- Overview of Big Data & Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator)
- Comprehensive knowledge of various tools that fall in Spark Ecosystem like Spark SQL, Spark MlLib, Sqoop, Kafka, Flume and Spark Streaming
- The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS
- The power of handling real time data feeds through a publish-subscribe messaging system like Kafka
- The exposure to many real-life industry-based projects which will be executed using easyScholars’s CloudLab
- Projects which are diverse in nature covering banking, telecommunication, social media, and government domains
- Rigorous involvement of a SME throughout the Spark Training to learn industry standards and best practices
value added features.
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frequently asked question.
You will never miss a lecture at easyScholars! You can choose either of the two options:
- View the recorded session of the class available in your LMS.
- You can attend the missed session, in any other live batch.
Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.
Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover the access to our 24×7 support team will be granted instantly as well. You can start learning right away.
Yes, the access to the course material will be available for lifetime once you have enrolled into the Apache Spark online course.
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.
Apache Spark is one of the leading Big Data frameworks that is in demand today. Spark is the next evolutionary change in big data processing environments as it provides batch as well as streaming capabilities. This makes it the ideal framework for anyone looking for speed data analysis. With companies showing eagerness to adopt Spark in their system, learning this framework can help you climb up your career ladder as well.
Scala stands for Scalable languages. easyScholars’s training program is what you need if you are looking to master Spark with Scala. Our course module starts from the beginning and covers every module necessary. With our instructor led sessions and a 24×7 support system, we make sure that you achieve your learning objectives.
Followings are the top 5 certification:
- Cloudera Spark and Hadoop Developer
- HDP Certified Apache Spark Developer
- MapR Certified Spark Developer
- Databricks Apache Spark Certifications
- O’Reilly Developer Apache Spark Certifications
Databricks Certified Associate developer for Apache Spark 3.0 certification tests your understanding of Spark DataFrame API. It also assesses your ability to use the Spark DataFrame API in order to perform basic data manipulation tasks within a Spark session. These tasks include manipulating, filtering, dropping and sorting columns, handling missing data, and combining, reading and writing DataFrames with schemas. They also involve working with UDFs or Spark SQL functions. The exam will also assess fundamental aspects of Spark architecture such as execution/deployment mode, execution hierarchy, fault tolerance and garbage collection.
Write Scala Programs to build Spark Application Master the concepts of HDFS Understand Hadoop 2.x Architecture Understand Spark and its Ecosystem Implement Spark operations on Spark Shell Implement Spark applications on YARN (Hadoop) Write Spark Applications using Spark RDD concepts Learn data ingestion using Sqoop Perform SQL queries using Spark SQL Implement various machine learning algorithms in Spark MLlib API and Clustering Explain Kafka and its components Understand Flume and its components Integrate Kafka with real time streaming systems like Flume Use Kafka to produce and consume messages Build Spark Streaming Application Process Multiple Batches in Spark Streaming Implement different streaming data sources