We at Ampersand Academy, provide the best extensive training for Apache Spark to Academic Institutions. We offer the best curriculum for Apache Spark with a course duration of 30hrs. The course delivery and method can also be customized according to the Academic Institute's requirement. We are flexible with the venue and can travel to client's preferred location for providing the training. We also offer customised workshops on Apache Spark as well as semester long course with hands on experience for Academic Institutions. Having developed the best approach for training Academic Institutions, and having industry experienced best trainer, we are the best Apache Spark training institute in Chennai.
Apache Spark is an open-source cluster computing framework which provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Apache Spark offers a unique application programming interface centred on a data structure called resilient distributed dataset. This allows implementation of iterative algorithms. It requires cluster management and distributed storage systems. For Cluster Management it supports Hadoop YARN and/or Apache Mesos. For Distributed Storage Systems it supports MapR File Systems, Cassandra, Hadoop Distributed File Systems, etc.
Spark Core is the foundation, it offers distributed task dispatching, scheduling and basic I/O functionalities exposed through application programming interface, e.g., Java, Python, Scala, centred on the resilient distributed dataset. Spark also provides two restricted forms of shared variables broadcast variables and accumulators.
Spark SQL is component present on top of the Spark Core that brings up new data abstraction called Data Frames. Data Frames supports structured and semi-structured data
Spark Streaming performs streaming analytics by leveraging Spark’s fast scheduling capability. It enters data in mini-batches and performs RDD transformation on those mini-batches of data.
MLib Machine Learning Library:
Spark’s MLlib is a distributed machine learning framework on top Spark Core. It is nine times as fast as disk-based implementation used by Apache Mahout and scales better than Vowpal Wabbit.
GraphX is a distributed graph processing framework present on Apache Spark. It offers API for graph computation and optimized runtime for abstraction
Prerequisite to join Apache Spark Course:
1. Basic computer handling skills.
2. Drive towards Analytics
3. Prior knowledge in Scala, R programming or Python. preferably Scala.