The amount of information processed in the world is out of control. With almost universal access to the internet and content-rich sites like social media, more data is produced every day than was produced over centuries of human existence. There is so much data at such a dizzying pace that the human ability to perceive and act on ALL the data is insufficient.
The amount of information processed in the world is out of control. With almost universal access to the internet and content-rich sites like social media, more data is produced every day than was produced over centuries of human existence. There is so much data at such a dizzying pace that the human ability to perceive and act on ALL the data is insufficient. Fortunately computers and software programs are now able to break down terabytes of metadata into chunks that can be perceived and acted upon.
Amazingly, processing power and computing ability make it possible to process and understand information as it is received. New products like Apache Spark as a Service allow companies to analyze data seamlessly in real time, which is really the only acceptable time frame for modern operating systems. So, what exactly is Apache Spark and what advantages does it offer in the realm of big data analytics?
Every company faces a common dilemma: data. There is so much of it coming in every second of every day. All the data is valuable in some way. For retailers, it’s customer buying habits and industry trends. For technology companies, it’s keeping up with the latest technology and what consumer demand will be in the future. For healthcare, it is minimizing costs and storing patient information securely. Regardless of the industry, there is a need to make data-driven decisions to keep up with competitors and offer competitive advantages to consumers.
What is Spark
Apache Spark is a tool used to make data-driven decisions using the Hadoop framework. Existing platforms like Hadoop, although superior for data processing, are incapable of real time analytics because they are by nature rigid. Hadoop as a platform processes information in batches, making it incapable of scaling to the volume and velocity of real time data. That is where Spark comes in. Spark connects to Hadoop computing and updates Hadoop with real time updates from a given data source in between data batches. As a product, Spark can connect to Hadoop data storage and function in the Hadoop data cluster. This keeps the data cluster constantly updated.
Live streaming data is the universally accepted standard for real time data updates. Coupled with Hadoop, there are several advantages to integrating Spark.
1) Speed-
Real time data transfers mean greater speed in accessing information. The point of real time is to cut the reaction time a company needs to respond to data received. During the recent Costco recall, if customer purchase records took two months to update, there wouldn’t have been any record to respond to the fruit recall. Speed also comes with the ease of integration into an existing data processing platform. Greater speed also allows businesses to capitalize on new means of analytics, such as this intelligent video analytics use case.
2) Shorter Data Transfer-
Spark is housed in the same cluster as Hadoop, meaning data has a shorter travel path than using a service housed outside of the cluster. A shorter travel path has many implications, including fewer processing errors and more efficiency. Speed combined with a shorter data path leads to reduced costs for a company while maintaining greater control over data as it moves.
3) Flexibility-
Spark coupled with Hadoop expands the possibilities for existing cloud resources. Real time insight gives more meaning to batch updates, especially with access to the same cloud storage. Points one and two also provide greater flexibility for companies, such as increased lead time to make decisions as circumstances change constantly.
Use of Real Time Analytics
Real time data analytics is like a burning fire. Consider this analogy. Once a spark catches, several chemical and physical changes occur. The fuel (data) is processed while heat (or data outcomes) is simultaneously emitted. Once the process of data streaming begins, it is seamless and very efficient. The process will continue to be efficient as long as data is fed into the system.
For large companies, data collection isn’t the issue. There is plenty of data collected through normal business operations. The challenge is understanding data through real time updates to the cloud computing system. Information streaming is the only way companies can keep pace with the blistering speed of life. One rogue statement by a CEO or one mis-tweet on a company Twitter account can throw an organization into crisis literally overnight. Industry changes and complicated company logistics feed the demand for real time solutions. Now it is evident that real time data isn’t just a good idea, it is a necessity.
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