“Big Data” is the term commonly used to refer to the application of predictive analytics, user behavior analytics, or other advanced data analysis methods that extract value from data and seldom to a particular size of data set. In simple terminology, big data is the name given to those chunky and difficult-to-understand data sets which are nearly impossible to deal with using traditional data processing application software.
Whether you want to enroll in the agile certified practitioner exam prep course or go for the data analytics certification, it is a prerequisite for you to have a solid knowledge of big data fundamentals. The Introduction to Big Data course from QuickStart can get this accomplished for you. This article is a detailed discussion of this certification training and related parts. But first, let us gain some more knowledge about big data and its components.
Big data technologies
Big data technologies like business intelligence, cloud computing, and databases are the main components of big data. Techniques for analyzing data such as A/B testing, machine learning, and natural language processing are also included in this category. Other important and commonly used big data technologies are visualization techniques such as charts, graphs, and other displays of the data.
Data analytics training
One of big data analytics’s most important defining characteristics is real or near-real-time information delivery. The data analytics certification training makes you familiar with the different advantages of shared storage in big data analytics. This training assists one in defining the problem at hand first.
It then helps you in doing analytic innovation by focusing on the business importance and letting you decide which tools to select for achieving timely results. The final step is to know how to implement a big data solution. This will include selecting suitable vendors and hosting options and balancing costs against business value so that you and your business stay ahead of the curve.
The big data introduction training course offered at quickstart.com will enable you to store, manage, process, and analyze massive amounts of unstructured data to convert it into a usable and meaningful database. It aims to let you learn how to leverage big data analysis tools and techniques to promote better business decision-making. This course will also tell you about ways of storing data for efficient processing and analysis.
Objectives of the big data certification course
Main objectives can be summarized as:
- Helping you plan and implement a big data strategy for your organization
- Making you learn how to store, manage, and analyze unstructured data
- Assisting you in selecting the correct big data stores for disparate data sets
- Allowing you to query large datasets in near real-time with Pig and Hive
- Enabling you to process large data sets using Hadoop to extract value
Getting to know the contents of big data course
This big data certification course starts with defining the big data, talking about its four dimensions: volume, velocity, variety, and veracity. It then introduces to you the Storage, MapReduce and Query Stack concepts. The Delivering business benefit from big data module makes you learn about the business importance of big data and various challenges associated with extracting useful data. It also tells you how to integrate big data with traditional data.
Advanced sessions will impart skills for storing big data and for analyzing your data characteristics. You will learn to select data sources for analysis and eliminate redundant data. It will also establish the role of NoSQL. You will get an overview of big data stores such as Hadoop Distributed File System, HBase, Hive, Cassandra, and Hypertable. To assist you in selecting big data stores, the course talks about choosing the correct data stores based on your data characteristics, moving code to data, implementing polyglot data store solutions, and aligning business goals to the appropriate data store.
Processing big data module starts with lessons on integrating disparate data stores talking about mapping data to the programming framework, connecting and extracting data from storage, transforming data for processing, and subdividing data in preparation for Hadoop MapReduce in detail. The following lesson on Employing Hadoop MapReduce deals with creating the components of Hadoop MapReduce jobs, distributing data processing across server farms, executing Hadoop MapReduce jobs, and monitoring the progress of job flows.
The Building Blocks of Hadoop MapReduce section will help you learn about distinguishing Hadoop daemons, investigating the Hadoop Distributed File System, and selecting appropriate local execution modes pseudo-distributed and fully distributed. The final chapter on handling streaming data tells about comparing real-time processing models, leveraging Storm to extract live events and lightning-fast processing with Spark and Shark.
Next comes the lesson on Tools and Techniques to Analyze big data, followed by the Abstracting Hadoop MapReduce jobs with Pig module that gives learning on communicating with Hadoop in Pig Latin, executing commands using the Grunt Shell, and streamlining high-level processing.
The Performing ad hoc big data querying with Hive lesson details about persisting data in the Hive MegaStore, performing queries with HiveQL, and investigating Hive file formats. Next module is named creating business value from extracted data. It helps you learn about mining data with Mahout, visualizing processed results with reporting tools, and querying in real-time with Impala.
The last section of the course is Developing a big data strategy. It starts with a lesson on defining a big data strategy for your organization, with sub-divisions named as establishing your big data needs, meeting business goals with timely data, evaluating commercial big data tools, and managing organizational expectations. The second last module is enabling analytic innovation, covering the topics like focusing on business importance, framing the problem, selecting the correct tools, and achieving timely results.
The final part of the module is named as Implementing a big data Solution. Here, you will learn about selecting suitable vendors and hosting options, balancing costs against business value, and keeping ahead of the curve.