Find the answer to how much does a Big Data Analyst earn in lieu of importing, cleaning, converting, and analyzing a huge amount of data? Learn about the salary of Big Data Analysts in various countries. Moreover, you’ll be able to understand how much you can earn as a Big Data Analyst in terms of experience and then finally identify the companies offering the best salary to the Big Data Analysts across the globe.
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Big Data analytics is the process of gathering, managing, and analyzing large sets of data (Big Data) to uncover patterns and other useful information. These patterns are a minefield of information and analysing them provide several insights that can be used by organizations to make business decisions. This analysis is essential for large organizations like Facebook who manage over a billion users every day, and use the data collected to help provide a better user experience.
Similarly, LinkedIn provides its users with millions of personalized suggestions on a regular basis. LinkedIn does it with the help of components like HDFS features and MapReduce in Big Data Analytics. Big Data has thus become an indispensable part of technology and our lives; and big data analyses provides solutions that are quick and require reduced effort to generate. It is no wonder then that big data has spread like wild fire and so have the solutions for its analyses.
According to a recent McKinsey report the demand for ‘Big Data’ professionals could outpace the supply by 50 to 60 percent in the coming years, and U.S.-based companies will be looking to hire over 1.5 million managers and big data analysts with expertise on how big data can be applied. Big Data investments have also sky rocketed, with several top profile companies spending their resources on Big Data related research and hiring big data analysts to change their technology landscape.
An IBM listing states that the demand for data science and analytics is expected to grow from 3,64,000 to nearly 27,20,000 by 2020. According to a recent study done by Forrester, companies only analyze about 12% of the data at their disposal. 88% of the data is ignored, mainly due to the lack of analytics and repressive data silos. Imagine the market share of big data if all companies start analysing 100% of the data available to them. Hence the conclusion is that there is no time like now to start investing in a career in big data. It is paramount that developers upskill themselves with analytical skills and get ready to take a share of the big data career pie.
Big data analytics certification is growing in demand and is most relevant in data science today than in other fields. The field of data analytics is new and there are not enough professionals with the right skills. Hence, the credibility of big data analytics certification promises many growth opportunities for organizations as well as individuals in the booming field of data science.
Many big companies like Google, Apple, Adobe, and so on are investing in Big Data. Let’s take a look at the benefits of Big Data that organizations and individuals are experiencing:
When the entire world is dependent on data, the Big Data Analyst profile plays a pivotal role in driving various businesses towards success. Now, let’s compare the salary of a Big Data Analyst in various countries in the following chart:
| Country Name | Currency | Salary (per annum) |
| India | Rupees (INR) | 4,14,628 |
| USA | Dollar ($) | 59,546 |
| UK | Pound Sterling (£) | 26,179 |
| Canada | Canadian Dollar (C$) | 55,004 |
The above data can be compared more clearly through the following figure in order to compare the country-wise earning of a Big Data Analyst:
(Fig: 1)
The above figure shows us that a Big Data Analyst based in the US earns a higher salary compared to the counterparts based in India, UK, and Canada. Now, let’s try to compare the salary earned by the Big Data Analysts across the globe based on experience through the following chart:
| Country Name | Experience | Currency | Salary (per annum) |
| India | Entry-level | Rupees (INR) | 3,21,470 |
| Mid-career | 6,12,850 | ||
| Experienced | 9,97,193 | ||
| USA | Entry-level | Dollar ($) | 53,958 |
| Mid-career | 66,347 | ||
| Experienced | 68,510 | ||
| UK | Entry-level | Pound Sterling (£) | 23,982 |
| Mid-career | 30,226 | ||
| Experienced | 33,202 | ||
| Canada | Entry-level | Canadian Dollar (C$) | 49,325 |
| Mid-career | 64,292 | ||
| Experienced | 69,216 |
Now, let’s try to analyze the above data with the help of the following figure:
(Fig: 2)
In Fig:2, we can draw a clear comparison between the experience-wise salary that a Big Data Analyst earns in India, USA, UK, and Canada. The figure makes it clear that the highest salary for the profile is earned in the US by the experienced Big Data Analysts. Moreover, the above chart further enables you to make a clear distinction between entry-level, mid-career and experienced Big Data Analyst.
So, that was about the salaries that a Big Data Analyst earns based on the location and experience. But the major question that arises here is, which are the companies that pay the highest salaries in the above countries. The following table will help you to check the salary paid by the top companies in India:
| Company Name | Salary (in Rupees per annum) |
| Tata Consultancy Services | 4,97,336 |
| Cognizant Technology Solutions | 5,21,638 |
| Accenture | 5,34,210 |
| Tech Mahindra | 4,62,673 |
| IBM | 4,30,624 |
| Amazon | 5,84,937 |
Fig: 1 & 2 give us a clear picture of the fact that the earning of a Big Data Analyst is more in the US compared to other nations. Now let’s take a look at the salary paid to Big Data Analysts by companies based in the US.
| Company Name | Salary (in US Dollars per annum) |
| CITI | 104,000 |
| HP Inc. | 77,000 |
| Auto Club of Southern California | 69,000 |
| JB Micro | 110,000 |
Now, let’s check out the salaries paid by companies to Big Data Analysts based in United Kingdoms.
| Company Name | Salary (in Pound Sterlings per annum) |
| Bloomberg L.P. | 42,515 |
| Her Majesty’s Revenue & Customs | 32,015 |
| Sport England | 46,296 |
| Sky | 41,786 |
The following table will help you to identify the salary paid by the companies based in Canada to the Big Data Analysts while enabling you to compare the same with the other countries:
| Company Name | Salary (in Canadian Dollars per annum) |
| TD | 67,699 |
| Scotiabank | 59,938 |
| Aimia | 67,663 |
| RBC | 56,545 |
| Rogers Communications | 55,000 |
Learn the basics of Apache Hadoop & data ETL, ingestion, and processing with Hadoop tools.
Understand how to join multiple data sets and analyze disparate data with the Pig framework.
How to organize data into tables, perform transformations, and simplify complex queries with Hive.
How to perform real-time interactive analyses on huge data sets stored in HDFS using SQL with Impala.
How to pick the best tool in Hadoop, achieve interoperability, and manage repetitive workflows.
There are no specific prerequisites required to learn Big Data.
Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the latest training!
Learn theory backed by practical case studies, exercises, and coding practice. Get skills and knowledge that can be applied effectively.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Learning Objective:
You will get introduced to real-world problems with Big data and will learn how to solve those problems with state-of-the-art tools. Understand how Hadoop offers solutions to traditional processing with its outstanding features. You will get to Know Hadoop background and different distributions of Hadoop available in the market. Prepare the Unix Box for the training.
Topics:
1.1 Big Data Introduction
1.2 Hadoop Introduction
Hands On:
Installation of Virtual Machine using VMPlayer on Host Machine. And work with Some basics Unix Commands needs for Hadoop.
Learning Objective:
You will learn what are the different Daemons and their functionality at a high level.
Topics:
Hands On:
Creates a Unix Shell Script to run all the deamons at one time.
Starting HDFS and MR separately.
Learning Objective:
You will get to know how to Write and Read files in HDFS. Understand how Name Node, Data Node and Secondary Name Node take part in HDFS Architecture. You will also know different ways of Accessing HDFS data.
Topics:
Hands On:
Writes a shell Script which write and read Files in HDFS. Changes Replication factor at three levels. Use Java for working with HDFS.Writes different HDFS Commands and also Admin Commands.
Learning Objective:
You will learn different modes of Hadoop, understand Pseudo Mode from scratch and work with Configuration. You will learn functionality of different HDFS operation and Visual Representation of HDFS Read and Write actions with their Daemons Namenode and Data Node.
Topics:
Hands On:Install Virtual Box Manager and install Hadoop in Pseudo distributed mode. Changes the different Configuration files required for Pseudo Distributed mode. Performs different File Operations on HDFS.
Learning Objective:
Understand different Phases in Map Reduce including Map, Shuffling, Sorting and Reduce Phases.Get a deep understanding of Life Cycle of MR in YARN submission. Learn about Distributed Cache concept in detail with examples.
Write Wordcount MR Program and monitor the Job using Job Tracker and YARN Console. Also learn about more use cases.
Topics:
Hands On:
6.1 PIG
Learning Objective:
Understand the importance of Pig in Big Data World, PIG architecture and PIG Latin commands for doing different complex operation on Relations, and also Pig UDF and Aggregation functions with piggy bank library. Learn how to pass dynamic arguments to Pig Scripts.
Topics
Hands On:
Login to Pig Grunt shell to issue Pig Latin commands in different Execution modes. Different ways of loading and transformation on Pig relations lazily. Registering UDF in grunt shell and perform Replicated Join Operations
6.2 HIVE
Learning Objective:
Understand importance of Hive in Big Data World. Different ways of configuring HIVE Metastore. Learn different types of tables in hive. Learn how to optimize hive jobs using Partitioning and Bucketing and Passing dynamic Arguments to Hive scripts. You will get an understanding of Joins,UDFS,Views etc.
Topics:
Hands On:
Executes Hive Queries in different Modes. Creates Internal and External tables. Perform Query Optimization by creating tables with Partition and Bucketing Concepts. Run System defined and User Define Functions including Explode and Windows Functions.
6.3 SQOOP
Learning Objectives:
Learn how to import normally and Incrementally data from RDBMS to HDFS and HIVE tables, and also learn how to export the data from HDFS and HIVE table to RDBMS.Learns Architecture of Sqoop Import and Export.
Topics:
Hands On:
Triggers Shell script to call Sqoop import and Export Commands. Learn to automate Sqoop Incremental imports with entering the last value of the appended Column. Run Sqoop export from HIVE table directly to RDBMS.
6.4 HBASE
Learning Objectives:
Understand different types of NOSQL databases and CAP theorem. Learn different DDL and CRUD operations of HBASE. Understand Hbase Architecture and Zookeeper Importance in managing HBase. Learns Hbase Column Family optimization and client Side Buffering.
Topics:
Hands On:
Create HBASE tables using Shell and perform CRUD operations with JAVA API. Change the column family properties and also perform sharding process. Also create tables with multiple splits to improve the performance of HBASE query.
6.5 OOZIE
Learning Objectives:
Understand Oozie Architecture and monitor Oozie Workflow using Oozie. Understand how Coordinator and Bundles work along with Workflow in Oozie. Also learn Oozie Commands to submit, Monitor and Kill the Workflow.
Topics:
Hands on:
Create the Workflow to incremental Imports of Sqoop. Create the Workflow for Pig, Hive and Sqoop Exports. And also execute Coordinator to Schedule the Workflows.
6.6 FLUME
Learning Objectives:
Understand Flume Architecture and its components Source, Channel and Sinks. Configure flume with Socket, File Sources and HDFS and Hbase Sink. Understand Fan In and Fan Out Architecture.
Topics:
Hands on:
Create flume Configurations files and configure with Different Source and Sinks.Stream Twitter Data and create hive table.
Learning Objective:You will learn Pentaho Big Data Best Practices, Guidelines, and Techniques documents.
Topics:
Hands on:You will use Pentaho as ETL tool for data analytics.
Learning Objective:
You will see different Integrations among hadoop ecosystem in a Data engineering Flow. Also understand how important it is to create a flow for ETL process.
Topics:
Hands On:Uses Storage Handlers for integrating HIVE and HBASE. Integrates HIVE and PIG as well.
Creating Recommendation system for Online Video Channels with the Historical Data using Cubing Comparing with the Benchmark Values.
Creating Sentimental Analytics by Downloading the Tweets from Twitter and Feeds the trending data to the Application.
Performing Clickstream Analytics on the Application data and engaging Customers by Customizing the Articles to the Customer for a UK Web Based Channel.
There are no prerequisites for attending this course.
Big Data analytics is important for companies and individuals to utilise data in the most efficient manner to cut costs. Tools such as Hadoop can help identify new sources of Data to help businesses to make quick decisions, understand market trends and develop new products
RAM: Minimum - 8 GB Recommended - 16GB DDR4
Hard Disk Space: 40 GB Recommended - 256 GB
Processor: i3 and above
The Big Data Analytics training does not have any restrictions although participants would benefit slightly if they’re familiar with basic programming languages.
All of the training programs conducted by us are interactive in nature and fun to learn as a great amount of time is spent on hands-on practical training, use case discussions, and quizzes. An extensive set of collaborative tools and techniques are used by our trainers which will improve your online training experience.
The Big Data Analytics training conducted at KnowledgeHut is customized according to the preferences of the learner. The training is conducted in three ways:
Online Classroom training: You can learn from anywhere through the most preferred virtual live and interactive training
Self-paced learning: This way of learning will provide you lifetime access to high-quality, self-paced e-learning materials designed by our team of industry experts
Team/Corporate Training: In this type of training, a company can either pick an employee or entire team to take online or classroom training. Flexible pricing options, standard Learning Management System (LMS), and enterprise dashboard are the add-on features of this training. Moreover, you can customize your curriculum based on your learning needs and also get post-training support from the expert during your real-time project implementation.
The sessions that are conducted include 30 hours of live sessions, with 15 hours MCQs and 8 hours of Assignments and 20 hours of hands-on sessions.
Course Duration information:
Online training:
Weekend training:
Yes, our lab facility at KnowledgeHut has the latest version of hardware and software and is very well-equipped. We provide Cloudlabs so that you can get a hands-on experience of the features of Big Data Analytics. Cloudlabs provides you with real-world scenarios can practice from anywhere around the globe. You will have an opportunity to have live hands-on coding sessions. Moreover, you will be given practice assignments to work on after your class.
Here at KnowledgeHut, we have Cloudlabs for all major categories like cloud computing, web development, and Data Science.
This Big Data Analytics training course have three projects, viz Recommendation Engine, Sentimental Analytics, Clickstream Analytics
The Learning Management System (LMS) provides you with everything that you need to complete your projects, such as the data points and problem statements. If you are still facing any problems, feel free to contact us.
After the completion of your course, you will be submitting your project to the trainer. The trainer will be evaluating your project. After a complete evaluation of the project and completion of your online exam, you will be certified a Big Data Analyst.
We provide our students with Environment/Server access for their systems. This ensures that every student experiences a real-time experience as it offers all the facilities required to get a detailed understanding of the course.
If you get any queries during the process or the course, you can reach out to our support team.
The trainer who will be conducting our Big Data Analytics certification has comprehensive experience in developing and delivering Big Data applications. He has years of experience in training professionals in Big Data. Our coaches are very motivating and encouraging, as well as provide a friendly learning environment for the students who are keen about learning and making a leap in their career.
Yes, you can attend a demo session before getting yourself enrolled for the Big Data Analytics training.
All our Online instructor-led training is an interactive session. Any point of time during the session you can unmute yourself and ask the doubts/ queries related to the course topics.
There are very few chances of you missing any of the Big Data Analytics training session at KnowledgeHut. But in case you miss any lecture, you have two options:
The online Apache Spark course recordings will be available to you with lifetime validity.
Yes, the students will be able to access the coursework anytime even after the completion of their course.
Opting for online training is more convenient than classroom training, adding quality to the training mode. Our online students will have someone to help them any time of the day, even after the class ends. This makes sure that people or students are meeting their end learning objectives. Moreover, we provide our learners with lifetime access to our updated course materials.
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
This will be live interactive training led by an instructor in a virtual classroom.
We have a team of dedicated professionals known for their keen enthusiasm. As long as you have a will to learn, our team will support you in every step. In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: https://www.knowledgehut.com/contact-us
We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, then we will facilitate a one-on-one discussion session with one of our trainers.
We accept the following payment options:
KnowledgeHut offers a 100% money back guarantee if the candidates withdraw from the course right after the first session. To learn more about the 100% refund policy, visit our refund page.
If you find it difficult to cope, you may discontinue within the first 48 hours of registration and avail a 100% refund (please note that all cancellations will incur a 5% reduction in the refunded amount due to transactional costs applicable while refunding). Refunds will be processed within 30 days of receipt of a written request for refund. Learn more about our refund policy here.
Typically, KnowledgeHut’s training is exhaustive and the mentors will help you in understanding the concepts in-depth.
However, if you find it difficult to cope, you may discontinue and withdraw from the course right after the first session as well as avail 100% money back. To learn more about the 100% refund policy, visit our Refund Policy.
Yes, we have scholarships available for Students and Veterans. We do provide grants that can vary up to 50% of the course fees.
To avail scholarships, feel free to get in touch with us at the following link: https://www.knowledgehut.com/contact-us
The team shall send across the forms and instructions to you. Based on the responses and answers that we receive, the panel of experts takes a decision on the Grant. The entire process could take around 7 to 15 days
Yes, you can pay the course fee in installments. To avail, please get in touch with us at https://www.knowledgehut.com/contact-us. Our team will brief you on the process of installment process and the timeline for your case.
Mostly the installments vary from 2 to 3 but have to be fully paid before the completion of the course.
Visit the following page to register yourself for the Big Data Analytics Training: https://www.knowledgehut.com/big-data/big-data-analytics-training/schedule
You can check the schedule of the Big Data Analytics Training by visiting the following link: https://www.knowledgehut.com/big-data/big-data-analytics-training/schedule
We have a team of dedicated professionals known for their keen enthusiasm. As long as you have a will to learn, our team will support you in every step. In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: https://www.knowledgehut.com/contact-us
We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, then we will facilitate a one-on-one discussion session with one of our trainers.
Yes, there will be other participants for all the online public workshops and would be logging in from different locations. Learning with different people will be an added advantage for you which will help you fill the knowledge gap and increase your network.
Big Data refers to large amounts of structured and unstructured data that can be analysed using traditional databases and multiple software techniques to reveal patterns that can be used to meet business objectives. Analyses of such large amounts of unstructured data helps in understanding and predicting human behaviour and solving complex business problems. Big Data is huge and consists of complex data sets that traditional data processing software cannot manage.
Big data contains in it patterns and information which when mined can given an insight into customer behavior and preferences. This leads to new innovations, satisfied customers, smoother operations and higher profits. Let’s see the attributes that are making Big Data so popular today:
With the help of Big Data technologies like Hadoop and cloud-based analysis, organizations find out more efficient ways of doing business and bring cost advantages when it comes to storing huge amounts of data.
With the help of Hadoop and in-memory analytics, organizations can quickly analyze the data and will be able to make decisions based on their learnings.
Big Data helps businesses gauge customer requirements and preferences, based on which they can develop new products or improve existing products to meet customer needs.
Tons of data are generated every second from our activities on social networks, the internet, or even from traditional business systems. This data generated from various sources is very complex and unstructured, and requires analysis to make it useful.
Data Analytics technologies provide organizations the means to analyse the data and draw conclusions, which further helps them improve their business models and create a better experience for their customers. Big Data Analytics is an advanced form of analytics which involves several elements like statistical analysis, what-if, and predictive models. There are a lot of tools and applications that enable analysts and data scientists to analyse different forms of data that cannot be handled by the usual BI applications.
The difference between Big Data and Big Data analytics is that the former is a dataset that is not refined and is unstructured and is collected from various sources. Big Data Analytics, on the other hand, is clean and clear data that is easier to access and more concise.
To elaborate, Data Analytics is more accurate and focused than Big Data because instead of gathering dumps of unstructured data, analysts have a specified goal and sort through data to look for solutions to specific problems. Whereas Big Data is just a collection of a huge volume of data that requires a lot of filtering to derive any sort of usage or insight from it. Additionally, another notable difference between them is that Big Data employs more complex tech tools to handle data, but Analytics uses statistics and predictive modelling with simpler tools.
Big Data Analytics tools include:
There are 7 widely used Big data techniques:
There are several techniques that are used for Big Data analysis. Some of them are:
Big Data comprises of specific attributes in the form of 4 V’s. They are:
Big data is present in 3 forms:
Structured data is the data that can be processed, stored, and retrieved in a preset form.
Unstructured data is the data that lacks any specific structure.
Semi-structured data is the data that is the combination of both the formats i.e. structured and semi-structured.
Data is basically the quantities, characters, or symbols on which operations are performed by a computer, which may be stored, transmitted or recorded. Big Data is also data but with a large size. Big Data is a term used to describe a collection of data that is large in size and may grow exponentially with time. Big data sets are so large and complex that none of the traditional data management tools are able to store or process it efficiently. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time.
Data Warehousing is extracting data from one or more homogeneous or heterogeneous data sources, transforming the data and loading that into a data repository to do data analysis which further helps in taking better decisions to improve one’s performance and in reporting.
Big data refers to volume, variety, and velocity of the data. How big the data is, the speed at which it is coming and the variety of data, determine the so-called “Big Data”. The 3 V’s of big data was articulated by industry analyst Doug Laney in the early 2000s.
Organizations want a big data solution because in many corporations there are many operations associated with a large amount of data. And in those corporations, if data contains much valuable information then it can lead to better decisions, which subsequently leads to more revenue, more profit, and more clients.
Organizations need a data warehouse in order to access reliable data and make informed decisions. Big data solution is a technology whereas data warehousing is an architecture.
Top big data technologies are divided into 4 fields which are classified as follows:
Data storage: For big data projects, cloud-based storage tools are vital to maximizing the amount of information you can store. Cloud storage options let you store data in a secure and accessible fashion, for ease of use.
Data mining: Once you have your data stored, you will need some tools to find the information you want to analyze or visualize. This tool helps you to extract the data you need without the hassle of manually trawling through it all (a task that's impossible for humans to do anyway if you hold thousands or more records).
Data analysis: Got the data you need? Now it's time to find the most powerful tools to help you analyze it in order to glean key insights into your business, your customers or the wider world.
Data Visualization: Not everyone is adept at taking key insights from a list of data points or understanding what they mean. The best way to present your data is by turning it into visuals that everyone can comprehend easily.
Big data analytics is the complex process of examining large and varied data sets, or big data, to uncover information -- such as hidden patterns, unknown correlations, market trends and customer preferences -- that can help organizations make informed business decisions. On a broad scale, data analytics technologies and techniques provide a means to analyze data sets and draw conclusions about them, which helps organizations make informed business decisions. Business intelligence (BI) queries answer basic questions about business operations and performance.
Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by high-performance analytics systems.
Driven by specialized analytics systems and software, as well as high-powered computing systems, big data analytics offers various business benefits, including:
The Big Data Analytics Certification course from KnowledgeHut has the following features:
Through this course, you will be able to learn the following:
All data architects, data analysts, data scientists, developers, BI analysts, BI developers, project managers, and SAS developers should take this course.
Here are a few tips that will help you in selecting the best data analytics course:
The basics that will be covered in the Big Data Analytics course are:
With a Big Data Analytics certification, you will be able to make a major career shift. They are proof of competency and are accepted worldwide. As technology keeps changing every few months, getting a full-fledged degree is challenging. Certifications are an easy way to build the required talent. Here are a few benefits of enrolling in the Big Data Analytics certification course:
No, there is a huge difference between data science and Big Data Analytics.
To learn Big Data Analytics from scratch, you should enrol yourself in a training institute like KnowledgeHut. KnowledgeHut provides 30 hours of live training on Big data and its frameworks. The whole curriculum has been created to focus on frameworks like HIVE and PIG. We use hands-on exercises and case studies for helping the participants learn practical applications of Big data and have a clear understanding of Hadoop ecosystem and integrations.
Here is what you can do with your data analytics certificate:
Big Data technology is used to store, retrieve and analyze large amounts of data to reveal hidden patterns, correlations, and other company-related insights. Big Data technology stacks help analyze data faster and help come up with better strategies to develop products that meet the customer’s requirements. Big Data analysis provides several advantages such as:
Big Data is used to discover hidden patterns, market trends and customer preferences. These in turn help organizations make informed business decisions, innovate products to meet customer preferences, cut costs, enhance efficiency and reduce time to market.
Data is the new oil and Big data is all about helping organizations beat competition and ensure market survival. In this volatile market where costs are hitting the roof and customers are volatile, it is important that businesses understand the pulse of the market and come up with innovations that meet the customers needs while also being cost, process and time efficient. The analyses of Big Data helps do all of that, and also helps in cost reduction and better decision making, which is why it has become indispensable to organizations.
Big Data analytics is indeed a revolution in the field of IT. It helps organizations to harness their data and use it to identify new opportunities and gain an insight to run their business efficiently. Companies can improve their strategies by keeping in mind customer preference. For example, Netflix Uses Big Data to Improve their Customer Experience.
Professionals who are experts in the field of big data analytics are in huge demand. High profile companies from all sectors hire Big Data Analysts to mine data for useful insights. Below are the list of Companies hiring Big Data Analysts
In Big Data lies the key to unlocking insights that help understand customer preferences and design business strategies. Big data helps a business to figure out what the customer wants and create new experiences, services, and products to meet these demands. Using big data has been crucial for many leading companies to beat competition and emerge successful. Today, Big Data has changed the way that businesses market themselves and their products. Big data has helped companies innovate, reduce operational costs, increase profits and gain customer loyalty.
There are many ways to learn Big Data Analytics. You can learn through tutorials, blogs, articles, books, etc. But the best way to learn about Big Data Analytics is by attending a training. The training will equip you with the skills to make a career shift to a promising field.
Below are the various resources to learn Big Data Analytics:
Big Data Course
Big Data Tutorials
Big Data Books
Blogs, articles
Yes, you need to have a good understanding of programming languages to learn Big Data Analytics.
No, Big Data is very easy to learn. Especially if you are familiar with a programming language.
To become a Big Data Analyst, you can take up the Big Data Analytics course. The course will help you understand the fundamentals and basics of Apache Hadoop and data ETL, ingestion, processing with Hadoop tools. Learn and understand the Pig framework and Hive framework. Moreover, you will get the opportunity to work on real-world projects as the course is curated by industry experts.
If you have not added Big Data Analytics skills to your resume yet, then this is the best time to do so. KnowledgeHut provides Big Data Analytics courses that have been crafted by experts which focus on job-readiness and hands-on skills. Build your concepts from scratch and assist your learning through step-by-step guidance on tools and techniques.
The Big Data Analytics course completion certificate by KnowledgeHut has lifetime validity.
Attending this Big Data Analytics training will help you in getting a job in the following ways:
Big Data Analytics expertise will benefit you in the following ways :
According to IDC, worldwide spending on big data and analytics is growing at a compound annual growth rate (GAGR) of 11.9 percent, and revenues will likely total more than $210 billion by 2020. This growth will only increase in the coming years as more and more data gets generated and the need to analyse it for business benefits increases. This has led to an increased demand for data scientists, analysts, and data management experts. Moreover, according to IDC, big data staffing shortage is expected to expand from analysts and scientists to include architects as well as experts in data management. Hence gaining expertise in this domain is sure to help you reap rich dividends in the future.
By the end of this course, you will learn about Big Data and Big Data Challenges. You will also gain knowledge of the Hadoop Daemon Processes, HDFS, Hadoop Installation Modes, and HDFS, Hadoop Developer Tasks, Hadoop Ecosystems, Data Analytics using Pentaho as an ETL tool.
Enterprises have realised the value that Big data analyses provides to their business, which is why there is an increase in demand for Big Data Analytics professionals. The following states the reasons why big data is in high demand:
Big Data Analysts could earn a gross pay of $84,955 per year.
Note: All information mentioned is based on data obtained in September 2019.
Today, Big Data Analysts are in great demand. Industries like Professional Services, Manufacturing, IT, Retail and Finance hire individuals who are experts in Big Data technology. Here is a list of some of the companies hiring Big Data professionals:
