Big Data and Analytics are transforming the way businesses take informed market-oriented decisions, craft strategies for targeting customer segments that are optimally promising, and remain shielded from market quirks and economic volatilities. These abilities are impacted by mining information that is locked in large data volumes generated online or from other connected sources.
Big Data can be reliably processed with the Apache Spark interface. Apart from facilitating seamless programming for data clusters, Spark also offers proper tolerance for faults and data parallelism. This implies that large datasets can be processed speedily by this open source platform. Apache Spark has an edge over Hadoop in terms of better and sophisticated capabilities on data handling, storing, evaluation and retrieving fronts. Spark framework comes integrated with modules for ML (Machine Learning), real-time data streaming, textual and batch data, graphics, etc., which makes it ideal for different industry verticals.
Scala or Scalable Language is a general-purpose object-oriented language with which Spark is written for supporting cluster computing. Scala offers support with immutability, type interference, lazy evaluation, pattern matching, and other features. Features absent in Java such as operator overloading, named parameters, no checked exceptions, etc. are also offered by Scala.
Data science offers unparalleled scope if you want to scale new heights in your career. Also, as part of an organization, if you are strategizing on cornering your niche market, you need to get focused insights into how the market is changing. With Apache Spark and Scala training, you can become proficient in analyzing patterns and making conclusive fact-driven assumptions.
There are many incentives for learning this framework-language combination as an aspirant or by exposing your organization’s chosen employees to this.
If your company is focusing on the Internet of Things, Spark can drive it through its capability of handling many analytics tasks concurrently. This is accomplished through well-developed libraries for ML, advanced algorithms for analyzing graphs, and in-memory processing of data at low latency.
Low latency data transmitted by IoT sensors can be analysed as continuous streams by Spark. Dashboards that capture and display data in real time can be created for exploring improvement avenues.
Spark has dedicated high-level libraries for analyzing graphs, creating queries in SQL, ML, and data streaming. As such, you can create complex big data analytical workflows with ease through minimal coding.
As a Data Scientist, you can utilize Scala’s ease of programming and Spark’s framework for creating prototype solutions that offer enlightening insights into the analytical model.
In the coming decade, Fog computing would gain steam and will complement IoT to facilitate de-centralized processing of data. By learning Spark, you can remain prepared for upcoming technologies where large volumes of distributed data will need to be analyzed. You can also devise elegant IoT driven applications to streamline business functions.
Spark can function atop HDFS (Hadoop Distributed File System) and can complement Hadoop. Your organization need not spend additionally on setting up Spark infrastructure if Hadoop cluster is present. In a cost-effective manner, Spark can be deployed on Hadoop’s data and cluster.
Spark is compatible with multiple programming languages such as R, Java, Python, etc. This implies that Spark can be used for building Agile applications easily with minimal coding. The Spark and Scala online community is very vibrant with numerous programmers contributing to it. You can get all the required resources from the community for driving your plans.
If your organization is looking to enhance data processing speeds for making faster decisions, Spark can definitely offer a leading edge. Data is processed in Spark in a cyclic manner and the execution engine shares data in-memory. Support for Directed Acyclic Graph (DAG) mechanism allows Spark engine to process simultaneous jobs with the same datasets. Data is processed by Spark engine 100x quicker compared to Hadoop MapReduce.
If you learn Spark and Scala, you can become proficient in leveraging the power of different data structures as Spark is capable of accessing Tachyon, Hive, HBase, Hadoop, Cassandra, and others. Spark can be deployed over YARN or another distributed framework as well as on a standalone server.
Completing an Apache Spark and Scala course from a renowned learning center would make you competent in leveraging Spark through practice sessions and real-life exercises. Once you become capable of using this cutting-edge analytics framework, securing lucrative career opportunities won’t be a challenge. Also, if you belong to an organization, gaining actual and actionable insights for decision making would be a breeze.