Apache Spark and Scala Certification Training Course

There are no specific prerequisites for our Spark Scala Certification Training. However, prior knowledge of Java Programming and SQL will be helpful but not mandatory.

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The Spark Certification Training is designed by industry experts to make you a Certified Spark Developer. The Spark Scala Course offers:

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• An overview of Big Data & Hadoop, including HDFS (Hadoop Distributed File System) and YARN (Yet Another Resource Negotiator).
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• Comprehensive knowledge of various tools that fall in the Spark Ecosystem, such as Spark SQL, Spark MlLib, Sqoop, Kafka, Flume, and Spark Streaming.\n
• The capability to ingest data in HDFS using Sqoop & Flume and analyze those large datasets stored in the HDFS.
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• The power to handle real-time data feeds through a publish-subscribe messaging system like Kafka.
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• Exposure to many real-life industry-based projects that will be executed using Edureka’s CloudLab.
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• Projects that are diverse in nature, covering banking, telecommunications, social media, and government domains.
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• Rigorous involvement of an SME throughout the Spark Training to learn industry standards and best practices.
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Spark is one of the fastest-growing and widely used tools for Big Data & Analytics. It has been adopted by multiple companies in various domains around the globe and, therefore, offers promising career opportunities. To take part in these kinds of opportunities, you need structured training that is aligned with Cloudera Hadoop and Spark Developer Certification (CCA175) and current industry requirements and best practices. Besides strong theoretical understanding, it is essential to have strong hands-on experience. Hence, during Edureka’s Spark and Scala course, you will be working on various industry-based use cases and projects incorporating big data and Spark tools as part of the solution strategy. Additionally, all your doubts will be addressed by industry professionals currently working on real-life big data and analytics projects.

Edureka’s Spark Training is designed to help you become a successful Spark developer. During this course, our expert instructors will train you to:

The market for Big Data Analytics is growing tremendously across the world, and the strong growth pattern followed by market demand is a great opportunity for all IT Professionals. Here are a few professional IT groups who are continuously enjoying the benefits and perks of moving into the Big Data domain:

[{"description":"The statistics provided below will give you a glimpse of the growing popularity and adoption rate of Big Data tools like Spark in the current and upcoming years:","Bullets":["56% of Enterprises Will Increase Their Investment in Big Data over the Next Three Years\ - Forbes""

You will execute all your Spark and Scala course assignments and case studies in the Cloud LAB environment provided by Edureka. You will access the Cloud LAB via a browser. In case of any doubts, Edureka’s support team will be available 24/7 for prompt assistance.

CloudLab is a cloud-based Spark and Hadoop environment that Edureka offers with the Spark Course. It allows you to execute all the in-class demos and work on real-life Spark case studies seamlessly. This not only saves you from the trouble of installing and maintaining Spark and Scala on a virtual machine but also provides you with an experience of a real big data and Spark production cluster. You can access the Spark Training CloudLab via your browser, which requires minimal hardware configuration. In case you get stuck at any step, our support team is ready to assist 24/7.

You don't have to worry about the system requirements as you will be executing your practicals in a Cloud LAB, which is a pre-configured environment. This environment already contains all the necessary tools and services required for Edureka's course.

CloudLab is a cloud-based Spark and Hadoop environment that Edureka offers with the Spark Course where you can execute all the in-class demos and work on real life spark case studies fluently. This will not only save you from the trouble of installing and maintaining Spark and Scala on a virtual machine, but will also provide you an experience of a real big data and spark production cluster. You’ll be able to access the Spark Training CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support team is ready to assist 24×7.

You don’t have to worry about the system requirements as you will be executing your practicals on a Cloud LAB which is a pre-configured environment. This environment already contains all the necessary tools and services required for Edureka's course.

Project 1- Domain: Financial Statement: A leading financial bank is trying to broaden the financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, it makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities. The bank has asked you to develop a solution to ensure that clients capable of repayment are not rejected and the loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful. Project 2- Domain: Transportation Industry Business challenge/requirement: With the spike in pollution levels and the fuel prices, many Bicycle Sharing Programs are running around the world. Bicycle sharing systems are a means of renting bicycles where the process of obtaining membership, rental and bike return is automated via a network of joint locations throughout the city. Using this system people can rent a bike from one location and return it to a different place as and when needed. Considerations: You are building a Bicycle Sharing demand forecasting service that combines historical usage patterns with weather data to forecast the Bicycle rental demand in real-time. To develop this system, you must first explore the dataset and build a model. Once it’s done you must persist the model and then on each request run a Spark job to load the model and make predictions on each Spark Streaming request