Trending February 2024 # Apache Solr Courses (4+ Hr Of Tutorials, & Apache Solr Certification # Suggested March 2024 # Top 7 Popular

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About Apache Solr Courses Training

Course Name Online Apache Solr Courses

Deal You get access to all 1 courses, Projects bundle. You do not need to purchase each course separately.

Hours 5+ Video Hours

Core Coverage The main aim of this course is to provide a wide understanding of Apache Solr and its functionalities to create applications.

Course Validity Lifetime Access

Eligibility Anyone serious about learning Apache Solr Courses

Pre-Requisites Basic knowledge about Java programming

What do you get? Certificate of Completion for each of the 1 courses, Projects

Certification Type Course Completion Certificates

Verifiable Certificates? Yes, you get verifiable certificates for each course with a unique link. These link can be included in your resume/Linkedin profile to showcase your enhanced skills

Type of Training Video Course – Self Paced Learning

Software Required None

System Requirement 1 GB RAM or higher

Other Requirement Speaker / Headphone

Curriculum

This course has been developed in a manner so that it could cover all the topics that one should master to work very effectively with this technology. This course comprises various demonstrations that have been selected very carefully to meet the expectations of the trainees. We have included all the topics in a single video tutorial to make it precise, efficient, and effective.

Against every topic, there will be a demonstration associated with that which will make it easy for the trainees to grasp the concept and will also help them to get a clear idea of every topic. The examples provided in this course have been selected very carefully so that it could deliver a precise idea by maintaining the simplicity level. The video tutorial is around three hours long and the main focus has been given to practice in this course. In addition to that, the theoretical concepts have also been detailed which could help anyone preparing for the interview for the technology.

The course will start from the unit named Apache Solr Tutorials which is the only unit in this course. All the concepts that we have covered here have been included in this course and you will get to know about various new things in this unit.

At the beginning of this course, you will be getting a brief introduction of what exactly it is and how it can help the businesses in terms of data searching. Once the introduction part is over, you will come across the beginner level concepts that are required to be understood before jumping to learn the medium and higher-level concepts. You will learn how to implement Apache Solr from the very beginning and how it could be configured to work effectively.

There will be some of the new concepts in the unit as we have added all the dependent technologies that endorse the working of Apache Solr. All the concepts that we have covered here have been detailed with the help of simple and precise examples that will make it easy for the trainees to dive deep into each concept. Once you complete this course, you will be able to work with Apache Solr to draft solutions for the problems that could be resolved using evolved searching techniques.

You will also be able to deploy this to handle the production data which is something very critical and useful to the organization. The folks who have opted for this course to prepare for the interview will feel ample confidence to appear there.

Apache Solr Courses- Certificate of Completion

What is Apache Solr?

Apache Solr can be defined as the open-source searching platform that enables one to perform an evolved search to extract accurate data. It can also be defined as the platform that allows one to perform complicated and quick searches based on the information stored in the index. It makes searching very easy by the virtue of indexing that it does. It performs a search for the complete string and also starts suggesting right after getting some inputs. Apache Solr has made it simple to perform a search to get the appropriate required data. It is very simple to work with and allows one to perform complicated queries as well. It can be considered as the platform that stores the data which could be further leveraged to manage it.

What skills will you learn in this Course?

Pre-requisites

For all the courses or training, usually, there is some secondary technology that one should have a good idea to learn the primary technology.

In this course, there are some technologies that one should have a good idea to learn this course. Database fundamentals are the first thing that you should be aware of. As the data is stored here in the way it does in the database, it is very nice to know the fundamentals of this database. It will help you to dive deep into the concepts and will also help you to get close exposure to the topics covered here.

Target Audience

Anyone willing to master this platform can be the best target audience for this course.

The professionals who are working in some other technology and want to master working with this platform can be the best target audience for this course. They will be learning about all the concepts that are expected by the Apache Solr profession to be expert in.

The educators who are already training folks into technologies related to this can also be the best target audience for this course.

FAQ’s- General Questions How long it may take to complete this course?

This course consists of all the topics in the single unit which is almost a three hours long video. One can finish this course right in two days maximum but the effort and time required to master this concept would be pretty long. The folks who are new to the prerequisites of this course may take around one and a half months to complete mastering this platform while the folks who are very familiar with the prerequisites may take around one month to master working with this. Once you are done with this course, you will be able to work very effectively with Apache Solr and will also be able to draft solutions for problems based on searching.

Why should I take this course?

This course is very effective, precise, and has almost everything that is required to work effectively with this platform. This course is a one-stop solution for all the folks who are looking for training on Apache Solr. Also, the educator has detailed everything in very simple words which helps the trainees to understand all the concepts very easily. If you are interested in learning all the concepts that fall under the court of Apache Solr, this course could be the best training choice for you.

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Career Benefits

Reviews

Apache Solr Course

The course is organized very well with a clear and concise explanation of every component of Solr. Every concept has been explained with easy practical implementation. This tutorial is a must if you plan on getting some real-time practice on Solr.

Sneha Rao

Enjoyed!

This course was very good and informative. I learned new skills and new software terminologies. I learned about the tool and its importance. The video was very good. The instructor was good and knowledgeable. The quality of the videos was nice and I will surely recommend this course to anyone who wants to learn app development.

Francisco Gil

Well Explained!

Nice and simple explanation, The tutor also has in-depth knowledge of Solr functionalities and explains very clearly the process and usage. Good and will recommend the same to others and also explained well how Solr got its name which many other online tutors did not explain.

Krishna Reddy

You're reading Apache Solr Courses (4+ Hr Of Tutorials, & Apache Solr Certification

Artificial Intelligence (Ai) In Hr

The use of artificial intelligence (AI) has led to a variety of positive outcomes in human resources (HR) departments.

AI helps HR professionals stay on top of trends, understand employee sentiment, streamline the acquisition of talent, and detect indications of dissatisfaction or imminent departure.

Fewer HR personnel are being asked to cover a larger number of employees.

Workforces have become increasingly dispersed and no longer right under the watchful eye of HR. 

There are many disparate systems offering data and potential inputs with regard to employee behaviors. It takes AI to pull these all together and provide sensible insight in a timely manner. 

See more: Artificial Intelligence Market

The use cases for AI-based HR include: 

Background checks: AI can improve the speed and accuracy of background checks. It can note red flags on resumes and spot indications of falsehood that might otherwise be missed. 

Detection of anomalies: With so many working from home, AI can look beyond simple indicators of who is logged in and who is not. It can spot regular work patterns and anomalies that may mean someone is avoiding work or trying to escape detection. 

Switch from generic to personalized communication: Traditional HR bulletins to all personnel can be transformed via AI. Perhaps the bulletin only needs to go to specific sets of employees. Including the person’s name, position, and other personalization features increases the likelihood of response and engagement. 

Risk management: HR can make use of AI algorithms to determine key personnel who may be at risk of leaving, being headhunted, or need a more defined career path. 

See more: Artificial Intelligence: Current and Future Trends

There are many ways in which companies are using AI in HR: 

Sonia Mathai, chief human resources officer at Globality, said that AI provides major assistance when it comes to 24/7 assistance and availability.

AI-powered chatbots are used to simulate live interaction and answer employee questions about hiring, benefits, training, and more. 

Sparkhound helped a large collision repair chain realize $1 million in turnover costs by addressing employee churn during a phase of high growth.

With almost 700 locations across the country and more than 10,000 employees, turnover at the auto chain reached 40% a year in some regions for key personnel, such as mechanics, painters, and customer support staff.

The result was employee retention and satisfaction rose rapidly, while reducing HR costs and helping increasing revenue.

Sandy Michelet, director of people strategy at Sparkhound, said AI allows HR to transfer time spent on repetitive and administrative tasks to more strategically valuable activities. 

ADP Research Institute (ADPRI) has devised a way to measure HR service quality and uncover the factors that influence the talent brand, intent to leave, and actually depart.

It gathered this data from sources across 25 countries by tracking a number of metrics and indicators. This results in an HR XPerience Score (HRXPS).

The metric has proven useful in determining how employees are twice as likely to value their company when they experience a single point of contact with HR. They are also 7.4 times more likely to say HR is value-promoting when they experience seven interactions with HR compared to no interactions.

The conclusion is that the more HR is engaged with an employee, the more likely the employee is to think well of HR and the company — and that direction impacts retention rates. 

“While companies have always tried to better understand what contributes to the talent brand, we now have a studied metric to effectively measure the HR function,” said Marcus Buckingham, head of people and performance research at the ADP Research Institute.

“Our research found that the HR function is critical to the talent brand — so much that every employee interaction that takes place, specific services used, and a personalized feel with a single point of contact are what influences a higher HRXPS. In fact, this high-ranking, single point of contact upends the current industry trend of doing away with HR.” 

Another area where HR receives material help from AI is automation.

Mathai of Globality noted that with many HR teams trying to do more with less, AI platforms are being used to relieve the burden.

AI is automating transactional and repetitive HR work, freeing them up to focus on tasks that involve direct interaction with personnel. 

See more: Artificial Intelligence and Automation

The administration of benefits is an area that consumes a tremendous amount of HR time.

AI-directed automation addressed to this area can eliminate much manual work and enable HR to better serve the employee base in this area.

Sparkhound implemented this approach internally, according to Michelet. An AI-based chatbot is used to answer benefits-related questions. A feedback button provides continuous improvement to the bot. 

See more: Top Performing Artificial Intelligence Companies

Top 6 Useful Tools For Hr Managers

Human Resources is one of the most crucial departments in a company or an organization. It is the only department that incorporates all the business departments and their employees. The function and influence of Human Resources are vastly diverse and accountable for various pivotal information.

It can be said that such devices have made work life a little easier for the Human Resources Managers. One such wonderful application you can opt for can be Aurion; it’s a magnificent go-to for many HR Managers and more.

Scroll down to know the best form of tools that can be implemented for HR Managers!

Top 6 tools for implementation for HR managers

Here are the productivity tools that can give you the ultimate solution to your problems –

1. Recruitment Tools

The recruitment procedure is a time-consuming process as well as hectic when it’s done manually.

Also read: Top 7 Best ECommerce Tools for Online Business

2. Interview Tools

After recruitment, the next procedure is interviewing the selected candidates. ATS can only help you to shortlist the applicants and list out the most suitable individuals according to your requirements, although it cannot give you the option of face-to-face interaction for an interview.

Video Calling or Video Interviewing Platforms help you to conduct online Interviewing sessions. During the pandemic, such interviewing tools have become a crucial part of the hiring process for the HR department in companies.

3. Skill Assessment Tools

More emphasis has been put upon individual skill proficiency than educational merits by various companies in recent years.

Such judgments require analytical comparisons and tests, considerably done for understanding and evaluating the right applicant for the job.

Also read: Top 10 Helpful GitHub Storage For Web Developers

4. Personality Assessment Tools

Jobs mainly relating to sales and other psychometric industries require specific skills beyond work experience, educational merits, and an overwhelming CV. These particular skills are assessed by digital HR tools called psychometric assessments.

Many platforms or applications are available in the market with extensive aptitude, psychometric, and personality tests for the candidates.

Such tests are designed and supervised by experienced professionals in that field. These tools help you to pick out the best individuals relating to your needs.

5. Employee Management Tools

Employee Management Tools are essential for any company to keep a check on the morale of the employees. There are certain benefits, bonuses, commissions that are often promised to the employees by the company.

Moreover, it is not easy to ascertain that the different benefits that might have been given to the employees are available for them to check if it is done manually. And that can cause a challenging situation.

Also read: How To Make 5K Dollars In A Month? 20+ Easy Ways To Make $5,000 Fast + Tips!

6. Employee Onboarding Tools

Employee Onboarding Tools help to board the teams, subgroups, and leaders according to the right circumstances easily. It increases workforce readiness, flexibility and transforms the company maintenance perfectly with the situations that may arise.

Final Thoughts

In the current age, which is surrounded by the pandemic and the rise of the digital world, software tools are a must upgrade to improve the efficiency of the company and the HR department. To stay at the top of the industry, adapting to developments and technology is essential to ascertain that top spot.

Benefits Of Digital Transformation: 4 Key Areas Of Digital Transformation

Have an insight into the 4 key areas of digital transformation.

The incorporation of digital technology into all aspects of a business is known as digital transformation. It has a significant impact on the way a company works. This method is being used by firms to restructure their operations to make them more effective and lucrative. Ninety percent of businesses operate in the cloud. Much of what they’re doing as they shift more data to the cloud is replicating current services in a digital version. True digital transformation entails much more. It establishes a technological foundation for transforming these services and data into actionable insights that may help a company improve almost every aspect of its operations. It enables for the reimagining of processes and procedures to work together wisely to deliver more comprehensive business insight, rather than just moving data to the cloud.  

Why is Digital Transformation Important?

As a result of the digital transformation, the way a firm operates is transforming. Every system, method, workflow, and culture is scrutinized. This transformation impacts every level of a company and brings data from many departments together so that they may collaborate more efficiently. Companies may read between the lines on the customer experience in ways that weren’t feasible previously by utilizing workflow automation and sophisticated processing, like artificial intelligence (AI) and machine learning (ML).  

8 Benefits of Digital Transformation

1. Enhanced data collection It establishes a method for various organizational functional units to convert raw data into insights across many touchpoints. It creates a single perspective of the customer experience, operations, production, finances, and business possibilities as a result of this.   2. Greater resource management Through digital transformation, information and services are unified into a set of business solutions. Rather than having disparate software and databases, it puts all of the organization’s assets together in one location. In 2023, enterprise firms will utilize an average of 900 apps. As a result, maintaining a consistent experience is difficult. Digital transformation may bring together applications, datasets, and software into a single corporate intelligence repository. There is no such thing as a digital transformation ministry or functional unit. It has an impact on every element of a business and may lead to process innovation and increased efficiency across divisions.   3. Data-driven customer insights Customer insights may be unlocked through data. You may build a customer-centric company plan by better knowing your customers and their demands. These insights can assist promote business success by combining structured data (individual customer information) with unstructured data (social media analytics). Data enables strategies to deliver information that is more relevant, customized, and adaptable.   4. Better customer experience When it pertains to their experience, customers have high expectations. Customers have grown accustomed to having an abundance of options, reasonable pricing, and quick delivery. The next battleground is customer experience (CX). More than 2-3rds of firms claim to concentrate primarily on customer satisfaction, as per Gartner. They predict that by 2023, that figure will have risen to 81 percent.   5. Encourages a digital culture Digital transformation fosters a digital culture by giving team members the proper tools, suited to their context. While these technologies make communication simple, they also aid in the digital transformation of the entire business. In the future, this digital culture will become even more essential. To realize the benefits of digitalization, team members must be upskilled and digitally educated.   6. Increased agility As a result of the digital transformation, businesses have become more agile. Businesses may boost their flexibility with digital transformation to increase performance and implement Continuous Improvement (CI) methods by borrowing from the field of software development. This allows for faster innovation and adaptation, as well as a path to growth.   7. Improved productivity Having the appropriate IT tools that operate together may help you boost productivity and optimize your workflow. It allows team members to work more effectively by automating numerous tedious activities and connecting data across the company.  

4 Key Areas of Digital Transformation

Technology The raw potential of new technologies is astonishing, from the Internet of Things to blockchain, data lakes, and artificial intelligence. Although many of them are getting easier to use, it is exceedingly difficult to comprehend how any given technology contributes to transformative opportunity, adapt that technology to the unique needs of the organization, and integrate it with current systems.   Data However, the majority of data in many companies worldwide fails to fulfill basic standards, and the demands of transformation need considerably enhanced data quality and analytics. Knowing different forms of unstructured data, massive amounts of data from outside your company, leveraging confidential data, and incorporating everything all while purging enormous amounts of data that will never be used is almost certainly part of the transformation process.   Process Transformation necessitates an end-to-end mentality, a reassessment of how to fulfill customer demands, the capacity to manage across silos in the future, and seamless integration of work processes. A process-oriented approach is best suited to these objectives.   Organizational Change Capability Leadership, teamwork, bravery, emotional intelligence, and other aspects of change management are included in this area. Luckily, there has been a lot published about this topic for many years, so we won’t go over it here except to say that anybody in charge of digital transformation needs to be well-versed in it.  

Conclusion

Know How To Step Up And Configure Apache Hadoop

Overview of Install Hadoop

Install Hadoop involves installing and configuring the related software and utilities associated with the Hadoop framework. Hadoop is an open-source framework which Licensed from Apache software foundation for big data processing. First, Java needs to be installed on the system. Hadoop is installed on a Linux Operating system like CentOS for its computation. After setting up the Java in the environment, the Hadoop package software that is downloaded through The Apache website needs to be installed. The related Name Node configurations the configuration XML is known as chúng tôi and for data nodes, the chúng tôi needs to be configured alongside chúng tôi for resource management.

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Hadoop, Data Science, Statistics & others

Hadoop Framework

The Apache Hadoop framework consists of the following key modules:

Apache Hadoop Common

Apache Hadoop Distributed File System (HDFS)

Apache Hadoop MapReduce

Apache Hadoop YARN (Yet Another Resource Manager)

1. Apache Hadoop Common

Apache Hadoop Common module consists of shared libraries that are consumed across all other modules, including key management, generic I/O packages, libraries for metric collection, and utilities for the registry, security, and streaming.

2. HDFS

The HDFS is based on the Google file system and is structured to run on low-cost hardware. In addition, HDFS is tolerant of faults and is designed for applications having large datasets.

3. MapReduce

MapReduce is an inherent parallel programming model for data processing, and Hadoop can run MapReduce programs written in various languages such as Java. MapReduce works by splitting the processing into the map phase and reduces the phase.

4. Apache Hadoop YARN

Apache Hadoop YARN is a core component, resource management, and job scheduling technology in the Hadoop distributed processing framework.

Steps to Install Hadoop

The following is a summary of the tasks involved in the configuration of Apache Hadoop:

Task 1: The first task in the Hadoop installation included setting up a virtual machine template that was configured with Cent OS7. Packages such as Java SDK 1.8 and Runtime Systems required to run Hadoop were downloaded, and the Java environment variable for Hadoop was configured by editing bash_rc.

Task 2: Hadoop Release 2.7.4 package was downloaded from the apache website and was extracted in the opt folder. Which was then renamed as Hadoop for easy access.

Task 3: Once the Hadoop packages were extracted, the next step included configuring the environment variable for the Hadoop user, followed by configuring Hadoop node XML files. In this step, NameNode was configured within chúng tôi and DataNode was configured within chúng tôi Finally, the resource manager and node manager were configured within yarn-site.xml.

Task 5: The next few steps were used to verify and test Hadoop. For this, we have created a temporary test file in the input directory for the WordCount program. Then, the map-reduce program chúng tôi was used to count the number of words in the file. Finally, results were evaluated on the localhost, and logs of the submitted application were analyzed. All MapReduce applications submitted can be viewed at the online interface, the default port number being 8088.

Task 6: We will introduce some basic Hadoop File System commands and check their usages in the final task. We will see how a directory can be created within the Hadoop file system to list the content of a directory, its size in bytes. We will further see how to delete a specific directory and file.

Results in Hadoop Installation

The following shows the results of each of the above tasks:

Result of Task 1:

A new virtual machine with a cenOS7 image has been configured to run Apache Hadoop. Figure 1 shows how CenOS 7 image was configured in the Virtual machine. Figure 1.2 shows the JAVA environment variable configuration within .bash_rc.

Virtual machine configuration

Java environment variable configuration

Result of Task 2:

Figure shows the task carried out to extract the Hadoop package into the opt folder.

Extraction of hadoop 2.7.4 package

Result of Task 3:

Figure shows the configuration for the environment variable for Hadoop user, Figure shows the configuration for XML files required for Hadoop configuration.

Configuring the environment variable for Hadoop user.

Configuration of core-site.xml.

Configuration of hdfs-site.xml.

Configuration of chúng tôi file.

Configuration of chúng tôi file.

Result of Task 4:

Figure shows the usage of the jps command to check relevant daemons are running in the background and the following figure shows Hadoop’s online user Interface.

jps command to verify running daemons.

Result of Task 5:

Figure shows the result for the MapReduce program called wordcount, which counts the number of words in the file. The next couple of figures displays the YARN resource manager’s online user interface for the submitted task.

MapReduce program results

Submitted Map-reduce application.

Logs for submitted MapReduce application.

Result of Task 6:

Figure shows how to create a directory within the Hadoop file system and perform a listing of the hdfs directory.

Creating a directory within the Hadoop file system.

Creating a file in HDFS.

New file created.

The next few figures show how to list the contents of particular directories:

Content of dir A

Content of dir B

The next figure shows how file and directory size can be displayed:

Display a file and directory size.

Deleting a directory or a file can be easily accomplished by the -rm command.

To delete a file.

Conclusion

Big Data has played a very important role in shaping today’s world market. Hadoop framework makes data analyst’s life easy while working on large datasets. The configuration of Apache Hadoop was quite simple, and the online user interface provided the user with multiple options to tune and manage the application. Hadoop has been used massively in organizations for data storage, machine learning analytics and backing up data. Managing a large amount of data has been quite handy because of Hadoop distributed environment and MapReduce. Hadoop development was pretty amazing when compared to relational databases as they lack tuning and performance options. Apache Hadoop is a user-friendly and low-cost solution for managing and storing big data efficiently. HDFS also goes a long way in helping in storing data.

Recommended Articles

This is a guide to Install Hadoop. Here we discuss the introduction to install hadoop, step-by-step installation of hadoop, and hadoop installation results. You can also go through our other suggested articles to learn more –

Comprehensive Introduction To Apache Spark, Rdds & Dataframes (Using Pyspark)

Introduction

Industry estimates that we are creating more than 2.5 Quintillion bytes of data every year.

Think of it for a moment – 1 Qunitillion = 1 Million Billion! Can you imagine how many drives / CDs / Blue-ray DVDs would be required to store them? It is difficult to imagine this scale of data generation even as a data science professional. While this pace of data generation is very exciting,  it has created entirely new set of challenges and has forced us to find new ways to handle Big Huge data effectively.

Big Data is not a new phenomena. It has been around for a while now. However, it has become really important with this pace of data generation. In past, several systems were developed for processing big data. Most of them were based on MapReduce framework. These frameworks typically rely on use of hard disk for saving and retrieving the results. However, this turns out to be very costly in terms of time and speed.

On the other hand, Organizations have never been more hungrier to add a competitive differentiation through understanding this data and offering its customer a much better experience. Imagine how valuable would be Facebook, if it did not understand your interests well? The traditional hard disk based MapReduce kind of frameworks do not help much to address this challenge.

In this article, I will introduce you to one such framework, which has made querying and analysing data at a large scale much more efficient than previous systems / frameworks – Read on!

P.S. This article is meant for complete beginners on the topic and presumes minimal prior knowledge in Big Data

Table of Contents

Challenges while working with Big Data

Introduction to Distributed Computing Framework

What is Apache Spark?

History of Spark

Common terms used

Benefits of Spark over traditional big data frameworks

Installation of Apache Spark (with Python)

Python vs Scala

Getting up to speed with RDD / Dataframe / Dataset

Solving a machine learning problem

Challenges while working with big data

Challenges associated with big data can be classified in following categories:

Challenges in data capturing: Capturing huge data could be a tough task because of large volume and high velocity. There are millions of sources emanating data at high speed. To deal with this challenge, we have created devices which can capture the data effectively and efficiently. For example, sensors which not only sense data like temperature of a room, steps count, weather parameters in real time, but send this information directly over to cloud for storage.

Challenges with data storage: Given the increase in data generation, we need more efficient ways to store data. This challenge is typically dealt by combination of various methods including increasing disk sizes, compressing the data and using multiple machines, which are connected to each other and can share data efficiently.

Challenges with Querying and Analysing data: This is probably the most difficult task at hand. The task is to not only to retrieve the past data, but also coming out with insights in real time (or as little time as possible). To handle this challenge, we can look at several options. One options is to increase the processing speed. However, this normally comes with increase in cost and can not scale as much. Alternately, we can build a network of machines or nodes known as “Cluster”. In this scenario, we first break a task to sub-tasks and distribute them to different nodes. At the end, we aggregate the output of each node to have final output. This distribution of task is known as “Distributed Computing”

Now that I have spoken of Distributed computing, let us get a bit deeper into it!

What is Distributing Computing Framework?

In simple terms, distributed computing is just a distributed system, where multiple machines are doing certain work at the same time. While doing the work, machines will communicate with each other by passing messages between them. Distributed computing is useful, when there is requirement of fast processing (computation) on huge data.

Let us take a simple analogy to explain the concept. Let us say, you had to count the number of books in various sections of a really large library. And you have to finish it in less than an hour. This number has to be exact and can not be approximated. What would you do? If I was in this position, I would call up as many friends as I can and divide areas / rooms among them. I’ll divide the work in non-overlapping manner and ask them to report back to be in 55 minutes. Once they come back, I’ll simply add up the numbers to come up with a solution. This is exactly how distributed computing works.

MapReduce is also used widely, when the task is to process huge amounts of data, in parallel (more than one machines are doing a certain task at the same time), on large clusters. You can learn more about MapReduce from this link.

What is Apache Spark?

Please note that Apache Spark is not a replacement of Hadoop. It is actually designed to run on top of Hadoop.

History of Apache Spark

Apache Spark was originally created at University of California, Berkeley’s AMPLab in 2009. The Spark code base was later donated to the Apache Software Foundation. Subsequently, it was open sourced in 2010. Spark is mostly written in Scala language. It has some code written in Java, Python and R. Apache Spark provides several APIs for programmers which include Java, Scala, R and Python.

Key terms used in Apache Spark:

Spark Context: It holds a connection with Spark cluster manager. All Spark applications run as independent set of processes, coordinated by a SparkContext in a program.

Driver and Worker: A driver is incharge of the process of running the main() function of an application and creating the SparkContext. A worker, on the other hand, is any node that can run program in the cluster. If a process is launched for an application, then this application acquires executors at worker node.

How Apache Spark is better than traditional big data framework?

Spark uses in-memory computations to speed up 100 times faster than Hadoop framework.

In Hadoop, tasks are distributed among the nodes of a cluster, which in turn save data on disk. When that data is required for processing, each node has to load the data from the disk and save the data into disk after performing operation. This process ends up adding cost in terms of speed and time, because disk operations are far slower than RAM operations. It also requires time to convert the data in a particular format when writing the data from RAM to disk. This conversion is known as Serialization and reverse is Deserialization.

Language Support: Apache Spark has API support for popular data science languages like Python, R, Scala and Java.

Supports Real time and Batch processing: Apache Spark supports “Batch data” processing where a group of transactions is collected over a period of time. It also supports real time data processing, where data is continuously flowing from the source. For example, weather information coming in from sensors can be processed by Apache Spark directly.

Lazy operation: Lazy operations are used to optimize solutions in Apache Spark. I will discuss about lazy evaluation in later part of this article. For now, we can think that there are some operations which do not execute until we require results.

Installation of Apache Spark with PySpark

We can install Apache Spark in many different ways. Easiest way to install Apache Spark is to start with installation on a single machine. Again, we will have choices of different Operating Systems. For installing in a single machine, we need to have certain requirements fulfilled. I am sharing steps to install for Ubuntu (14.04) for Spark version 1.6.0. I am installing Apache Spark with Python which is known as PySpark (Spark Python API for programmer). If you are interested in the R API SparkR, have a look at this learning path.

OS: Ubuntu 14.04, 64 bit . (If you are running on Windows or Mac and are new to this domain, I would strongly suggest to create a Virtual Ubuntu machine with 4 GB RAM and follow the rest of the process).

Softwares Required: Java 7+, Python 2.6+, R 3.1+

Installation Steps:

Step 0: Open the terminal.

Step 1: Install Java

$ sudo apt-add-repository ppa:webupd8team/java $ sudo apt-get update $ sudo apt-get install oracle-java7-installer $ java -version

Step 2 : Once Java is installed, we need to install Scala

$ sudo dpkg -i scala-2.11.7.deb $ scala –version

This will show you the version of Scala installed

Step 3: Install py4j

Py4J is used on the driver for local communication between the Python and Java SparkContext objects; large data transfers are performed through a different mechanism.

$ sudo pip install py4j

Step 4: Install Spark.

By now, we have installed the dependencies which are required to install Apache Spark. Next, we need to download and extract Spark source tar. We can get the latest version Apache Spark using wget:

$ tar xvf spark-1.6.0.tgz

Step 5: Compile the extracted source

 sbt is an open source build tool for Scala and Java projects which is similar to Java’s Maven.

$ sbt/sbt assembly

This will take some time to install Spark. After installing, we can check whether Spark is running correctly or not by typing.

$ ./bin/run-example SparkPi 10

this will produce the output:

Pi is roughly 3.14042

To see the above results we need to lower the verbosity level of the log4j logger in log4j.properties.

$ cp conf/log4j.properties.template conf/log4j.properties $ nano conf/log4j.properties

log4j.rootCategory=INFO, console

log4j.rootCategory=ERROR, console

Step 6: Move the files in the right folders (to make it convenient to access them)

$ sudo ln -s /opt/spark-1.6.0 /opt/spark

Add this to your path by editing your bashrc file:

Step 7: Create environment variables. To set the environment variables, open bashrc file in any editor.

$ nano ~/.bashrc

Set the SPARK_HOME and PYTHONPATH by adding following lines at the bottom of this file

export

SPARK_HOME=/opt/spark

export

PYTHONPATH=

$SPARK_HOME

/python

Next, restart bashrc by typing in:

$ . ~/.bashrc

Let’s add  this setting for ipython by creating a new python script to automatically export settings, just in case above change did not work.

$ nano

~

/.ipython/profile_default/startup/load_spark_environment_variables.py

Paste some lines in this file.

import

os

import

sys

if

'

SPARK_HOME

'

not

in

os.environ: os.environ[

'

SPARK_HOME

'

]

=

'

/opt/spark

'

if

'

/opt/spark/python

'

not

in

sys.path: sys.path.insert(

0

,

'

/opt/spark/python

'

)

Step 8: We are all set now. Let us start PySpark by typing command in root directory:

$ ./bin/pyspark --packages

We can also start ipython notebook in shell by typing:

$ PYSPARK_DRIVER_PYTHON=ipython ./bin/pyspark

When we launch the shell in PySpark, it will automatically load spark Context as sc and SQLContext as sqlContext.

Python vs Scala:

One of the common question people ask is whether it is necessary to learn Scala to learn Spark? If you are some one who already knows Python to some extent or are just exploring Spark as of now, you can stick to Python to start with. However, if you want to process some serious data across several machines and clusters, it is strongly recommended that you learn Scala. Computation speed in Python is much slower than Scala in Apache Spark.

Scala is native language for Spark (because Spark itself written in Scala).

Scala is a compiled language where as Python is an interpreted language.

Python has process based executors where as Scala has thread based executors.

Python is not a JVM (java virtual machine) language.

Apache Spark data representations: RDD / Dataframe / Dataset

Spark has three data representations viz RDD, Dataframe, Dataset. For each data representation, Spark has a different API. For example, later in this article I am going to use ml (a library), which currently supports only Dataframe API. Dataframe is much faster than RDD because it has metadata (some information about data) associated with it, which allows Spark to optimize query plan. Refer to this link to know more about optimization. The Dataframe feature in Apache Spark was added in Spark 1.3. If you want to know more in depth about when to use RDD, Dataframe and Dataset you can refer this link.

In this article, I will first spend some time on RDD, to get you started with Apache Spark. Later, I will spend some time on Dataframes. Dataframes share some common characteristics with RDD (transformations and actions). In this article, I am not going to talk about Dataset as this functionality is not included in PySpark.

RDD:

After installing and configuring PySpark, we can start programming using Spark in Python. But to use Spark functionality, we must use RDD. RDD (Resilient Distributed Database) is a collection of elements, that can be divided across multiple nodes in a cluster to run parallel processing. It is also fault tolerant collection of elements, which means it can automatically recover from failures. RDD is immutable, we can create RDD once but can’t change it. We can apply any number of operation on it and can create another RDD by applying some transformations. Here are a few things to keep in mind about RDD:

We can apply 2 types of operations on RDDs:

Action: Actions refer to an operation which also apply on RDD that perform computation and send the result back to driver.

Example: Map (Transformation) performs operation on each element of RDD and returns a new RDD. But, in case of Reduce (Action), it reduces / aggregates the output of a map by applying some functions (Reduce by key). There are many transformations and actions are defined in Apache Spark documentation, I will discuss these in a later article.

Accumulator: In Accumulator variables are used for aggregating the information.

How to Create RDD in Apache Spark

Existing storage: When we want to create a RDD though existing storage in driver program (which we would like to be parallelized). For example, converting a list to RDD, which is already created in a driver program.

External sources: When we want to create a RDD though external sources such as a shared file system, HDFS, HBase, or any data source offering a Hadoop Input Format.

Writing first program in Apache Spark

I have already discussed that RDD supports two type of operations, which are transformation and action. Let us get down to writing our first program:

Step1: Create SparkContext

First step in any Apache programming is to create a SparkContext. SparkContext is needed when we want to execute operations in a cluster. SparkContext tells Spark how and where to access a cluster. It is first step to connect with Apache Cluster. If you are using Spark Shell, we will find that this is already created. Otherwise, we can create the Spark Context by importing, initializing and providing the configuration settings. For example:

from pyspark import SparkContext sc = SparkContext()

Step2: Create a RDD

I have already discussed that we can create RDD in two ways: Either from an existing storage or from an external storage. Let’s create our first RDD. SparkContext has parallelize method, which is used for creating the Spark RDD from an iterable (like list, tuple..) already present in driver program.

Lets create the first Spark RDD called rdd.

data = range(1,1000) rdd = sc.parallelize(data)

We have a collect method to see the content of RDD.

rdd.collect()

To see the first n element of a RDD we have a method take:

rdd.take(2) # It will print first 2 elements of rdd

We have 2 parallel operations in RDD which are Transformation and Action. Transformation and Action were already discussed briefly earlier. So let’s see how transformation works. Remember that RDDs are immutable – so we can’t change our RDD, but we can apply transformation on it. Let’s see an example of map transformation to demonstrate how transformation works.

Step 3: Map transformation.

Map transformation returns a Mapped RDD by applying function to each element of the base RDD. Let’s repeat the first step of creating a RDD from existing source, For example,

data = ['Hello' , 'I' , 'AM', 'Ankit ', 'Gupta'] Rdd = sc.parallelize(data)

Now a RDD (name is ‘Rdd’) is created from the existing source, which is a list of string in a driver program. We will now apply lambda function to each element of Rdd and return the mapped (transformed) RDD (word,1) pair in the Rdd1.

Rdd1 = Rdd.map(lambda x: (x,1))

Let’s see the out of this map operation.

Rdd1.collect() output: [('Hello', 1), ('I', 1), ('AM', 1), ('Ankit ', 1), ('Gupta', 1)]

If you noticed, nothing happened after applying the lambda function on Rdd1 (we won’t see any computation happening in a cluster). This is called the lazy operation. All transformation operations in Spark are lazy, which means that we will not see any computations on RDD, until we need them for further action.

Spark remembers which transformation is applied to which RDD with the help of DAG (Directed a Cyclic Graph). The lazy evaluation helps Spark to optimize the solution because Spark will get time to see the DAG before actually executing the operations on RDD. This enables Spark to run operations more efficiently.

In the code above, collect() and take() are the examples of an action.

There are many number of transformation defined in Apache Spark. We will talk more about them in a future post.

Solving a machine learning problem:

We have covered a lot of ground already. We started with understanding what Spark brings to the table, its data representations, installed Spark and have already played with our first RDD. Now, I’ll demonstrate solution to “Practice Problem: Black Friday” using Apache Spark. Even if you don’t understand these commands completely as of now, it is fine. Just follow along, we will take them up again in a future tutorial.

Let’s look at the steps:

Reading a data file (csv)

For reading the csv file in Apache Spark, we need to specify the library in python shell. Lets read the the data from a csv files to create the Dataframe and apply some data science skills on this Dataframe like we do in Pandas.

For reading the csv file, first we need to download Spark-csv package (Latest) and extract this package into the home directory of Spark. Then, we need to open a PySpark shell and include the package (I am using “spark-csv_2.10:1.3.0”).

$ ./bin/pyspark --packages com.databricks:spark-csv_2.10:1.3.0

In Apache Spark, we can read the csv file and create a Dataframe with the help of SQLContext. Dataframe is a distributed collection of observations (rows) with column name, just like a table. Let’s see how can we do that.

Please note that since I am using pyspark shell, there is already a sparkContext and sqlContext available for me to use. In case, you are not using pyspark shell, you might need to type in the following commands as well:

sc = sparkContext() sqlContext = SQLContext(sc)

First download the train and test file and load these with the help of SparkContext

train = sqlContext.load(source="com.databricks.spark.csv", path = 'PATH/train.csv', header = True,inferSchema = True) test = sqlContext.load(source="com.databricks.spark.csv", path = 'PATH/test-comb.csv', header = True,inferSchema = True)

PATH is the location of folder, where your train and test csv files are located. Header is True, it means that the csv files contains the header. We are using inferSchema is True for telling sqlContext to automatically detect the data type of each column in data frame. If we do not set inferSchema to true, all columns will be read as string.

Analyze the data type

To see the types of columns in Dataframe, we can use the method printSchema(). Lets apply printSchema() on train which will Print the schema in a tree format.

train.printSchema()

Previewing the data set

To see the first n rows of a Dataframe, we have head() method in PySpark, just like pandas in python. We need to provide an argument (number of rows) inside the head method. Lets see first 10 rows of train:

train.head(10)

To see the number of rows in a data frame we need to call a method count(). Lets check the number of rows in train. The count method in pandas and Spark are different.

train.count()

Impute Missing values

We can check number of not null observations in train and test by calling drop() method. By default, drop() method will drop a row if it contains any null value. We can also pass ‘all” to drop a row only if all its values are null.

train.na.drop().count(),test.na.drop('any').count()

Here, I am imputing null values in train and test file with -1. Imputing the values with -1 is not an elegant solution. We have several algorithms / techniques to impute null values, but for the simplicity I am imputing null with constant value (-1). We can transform our base train, test Dataframes after applying this imputation. For imputing constant value, we have fillna method. Lets fill the -1 in-place of null in all columns.

train = train.fillna(-1) test = test.fillna(-1)

Analyzing numerical features

We can also see the various summary Statistics of a Dataframe columns using describe() method, which shows statistics for numerical variables. To show the results we need to call show() method.

train.describe().show()

Sub-setting Columns

Let’s select a column called ‘User_ID’ from a train, we need to call a method ‘select’ and pass the column name which we want to select. The select method will show result for selected column. We can also select more than one column from a data frame by providing columns name separated by comma.

train.select('User_ID').show()

Analyzing categorical features

To start building a model, we need to see the distribution of categorical features in train and test. Here I am showing this for only Product_ID but we can also do the same for any categorical feature. Let’s see the number of distinct categories of “Product_ID” in train and test. Which we can do by applying methods distinct() and count().

train.select('Product_ID').distinct().count(), test.select('Product_ID').distinct().count() Output:(3631, 3491)

After counting the number of distinct values for train and test we can see the train has more categories than test. Let us check what are the categories for Product_ID, which are in test but not in train by applying subtract chúng tôi can also do the same for all categorical feature.

diff_cat_in_train_test=test.select('Product_ID').subtract(train.select('Product_ID')) diff_cat_in_train_test.distinct().count()# For distict count Output: 46

Above you can see that 46 different categories are in test not in train. In this case, either we collect more data about them or skip the rows in test for those categories(invalid category) which are not in train.

Transforming categorical variables to labels

We also need to transform categorical columns to label by applying StringIndexer Transformation on Product_ID which will encode the Product_ID column of labels to a column of label indices. You can see more about this from the link

from pyspark.ml.feature import StringIndexer plan_indexer = StringIndexer(inputCol = 'Product_ID', outputCol = 'product_ID') labeller = plan_indexer.fit(train)

Above, we build a ‘labeller’ by applying fit() method on train Dataframe. Later we will use this ‘labeller’ to transform our train and test. Let us transform our train and test Dataframe with the help of labeller. We need to call transform method for doing that. We will store the transformation result in Train1 and Test1.

Train1 = labeller.transform(train) Test1 = labeller.transform(test)

Lets check the resulting Train1 Dataframe.

Train1.show()

The show method on Train1 Dataframe will show that we successfully added one transformed column product_ID in our previous train Dataframe.

Selecting Features to Build a Machine Learning Model

Let’s try to create a formula for Machine learning model like we do in R. First, we need to import RFormula from the pyspark.ml.feature. Then we need to specify the dependent and independent column inside this formula. We also have to specify the names for features column and label column.

from pyspark.ml.feature import RFormula formula = RFormula(formula="Purchase ~ Age+ Occupation +City_Category+Stay_In_Current_City_Years+Product_Category_1+Product_Category_2+ Gender",featuresCol="features",labelCol="label")

After creating the formula we need to fit this formula on our Train1 and transform Train1,Test1 through this formula. Lets see how to do this and after fitting transform train1,Test1 in train1,test1.

t1 = formula.fit(Train1) train1 = t1.transform(Train1) test1 = t1.transform(Test1)

We can see the transformed train1, test1.

train1.show()

After applying the formula we can see that train1 and test1 have 2 extra columns called features and label those we have specified in the formula (featuresCol=”features” and labelCol=”label”). The intuition is that all categorical variables in the features column in train1 and test1 are transformed to the numerical and the numerical variables are same as before for applying ML. Purchase variable will transom to label column. We can also look at the column features and label in train1 and test1.

train1.select('features').show() train1.select('label').show()

Building a Machine Learning Model: Random Forest

After applying the RFormula and transforming the Dataframe, we now need to develop the machine learning model on this data. I want to apply a random forest regressor for this task. Let us import a random forest regressor, which is defined in pyspark.ml.regression and then create a model called rf. I am going to use default parameters for randomforest algorithm.

from pyspark.ml.regression import RandomForestRegressor rf = RandomForestRegressor()

After creating a model rf we need to divide our train1 data to train_cv and test_cv for cross validation.

Here we are dividing train1 Dataframe in 70% for train_cv and 30% test_cv.

(train_cv, test_cv) = train1.randomSplit([0.7, 0.3])

Now build the model on train_cv and predict on test_cv. The results will save in  predictions.

model1 = rf.fit(train_cv) predictions = model1.transform(test_cv)

If you check the columns in predictions Dataframe, there is one column called prediction which has prediction result for test_cv.

model1 = rf.fit(train_cv) predictions = model1.transform(test_cv)

Lets evaluate our predictions on test_cv and see what is the mean squae error.

To evaluate model we need to import RegressionEvaluator from the pyspark.ml.evaluation. We have to create an object for this. There is a method called evaluate for evaluator which will evaluate the model. We need to specify the metrics for that.

from pyspark.ml.evaluation import RegressionEvaluator evaluator = RegressionEvaluator() mse = evaluator.evaluate(predictions,{evaluator.metricName:"mse" }) import numpy as np np.sqrt(mse), mse

After evaluation we can see that our root mean square error is 3773.1460883883865 which is a square root of mse.

Now, we will implement the same process on full train1 dataset.

model = rf.fit(train1) predictions1 = model.transform(test1)

After prediction, we need to select those columns which are required in Black Friday competition submission.

df = predictions1.selectExpr("User_ID as User_ID", "Product_ID as Product_ID", 'prediction as Purchase')

Now we need to write the df in csv format for submission.

df.toPandas().to_csv('submission.csv')

After writing into the csv file(submission.csv). We can upload our first solution to see the score, I got the score 3822.121053 which is not very bad for first model out of Spark!

End Note:

In this article, I introduced you to the fascinating world of Apache Spark. This is only the start of things to come in this series. In the next few weeks, I will continue to share tutorials for you to master the use of Apache Spark. If this article feels like a lot of work, it is! So, take your time and digest this comprehensive guide.

In the meanwhile, if you have any questions or you want to give any suggestions on what I should cover, feel free to drop them in the notes below.

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