Futuristic Big Data Interview Questions and Answers

1st Round: Big Data Basic interview questions and answers:

Q1. Spell out a few interesting facts about Big Data?

Ans. Big data is big, and there is no doubt about it. These are some of the facts about Big Data:

  • Net users generate (create or send) 200 billion mails every day.
  • Half a billion tweets are sent every day.
  • Gartner estimates that data volume will grow by at least 800 percent over the next five years.
  • By 2020, each person on this planet will be consuming about 5200 GB of data.
  • By 2025, global data is expected to reach 163 ZB, according to IDC.
Q2. How much data is sufficient to get valid outcome?

Ans.  This is like asking how much alcohol a person must consume to get on a high. It varies from one person to another. A number between too less or too much has to be found. The same goes for data because different businesses work differently and use and measure data in different ways. It is finally up to the individual. The ideal volume is the one that enables you to get the right results.

Q3. Explain the four features of Big Data?

Ans.  The four features of Big Data are indicated by the four V’s to help understand the value of data and improve operational efficiency:

  • Volume
  • Velocity
  • Variety
  • Veracity
Q4. Describe logistic regression?

Ans. Logistic Regression is a technique with which the binary result from a linear amalgamation of predictor variables is predicted. It is also known as logit model.

Q5. Describe the method by which A/B testing works?

Ans.  A highly versatile method for zeroing in on the ideal online promotional and marketing strategies for any organization, A/B testing can be used to figure out everything from mails to search ads to website copy. The core objective is figuring out any modification to a

webpage to make the best use of the result of an interest.

Q6. Which three modes can Hadoop run on?

Ans. Following are the models:

  • Standalone mode
  • Pseudo Distributed mode (Single node cluster)
  • Fully distributes mode (Multiple node cluster)
Q7. Which are the important tools useful for Big Data analytics?

Ans. Important tools useful for Big Data Analytics.

  • NodeXL
  • KNIME
  • Tableau
  • Solver
  • OpenRefine
  • Rattle GUI
  • Qlikview
Q8. Explain collaborative filtering?

Ans. Collaborative filtering is a set of technologies that predicts what a particular consumer will like depending on the preferences of several individuals. We could call it a technical word for asking suggestions from individuals.

Q9. Explain block in Hadoop Distributed File System (HDFS)?

Ans.  This is how block in Hadoop Distributed File System is understood: A file that is stored in HDFS breaks down all file systems into a set of blocks and HDFS oblivious of what is stored in the file. It is necessary for a block size to be 128MB in Hadoop. This value can be altered for individual files.

Q10. Define checkpoint

Ans.  The checkpoint is the main part of maintaining filesystem metadata in HDFS. The HDFS creates checkpoints of file system metadata by connecting fsimage with the edit log. This new version of fsimage is named as Checkpoint.

Q11. What are Active and Passive Namenodes?

Ans.  While Active NameNode runs and works in the cluster; Passive NameNode has comparable data like active NameNode.

Q12. For what purpose is JPS used?

Ans. The JPS command is one that is used to check if NodeManager, NameNode, ResourceManager and Job Tracker are working on the machine.

Q13. To what use do you put missing data?

Ans.  Missing data is a situation in which no data is stored for the variable and the data collection is insufficient. Data analysts should analyze data and determine if it is sufficient and what to with it.

Q14. Mention key components of a Hadoop application

Ans. Key components of Hadoop Application:

  • HDFS
  • YARN
  • MapReduce
  • Hadoop Common
Q15. What responsibilities does the role of a data analyst carry?

Ans. A data analyst:

  • Assists marketing executives in understanding the performance of each product or service by various criteria such as age, region, gender, season or such others
  • Tracks external trends in relation to demography or geographic location of the market to help understand the status of products in each region
  • Bring about greater understanding between the customers and the business

2nd Round: Big Data Technical Interview Questions and answers:

Q1. In what way is Hadoop related to Big Data? What are Hadoop’s components?

Ans: Hadoop by Apache is an open-source framework that is used to store, process, and analyze complex unstructured data sets with which one can derive insights and actionable intelligence for businesses.

These are the three main components of Hadoop –

  • MapReduce – This is a programming model which processes large datasets parallel to each other.
  • HDFS – HDFS is a distributed file system that stores data storage without prior organization. It is Java-based.
  • YARN – This is a framework that manages resources and handles requests from distributed applications.
Q2. Why is Hadoop required for Big Data Analytics?

Ans: Since Big Data is huge and unstructured data, is becomes unwieldy and difficult to analyze and explore in the absence of analysis tools. Hadoop, in offering storage, processing, and data collection capabilities, fills this purpose. Hadoop stores data in its raw forms without using any schema and gives the option of adding any number of nodes.

Another advantage of Hadoop is that since it is open-source and is run on commodity hardware; it is relatively inexpensive for the purpose it serves.

Q3.   Which command is used for shutting down all the Hadoop Daemons together?

Ans: ./sbin/stop-all.sh

Q4. Describe the components of YARN?

Ans: These are the two main components of YARN (Yet Another Resource Negotiator):

  • Resource Manager
  • Node Manager
Q5. Explain the various features of Hadoop. 

Ans: Listed in many Big Data Interview Questions and Answers, the answer to this is-

  • It is Open-Source- Open-source frameworks come with a source code that is available and accessible by all over the World Wide Web. It is something like the doc files that we share online, which anyone with the permission can edit. What this feature enables is for the code snippets to be edited, rewritten, and modified according to user and analytics requirements.
  • It is scalable – Hadoop runs on commodity hardware; yet, it allows the addition of extra hardware resources to new nodes.
  • Its data is recoverable – One of the core features of Hadoop is that its data can be recovered. This is done by splitting blocks into three replicas across clusters. With Hadoop, users can recover data from node to node in cases of failure. It also enables recovery of tasks/nodes automatically during such instances.
  • Hadoop is extremely user-friendly – Users that are new to Data Analytics will swear by the user friendliness that Hadoop brings. It is the ideal framework to use, as its user interface is very simple. Also, clients don’t need to handle distributed computing processes, since the framework takes care of it.
  • It has Data Locality – With the Hadoop Data Locality feature, computation is moved to data instead of data to computation. Data, on the other hand, is moved to clusters rather than being brought to the location where MapReduce algorithms are processed and submitted.
Q6. Which are the different tombstone markers used for deletion purposes in HBase?

Ans: These are the three main tombstone markers used for deletion in HBase:

  1. Family Delete Marker, which marks all the columns of a column family.
  2. Version Delete Marker, which marks a single version of a single column.
  3. Column Delete Marker, which marks all the versions of a single column.

HR Round:

Which are the common challenges faced by Hadoop developers?

What difference did you bring into a project involving Big Data that you worked on?

What future do you see for Hadoop and Big Data over the next five to 10 years?

Do you believe that Big Data can change the face of human life? In what ways do you think this can be done and when do you think we can achieve it?

Conclusion:

With the world set to see critical changes in the areas covered by Big Data; it is not surprising that Big Data professionals are set to be in very high demand. Big Data is the real engine that powers everything on the www. Technology futurists are foreseeing a world powered by Big Data, Machine Learning, Artificial Intelligence and data science that will be so dramatically different from the one in which we live that no facet of human life is going to remain the same. Big Data can change everything from healthcare to banking and from genetics to the environment. There is no better time to be in Big Data than now because this is an opportunity to be at the forefront of revolutionary changes that this technology could bring to mankind.

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Amazon S3 For Beginners – Amazon Professional web hosting

About this Course

As a website owner, you will face many challenges when it comes to hosting your blog, website, and online business presence.

Amazon S3 For Beginners - Amazon Professional web hosting

This is because you are relying purely on your web hosting company to support you.

What happens when you get bigger, in terms of receiving lots of visitors?

What happens when you launch a product or service and you get a flood of traffic that will crash your server?

What usually happens is that your website slows down, and your user experience becomes painful and visitors just leave.

Or worse, your web hosting company decides to terminate your account because you’re using too many server resources, or they ask you to pay for a dedicated server which can cost you $150-$300 extra per month.

You cannot afford to lose money due to a minor oversight that would’ve taken just a few hours of your time.

To prevent this from happening, you typically want to host your files on an external server. However, the problem with this is that those costs will add up fast and you’ll simply run into the same situation.

So, in other words, we recommend that you host your images, large video, audio files, or other files on Amazon S3.

Amazon S3 allows you to host very large files and utilize their global reach and super-fast speeds for a very low cost.

The problem with this though is that if you read their technical documentation, it is very difficult to understand for someone who is just getting started.

So, if you don’t have hours to spend wading through the text, we’ve decided to create a video course that will allow you to understand how to do all of this in less than a couple of hours.

Soon you will be on your way to hosting your big files and protecting them.

Basic knowledge
  • You will need a computer, a website (or blog) and basic knowledge in all of the above
What you will learn
  • Whoever takes this course is already thinking big about their website or blog. After this course, you will be able to launch a product or service and get a flood of traffic that wouldn’t hurt your server. You will be ready to get bigger in terms of receiving a lot of visitors

To read more:

Oracle Goldengate 12c

About this Course

Learn Oracle Goldengate and be Oracle Goldengate Specialist.

The course contains the topics from basics to advanced level. The course is designed in such a way that it caters to everyones need. The course is descriptive and practical. The course will help you in achieving expertise on Oracle Goldengate.

Programming And Coding Interviews1

With this course you will learn:

  • Oracle Goldengate Architecture
  • Oracle Goldengate Introduction,Download and Installation
  • Set up a unidirectional and bidirectional replication
  • Understanding Parameter files
  • Perform data filteration and transformation
  • Implementing DDL Replication
  • Implement integrated Extract
  • Implementing Integrated Replicat
  • Strategy for Monitoring and troubleshooting an Oracle GoldenGate configuration,
  • Configuring Oracle Goldengate security to meet customer needs
  • Understanding and Configuring Oracle Goldengate Parameter Files
  • Preparing for the interview
  • Test cases
  • Oracle Goldengate 12c new features
  • Hands on Activity Guides
  • Interview Questions for GG

Post the completion of this course attend the Goldengate certification course (Certification Oracle Goldengate Implementation Essentials) from Ashish Agarwal and be Oracle Goldengate certified. So u will have expertise and certification both with this and certification course!

Content and Overview 

This course is suitable for for Database Administrators and Developers., The course has descriptive and demonstrated 18 hours video sessions which will help you learn Oracle GoldenGate 12c fundamentals. The lectures are supported with exercises and documentations.

The course starts with description of Oracle Goldengate Architecture and installation of Oracle Goldengate on Linux Platform. The course has a wonderful presentation on all the topics including parameter files of Oracle Goldengate. The course also guides you on how to crack the interview. With the course you get Goldengate Interview Q and A. Also you can get the guide to setup the lab with this course.

This course give you the chance to grab the oppurtunity to work with instructor with such a wide experience and gaining real time scenarios.

Upon completing this course, you will be able to configure Oracle Goldengate and support Goldengate in customer environment. So, come join in the amazing journey of learning this hot technology.

Who is the target audience?

  • Database Administrators
  • Database Analysts
  • Solution Architects
  • Technical Architects
Basic knowledge
  • We assume the audience have the basic knowledge of the Oracle Database Administration fundamentals
  • Database developers and data integration specialist could also benefit from this course
What you will learn
  • Installaton of Oracle Goldengate
  • Architecture of Oracle Goldengate – Process Data Flow
  • Implementation and Configuration of Goldengate processes – Integrated and Classic modes
  • Configure unidirectional and bidirectional setup
  • Data Filteration and Transformation
  • Logdump utility
  • Zero Downtime migration and upgradation using Oracle Goldengate
  • DDL Replication
  • Troubleshooting strategy
  • Understanding and Configuration of Parameter files
  • HANDLECOLLISIONS

To Know More:

Why Java is the Future of Big Data and IoT 2018

Digitization has changed the business model in companies. Today, every market analysis is dependent on data. As a result, the rate at which data is being generated is outpacing our analysis capability. Hence, big data analysis is in place with high-end analytic tools like Hadoop. Hadoop is a Java-based programming framework with high-level computational power that enables to process large data sets.

On the other hand, after the internet, the next thing that would take the world by storm may be the Internet of Things (IoT). This technology is based on artificial intelligence and embedded technology. This new wave of technology is meant to enable machines to human-like performance. However, the implementation of an embedded system needs many considerations; and here comes the role of Java in IoT.

Being in the technology space for more than 20 years as a trusted platform for development, Java has not been outdated. Furthermore, its role is ubiquitous even with the latest technology inventions.

In this blog, we will discuss the role of Java in big data and IoT and its credibility in future as well.

What does IoT do?

IoT is a means or technology to collect and manage massive amounts of data from a vast network of electronic devices and sensors, then processing the collected data, and sharing it with other connected devices or units to make real-time decisions. Basically, it creates intelligent devices. Example of such intelligent networking system is an automated security system of a house.

However, enabling the IoT would need programs which will help to easily connect it with other devices to maintain the connectivity all around the system. Here comes Java into the picture with its networking programming capability.

What does IoT do

Role of Java in IoT

Here are the features of Java which play critical roles in developing an IoT system.

Platform independence

Platform independence is an important feature when you are developing an IoT system. During the development of an embedded application, you need to consider about the below factors –

  • Processor,
  • The real-time operating system,
  • Different protocols which will be used to connect the devices.

Java ME abstracts all of the above factors. Hence, the developed IoT application can run across the many different devices without changing the code of those applications. It helps to implement write once and prototype anywhere (on different types of hardware platform) facility. As IoT mainly handles embedded system, the developers need to use the software on different chipsets or operating systems as per the requirements.

Portability

Portability over the network is one of the primary reasons for choosing Java for IoT development for almost all devices from desktop computer to mobile use Java. Also with its networking capability, it is an integral part of the internet that makes it a good fit for IoT.

Easy accessibility with the best functionalities

A developer can easily learn Java, and with its best level of object-oriented features, it provides the best level of services in an application. For example, security and scalability are two important parameters in the industry while dealing with IoT devices and Java meets that requirement. With its huge ecosystem in the place, Java makes itself more suitable for the IoT. Hence, developers with advanced Java knowledge are working on innovative IoT solutions to create a connected digital world.

Easy accessibility with the best functionalities.jpg

Extensive APIs

Java offers its users the advantage of using an extensive list of APIs which they need to apply rather than rewriting during the making of an embedded application. It makes Java a perfect choice for IoT programmers.

Flexible and easy to migrate

One of the primary reasons IoT programmers incline towards Java is because of its flexibility and virtual availability everywhere. Hence, they can do anything with Java. Additionally, the migration capability of any Java application is high. The reason behind it is if an application is developed using Java, there will not be many issues during the migration to a new platform, and the overall process will be less prone to error.

What are the benefits of using Java for IoT

When we embed Java for IoT, as a user we receive numerous benefits which ultimately reap in the business along with technical enhancement.

Here are some of the benefits mentioned below –

  • Higher Resource Availability – Being in technology space over long period Java has built up a strong community that consists of millions of developers around the world. It is a diverse ecosystem, and with a strong community back up, it is easier for a developer to learn Java easily. Hence, it helps to meet the goal of achieving a connected system.
  • Enhanced Device Performance – In IoT mainly Java Embedded is used which helps in more enhanced information exchange among devices on a timely basis which makes devices more integrated.
  • Enhanced Product Life-cycle due to High Adaptability – With Java, a product gets the ability to upgrade itself according to the business requirements and changes coming up in the market. Moreover, it manages itself with the changes without any glitch. Hence, the overall product life cycle enhances with the use of Java.
  • Increased Marketability – Since the product lifecycle gets increased and reusability of the modules, the overall market credibility of the product increases automatically.
  • Reduced Support Cost – As Java Embedded provides the ability to auto update and managing a product, the support cost gets reduced significantly.
  • Secure and Reliable – With enhanced security feature of Java, any IoT device will get security and reliability assurance over the internet.

What is the Role of Java in Big Data?

When we talk about Big data, the first thing comes in our mind is what does it actually do? Well, big data deals with enormous data set, either formatted or unformatted and process them to provide a valid output to the businesses in the required format. Here are few main purposes of big data-

  • To process a huge set of data to get insights of a trend
  • To use processed data for machine learning purpose to create automated process or system
  • Using big  data for complex pattern analysis

For the functionalities as mentioned earlier, mainly tools are used. Some of the popular tools are Apache Hadoop, Apache Spark, Apache Storm and many more. Most of these tools are Java-based, and Java concepts are widely used for data processing.

Big Data and Internet of Things are Interrelated

As IoT continues to grow, it has become one of the key sources of an infrequent amount of data. The data may be sourced from hundreds to thousands or even larger number of IoT devices as random data. This huge set of data also needs analysis through big data. Thus there is an interdependency of both the technologies where Java works as a common platform.

Big Data and Internet of Things are Interrelated.jpg

What will be the Role of Java in Big Data and IoT in Future?

Internet of Things is triggering millions of devices to connect online which is resulting in data more than ever. This huge data needs enough storage and management. For this purpose, big data technologies must be augmented to handle this data effectively. Interestingly the technology giants like Google and Apache are contributing more libraries for these technologies advancement. As we have discussed the role of Java in big data and IoT, it is expected that Java development will play the more aggressive role for the future benefit of these technologies.

 Overall, Java has always been considered as a popular and useful technology which is also a trusted platform when compared to all the others programming languages on the market. Though there are numerous programming languages are in place with easier interfaces like Pig, Ruby and many more; still, people show their gravity towards Java. As a result, the numbers of Java programmers are increasing every day.

Thus, whether or not the technologies like big data and IoT change rapidly, the role of Java in Big data and IoT will always remain the same.

Conclusion: To conclude, the bottom line is – Java is everywhere. However, if you want to walk with the changing industry trends, then Java is not the ultimate answer for achieving a promising career. You need to ramp up with latest technologies like Big data, Machine learning, IoT, Cloud or similar technologies. However, an effective upgradation needs proper guidance and roadmaps and here comes the role of Whizlabs to help you out in your path of success.

Do you want free online courses Click here

Best Big Data Hadoop Architect- Hadoop Online Courses | Simpliv

Hadoop Big Data

HBase – The Hadoop Database

Prerequisites: Working with HBase requires knowledge of Java

Record and run settings a team which includes 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data processing jobs.

Relational Databases are so stuffy and old! Welcome to HBase – a database solution for a new age.

HBase: Do you feel like your relational database is not giving you the flexibility you need anymore? Column oriented storage, no fixed schema and low latency make HBase a great choice for the dynamically changing needs of your applications.

What’s Covered:

  • 25 solved examples covering all aspects of working with data in HBase
  • CRUD operations in the shell and with the Java API, Filters, Counters, MapReduce
  • Implement your own notification service for a social network using HBase
  • HBase and it’s role in the Hadoop ecosystem, HBase architecture and what makes HBase different from RDBMS and other Hadoop technologies like Hive
  • Using discussion forums
  • Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(
  • We’re super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices
  • The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person.* The truth is, direct support is hugely expensive and just does not scale.
  • We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose

Click here continue to improve your Knowledge

 

Hadoop, MapReduce for Big Data problems

Big Data Hadoop

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

This course is a zoom-in, zoom-out, hands-on workout involving Hadoop, MapReduce and the art of thinking parallel.

Let’s parse that.

Zoom-in, Zoom-Out: This course is both broad and deep. It covers the individual components of Hadoop in great detail, and also gives you a higher level picture of how they interact with each other.

Hands-on workout involving Hadoop, MapReduce : This course will get you hands-on with Hadoop very early on. You’ll learn how to set up your own cluster using both VMs and the Cloud. All the major features of MapReduce are covered – including advanced topics like Total Sort and Secondary Sort.

The art of thinking parallel: MapReduce completely changed the way people thought about processing Big Data. Breaking down any problem into parallelizable units is an art. The examples in this course will train you to “think parallel”.

What’s Covered: Lot’s of cool stuff ..

Using MapReduce to:

Big Data Hadoopa

Recommend friends in a Social Networking site: Generate Top 10 friend recommendations using a Collaborative filtering algorithm.

Build an Inverted Index for Search Engines: Use MapReduce to parallelize the humongous task of building an inverted index for a search engine.

Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text.

Build your Hadoop cluster:

Install Hadoop in Standalone, Pseudo-Distributed and Fully Distributed modes

Set up a hadoop cluster using Linux VMs.

Set up a cloud Hadoop cluster on AWS with Cloudera Manager.

Understand HDFS, MapReduce and YARN and their interaction

Customize your MapReduce Jobs:

Chain multiple MR jobs together

Write your own Customized Partitioner

Total Sort : Globally sort a large amount of data by sampling input files

Secondary sorting

Unit tests with MR Unit

Integrate with Python using the Hadoop Streaming API

.. and of course all the basics:

MapReduce : Mapper, Reducer, Sort/Merge, Partitioning, Shuffle and Sort

HDFS & YARN: Namenode, Datanode, Resource manager, Node manager, the anatomy of a MapReduce application, YARN Scheduling, Configuring HDFS and YARN to performance tune your cluster.

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We’re super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

Click here continue to improve your Knowledge

 

Complete Google Data Engineer and Cloud Architect Guide

Big Data Hadoopf

This course is a really comprehensive guide to the Google Cloud Platform – it has 25 hours of content and 60 demos.

The Google Cloud Platform is not currently the most popular cloud offering out there – that’s AWS of course – but it is possibly the best cloud offering for high-end machine learning applications. That’s because TensorFlow, the super-popular deep learning technology is also from Google.

What’s Included:

  • Compute and Storage – AppEngine, Container Enginer (aka Kubernetes) and Compute Engine
  • Big Data and Managed Hadoop – Dataproc, Dataflow, BigTable, BigQuery, Pub/Sub
  • TensorFlow on the Cloud – what neural networks and deep learning really are, how neurons work and how neural networks are trained.
  • DevOps stuff – StackDriver logging, monitoring, cloud deployment manager
  • Security – Identity and Access Management, Identity-Aware proxying, OAuth, API Keys, service accounts
  • Networking – Virtual Private Clouds, shared VPCs, Load balancing at the network, transport and HTTP layer; VPN, Cloud Interconnect and CDN Interconnect
  • Hadoop Foundations: A quick look at the open-source cousins (Hadoop, Spark, Pig, Hive and HBase)

Click here continue to improve your Knowledge

 

Top 3 Hadoop Big Data courses to help you break into the industry

Hbase & Hadoop Tutorial Step by Step for Beginners

Hadoop Online Training.jpg

Prerequisites: Working with HBase requires knowledge of Java

Record and run settings a team which includes 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data processing jobs.

Relational Databases are so stuffy and old! Welcome to HBase – a database solution for a new age.

HBase: Do you feel like your relational database is not giving you the flexibility you need anymore? Column oriented storage, no fixed schema and low latency make HBase a great choice for the dynamically changing needs of your applications.

What’s Covered:

  • 25 solved examples covering all aspects of working with data in HBase
  • CRUD operations in the shell and with the Java API, Filters, Counters, MapReduce
  • Implement your own notification service for a social network using HBase
  • HBase and it’s role in the Hadoop ecosystem, HBase architecture and what makes HBase different from RDBMS and other Hadoop technologies like Hive
  • Using discussion forums
  • Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(
  • We’re super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices
  • The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person.* The truth is, direct support is hugely expensive and just does not scale.
  • We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose

 

Click here continue to improve your Knowledge

 

Apache Hadoop Mapreduce Architecture Online Course

Hadoop Big Data.jpg

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

This course is a zoom-in, zoom-out, hands-on workout involving Hadoop, MapReduce and the art of thinking parallel.

Let’s parse that.

Zoom-in, Zoom-Out: This course is both broad and deep. It covers the individual components of Hadoop in great detail, and also gives you a higher level picture of how they interact with each other.

Hands-on workout involving Hadoop, MapReduce : This course will get you hands-on with Hadoop very early on. You’ll learn how to set up your own cluster using both VMs and the Cloud. All the major features of MapReduce are covered – including advanced topics like Total Sort and Secondary Sort.

The art of thinking parallel: MapReduce completely changed the way people thought about processing Big Data. Breaking down any problem into parallelizable units is an art. The examples in this course will train you to “think parallel”.

What’s Covered: Lot’s of cool stuff ..

Big Data qs

Using MapReduce to:

Recommend friends in a Social Networking site: Generate Top 10 friend recommendations using a Collaborative filtering algorithm.

Build an Inverted Index for Search Engines: Use MapReduce to parallelize the humongous task of building an inverted index for a search engine.

Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text.

Build your Hadoop cluster:

Install Hadoop in Standalone, Pseudo-Distributed and Fully Distributed modes

Set up a hadoop cluster using Linux VMs.

Set up a cloud Hadoop cluster on AWS with Cloudera Manager.

Understand HDFS, MapReduce and YARN and their interaction

Customize your MapReduce Jobs:

Chain multiple MR jobs together

Write your own Customized Partitioner

Total Sort : Globally sort a large amount of data by sampling input files

Secondary sorting

Unit tests with MR Unit

Integrate with Python using the Hadoop Streaming API

 

Click here continue to improve your Knowledge

 

Complete Google Cloud Data Engineer Certification

Google Data Engineera.jpg

This course is a really comprehensive guide to the Google Cloud Platform – it has 25 hours of content and 60 demos.

The Google Cloud Platform is not currently the most popular cloud offering out there – that’s AWS of course – but it is possibly the best cloud offering for high-end machine learning applications. That’s because TensorFlow, the super-popular deep learning technology is also from Google.

What’s Included:

  • Compute and Storage – AppEngine, Container Enginer (aka Kubernetes) and Compute Engine
  • Big Data and Managed Hadoop – Dataproc, Dataflow, BigTable, BigQuery, Pub/Sub
  • TensorFlow on the Cloud – what neural networks and deep learning really are, how neurons work and how neural networks are trained.
  • DevOps stuff – StackDriver logging, monitoring, cloud deployment manager
  • Security – Identity and Access Management, Identity-Aware proxying, OAuth, API Keys, service accounts
  • Networking – Virtual Private Clouds, shared VPCs, Load balancing at the network, transport and HTTP layer; VPN, Cloud Interconnect and CDN Interconnect
  • Hadoop Foundations: A quick look at the open-source cousins (Hadoop, Spark, Pig, Hive and HBase)

Who is the target audience?

Complete Google Data Engineer and Cloud Architect Guide.jpg

  • Yep! Anyone looking to use the Google Cloud Platform in their organizations
  • Yep! Any one who is interesting in architecting compute, networking, loading balancing and other solutions using the GCP
  • Yep! Any one who wants to deploy serverless analytics and big data solutions on the Google Cloud
  • Yep! Anyone looking to build TensorFlow models and deploy them on the cloud
Basic knowledge
  • Basic understanding of technology – superficial exposure to Hadoop is enough.
What you will learn
  • Deploy Managed Hadoop apps on the Google Cloud
  • Build deep learning models on the cloud using TensorFlow
  • Make informed decisions about Containers, VMs and AppEngine
  • Use big data technologies such as BigTable, Dataflow, Apache Beam and Pub/Sub

 

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Easy to Advanced Data Structures

Easy to Advanced Data Structures5.jpg
DESCRIPTION

Data structures are amongst the most fundamental ingredients in the recipe for creating efficient algorithms and good software design. Knowledge of how to create and design good data structures is an essential skill required in becoming an exemplary programmer. This course will teach you how to master the fundamental ideas surrounding data structures.

Learn and master the most common data structures in this comprehensive course:

  • Static and dynamic arrays
  • Singly and doubly linked lists
  • Stacks
  • Queues
  • Heaps/Priority Queues
  • Binary Trees/Binary Search Trees
  • Union find/Disjoint Set
  • Hash tables
  • Fenwick trees
  • AVL trees

Course contents

Easy to Advanced Data Structures 1

This course provides you with high quality animated videos explaining a multitude of data structures and how they are represented visually. You will learn how to code various data structures together with simple to follow step-by-step instructions. Every data structure presented will be accompanied by some working source code (in Java) to solidify your understanding of that particular data structure. I will also be posting various coding exercises and multiple choice questions to ensure that you get some hands on experience.

Who is the target audience?

BASIC KNOWLEDGE
  • Basic computer science knowledge
WHAT YOU WILL LEARN
Easy to Advanced Data Structures4
  • Mature understanding of data structures
  • Algorithms associated with data structures
  • Dynamic arrays
  • Singly and doubly linked list
  • Queues & Stacks
  • Binary Trees and Binary search trees
  • Heaps & Priority queues
  • Union find/Disjoint set
  • Hash table/Associative array
  • Fenwick tree/Binary indexed tree

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