Spark online training course is designed to build complete, unified Big Data applications combining batch, streaming, and interactive analytics of data,
Instructor-Led Live Online Training
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
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 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 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 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 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 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.
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 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.
Average Spark Salary in USA is increasing and is much better than other products.
Average Spark Salary in India.
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.
Benefits to our Global Learners
- Tekslate services are Student-centered learning.
- Qualitative & cost effective learning at your pace.
- Geographical access to learn from any part of the world.
Spark Certification Training in Your City
- Spark Training India
Tekslate provides instructor-led live online training and corporate training. Spark Training provides you hands on real-time project experience. Our Spark trainers are certified industry experts and work professionals. We provide customized training for beginners as well working professionals. You can avail training in your cities like Hyderabad, Bangalore, Delhi, Mumbai, Pune, Chennai and more.
- Spark Training United States
Our trainers in US are certified and have in-depth knowledge regarding 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.
- 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.
- Spark Training Canada
There are many companies that offer Spark training in Canada. Our 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. 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.
What Are The Modes Of Training?
Tekslate basically offers the online instructor-led training. Apart from that we also provide corporate training for enterprises.
Who Are The Trainers?
Our trainers have relevant experience in implementing real-time solutions on different queries related to different topics. Tekslate also verifies their technical background and expertise.
What If I Miss A Class?
We record each LIVE class session you undergo through and we will share the recordings of each session/class.
Can I Request For A Support Session If I Find Difficulty In Grasping Topics?
If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers.
What Kind Of Projects Will I Be Working On As Part Of The Training?
You will work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various aspect and components making you perfectly industry-ready.
How Will I Execute The Practical?
Our Trainers will provide the Environment/Server Access to the students and we ensure practical real-time experience and training by providing all the utilities required for the in-depth understanding of the course.
If I Cancel My Enrollment, Will I Get The Refund?
If you are enrolled in classes and/or have paid fees, but want to cancel the registration for certain reason, it can be attained within 48 hours of initial registration. Please make a note that refunds will be processed within 30 days of prior request.
Will I Be Working On A Project?
The Training itself is Real-time Project Oriented.
Are These Classes Conducted Via Live Online Streaming?
Yes. All the training sessions are LIVE Online Streaming using either through WebEx or GoToMeeting, thus promoting one-on-one trainer student Interaction.
Is There Any Offer / Discount I Can Avail?
There are some Group discounts available if the participants are more than 2.
Who Are Our Customers & Our Location?
As we are one of the leading providers of Online training, We have customers from USA, UK, Canada, Australia, India and other parts of the world.
The online course session was good with a lot of discussion on the subject in depth. Demo's were good and the trainer cleared my doubts clearly.The trainer having a very good command on the subject.
Real time/live scenario's were included in the training sessions. The trainer's has very good command on the subject. Thank you..
I like the training period from Tekslate. The course is very well designed that helps to keep track until we demonstrate subject mastery.
Using most innovative teaching techniques, Tekslate intended to help students to learn through online. A great part of the coursework is allowed to use and earn certification by the time they finish t ...