Nearly every CEO aspires to make their company data-driven, infuse a data culture, and advance along the data maturity curve. If they fail, their board of directors will seek someone who can harness the value of data. Companies and other organizations succeeding on this path have invested in both the skills and culture, but also a fair amount of technology. Data is top of mind, not just for the CEO, but also for leaders throughout the organization.

Big data and modern data architectures have been central to these advances. In particular, Apache SparkTM has enabled analytics advances that were unthinkable just a few years ago. Like with any emerging technology, reaching optimal results is not always a sure thing. In order to meet the CEO’s needs, while remaining agile and managing expenses, most companies will need to improve Spark's performance.

Achieving these goals is currently within reach without requiring a huge disruption to existing infrastructure. Explore how a new technology called hyperacceleration can resolve your biggest challenges.

 

Businesses Need Answers Fast

faster insights Companies depend on Spark to gain insights and support decision-making. The ability to be agile and adapt to changing conditions while accessing more data sets and larger data volumes can directly yield better answers. Part of Spark's promise is the ability to shift a batch process approach to real-time streaming, thus providing real-time insights. By running more in memory, Spark’s architecture is ideal for speed. 

Harnessing this powerful data on Spark is the start. However, most Spark environments now encounter new lags and bottlenecks. Data architects and data engineers are crafty at plugging the gaps, but Spark can work only so hard without the intervention of expansion or acceleration. 

Quality, accuracy, and speed are all competing priorities in the big data analytics environment, and too often, teams are forced to compromise on one or more. But remember that satisfying all of these priorities is essential to empower the proactive CEO and the leadership team.

Faster Answers Mean Competitive Advantage

For example, retail banks need to remain customer obsessed in an increasingly digital world. Competition for customers is fierce, and younger customers, in particular, bring different expectations of 24/7 access across multiple channels. A 2016 FICO survey found that millennials are 2-3 times more likely to switch banks.

Whether it’s via a mobile device, a branch visit, or a phone call, customers expect banks to have answers at their fingertips. Spark is a central element of many banks’ data environments, both for serving up basic transaction information and for analytically driven recommendations and alerts. When these systems are finely tuned, the bank becomes a trusted advisor. When these systems are bogged down, frustrated customers will look for another bank.

 

Are Your Data Scientists Performing High-Level Tasks?improved analytics

Data scientists are a scarce and expensive resource. Many have PhDs or have invested equivalent years of hands-on experience building their expertise. When the data environment is not optimized, the data scientists spend more time cleaning data or simply waiting for processing and query results. Neither the organization nor the data scientists benefit from this inefficiency. Data scientists push data engineers and architects to improve pipelines, and these architects scramble to plug gaps and optimize the hardware.

Unfortunately, data scientists’ skills are wasted when they focus on data preparation, and data engineers and architects lack the skills to program advanced hardware. To date, there have been few practical solutions absent heavy infrastructure expansion or specialized programming.

 

Improve Spark While Managing Costs

lower tcoOne of the other key promises of Spark and other big data platforms is the ability to scale in an easy and linear fashion. Need more performance, processing, or storage? “Add more nodes (scale out) or upgrade nodes (scale up),” is the common refrain. Organizations are learning that this promise does not always deliver. Expansion of servers is expensive, it’s not always physically feasible, and performance growth typically tapers off significantly in larger clusters. 

Unfortunately, the CEO's license to be data-driven never comes with a blank check. Analytics budgets have grown slowly, despite exponential growth in data and its value. Even if adding nodes did scale linearly, successful data leaders will find ways to lower their total cost of ownership (TCO) while maintaining and increasing their analytics capabilities. 

 

Improving Spark’s Performance Is Possible with Hyperacceleration

Hyperacceleration can be the answer for analytics leaders who want to reach deeper insights with optimal speed and cost efficiency. Hyperacceleration is a novel approach to big data analytics platform performance that automatically programs advanced hardware.

A Look at Hyperacceleration on Spark

The central challenge with introducing specialized hardware to Spark has been extensive programming requirements. To help organizations tackle this challenge, Bigstream has developed software that attaches to the hardware and automates the acceleration. 

This groundbreaking technology is the hyperacceleration software layer. It seamlessly integrates with Spark, leveraging the power of field-programmable gate arrays (FPGAs) without the need for Spark code changes or special programming. The layer automates the programming of applications onto the FPGA. 

The acceleration impacts every key step of the big data pipeline, delivering end-to-end speed increases. Because it does not require Spark code changes or alter your existing infrastructure, it is easy to deploy and implement. 

Spark users might not even be conscious of the addition of hyperacceleration while managing the code. But they will definitely notice the results. Once it’s implemented, organizations can provide faster time to insight and improved analytics, all while reducing their TCO by up to 40 percent. 

 

Faster Spark Analytics with Less Effort and Investment

Meeting the demands of big data analytics is not easy, but you can empower your team and stakeholders by introducing hyperacceleration. 

Learn more about how it works and why it's a game changer by downloading our eBook, Accelerating AI & Analytics Workloads: How the SmartSSD Delivers Acceleration for Better Performance, today.

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