Big Data & Hadoop Developer

What are the tools needed to analyze Big Data?

Given below are the tools to analyze Big Data:

1. Jaspersoft BI Suite: The Jaspersoft package is one of the open source leaders for producing reports from database columns. The software is very refined and pre-installed in many businesses converting SQL tables into PDFs that most can analyze at meetings.
2. Pentaho Business Analytics: Pentaho is a software platform that started off as a report generating engine, like JasperSoft, and is branching into Big Data by making it easier to absorb information from the new sources. One can connect Pentaho's tool to several NoSQL databases such as MongoDB and Cassandra and after connection, one can drag and drop the columns into views and reports like the information came from SQL databases.
3. Karmasphere Studio and Analyst: Karmasphere Studio consists of a group of plug-ins built over Eclipse. This refers to a customized IDE that makes it simpler to generate and execute Hadoop jobs.
4. Talend Open Studio: Talend is an Eclipse-based IDE for stringing together data processing jobs with Hadoop. Talend tools are designed to support data integration, data quality, and data management, all with subroutines tuned to these jobs. Talend Studio permits one to create jobs by dragging and dropping small icons onto a canvas.
5. Skytree Server: Skytree hands out a bundle that performs several complex machine-learning algorithms. It just requires typing the right command into a command line. Skytree is concentrated on the core functions than appealing GUI. The implementation is around 10,000 times faster than its competitors.
6. Tableau Desktop and Server: Tableau Desktop constitutes a visualization tool enables simpler ways to look at data in newer ways, and then splice the data enabling viewing data in different ways. Tableau is optimized to give one all the columns for the data and lets one mix them before putting them in graphical templates given.
7. Splunk: Splunk generates an index of data likening data to a book or a block of text. It is similar to databases which also build indices, but Splunk's approach is really similar to a text search process.

What is Big Data? Why should the organizations adopt Big Data?

Conventional data processing applications are insufficient to handle massive or complicated data, whereas Big Data can do that. The main challenges in handling these massive data sets comprise of data capture, data curation, and analysis.

Other areas include data search, transfer, sharing, visualization, querying, updating and information privacy. Big Data frequently refers to the use of user behavior analytics, predictive analytics, or certain other advanced data analytics methods that mine value from data, and seldom to a particular size of data set.

Given below are the reasons why Big Data is essential for the Enterprise of today:

1. Building new applications: Big Data permits a business to gather billions of real-time data points on its resources, products, and customers post which it repackages that data instantaneously and optimizes customer experience and resource utilization.
2. Improving the effectiveness and lowering the cost of existing applications: Big Data technologies are able to phase out expensive and highly customized legacy systems with a standard solution that runs on commodity hardware. Most Big Data technologies are open source and can be applied and executed far more cheaply than proprietary paid technologies.
3. Realizing new sources of competitive advantage: Big Data enable businesses to function in an agile environment, empowering them to adapt to the changes faster than their competitors.
4. Increasing customer loyalty: Maximise the amount of data shared within the enterprise and aptness with which it is updated increase the customer loyalty. This, in turn, permits businesses and other enterprise related organizations to respond to customer demand faster and more accurately.

What is the principle good information stewardship says to the organization who wants to adapt to big data?"

Information Stewardship (IS) is the principle that ensures that every byte of information entering the organization will be according to policy in place defining how that information is to be managed, stored, and protected throughout its life. It consists of several disciplines:

1. Compliance management
2. Information protection
3. Disaster resilience
4. Information lifecycle management
5. Data quality management

Implementing IS principles in Big Data is the road to sustainable success and the chance to extract big insights out of the data. Long-term success using big data analytics depends on proper management of the data.

Big data focuses on storage infrastructures, networks, and tools. The volume of data, its growth rate, performance requirements, and needs for protection in disaster put together generate challenges for existing systems. To outlast the transition to big data, corporations must exercise good information stewardship by defining policy to guide each byte of information through acquisition and classification, its lifecycle in storage, its protection and insurance against disaster, and ultimate disposition at the end of its life.

Guided by the principles of good stewardship, and using some combination of new analytics and storage architectures plus the mixing of public and private cloud resources, it should provide an infrastructure which can handle big data. For this, it needs to understand the objectives and inputs to all Big Data projects making the best storage architecture choices for supporting them and know the full costs of internal compute and storage to make a proper evaluation of possible cloud options.

Describe the HDFS Deep Dive architecture.

The following points describe the HDFS Deep Dive Architecture.

1. It is a distributed file system handling a large number of files which streams data access and has fault tolerance.
2. It is able to overcome some of the limitations which Network File system has.
3. Its design is derived from Google File System.
4. It is Write Once Read Many.
5. It is a block-structured file system.
6. It is usually assembled by joining multiple hard drives from different file systems.
7. Data sets are available in the form of social media, page access etc.
8. If a block of data is there, then its two copies will be existing in one rack and the one copy will be existing in a different rack. Thus, data is kept in different racks.
9. It uses Master/Slave architecture where one master controls different slaves.

Differentiate Sqoop and Flume.

Difference between Sqoop and Flume is:

List the diagnostic operators in Apache Pig.

List of the diagnostic operators in Apache Pig is shown below:

1. Dump Operator: to run Pig Latin statements and displaying the results.
2. Describe operator: to view schemas of a relation.
3. Explanation operator: to display the logical and physical execution plans of a relation.
4. Illustration operator: step by step execution of a sequence of statements.

When should a combiner be used in Map Reduce job?

It is mainly used to reduce the volume of data transfer between Map and Reduce. The output of map class is usually large and data being transferred to the reduce class is very high. Thus, here the use of combiner comes. Running MapReduce job leads to large bits of data being generated after being run on the huge dataset and passed on to Hadoop reducer. This leads to huge network congestion and has to be resolved to perform further processing. Thus, we need combiner in this case. a combiner is also called as a mini-reducer. It processes output data from Hadoop mapper and then passes it to Hadoop reducer. This indicates that Hadoop combiner and Hadoop reduce work on the same code.

What are the different Hive meta store configuration?

Different Hive meta store configurations include:

1. Embedded meta store: Derby being its default database, this configuration is the simplest to get started with the hive. It embeds database as well as services of meta store in main HiveServer. It allows support to only one user and certification is not given for production purposes.
2. Local meta store: In this meta store, only services of meta store are run in HiveServer but the database has to be run on a different processor from a different host.
3. Remote meta store: Databases, services of meta store are on the same host but server processes run on separate host enable better availability. Communication between services of meta store and databases occur through JDBC. Metastore services keep on running in its own JVM.

What are the different job titles for big data analyzer?

1. Data scientists: A combination of mathematics and statistics, people can take up this designation when deciding the types of analysis that would give the best results.
2. Data architects: These professionals build data models and plan roadmaps of how different data sources and analytical tools will go online and fit together.
3. Data visualizers: These professionals are responsible for extracting data and presenting them in a visual format such as charts and graphs or even dashboards.
4. Data change agents: these professionals are responsible for driving changes in internal operations and even processes that are based on data analytics.
5. Data engineer/operators: Professionals that make the Big Data infrastructure function on a daily basis and develop an architecture that helps analyze and supply data as required by business needs, and maintaining.

What is the salary curve for big data analytics?

As demand for predictive analytics professionals increases, median base salaries continue to rise as more companies in more industries use big data.

A leading salary survey of predictive analytics professionals dating Sept. 3, the executive recruiting consultant, median base salaries over the last 12 months went from $95,000 for data analysts to $145,000 for managers.

Seventy-one percent of data workers who were surveyed said they were eligible for bonuses while 91 percent of managers were already in line for bonuses. The median or average bonus for workers was $11,000 and $27,400 for managers, according to the survey of 1,586 data professionals working for more than 750 different companies.

The managing director of the leading survey firm was fast to differentiate predictive analytics professionals from, data scientists as big data definitions have continued to change over time. Predictive analysis is known to work with large volumes of data, referring insights through the development of models and prescribing actions aimed at generating profits and reducing corporate risk.

What are the salary trends in the market across tools and skills in advanced analytics?

Advanced analytics/ predictive modeling professionals get the highest salaries compared to their analytics peers reaching 12.1 Lacs on average. This is followed closely by MangoDB professionals who command on an average of 11.1 Lacs in pay per annum. Next follows the privileged Big Data Professionals who draw an average salary of 9.7 Lacs which is the same for data mining professionals. MIS professionals command close to 7 Lacs. Qlikview and Tableau professionals have salaries at 9.5 and 9 lacs respectively.

Describe the salary trends across metropolitan cities in India in the Analytics Domain.

The current year has seen almost a 15% increase in salary for Mumbai, starting from an average analytics salary of 9.9 Lacs last year to 11.4 Lacs this year. The second one is Bangalore with 10.3 Lacs salary, which is a 5% increase from last year. The third contender is Delhi-NCR, where the pay of analytics professionals has increased from 9.4 Lacs to 9.9 Lacs. When it comes to the difference in pay between the years, it ties with Bangalore as it is also a 5% increase in one year. In the 6-10 Lacs bracket, Bangalore leads with 26% earning within this bracket, followed closely only by Pune and Hyderabad. Coming to the 0-6 Lacs bracket, Hyderabad leads with 46% of analytics professionals earning below 6 Lacs followed closely by Pune at 42%.

Explain Salary Trend for Skills across Cities in India in the Analytics domain.

The salaries for analytics across the Indian metropolitan cities, with respect to the analytics skill sets, are pretty diverse. When it comes to predictive modeling / advanced analytics, Delhi/ NCR has relatively higher pay scales than Bangalore. Mumbai ranks number one in awarding high salaries across all skill sets for Analytics. On the flip side, the salaries across all skills decrease sharply for cities beyond Mumbai & Delhi/ NCR, Bangalore. Pune pays higher for Data Mining than Chennai and Hyderabad While Bangalore and Mumbai are tied for Big Data skills.

Discuss Salary Trends across Company Type Cities in India in the Analytics domain.

The categories of the companies are:

1. Large IT players
2. Boutique analytic firms
3. Consulting firms
4. Domestic players and
5. Captive centres

Boutique analytic firms have a specific focus on analytics as their services, and therefore the smallest in size compared to the other three. Large IT players are those whose primary services are IT, but they have internal analytics teams. Consulting services offer a plethora of services, wherein one of those is analytics. On the other hand, the captive centers are those international firms, which have back offices in India, like Dell, HP etc. are not analytics service providers, but have internal analytics teams.

Why should the organization's move to big data?

Enterprises of every size are on the path to Big Data awareness and use. In case you are not one of those companies that haven’t caught up to Big Data, here are a few reasons to use Big Data to your benefit on an organizational level.

1. To Manage Data Better Present day data processing platforms allow data scientists to gather, wade through or filter and finally analyze different types of data. Most of today's data processing platforms let data scientists analyze, collect and sift through various types of data.
2. To benefit From Speed, Capacity and Scalability of Cloud Storage The third-party cloud service providers will help a company cut costs on hardware and also reduce training costs by adding an abstraction layer. They can help in storage and computing power to process data for a specific period.
3. To Enable End Users to Visualize Data Since Business Intelligence is a force to be reckoned with in the market, Big Data initiatives are set up to enable data visualization. This increases the effectiveness of data usage by all employees of the organization.
4. The Company Can Find New Business Opportunities Big Data, a holistic approach to data mining and application give a company an edge over its current market competition.
5. Your Data Analysis Methods, Capabilities Will Evolve The existing data analysis methods in companies for audio, video and text files are able to provide actionable insight with the right tools to recognize customer patterns and drive sales number up.

What are the sectors that Big Data is used?

The sectors that Big Data are used in are

1. Banking and Securities: This industry deals with tick analytics, securities fraud early warning, social analytics for trading, archival of audit trails, and IT operations analytics.
2. Communications, Media, and Entertainment: The customer data along with behavioral data is analyzed to create detailed customer profiles that are utilized to:
   • i. Generate content for varying target audiences
   • ii. Suggest content on demand
   • iii. Evaluate content performance.
3. Healthcare Providers: This sector accesses large amounts of data and has been frequented by massive failures in using data to limit the cost of increasing healthcare. And also, a problem is inefficient systems that limit speedier and healthcare benefits.
4. Education: Big Data is used considerably in higher education to track the time a student spends on particular educational web pages on an LMS. This is used in effect to track the holistic progress of a student through time.
5. Manufacturing and Natural Resources: Here Big Data is used to aid predictive modeling to in turn support decision making to take and consolidate massive amounts of data from areas such as graphical data, temporal data and geospatial data and a whole lot more.
6. Government: In public services, Big Data has a broad range of applications comprising of fraud detection, energy exploration, health-related research, financial market analysis and environmental protection.
7. Insurance: In this industry, there is a considerable deficiency in customer personalization, insurance being a sensitive issue and purchase for customers. This requires targeted pricing for customers and calculated division of particular market segments for convenience.
8. Retail and Wholesale Trade: This industry is a veritable gold mine of data that is available from physical retailers to online stores. RFID tag data (tagged products), customer loyalty points data and POS scanners are just the tips of the iceberg when it comes to data available in this domain.
9. Transportation: Monitoring of travel data and its usage in handling customers is still in its infancy. But the data that is pouring in from the travel industry is increasing exponentially with each passing year.
10. Energy and Utilities: Current energy meters are calibrated for recording energy readings every few minutes as opposed to just once a day with older analog energy meters. The kind of discrete and measurable data make cities more energy efficient by re-directing power to where it is really required.

What are the Prerequisites of Big Data and Hadoop Developer Course?

Knowledge in Java and Data structure is very helpful. For practical experience, knowledge on machine learning and Python is an added advantage. Java is not strictly a prerequisite for working with Hadoop. In addition, knowing query language like SQL is a boon to learning complex concepts such as Hive, Pig, and HBase.

What are the system requirements to attend the live sessions?

1. Processor I3 with 4GB RAM, OS can be 32 or 64 bit (Laptop/Desktop)
2. Internet connection with Min 1 MBPS speed
3. Good quality headset
4. Power back up
5. You can also log in through your Android mobile phone/ Tablet with 4G internet connectivity

What if the trainee miss any session?

The trainee can watch the recorded video of all the sessions in the LMS or Trainee can attend the missed session in the upcoming batches.

What do the trainee get from the LMS?

The trainee will have the access to Recorded sessions, Assignments, Quizzes, Case Studies, few course documents posted by trainers, Placement related docs etc.

What is the validity of the LMS access? What if the LMS access is expired.

The trainee will get 1-year access to the LMS. You can contact our support team to extend the validity of the LMS.

Will the trainee get any project to work on with Big data and Hadoop developer course?

Yes, Of course! The trainee will get the project at the end of the course, you need to submit a project. Our trainers will assist you to complete the project.

How are the practicals done?

The trainee will get step by step assistance on VM installation from our expert trainers during the practical sessions, post live sessions, you can practice at your end and submit your queries if any to our support team support@bumacoglobal.com for further assistance.

What are the types of training we offer?

1. WBLT- Web-based live Training
2. WBVT- Web-based Video Training
3. One on One live training
4. Self-paced training
5. In class training

What are the benefits of online training ?

1. Flexible location
2. Flexible schedule
3. Travel free
4. Time saving
5. Cost saving
6. LMS access
7. You will never miss a class
8. Two-way interactive
9. Fast learning
10. Trainer support for 1 year

Who are our Trainers?

Our trainers are industry experts having 10 to 15 years of industry experience and 3-4 years of training experience. Most of the trainers are working professionals who teach the real time scenarios which will help the students to learn the courses in an effective manner.

Will the trainee get the certification post the course completion?

Yes, Trainee will get the participation certificate from Bumaco Global upon successfully completing the course.

What if the trainee has more queries and need assistance?

The trainee can drop an email to support@bumacoglobal.com an automatic ticket will get generated. Our support team works 24/7 to assist you with all your queries.