Apache Spark and Big Data: What’s Ahead
What is Apache Spark?
Apache Spark is a cluster computing technology which is exclusively designed for fast computation. It is based on Hadoop MapReduce and it includes interactive queries and stream processing as well. The in-memory cluster computing increases the processing speed of any application which acts as a catalyst to enable a lot of processes. Apache Spark is quite popular among developers because of its support to multiple languages like Python, Java, Scala, etc. Speed is the major booster for this technology and hence it provides enhanced performance in analytics.
What is Big Data?
As the name suggests big data can be simply defined as extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. This is especially significant when volume of data is huge and needs to be managed in a better way. Traditional approaches to handle such huge amount of data delivers a poor performance which led to invention of more scientific ways to handle such data. Nowadays, big data is part of our daily life. Any sponsored ad that you see on Facebook or any other app is nothing but a result of analytics on some big data stored at one place or at different places.
Relationship between Apache Spark and Big Data
It can now be easily understood that Big data and Apache spark can be considered as dependent entities to some extent. Big data is basically the input for Apache spark. Apache spark is designed in such a way that the processing speed is enhanced to a large extent when in memory clusters are used. So, for storing data, Apache spark does not rely on storing data externally when multiple queries are used. Instead, it relies upon in memory clusters which temporarily stores data. Also, it enables a wide range of workloads such as batch applications, iterative algorithms, streaming and interactive queries.
Along with various benefits, big data comes with a lot of challenges. The biggest question will always be the never-ending volumes of data. To be able to analyze, this data needs to be stored at a common place. Most of the times the data is unstructured or semi structured. Data from various sources needs to be standardized to make it valuable. Also, security is a major concern when such huge chunks of data is stored and analyzed. Thus, proper care needs to be taken without any compromise for security.
With several integrations and adapters on Spark, one can easily combine other technologies with Spark. This provides a major advantage over other big data processing technologies. The boost for big data analytics is an add on for Apache Spark which guarantees a challenging but bright future for these technologies.
About the Author
DataFactZ is a professional services company that provides consulting and implementation expertise to solve the complex data issues facing many organizations in the modern business environment. As a highly specialized system and data integration company, we are uniquely focused on solving complex data issues in the data warehousing and business intelligence markets.