Course Details

What is Apache Spark?

Apache Spark is a collection platform designed to be general purpose and fast. Spark is created to be highly accessible, offering simple APIs in Scala, SQL, Python, and Java and rich built-in libraries.

Apache Spark is developed as the next generation big data processing engine, and is being applied throughout the industry faster than ever. Apache Spark improves over Hadoop Map Reduce, which helped ignite the big data revolution, in several key dimensions: it is faster, much easier to use due to its rich API’s and its goes far above batch applications to support a variety of including interactive queries, workloads, machine learning, graph processing, and streaming.

Why to attend Tekslate Online Training ?​

Classes are conducted by Certified Spark Working Professionals with 100 % Quality Assurance.

With an experienced Certified practitioner who will teach you the essentials you need to know to kick-start your career on Spark. Our training make you more productive with your Spark Training Online. Our training style is entirely hands-on. We will provide access to our desktop screen and will be actively conducting hands-on labs with real-time projects.

Spark Training Course Curriculum

Introduction To Big Data and Spark

Introduction to Big Data, Challenges with Big Data, Batch Vs. Real Time Big Data Analytics, Batch Analytics – Hadoop Ecosystem Overview, Real Time Analytics Options, Streaming Data – Storm, In Memory Data – Spark, What is Spark?, Modes of Spark, Spark Installation Demo, Overview of Spark on a cluster, Spark Standalone Cluster

Spark Baby Steps

Invoking Spark Shell, Creating the SparkContext, Loading a File in Shell, Performing Some Basic Operations on Files in Spark Shell, Building a Spark Project with sbt, Running Spark Project with sbt, Caching Overview, Distributed Persistence, Spark Streaming Overview, Example: Streaming Word Count

Playing With RDDs In Spark

RDDs, Spark Transformations in RDD, Actions in RDD, Loading Data in RDD, Saving Data through RDD, Spark Key-Value Pair RDD, Map Reduce and Pair RDD Operations in Spark, Scala and Hadoop Integration Hands on

Shark – When Spark Meets Hive

Why Shark?, Installing Shark, Running Shark, Loading of Data, Hive Queries through Spark, Testing Tips in Scala, Performance Tuning Tips in Spark, Shared Variables: Broadcast Variables, Shared Variables: Accumulators

Spark Overview

Apache Spark is an exciting new technology that is rapidly superseding Hadoop as the preferred Big Data processing platform. Its unique design, which allows for keeping large parts of data in memory, offers tremendous performance improvements. Spark programs can be even 100 times faster than their Hadoop counterparts. Spark’s elegant API and runtime architecture allow you to write distributed programs in a manner similar to writing the local ones, using functional programming methods, which are very appropriate for data processing. By supporting Python, Java and Scala, Spark is open to a wide range of users: to the science community traditionally favoring Python, to the still widespread Java community and to people using the ever more popular Scala, offering functional programming on the JVM. Finally, Spark combines MapReduce-like capabilities for batch programming, real-time data processing functions, SQL-like handling of structured data, graph algorithms, and machine learning, all in a single framework. This makes it a one-stop-shop for all your BigData crunching needs.

Features of Spark

Fast: Data processing up to 100x faster than MapReduce, both in-memory and on disk

Powerful: Write sophisticated parallel applications quickly in Java, Scala, or Python without having to think in terms of only “map” and “reduce” operators

Integrated: Spark is deeply integrated with CDH, able to read any data in HDFS and deployed through Cloudera Manager

Advantages of Spark

In practice, it is said that Spark is 10x faster than Hadoop on average.

Agility compared to the monolithic aspect of Hadoop: Spark allows rapid changes, thanks to loading the data into memory and interacting with it in a rapid manner. The shell (REPL) is great to test things out.

Data scientists & non-data engineers can use Spark through Python.

Newer platform with multiple tools like Machine learning, Graph and streaming included, with strong community support.

Scala is superior for data processing thanks to its higher level of abstraction. Although Spark supports Java, it is recommended to use Scala in Spark as a non-functional programming will make coding less intuitive and lower level. Also using Scala will make debugging will be easier. A combination of using an IDE (for API auto completion and data typing) and REPL (interactive shell) s actually best for efficiency.

SPARK’S Performance

Spark’s core concept is an in-memory execution model which enables caching of job data in memory, instead of fetching it from disk every time, like MapReduce does. This can speed up the execution of jobs up to 100 times, compared to the same jobs in Hadoop. This has the biggest effect on iterative algorithms, like machine learning, graph algorithms and other types of workloads that need to reuse data and it is a huge performance improvement over classic MapReduce jobs.

Spark Components

Spark Core is the basic component on which the other four components depend. Also pictured are various storage systems where Spark can access data from: distributed storage systems like HDFS, GlusterFS, S3 and similar; distributed databases like Cassandra, Impala, Hive, HBase, Hypertable, and others; but also classic relational databases, such as Oracle, PostgreSQL or DB2.

Spark Core

Spark Core contains basic Spark functionalities required for running jobs and needed by other components. The most important of these is the RDD concept, or resilient distributed dataset, which is the main element of Spark API. It is an abstraction of a distributed collection of items with operations and transformations applicable to the dataset. It is resilient because it is capable of rebuilding datasets in case of node failures. Spark Core also provides means of information sharing between computing nodes with broadcast variables and accumulators. Other fundamental functions, like networking, security, scheduling and data shuffling, are also part of the Spark Core.

Spark SQL

Spark SQL is the newest Spark component, but very actively developed. It provides functions for manipulating large sets of distributed, structured data using SQL (actually, an SQL subset supported by Spark) and Hive SQL language (HQL). Spark SQL can also be used for querying JSON data as well as for writing and reading Parquet files, which is an increasingly popular file format that allows for storing schema along with the data. It provides a query optimization framework called Catalyst that can be extended by custom optimization rules and includes a Thrift server, which can be used by external systems, such as BI tools, to query data through Spark SQL using classic JDBC and ODBC protocols.

Spark Streaming

Spark Streaming is a framework for ingestion of real-time streaming data from various sources. The supported sources include HDFS, Kafka, Flume, Twitter, ZeroMQ and custom ones. Its operations recover from failure automatically which is, of course, very important for online data processing. Spark Streaming can be combined with other Spark components in a single program unifying real-time processing with machine learning, SQL and graph operations, which is something not seen in the Hadoop ecosystem.

Spark GraphX

Graphs are data structures comprised of vertices and edges connecting them. GraphX provides functions for building graphs and implementations of the most important algorithms of the graph theory, like page rank, connected components, shortest paths, SVD++ and others. It also provides Pregel message-passing API, the same API for large scale graph processing implemented by Apache Giraph, a project with implementations of graph algorithms and running on Hadoop.

Spark MLlib

Spark MLlib is a library of machine learning algorithms grown from MLbase project at UC Berkeley. Supported algorithms include logistic regression, naive Bayes classification, SVM, decision trees, random forests, linear regression, k-means clustering and others.

Salary Trends

Average Spark Salary in USA is increasing and is much better than other products.

Ref: Indeed.com

Average Spark Salary in India.

Ref: Glassdoor.com

Spark Certification

Spark Developer credential is designed for Engineers, Programmers, and Developers who prepare and process large amounts of data using Spark.

• Having Spark certification distinguishes you as an expert.
• For Spark certification, you need not go to a test center, as the certification is available online.
• You need to register yourself at https://www.mapr.com/services/mapr-academy/mapr-certified-spark-developer to give your Spark Exam.

Exam Details: http://learn.mapr.com/mcsd-mapr-certified-spark-developer

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Apache Spark Certification Training in Your City

Apache Spark Training United States

Our trainers in US are certified and have in-depth knowledge regarding Apache Spark Concepts. Tekslate superior quality training is what makes us stand apart from others. Case studies are included in the curriculum of training programs irrespective of the mode you chose. You can avail training in your cities like New York, Los Angeles, Chicago, Houston, and more.

Apache Spark Training United Kingdom

For experienced professionals in UK, special batches are conducted in different timings. Customized approach to imparting training has made us different from others. You can clarify your doubts after completing the class. You can avail training in your cities like London, Birmingham, Leeds, Glasgow and more.

Apache Spark Training Canada

There are many companies that offer Apache Spark training in Canada. Our Apache Spark course provides basic understanding about the introduction and overview. It is the course that can be educate right from the beginner to the intermediate and advanced level. Apache Spark Training is provided by Real Time Industry Experts who has huge subject knowledge, skills and enhances the skills of students in the best way. You can avail training in your cities like Montreal, Winnipeg, Mississauga, Ottawa and more.

Apache Spark Training India

Tekslate provides instructor-led live online training and corporate training. Apache Spark Training provides you hands on real-time project experience. Our Apache Spark trainers are certified industry experts and work professionals. We provide customized training for beginners as well working professionals.

• Apache Spark Training in Hyderabad – We at TekSlate offer interactively designed Apache Spark Certification training. The Apache Spark Training course design in Hyderabad aims not only imparting theoretical concepts, but also aid students explore and experiment the subject. By the end of our training program, students can confidently update their profiles with knowledge and Hands on experience.
• Apache Spark Training in Bangalore – TekSlate masters in IT Online Training services. We are aware of industry needs and we are offering Apache Spark Training in Bangalore in a more practical way. We guarantee efficient training offered by real-time experts in the industry.
• Apache Spark Training in Chennai – TekSlate is one of the top-ranked Institute in Apache Spark training in Chennai. We provide best quality training for Apache Spark online with well-experienced professionals. Our unique blend of hands-on training enables students with the productive skills to improve their performance.
• Apache Spark Training in Pune – TekSlate offers Instructor-led online training by Top-Notch Trainers in Pune. Every session will be recorded and provided to you for future reference. Good quality Material will help students explore the subject confidently.
• Apache Spark Training in Mumbai – TekSlate offers best Apache Spark Training in Mumbai with most experienced professionals. Our Instructors are working professionals in the related technologies. Our team of trainers provides training services in a practical way with a framed syllabus to match with the real world requirements for both beginner level to advanced level.
• Apache Spark Training in Delhi – Apache Spark Training helps you to develop your IT skills through our wide variant training curricula. TekSlate in Delhi has immense experienced real-time professionals having years of experience. Our training program is very much mixed with both practical and interview point of questions to achieve the expertise in the subject.