Big Data : It refers to huge data sets characterized by larger Volumes, larger Variety (more diverse, including structured, semi-structured and unstructured data) and getting generated real time at higher Velocity than any individual or organization has had to deal with before with enormous Veracity (uncertainty of data accuracy). These four Vs characterize what Big Data is all about.
This flood of data is generated by connected devices – from PCs and smart phones to sensors such as RFID readers and traffic cams etc. They come in many formats, including text, document, image, audio, video and more. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search: transforming such content into a structured format for later analysis is a major challenge. Unstructured data is growing faster than structured data. Big data is data that is too large and complex for conventional data tools to capture, store and analyze. However, the real value of Big Data is in the insights it produces when analyzed – finding trends & patterns, deriving meaning, making decisions, getting predictions and ultimately responding to the world with intelligence.
Organizations can leverage Big Data Analytics to differentiate themselves from their competitors and outperform them in terms of operational efficiency if they can effectively capture, analyze, visualize, and apply Big Data insights to their business goals. A big data platform will enable an organization to tackle complex problems that previously could not be solved. It can even create new business opportunities that were previously lost. It will help the industries like Automotive, Banking, Consumer Products, Energy and Utilities, Government, Healthcare, Insurance, Oil & Gas, Retail, Telecommunication, Travel & Transportation to name a few. Big Data can be used in multiple ways and for multiple reasons in every industry.
When it comes to selection of Big Data Analytics tools, the adoption of Apache Hadoop framework continues to be the popular choice.
Hadoop : Hadoop is not a database but an enabler of certain NoSQL databases. Apache Hadoop is an open-source software framework for Big Data Analytics that provides a simple programming model to enable distributed processing of large data sets on cluster of computers built from commodity hardware. It includes a distributed file system (HDFS) for storage, a parallel processing framework and several of the components that support ingestion of data, coordination of workflows, management of jobs, and monitoring the cluster. Hadoop is more cost-effective at handling large unstructured data sets than traditional approaches. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common and should be automatically handled by the framework. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.
SPARK: Apache Spark is an advanced DAG execution engine that supports acyclic data flow and in-memory computing and can cache to disk if needed. Designed for fast, large-scale data processing capabilities for both batch as well as streaming workloads, making it a preferred choice of platform for speedy data analysis. Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3. Allows to write applications quickly in java, SCALA, Python, and R. You may combine a stack of libraries for Machine Learning, Streaming and SQL seamlessly in the same application.
Apache Spark in one of the fastest growing big data community. Spark is a fascinating platform for data scientists too because of it’s in-memory computing ability, which helps speed up machine learning workload, thereby making Spark training obligatory.
Hadoop and Spark training can give you steep competitive edge because of soaring demand for Spark professionals across a wide range of industries. Spark developers earn highest average salary amongst other developers.
By the end of Hadoop and Spark training, you will be able to learn and deeply understand the Hadoop Ecosystem along with adequate hands-on sessions and work on real live business cases using Hadoop and Spark. Followings are coverage of topics in our Hadoop Spark training curriculum. You can get a detailed syllabus in your email inbox, as soon as you share your email id with us.
- Big Data and Hadoop Fundamentals
- Hadoop Ecosystem
- Hadoop Installation and Configuration
- Hadoop Shell Commands
- Hadoop Distributed Filing System (HDFS)
- Hadoop MapReduce Framework
- MapReduce Architecture
- Advance MapReduce with hands-on
- Hadoop 2.0, YARN, MRv2
- Assignment of MapReduce
- SPARK Architecture
- RDD Operations in SPARK
- SPARK Runtime Architecture
- Deploying & Debugging Spark Application
- Spark SQL
- Apache Hive and HiveQL
- Apache Pig and Pig Latin
- NoSQL Databases
- Airflow Features and Architecture
- Scheduling jobs using Airflow
Majority of organizations are in process of implementing Big Data Analytics and many are actively planning to implement. Hence, the demand for Big Data expertise across a range of occupations saw significant growth over past few years and the number of job opportunities are steadily increasing. Owing to the high demand of this skillset, qualified professionals with right skillset are being highly paid in comparison to other technology skill. Therefore, many IT professionals have been investing time and money on Big Data Hadoop training and Big Data Hadoop certification.
At MARSIAN, we provide Big Data Analytics training in Pune for individuals as well as corporates. We provide flexible learning options with customized Big Data courses, catering to different target audiences with different duration & delivery mechanisms upon need based Big Data and Hadoop training in Pune.
Our Big Data Hadoop Spark training curriculum provides a comprehensive understanding of the entire Hadoop Ecosystem along with in-depth coverage of Spark. This curriculum is designed with a focus on more Hands-on sessions with numerous case studies and applications with real world business challenges that enables immediate and effective participation in Big Data projects. Our Hadoop and Spark training curriculum includes necessary topics that are required for hadoop certification and spark certification.
With an attempt to provide the best Hadoop training in Pune, we review our curriculum frequently and keep updated all the time, to ensure you're learning what's most relevant to employers and qualify for big data certification.
Our trainers are senior working professionals from the industry with extensive experience in Big Data Hadoop and Spark. We provide necessary support for big data analytics certification after completion of the Hadoop and Spark training.
Knowledge of Core Java and SQL.
Earn a MARSIAN certificate of completion at end of the course. Additionaly, you will also get necessary support for any external certification preparation.