firm implemented ‘Spark’ under the leadership of Matarese which enabled BlackArrow to collect data from cable operators seamlessly. The data collected contained information regarding basic information about viewers and shows watched by them, and also about platforms used to broadcast TV shows. Using this information, the firm determined what types of commercials are needed to be played at the appropriate time based on the characteristics of viewers. Spark then moved the collected data forward and with self-scrutiny features it furnished information about which ads viewers viewed or skipped through Infobright analytical database.

The data is stored in the form of reports which can be accessed by cable operator customers to view or carry out polarized analysis on effectuality of ads. It is evident now how easy it is to use the Spark with visualization tools and Pentaho’s business intelligence solution embedded within it. “We chose Pentaho over other data visualization vendors because it offers greater control over how reports are surfaced to BlackArrow's clients. Spark also has good adoption and a development community, which is why we love it, but it's still a relatively young technology. The attraction is that there are a lot of capabilities, and we expect to make use of those capabilities,” says Joe Matarese, CTO, BlackArrow.

Spark functions with effective data processing speed with SQL queries to fetch and liberate information. “The company previously used a homegrown reporting system, but customers started wanting to do more ad hoc querying, which prompted BlackArrow to decide it needed a packaged tool that would support static reports as well as user queries,” said Matarese. 

How Safe Is ‘Spark’?

Databricks, creator of ‘Spark’, has already elucidated that it is free of warts as it bring out stability in processes to large extent with seamless data processing speed. Its ability to incorporate latest IT technology feature is like icing on the cake as it fulfills every aspects of data analytics. But you may encounter inconsistency between organization processes and the new and youngest of ‘Spark’ version due to the developmental differences. Matarese further opined that, “Solving those kinds of issues takes persistence and an understanding that a Spark implementation is going to require ongoing development and maintenance. You're going to have to deal with a few hurdles.” But if your system runs on any of the technology supported by Spark, it will be easy for you to get analytics easily done.

Reaching out to customers with valid data is the first priority of every organization and ‘Spark’ surpasses the expectations with its potential analytical features. Spark can be viewed as a sequel of Hadoop data analytics solution while others view it as a replacement for Hadoop. “It all goes back to understanding what you're trying to do and the problems you're trying to solve,” Matarese advises.

How Apache Spark Is the Next Big Data Analysis Thing!

Big data is not just about data but it is more about what you do with the data. With apposite analytical tools and statistical principles, vital information can be extracted from data that can help CIOs to boost up business. In the same way, the ‘Apache Spark’ is viewed as next big data analytical tool for advertising companies.

CIOs have access to various analytical tools available in the market but MapReduce had always been the chosen one. It allowed them to manipulate large chunk of data on distributed computing platform. But despite being one-of-its-kind, MapReduce was hit back due to slower speed. MapReduce users could have experienced discomfort and they were in search of robust and feasible solutions that could help them to overcome the issue. By understanding the need, Databricks assembled the ‘Spark’ as a next generation data processing engine. But, since the inception of the ‘Spark’, it has always been compared with MapReduce as expectations with the new tool were sky high. “MapReduce is an implementation of a design that was created more than 15 years ago. Spark is a from-scratch, reimagined or re-architecting of what you want out of an execution engine given today's hardware,” said Patrick Wendell, Co-founder, Databricks.

Unlike MapReduce, the Spark does not work in batches instead it processes chunk of data together to streamline data analytics process. The Spark also eliminates limitations of data in-memory process faced in MapReduce. By overcoming all the hurdles faced by using MapReduce, the ‘Spark’ was started to seem as a ray of hope in data analytics platform by CIOs. It doesn’t end there as Databricks further stated that the data analytics tool will also be compatible with Hadoop and could redefine big data ecosystem. Large organizations such as Novartis, Comcast, and Goldman Sachs are already praising about what miraculous changes Spark can bring out in data analytics. “I do think Spark has a role to play and a life that's outside of the Hadoop environment. I hope Spark transcends the label, and I think to a large extent, we've done that,” Wendell added.