In today’s extremely competitive market, making quick but sound decisions is vital in a business afloat and ahead of the competition. Technology has made leaps and bounds in helping businesses move forward, but this has also led to the exponential rise of data that must be processed and analyzed. This presents new and often complex challenges that businesses need to overcome and eventually integrate into their workflow. Technologies like data analytics, internet of things (IoT), artificial intelligence (AI), and blockchain all but push companies to undergo a digital transformation as soon as possible.
To be successful in this endeavor, businesses need to rethink their IT approach and modernize legacy systems by scaling them to integrate with more modern application environments. Today’s businesses have realized the importance of being “digitally savvy,” with 70% of global business executives already integrating AI and blockchain into their supply chain and 45% agreeing that digital transformation will drive revenue growth. More than 80% of companies also expect an increase in profitability due to undergoing some form of digital transformation.
In recent years, in-memory data grids (IMDG) have shown great promise in handling large amounts of streaming data through the ability to store fast-changing application data and scale application performance. Specifically, the integration of map/reduce analytics into the data grid has made data analysis more powerful and manageable by enabling real-time decision making. The map/reduce analysis model is aimed at accelerating data analysis for disk-based data, which can easily go from gigabytes to petabytes in a short period. Speed is the common metric used to measure these solutions, with the goal being the reduction of batch job processing times from hours to minutes. Map/reduce implementations are usually offered as computing framework components and can take many forms depending on business need.
Top IMDG Benefits
Aside from using map/reduce to quickly and easily mine data, an IMDG also offers the following benefits.
- Fast access times for live, fast-moving data, featuring speeds up to more than 100 times faster than disk-based solutions
- Data loss prevention through high availability despite failure of data grid server
- Low response times due to scalable throughput and storage capacity
- Shared data access across the server farm
- Global data access across several sites and even the cloud
One of the major differences between traditional data analysis platforms and IMDG is in the way they process data. Traditional platforms analyze very large but static datasets, which is often copied from disk-based storage to a distributed file system before analysis. IMDG’s, on the other hand, analyze data in the stream, which means analysis is done on fast-changing operational data. Data motion is minimized to allow for continuous and quick analysis. Common use cases for IMDG’s include eCommerce for optimization of real-time shopping activity, credit cards for real-time fraud detection, and equity trading for minimizing risk during a trading day.
IMDG for Analytics
The map/reduce platform may be a boon for big data analysis, but its disk-based incarnation presented challenges because it was too complex for applications that must analyze hundreds of terabytes of fast-changing datasets in seconds. The integration of map/reduce analysis into IMDG has transformed it from a scalable disk-based data store into a parallel computing platform that can analyze data in near real-time.
Minimizing data movement
An IMDG analyzes data where it resides instead of migrating data from disk into memory, minimizing data movement both within the network and to and from disk. With results stored and combined in memory, access to it becomes quicker while data movement is minimized. Data movement to and from disk-based storage is the common cause of bottlenecks; an IMDG does away with this by minimizing the need to access the disk, resulting in low latency and high throughput. This also keeps data synchronized and makes data updating and retrieval easier, leading to quicker and more efficient application development.
Minimizing Complexity
The simplified programming model of an IMDG makes it a preferred platform when compared to mainstream disk-based platforms. IMDG’s make use of object-oriented queries to select objects so application developers don’t have to create a key space for identifying objects to be analyzed. Map/reduce analysis is also made simpler by structuring both the analysis (map) and merge (reduce) codes as straightforward, object-oriented methods. This simpler parallel execution model significantly reduces development time and makes tuning a thing of history. This way, developers spend less time learning about the model and how it works and more time addressing the analytical challenges of any given business problem.
Maximizing Performance
IMDG is able to process complex data at scale because it “distributes” the workload to several computers within the network, combining both RAM and computing power of all available computers. By processing against the full dataset, the amount of data can exceed the amount of memory. Known as “persistent store,” it also helps optimize data so that those that are most frequently used are kept both in memory and on disk, making data access and updating faster while also limiting data movement. An IMDG also allows for the collocation of both the application and its data in the same memory space to minimize latency.
Conclusion
Today’s fast-paced, ever-changing landscape calls for solutions that provide results at the soonest possible time. As the amount of data grows larger, processing and analyzing them becomes more complex and challenging—and businesses must find ways to overcome this. In-memory data grids are a viable computing solution because it all but eliminates the learning curve by simplifying the application development model without compromising performance. Ensuring high throughput and low latency by using RAM, movement of data is minimized and network bottlenecks are significantly reduced. The IMDG also leverages features like load balancing, partitioning, and peer-to-peer architecture for maximizing high availability and easy scalability.
