Big Data to Data Science: Career Transition

Big Data to Data Science: Career Transition

Career Transition to Data Science Success Story

Knowing ML algorithms is not enough. In-depth understanding to build ML models is more important.

– Darshan Jayanna

Background

Education: B.E. Electronics

Previous Profile

Company: Accenture 
Profile: Big Data Engineer 
Domain: Healthcare & Public Sector 
Location: Hyderabad
Experience: 1.5 Years

Current Profile

Company: The Math Co.
Profile: Data Scientist
Domain: IT 
Location: Bangalore

My journey into Data Science

Why Data Science?

Everyone in the IT industry knows Machine Learning is the future. Everything will be automated soon and if you can’t catch up, your job will be outdated soon. I realized and accepted that I will have to buck up. Coming from Big Data background, Data Science was a simple choice.

Why Dimensionless?

Honestly, I thought I could learn through free courses, so for almost a year, I tried to learn by myself online. I know I wasted a year, but I guess every IT person must’ve tried learning that way while getting to know new technology. Though there was a lot of knowledge around, it’s not in-depth. Like they give you the algorithms to study, but no one tells you the maths and logic behind those algos. That’s why I wanted a proper coaching class where someone can go in a step deeper and teach me the logic.

Also, since I was working, I wanted something online and something that did not cost a lot.

After checking plenty of courses, I came across Dimensionless. The reviews were all-star, the fee was reasonable, and the course curriculum and duration looked justified. I attended the Demo, it was quite impressive. For me, the decision-making point was that they started the course with the basics of Stats and Programming, so I could start learning from scratch.

Experience with Dimensionless?

The program was very flexible and highly interactive. Every day I had the option to join the class in the morning or the evening. Later a recording of the class was also available even if I missed my class. And 3 hours of self-study on the weekend was more than enough to cover up the weekly syllabus.

Frankly, implementation of ML on data does not involve much coding, rather it is about the logic behind the algorithm. Dimensionless helped me understand what’s going on in the backend.

Career Transition to Data Science

Once I was comfortable in the course, the transition felt natural. And after solving case studies under the guidance of Dimensionless I was able to smoothly switch to a Data Science profile within the company itself.

To start my career transition, first I got a project with Dimensionless and got some hands-on experience along with my job.

Initially, I was trying to transition within my company to gain some experience and feel more confident but I was not getting a release from the project due to company policies. But I did not want to get stuck and decided to move out. So I started giving interviews outside and actually ended with almost 100% hike in my salary.

The interviewers mostly asked questions on understanding of these algorithms. There were 2-3 hours of theory at Dimensionless before covering the algorithms, this made it simpler to understand and implement the algorithms. It helped me a lot during the interviews.

Through Dimensionless, I got selected in Motilal Oswal as a Data Scientist in Mumbai. Later I got 2 more offers, one from ePay and other from The Math Co., and as you already know…
I took the one at The Math Co. with a One Hundred Percent salary hike!

I strongly believe that as success comes to those who reach for it, similarly career-growth comes by constantly upgrading oneself.


Big Data and Potential Career Opportunities

Big Data and Potential Career Opportunities

Big Data is the term that is circling everywhere in the field of analytics in the modern era. The rise of this term came about as the result of the enormous volume of unstructured data that is getting generated from a plethora of sources. Such voluminous unstructured data carries huge information which if mined properly could help a business achieve groundbreaking results.

Hence, it’s a wide range of applications has made Big Data popular among masses and everyone wants to master the skill associated with it to embrace the lucrative career opportunities that lie ahead.  For the data professionals, many companies have various open positions in the job market and the number is only going to increase in the future.

Reason of the craze behind Big Data

The opportunities in the domain of Big Data is diverse and hence its craze is spreading rapidly among professionals from different fields like Banking, Manufacturing, Insurance, Healthcare, E-Commerce, and so on.  Below are some of the reasons why its demand keeps on rising.

  • Talent shortage in Big data – Despite its every increasing opportunity, there is a significant shortage in the number of professionals who are actually trained to work in this field. Those who work in IT are generally accustomed to software development or testing, while people from other fields are familiar with spreadsheets, databases and so on.

However, the required skill to load and mine Big Data is missing significantly which makes it the job which is up for grabs for anyone who could master the skills. Business Analysts and managers along with the engineers need to be familiar with the skills required to work with Big data.

  • Variety in the types of jobs available – The term Big Data is somewhat holistic and could be misleading in defining the job descriptions for an open position. Even many people use this term in several situations without actually understanding the meaning behind its implementation. 

There could be several job types available in the market which has the term Big Data in it. The domain of work could vary from Data analytics to Business analysis to Predictive analytics. It makes easier for one to choose among the various types and train oneself accordingly. Companies like Platform, Teradata, Opera, etc., have many opportunities in big data for their different business needs.

  • Lucrative salary – One of the major reasons why professionals are hopping onto the big data ecosystem is the salary that it offers. As it’s a niche skill, hence companies are ready to offer competitive packages to the employees. Those who want a learning curve and sharp growth in their career, big data could prove to be the perfect option for them.

As mentioned before, there are a variety of roles which requires big data expertise. Below are the opportunities based on the roles in the field of big data.

  • Big Data Analyst – One of the most sought after roles in Big Data is that of a Big Data Analyst. To interpret data and extract meaningful information from it which could help the business grow and influence the decision-making process is the work that a big data analyst does.

The professional also needs to have an understanding of tools such as Hadoop, Pig, Hive, etc. Basic statistics and algorithms knowledge along with the analytics skills is required for this role. For the analysis of data, domain knowledge is another important factor needed. To flourish in this role some of the qualities that are expected from a professional are –

  1. Reporting packages and data model experience.
  2. The ability to analyze both structured and unstructured data sets.
  3. The skill to generate reports that could be presented to the clients.
  4. Strong written and verbal communication skills.
  5. An inclination towards problem-solving and an analytical mind.
  6. Providing attention to detail.

The job description for a Big Data analyst includes –

  1. Interpretation and the collection of data.
  2. To the relevant business members, reporting the findings.
  3. Identification of trends and patterns in the data sets.
  4. Working alongside the management team or business to meet business needs.
  5. Coming up with new analysis and data collection process.
  • Big Data Engineer – The design of a Big Data solutions architect is built upon by the Big Data engineer. Within the organizations, the development, maintenance, testing, and evaluation of the Big Data solutions is done by the Big Data engineer. They also tend to have experience in Hadoop, Spark, and so on, and hence are involved in designing Big Data solutions. An expert in data warehousing, who builds data processing systems and are comfortable working in the latest technologies. 

In addition to this, the understanding of software engineering is also important for someone moving into the Big Data domain. Experience in engineering large-scale data infrastructures and software platforms should be present as well. Some of the programming or scripting languages a Big Data engineer should be familiar with are Java, Linux, Python, C++, and so on. Moreover, the knowledge of database systems like MongoDB is also crucial. Using Python or Java, a Big Data engineer should have a clear sense of building processing systems with Hive and Hadoop.

  • Data Scientist – Regarded as the sexiest job of the 21st century, a Data Scientist is regarded as the captain of the ship in the analytical Eco space. A Data Scientist is expected to have a plethora of skills stating from Data Analysis to building models to even client presentations.

In traditional organizations, the role of a Data Scientist is getting more importance as the way the old-school organizations used to work are now changing with the advent of Big Data. It’s now easier than ever to decipher the data starting from HR to R&D.

Apart from analyzing the raw data and drawing insights using Python, SQL, Excel, etc., a Data Scientist should also be familiar with building predictive models using Machine Learning, Deep Learning, and so on. Those models could save time and money for a business.

  • Business Intelligence Analyst – This role revolves around gathering data via different sources and also compare that with a competitor’s data. A picture of the company’s competitiveness would be developed by a Business Intelligence Analyst compared to other players in the market. Some of the responsibilities of a Business Intelligence Analyst are –
  1. Managing BI solutions.
  2. Through the applications lifecycle, provide reports and Excel VBA applications.
  3. Analyze the requirements and the business process.
  4. Requirements, design, and user manual documentations.
  5. Identifying the opportunities with technology solutions to improve strategies and processes.
  6. Identifying the needs to streamline and improve operations.

 

  • Machine Learning Engineer – A software engineer specialized in machine learning fulfils the role of a Machine Learning Engineer. Some of the responsibilities that a Machine Learning Engineer carries out are –
  1. Running experiments with machine learning libraries using a programming language.
  2. The production deployment of the predictive models.
  3. Optimizing the performance and the scalability of the applications.
  4. Ensuring a seamless data flow between the database and backend systems.
  5. Analyzing data and coming up with new use cases.

 

Global Job Market of Big Data

source: Datanami

Businesses and organizations have now put special attention to the full potential of Big Data. India has a large concentration of the jobs available in the Big Data market. Below are some of the notable points related to the job market of Big Data.

  • It is estimated that by 2020, the number would be approximately seven lakhs for the opportunities surrounding the role of Data Engineers, Big Data Developers, Data Scientists., and so on.
  • The average time for which an analytics job stays in the market is longer than the other jobs. The compensation for Big Data professionals is also 40%t more than other IT skills.
  • Apache Spark, Machine Learning, Hadoop, etc., are some of the skills in the Big Data domain which are the most lucrative. However, hiring such professionals require higher cost and hence it is necessary that better training programs are provided.
  • Retail, manufacturing, IT, finance is some of the industries which hire Big data expertise people.
  • People with relevant Big Data skills are a rarity and hence there is a gap between demand and supply. Hence, the average salary is high for people who are working in this field which is more than 98% than in general.

 

How to be job-ready?

Despite the rising opportunities in Big Data, there is still a lack of relevant skills among the professionals. Hence, it is necessary to get your basics right.  You should be familiar with the tools and technique coupled up with the domain knowledge would certainly put you in the driving seat.

Tools like Hive, Hadoop, SQL, Python, Spark are mostly used in this space and hence you should know most of them. Moreover, one should get their hands dirty and work in as many productions based projects as possible to tackle any kind of issues faced during analysis.

Conclusion

There is a huge opportunity for Big Data and now is the best time than ever to keep on learning and improving your skills.

If you are willing to learn more about Big Data or Data Science in general, follow the blogs and courses of Dimensionless.

Follow this link, if you are looking to learn more about data science online!

Additionally, if you are having an interest in learning Data Science, Learn online Data Science Course to boost your career in Data Science.

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Top 10 reasons why Dimensionless is the Best Data Science Course Provider Online

Top 10 reasons why Dimensionless is the Best Data Science Course Provider Online

Introduction

Data Science was called “The sexiest work of the 21st Century” by the Harvard Review. Data researchers as problematic solvers and analysts identify patterns, notice developments and make fresh findings and often use real-time information, machine learning, and IA. This is where Data Science Course comes into the picture.

There is a strong demand for information researchers and qualified data scientists. Projections from IBM suggest that by 2020 the figure of information researchers will achieve 28%. In the United States alone, there will be 2,7 million positions for all US information experts. In addition, we were provided more access to detailed analyzes by strong software programs.

Dimensionless Tech offers the finest online data science course and big data coaching to meet the requirement, offering extensive course coverage and case studies, completely hands-on-driven meetings with personal attention to each individual. This assessment is a gold mine with invaluable insights. To satisfy the elevated requirement. We only provide internet LIVE instruction for instructors and not instruction in the school.

About Dimensionless Technologies

Dimensionless Technologies is a training firm providing online live training in the sector of data science. Courses include–R&P data science, deep learning, large-scale analysis. It was created in 2014, with the goal of offering quality data science training for an inexpensive cost, by 2 IITians Himanshu Arora & Kushagra Singhania.
Dimensionless provides a range of internet Data Science Live lessons. Dimensionless intends to overcome the constraints by giving them the correct skillset with the correct methodology, versatile, adaptable and versatile at the correct moment, which will assist learners to create informed business choices and sail towards a successful profession.

Why Dimensionless Technologies

Experienced Faculty and Industry experts

Data science is a very vast field and hence a comprehensive grasp over this subject requires a lot of effort. With our experienced faculties, we are committed to impart quality and practical knowledge to all the learners. Our faculty through their vast experience (10 plus industry experience) in the data science industry is best suited to show the right path to all students towards their success journey on the path of data science. Our trainer’s boast of their high academic career as well (IITian’s)!

End to End domain-specific projects

We, at Dimensionless, believe that concepts can be learned best when all the theory learned in the classroom can actually be implemented. With our meticulously designed courses and projects, we make sure our students get hands-on the projects ranging from pharma, retail, and insurance domains to banking and financial sector problems! End-to-end projects make sure that students understand the entire problem-solving lifecycle in data science

Up to date and adaptive courses

All our courses have been developed based on the recent trends in data science. We have made sure to include all the industry requirements for data scientists. Courses start from level 0 and assume no prerequisites. Courses make learners traverse from basic introductions to advanced concepts gradually with the constant assistance of our experienced faculties. Courses cover all the concepts to a great depth such that learners are never left wanting for more! Our courses have something or other for everyone whether you are a beginner or a professional.

Resource assistance

Dimensionless technologies have all the required hardware setup from running a regression equation to training a deep neural network. Our online-lab provides learners with a platform where they can execute all their projects. A laptop with bare minimum configuration (2GB RAM and Windows 7) is sufficient enough to pave your way into the world of deep learning. Pre-setup environments save a lot of time of learners in installing all the required tools. All the software requirements are loaded right in front of the accelerated learning

Live and interactive sessions

Dimensionless provides classes through live interactive classes on our platform. All the classes are taken live by instructors and are not in any pre-recorded format. Such format enables our learners to keep up their learning in the comfort of their own homes. You don’t need to waste your time and expenses in any travel and can take classes from any location of your preference. Also, after each class, we provide the recorded video of it to all our learners so that they can go through it to clear all their doubts. All trainers are available to post classes to clear the doubts as well

Lifetime access to study materials

Dimensionless provides lifetime access to the learning material provided in the course. Many other course providers provide access only till the time one is continuing with classes. With all the resources available thereafter, learnings for our students will not stop even after they have taken up our entire course

Placement assistance

Dimensionless technologies provide placement assistance to all its students. With highly experienced faculties and contacts in the industry, we make sure our students get their data science job and kick start their career. We help in all stages of placement assistance. From resume-building to final interviews, Dimensionless technologies is by your side to help you achieve all your goals

Course completion certificate

Apart from the training, we issue a course completion certificate once the training is complete. The certificate brings credibility to the resume of the learners and will help them in fetching their data science dream jobs

Small batch sizes

We make sure that we have small batch sizes of students. Keeping the batch size small allows us to focus on students individually and impart them a better learning experience. With personalized attention, we make sure students are able to learn as much possible and helps us to clear all their doubts as well

Conclusion

If you want to start a profession in data science, dimensionless systems have the correct classes for you. Not just all key ideas and techniques are covered but they are also implemented and used in real-world company issues.

Follow this link, if you are looking to learn data science online!

You can follow this link for our Big Data course! This course will equip you with the exact skills required. Packed with content, this course teaches you all about AWS tools and prepares you for your next ‘Data Engineer’ role

Additionally, if you are having an interest in learning Data Science, click here to start the Online Data Science Course

Furthermore, if you want to read more about data science, read our Data Science Blogs

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Visualization Techniques for Datasets in Big Data

Visualization Techniques for Datasets in Big Data

Introduction

Data visualization is an important component of many company approaches due to the growing information quantity and its significance to the company. In this blog, we will be understanding in detail about visualisation in Big Data. Furthermore, we will be looking into the areas like why visualisation in big data is a tedious task or are there any tools available for visualising Big Data

 

What is Data Visualisation?

Data display represents data in a systematic manner, including information unit characteristics and variables. Data discovery techniques based on visualization enable company consumers to generate customized analytical opinions using disparate information sources. Advanced analytics can be incorporated into techniques for the development on desktop and laptop or mobile devices like tablets and smartphones of interactive and animated Graphics.

 

What is Big Data Visualisation?

Big data are large volumes, elevated speed and/or high-speed information sets that involve fresh types of handling to optimize processes, discover understanding and make choices. Data capture, storage, evaluation, sharing, searches and visualization face great challenges for big data. Visualization could be considered as “large information front end. There’s no data visualization myth.

  1. It is important to visualize only excellent information: an easy and fast view can show something incorrect with information just like it helps to detect exciting patterns.
  2. Visualization always manifests the correct choice or intervention: visualization is not a substitute for critical thinking.
  3. Visualization brings assurance: data are displayed, not showing an exact image of what is essential. Visualization with various impacts can be manipulated.

 

Tables, diagrams, pictures and other intuitive display methods to represent the information are created using visualization methods. Visualizing large information is not as simple as conventional tiny information sets. The expansion of traditional methods to visualization was already evolved but far enough. Many scientists use feature extraction and geometrical modeling in large-scale data visualization to significantly decrease the volume of information before real information processing. When viewing big data, it is also very essential to select the correct representation of information.

 

Problems in Visualising Big Data

In the visual analysis, scalability and dynamics are two main difficulties. The visualization of big data (structured or unstructured) with diversity and heterogeneity is a big difficulty. For big data analysis, speed is the required variable. Big information does not make it simple to design a fresh visualization tool with effective indexing. In order to improve the handling of Big Data scalability factors that influence information viewing decisions, cloud computing, and the sophisticated graphical user interface can be combined with Big Data. 

Unstructured information formats such as charts, lists, text, trees, and other information must be used by visualization schemes. Often large information has unstructured formats. Due to the constraints on bandwidth and power consumption, visualization should step nearer to the data to effectively obtain significant information. The software for visualization should be executed on location. Due to the large volume of the information, visualization requires huge parallelisation. The difficulty in simultaneous viewing algorithms is to break down an issue into autonomous functions that can be carried out at the same time.

 

There are also the following problems for big data visualization:

  • Visual noise: Most items on the dataset are too related to each other. There are also the following issues when viewing large-scale information. Users can not split them on the display as distinct items.

  • Info loss: Visible data sets may be reduced, but information loss may occur.

  • Broad perception of images: data display techniques are restricted not only by aspect ratio and device resolution but also by physical perception limitations.

  • The elevated pace of changes in the picture: users view information and are unable to respond to the amount of changes in information or its intensity.

  • High-performance requirements: In static visualization it is hard to notice because of reduced demands for display velocity— high performance demands.

     

Choice of visualization factors

 

  • Audience: The information depiction should be adjusted to the target audience. If clients are ending up in a fitness application and are looking at advancement, then simplicity is essential. On the other side, when information ideas are for scientists or seasoned decision-makers, you can and should often go beyond easy diagrams.

  • Satisfaction: The data type determines the strategies. For instance, when there are metrics that change over the moment, the dynamics will most likely be shown with line graphs. You will use a dispersion plot to demonstrate the connection between two components. Bar diagrams are ideal for comparison assessment, in turn.

  • Context: The way your graphs appear can be taken with distinct methods and therefore read according to the framework. For instance, you may want to use colors of one color to highlight a certain figure, which is a major profit increase relative to other years, and choose a shiny one as the most important component on the graph. Instead, contrast colors are used to distinguish components.

  • Dynamics: Dynamics. Data are distinct and each means a distinct pace of shift. For example, each month or year the financial results can be measured while time series and data tracking change continuously. Dynamic representation (steaming) or a static visualization can be considered, depending on the type of change.

  • Objective: The objective of viewing the information also has a major effect on the manner in which it is carried out. Visualizations are built into dashboards with checks and filters to carry out a complicated study of a scheme or merge distinct kinds of information for a deeper perspective. Dashboards are, however, not required to display one or more occasional information.

 

Visualization Techniques for Big Data

1. Word Clouds

Word clouds work easy: the larger and bolder the word is in the term cloud the more a particular word is displayed in a source of text information (such as a lecture, newspaper post or database).

Here is an instance of USA Today using the United States. State of Union Speech 2012 by President Barack Obama:

instance of USA Today

As you can see, words like “American,” “jobs,” “energy” and “every” stand out since they were used more frequently in the original text.

Now, compare that to the 2014 State of the Union address:

State of the Union address for american jobs

You can easily see the similarities and differences between the two speeches at a glance. “America” and “Americans” are still major words, but “help,” “work,” and “new” are more prominent than in 2012.

2. Symbol Maps

Symbol maps are merely maps shown over a certain length and latitude. You can rapidly create a strong visual with the “Marks” card at Tableau, which tells customers of their place information. You can also use the information to manage the form of the label on the map using the illustration in the Pie chart or forms for a different degree of detail.

These maps can be as simple or as complex as you need them to be

US maps for oil consumption

 

3. Line charts

Alternatively known as a row graph, a row graph is a graph of the information shown using a number of rows. Line diagrams show rows horizontally through the diagram, with the scores axis on the left hand of the diagram. An instance of a line chart displaying distinctive Computer Hope travelers can be seen in the image below.

line graph for distinctive Computer Hope travelers

As can be seen in this example, you can easily see the increases and decreases each year over different years.

4. Pie charts

A diagram is a circular diagram, split into sections like wedges, which shows the amount. The complete valuation of each coin is 100% and is a proportional portion of the whole.

The portion size can easily be understood on a look at pie charts. They are commonly used to demonstrate the proportion of expenditure, population sections or study responses across a big number of classifications.

pie chart for website traffic

5. Bar Charts

A bar graph is a visual instrument which utilizes bars to match information between cities. bars are also called a bar chart or bar diagram. A bar chart can be executed horizontally or vertically. What we need to understand is that the longer the bar is, the more valuable it is. Two axes are the bar graphs. The horizontal axis (or x-axis) is shown on a graph of the vertical bar, as shown above. They are years in this instance. The vertical axis is the magnitude. The information sequence is the colored rows.

Bar charts have three main attributes:

  • A bar character allows for a simple comparison of information sets among distinct organizations.
  • The graph shows classes on one axis and on the other a separate value. The objective is to demonstrate the connection between the two axes.
  • Bar diagrams can also display over moment large information modifications.

 

Data visulaisation in chart

6. Heat Maps

A heat map represents information that are displayed two-dimensionally by color values. An instant visual overview of the data is provided by a straightforward heat chart. 

There can be numerous methods to show thermal maps, but they all share one thing in common: to transmit interactions between information values in a tablet, they use a color that would be much difficult to comprehend.

Data visulaisation through heat maps

 

Visualisation Tools for Big Data

1. Power BI

Power BI is a company analysis option that enables you to view and share your information or integrate them into your app or blog. Connect to hundreds of information sources and live dashboards and accounts to take your information to life.

Microsoft Power BI is used to discover perspectives into the information of an organization. Power BI can communicate, convert and wash information into the data model and generate chart or diagram to display information graphics. All this can be communicated within the organisation with other consumers of Power BI.

Data models generated by Power BI can be used by organizations in many ways, including story telling through charts and views of data and “what if” scenarios inside the data. Power BI accounts can also respond to issues in real time and assist predict how departments will fulfill company criteria.

The Power BI can also provide executives or executives with corporate dashboards to provide them with an understanding of the agencies.

power BI dashboard

2. Kibana

Kibana is an open-source log analysis and time series analysis information visualization and exploring device for the surveillance of applications and operational intelligence instances. It provides strong and easy-to-use characteristics like histograms, diagrams, pie charts, thermal maps and integrated geospatial assistance. In addition, it ensures close inclusion with the famous analytics and search engine Elasticsearch, which makes Kibana the main option for viewing the information saved in Elasticsearch.

Kibana has been intended with Elasticsearch to render large and complicated information flows understandable by visual depiction more rapidly and smoothly. Elasticsearch analytics provide both information and improved aggregation mathematical transformations. The application produces a versatile, vibrant dashboard with PDF records on request or on timetable. The generated documents can depict information with customisable colors and highlighted search outcomes in the form of bar, row, scatter plot and paste graph sizes. Kibana also involves visualized data sharing instruments.

kibana Dashboard

 

3. Grafana

Grafana is a metrics & visualizing package of open source analysis. It is used most frequently for moment serial data visualization for infrastructure and implementation analysis, but many use it in other areas including agricultural equipment, domestic automation, climate, and process control.

Grafana is a temporary information sequence display instrument. A graphical description can be obtained from a lot of gathered information of the position of a business or organisation. How are they doing it? The collaborative editing of Wikidata, an extensive database of information, that increasingly builds papers in Wikipedia, utilizes the grafana.wikimedia.org to demonstrate openly (in our situation we do so on a regular basis) the publishings conducted out by associates and computers, in a certain span of moment produced and edited’ websites,’ or information sheets:

Gafrana Dashboard

 

4. Tableau

Tableau has been utilized in the business intelligence industry as a strong and rapidly increasing information vision instrument. It makes it readily understandable to simplify raw information.

Data analysis with Tableau is very quick and the visualizations are in the shape of dashboards and tablets. The information produced using Tableau can be comprehended at every stage in an organisation by the specialist. It even enables a non-technical user a personalized dashboard to be created.

The best feature Tableau are

  • Data Blending
  • Real-time analysis
  • Collaboration of data

Tableau software is fantastic because it does not require any technical or programming abilities to function. The instrument has attracted individuals from all sectors, such as company, scientists, various industries, etc.

Tableau dashboard

 

Summary

Static or vibrant visualizations can be interactive viewing often results in discovery and works better than static information instruments. Interactive views can assist you to get an overview of big data. The scientific method can be facilitated by interactive brushing and connecting visualisation methods to networks or web-based instruments. The web-based display enables to ensure dynamic data is kept up to date and updated.

There is not sufficient room for extending some standard visualization methods to manage big data. More fresh Big Data viewing techniques and instruments for various Big Data apps should be created

Follow this link, if you are looking to learn data science online!

You can follow this link for our Big Data course!

Additionally, if you are having an interest in learning Data Science, click here to start the Online Data Science Course

Furthermore, if you want to read more about data science, read our Data Science Blogs

A Comprehensive Guide to Data Mining: Techniques, Tools and Application

A Comprehensive Guide to Data Mining: Techniques, Tools and Application

 

 

Different Ways to Manage Apache Spark Applications on Amazon EMR

Image result for apache spark on amazon emr

source: SlideShare

 

Technology has seen advancing rapidly in the last few years and so is the amount the data that is getting generated. There are a plethora of sources which generates unstructured data that carries a huge amount of information if mined correctly. These varieties of voluminous data are known as the Big Data which traditional computers or storage systems are incapable to handle.

To mine big data, the concept of parallel computing or clusters came into place popularly known as the Hadoop. Hadoop has several components which not only stores the data in the form of clusters but processes them in parallel as well. The HDFS or the Hadoop storage file system stores the big data while using the Map Reduce technique the data is processed.

However, most applications nowadays generate data in real-time which requires real-time analysis. Hadoop doesn’t allow real-time data storage or analysis as the data is processed in batches in Hadoop. To resolve such instances, Apache introduced Spark which is faster than Hadoop and allows data to be processed in real time. More and more companies have since transitioned from Hadoop to Spark as their application depends on real-time data analytics. You could also perform Machine Learning operations on Spark using the MLlib library.

The computation in spark is done in memory, unlike Hadoop which relies on disk for computation. It is an elegant and expressive development application programming interface which allows fast and efficient SQL and ML operations on iterative datasets. Applications could be created everywhere and the power of Spark could be exploited as Spark runs on Apache Hadoop YARN. Within a single dataset in Hadoop, insights could be derived and the data science workloads could be enriched.

A common cluster could be shared by Spark and other applications by maintaining service and response consistency which is a foundation provided by the Hadoop YARN-based architecture. Working with YARN in HDP, one of the many data access engines now is Spark. A Spark Core and other libraries are present in the Apache Spark.

The abstraction in Spark makes data science easier. Machine Learning is a technique where algorithms learn from data. The data processing speeds up by caching the dataset by Spark which is ideal for implementing such algorithms. To model an entire Data Science workflow, a high-level abstraction is provided by Spark’s Machine Learning Pipeline API. Abstractions like Transformer, Estimator, and so on are provided by the Spark’s Machine Learning pipeline package which increases the productivity of a Data Scientist.

So far we have discussed Big Data and how it could be processed using Apache Spark. However, to run Apache Spark applications, proper infrastructure needs to be in place and thus Amazon EMR provides a platform to manage applications built on Apache Spark.

 

Managing Spark Applications on Amazon EMR

 

Image result for manage apache spark applications on amazon emr

source: medium

 

Amazon EMR is one of the most popular cloud-based solutions to extract and analyze huge volumes of data from a variety of sources. On AWS, frameworks such as Apache Hadoop and Apache Spark could be run with the help of the Amazon EMR. In the matter of a few minutes, with the help of multiple instances, organizations could spin up a cluster which is enabled by the Amazon EMR. Through parallel processing, various data engineering and business intelligence workloads to be processed which reduces effort, cost, and time of the data processing involved in setting up the cluster.

As Apache Spark is a fast, an open-source framework, it is used in the processing of the big data. To reduce I/O, in memory across nodes, Apache Spark performs parallel computing in memory and thus the reliability of cluster memory (RAM) is heavy. On Amazon EMR, to run a Spark application, the following steps need to be performed –

  • To the Amazon S3, the Spark application package is uploaded.
  • With the configured Apache Spark, the Amazon EMR cluster is configured and launched.
  • Onto the cluster, from the Amazon S3, the application package is installed and then the application is run.
  • After the application is completed, the cluster is terminated.

 

For a successful operation, based on the data and processing requirements, the Spark application needs to be configured. There could be memory issues if Spark is configured with the default settings. Below are some of the memory errors occurs while maintaining Apache Spark on Amazon EMR in a default setting.

  • The loss of memory error when the Java Heap space is not empty – lang.OutOfMemoryError: Java heap space
  • When the physical memory exceeds, you get the out of memory error Error: ExecutorLostFailure Reason: Container killed by YARN for exceeding limits
  • If the Virtual memory is exceeded, you would also get the out of memory error.
  • The Executor memory also gives the out of memory error if it’s exceeded.

 

Some of the reasons why these issues occur are –

  • While handling large volumes of data, due to the inappropriate settings of the number of cores, executor memory, or the number of Spark executor instances.
  • The memory allocated by YARN is exceeded by the Spark executor’s physical memory. In such cases, to handle memory intensive operations, the memory of the Spark executor and the overhead together is not enough.
  • In the Spark executor instance, to handle operations like garbage collection, enough memory is not present.

 

Below are the ways in which Amazon Spark could be successfully configured and maintained on Amazon EMR.

Based on the needs of the application, the number of instances and type should be determined. There are three types of nodes in the Amazon EMR –

  • The master acts as the resource manager.
  • The Core nodes which are managed by the master that executes tasks and manages storage.
  • The Task which performs only tasks but no storage. 

The right instance type should be chosen based on the application whether it is memory intensive or compute intensive. The R-type instances are preferred for the memory intensive applications while the C-Type instances are preferred for the compute-intensive applications. For each node types, the number of instances are decided after the type of the instance is decided. The number is dependent on the frequency requirements, the execution time of the application, and the input dataset size.

The Spark Configuration parameters need to be determined. Below is the diagram representing the executor container memory.

 

Image result for manage apache spark applications on amazon emr

source: Amazon Web Services

 

There are multiple memory compartments in the executor container. However, for task execution, only one is used and for seamlessly running of the task, these need to be configured properly.

Based on the task and core instance types, the values for the Spark parameters are automatically set by the in spark-defaults. The maximize resource allocation is set to True to use all the resources in the cluster. Based on the workloads, the number of executors used could be dynamically scaled by Spark on YARN. In an application, to use the right number of executors, in most cases, the sub-properties are required which requires a lot of trial and error. These could often lead to the wastage of memory if the trial and error are not right.

  • Memory should be effectively cleared with the implementation of a proper garbage collector. In certain cases, out of memory error could occur due to the garbage collection especially when in the application, there are multiple RDDs. between the RDD cached memory and the task memory, when there is an interference, such instances might occur. Multiple garbage collectors could be used and in the memory, the new ones could be placed. However, the latency is overcome by the latest Garbage First Garbage Collector (G1GC).
  • The configuration parameters of YARN should be set. As the operating system bumps up the virtual memory aggressively, the virtual out of memory could still occur, even if all properties of Spark are configured correctly. The virtual memory and the physical memory check flag should be set to False to prevent such application failures. 
  • Monitoring and Debugging should be performed. With the verbose option, the spark-submit should be run and the Spark configuration details could be known. To monitor Network I/O, the application progress, Spark UI and Ganglia could be used. A Spark application could process ten terabytes of data successfully if it is configured using 170 executor instances, 37GB memory, eight terabytes of RAM, five virtual CPUs, a twelve times large master and core nodes and 1700 equaled parallelism.

 

Conclusion

Apache Spark is being used by most industries these days and thus building a flawless application using Spark is a necessity which could help the business in their day to day activities.

Amazon EMR is one of the most popular cloud-based solutions to extract and analysis huge volumes of data from a variety of sources. On AWS, frameworks such as Apache Hadoop and Apache Spark could be run with the help of the Amazon EMR. This blog post covered various memory errors, the causes of the errors and how to prevent them when running Spark applications on Amazon EMR.

Dimensionless has several blogs and training to get started with Python, and Data Science in general.

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Also Read:

What is Map Reduce Programming and How Does it Work

How to Visualize AWS Cost and Usage Data Using Amazon Athena and QuickSight

 

 

What is Map Reduce Programming and How Does it Work

Image result for map reduce

source : Talend

Introduction

Data Science is the study of extracting meaningful insights from the data using various tools and technique for the growth of the business. Despite its inception at the time when computers came into the picture, the recent hype is a result of the huge amount of unstructured data that is getting generated and the unprecedented computational capacity that modern computers possess.

However, there is a lot of misconception among the masses about the true meaning of this field with many of the opinion that it is about predicting future outcomes from the data. Though predictive analytics is a part of Data Science, it is certainly not all of what Data Science stands for. In an analytics project, the first and foremost role is to get the build the pipeline and get the relevant data to perform predictive analytics later on. The professional who is responsible for building such ETL pipelines and the creating the system for flawless data flow is the Data Engineer and this field is known as Data Engineering.

Over the years the role of Data Engineers has evolved a lot. Previously it was about building Relational Database Management System using Structured Query Language or run ETL jobs. These days, the plethora of unstructured data from a multitude of sources has resulted in the advent of Big Data. It is nothing but a different forms of voluminous data which carries a lot of information if mined properly.

Now, the biggest challenge that professionals face is to analyse these huge terabytes of data which traditional file storage systems are incapable of handling. This problem was resolved by Hadoop which is an open-source Apache framework built to process large data in the form of clusters. Hadoop has several components which takes care of the data and one such component is known as Map Reduce.

 

What is Hadoop?

Created by Doug Cutting and Mike Cafarella in 2006, Hadoop facilitates distributed storage and processing of huge data sets in the form parallel clusters. HDFS or Hadoop Distributed File System is the storage component of Hadoop where different file formats could be stored to be processed using the Map Reduce programming which we would cover later on in this article.

The HDFS runs on large clusters and follows a master/slave architecture. The metadata of the file i.e., information about the relative position of the file in the node is managed by the NameNode which is the master and could save several DataNodes to store the data. Some of the other components of Hadoop are –

  • Yarn – It manages the resources and performs job scheduling.
  • Hive – It allows users to write SQL-like queries to analyse the data.
  • Sqoop – Used for to and fro structured data transfer between the Hadoop Distributed file System and the Relational Database Management System.
  • Flume – Similar to Sqoop but it facilitates the transfer of unstructured and semi-structured data between the HDFS and the source.
  • Kafka – A messaging platform of Hadoop.
  • Mahout – It used to create Machine Learning operations on big data.

Hadoop is a vast concept and in detail explanation of each components is beyond the scope of this blog. However, we would dive into one of its components – Map Reduce and understand how it works.

 

What is Map Reduce Programming

Map Reduce is the programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop Cluster, i.e. suppose you have a job to run and you write the Job using the MapReduce framework and then if there are a thousand machines available, the Job could run potentially in those thousand machines.

The Big Data is not stored traditionally in HDFS. The data gets divided into chunks of small blocks of data which gets stored in respective data nodes.  No complete data’s present in one centralized location and hence a native client application cannot process the information right away. So a particular framework is needed with the capability of handling the data that stays as blocks of data into respective data nodes, and the processing can go there to process that data and bring back the result. In a nutshell, data is processed in parallel which makes processing faster.

To improve performance and for better efficiency, the idea of parallelization was developed. The process is automated and concurrently executed. The instructions which are fragmented could also run on a single machine or on different CPU’s. To gain direct disk access, multiple computers uses SAN or Storage Area Networks which is a common type of Clustered File System unlike the Distributed File Systems which sends the data using the network.

One term that is common in this maser/slave architecture of data processing is Load Balancing where among the processors the tasks are spread to avoid overload on any DataNode. Unlike the static balancers, there is more flexibility provided by the dynamic balancers.

The Map-Reduce algorithm which operates on three phases – Mapper Phase, Sort and Shuffle Phase and the Reducer Phase. To perform basic computation, it provides abstraction for Google engineers while hiding fault tolerance, parallelization, and load balancing details.

  • Map Phase – In this stage, the input data is mapped into intermediate key-value pairs on all the mappers assigned to the data.
  • Shuffle and Sort Phase – This phase acts as a bridge between the Map and the Reduce phase to decrease the computation time. The data here is shuffled and sorted simultaneously based on the keys i.e., all intermediate values from the mapper phase is grouped together with respect to the keys and passed on to reduce function.
  • Reduce Phase– The sorted data is the input to the Reducer which aggregates the value corresponding to each key and produces the desired output.

 

How Map Reduce works

  • Across multiple machines, the Map invocations are distributed and the input data is automatically partitioned into M pieces of size sixteen to sixty four megabytes per piece. On a cluster of machines, many copies of the program are then started up.
  • Among the copies, one is the master copy while the rest are the slave copies. The master assigns M map and R reduce tasks to the slaves. Any idle worker would be assigned a task by the master.
  • The map task worker would read the contents of the input and pass key-value pairs to the Map function defined by the user. In the memory buffer, the intermediate key-value pairs would be produced.
  • To the local disk, the buffered pairs are written in a periodic fashion. The partitioning function then partitions them into R regions. The master would forward the location of the buffered key-value pairs to the reduce workers.
  • The buffered data is read by the reduce workers after getting the location from the master. Once it is read, the data is sorted based on the intermediate keys grouping similar occurrences together.
  • The Reduce function defined the user receives a set of intermediate values corresponding to each unique intermediate key that it encounters. The final output file would consists of the appended output from the Reduce function.
  • The user program is woken up by the Master once all the Map and Reduce tasks are completed. In the R output files, the successful MapReduce execution output could be found.
  • Each and every worker’s aliveness is checked by the master after the execution by sending periodic pings. If any worker does not respond to the ping, it is marked as failed after a certain point if time and its previous works are reset.
  • In case of failures, the map tasks which are completed would be re-executed as their output would be inaccessible in the local disk. Output which are stored in the global file system need not to be re-executed.

 

Some of the examples of Map Reduce programming are –

  • Map Reduce programming could count the frequencies of the URL access. The logs of web page would be processed by the map function and stored as output say <URL, 1> which would be processed by the Reduce function by adding all the same URL and output their count.
  • Map Reduce programming could also be used to parse documents and count the number of words corresponding to each document.
  • For a given URL, the list of all the associated source URL’s could be obtained with the help of Map Reduce.
  • To calculate per host term vector, the map reduce programming could be used. The hostname and the term vector pair would be created for each document by the Map function which would be processed by the reduce function which in turn would remove less frequent terms and give a final hostname, term vector.

 

Conclusion

Data Engineering is a key step in any Data Science project and Map Reduce is undoubtedly an essential part of it. In this article we have a brief intuition about Big Data and provided an overview of Hadoop. Then we explained Map Reduce programming and its workflow and gave few real life applications of Map Reduce programming as well.

Dimensionless has several blogs and training to get started with Python, and Data Science in general.

Follow this link, if you are looking to learn more about data science online!

Additionally, if you are having an interest in learning Data Science, Learn online Data Science Course to boost your career in Data Science.

Furthermore, if you want to read more about data science, you can read our blogs here