10 Reasons Why Big Data Analytics is the Best Career Move

10 Reasons Why Big Data Analytics is the Best Career Move

Big Data is undoubtedly the next big thing! Currently, Big Data Analytics is in the forefront of IT and it plays an extremely crucial role

Big Data is undoubtedly the next big thing! Currently, Big Data Analytics is in the forefront of IT and it plays an extremely crucial role because it can make a huge impact when it comes to improving business, making and retaining decisions and policies and providing the biggest possible edge over competitors.

So if you think you are cut out for the big, bad and brutal Analytics game, then there are a plethora of options waiting for you on your favorite job portal. Let’s take a look at why Big Data Analytics is the smartest career move you can make right now:

Soaring Demand for Analytics Professionals

The demand for skilled professionals in the field of Analytics has is quite recent! But, experts believe that in the next few years the market will expand and evolve to such an extent that the global IT market will be more or less occupied with Big Data Analytics professionals. If you invest in training right now, you will reap what you will sow!

Huge Job Opportunities & Meeting the Skill Gap

A close look at the job portal of your choice and you will know that there is a large number of vacancies worldwide in this particular sector and that’s solely because of the dearth of skilled professionals. So if you are thinking of Big Data Analytics as a career option, the dismal demand-supply gap will ensure that you will never be unemployed.

Salary Aspects

Due to the high demand for analytics professionals, the range of salary is also skyrocketing. Studies suggest that the annual pay hike for Analytics professionals in India is almost 50% more than other IT professionals. The salary trend in U.K has also seen steady and exponential growth in the last two years.

Top Priority in a lot of Organizations

Big Data Analytics is undoubtedly one of the top priorities of the leading organizations because it helps an organization grow by collecting and storing data that might get scattered and lost. Surveys have revealed that Big Data Analytics is one of the central factors behind boosting an organization’s social media marketing chops.

Adoption of Big Data Analytics is Growing

With new and advanced technologies coming in, Analytics has become all the more intricate and sophisticated as it can now be performed on large and varied datasets. People are now investing more time and effort in dealing with the strategy setup for Big Data Analytics.

A Key Factor in Decision Making

Analytics now has an upper hand in the decision-making process of the organizations because it is a strong and effective strategic influence that works remarkably in the long run.

The Rise of Unstructured and Semistructured Data Analytics

Surprisingly, there has been considerable growth in the sector of unstructured and semi-structured data analytics including the analysis of weblogs, social media, photos, videos, and even e-mail.

Usage in Every Section

The biggest advantage of Big Data Analytics is that it can be used almost anywhere and everywhere. From Healthcare to Energy to Technology to Banking, Big Data Analytics can be used in every single domain making it as flexible as flexible can get.

Surpassing Market Forecast / Predictions for Big Data Analytics

According to a renowned international survey, Big Data Analytics will be one of the most disruptive technologies in three years’ time. Researchers are of opinion that in the near future, Big Data Analytics tools will be used for the first line of defense, combining machine learning, text mining and ontology modeling to provide holistic and integrated security threat prediction.

Numerous Choices in Job Titles and Type of Analytics

A quick search on a trustworthy job portal will tell you that this particular field comes with a bouquet of specialized jobs you can choose from including Big Data Analytics Architect, Big Data Engineer, Big Data Analyst etc. Leading organizations like Microsoft and Oracle have an excellent range of Big Data Analytics jobs for you to choose from. So if you are armed with the required skill, Big Data is going to be your best friend!

Integrating Kafka With Spark Structured Streaming

Integrating Kafka With Spark Structured Streaming

Learn the method to integrate Kafka with Spark for consuming streaming data amd discover how to unleash your streaming analytics needs...

Learn the method to integrate Kafka with Spark for consuming streaming data amd discover how to unleash your streaming analytics needs...

Kafka is a messaging broker system that facilitates the passing of messages between producer and consumer. On the other hand, Spark Structure streaming consumes static and streaming data from various sources (like Kafka, Flume, Twitter, etc.) that can be processed and analyzed using a high-level algorithm for Machine Learning and pushes the result out to an external storage system. The main advantage of structured streaming is to get continuous incrementing of the result as the streaming data continue to arrive.

Kafka has its own stream library and is best for transforming Kafka topic-to-topic whereas Spark streaming can be integrated with almost any type of system. For more detail, you can refer to this blog.

In this blog, I’ll cover an end-to-end integration of Kafka with Spark structured streaming by creating Kafka as a source and Spark structured streaming as a sink.

Let’s create a Maven project and add following dependencies in pom.xml.


Now, we will be creating a Kafka producer that produces messages and pushes them to the topic. The consumer will be the Spark structured streaming DataFrame.

First, setting the properties for the Kafka producer.

val props = new Properties()
props.put("bootstrap.servers", "localhost:9092")
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")

  • bootstrap.servers: This contains the full list of servers with hostname and port. The list should be in the form of host1: port, host2: port , and so on.

  • key.serializer: Serializer class for the key that implements serializer interface.

  • value.serializer: Serializer class for the key that implements the serializer interface.

Creating a Kafka producer and sending topic over the stream:

val producer = new KafkaProducer[String,String](props)
for(count <- 0 to 10) 
  producer.send(new ProducerRecord[String, String](topic, "title "+count.toString,"data from topic"))
println("Message sent successfully")

The send is asynchronous, and this method will return immediately once the record has been stored in the buffer of records waiting to be sent. This allows sending many records in parallel without blocking to wait for the response after each one. The result of the send is a RecordMetadata specifying the partition the record was sent to and the offset it was assigned. After sending the data, close the producer using the close method.

Kafka as a Source 

Now, Spark will be a consumer of streams produced by Kafka. For this, we need to create a Spark session.

val spark = SparkSession
  .config("spark.master", "local")

This is getting the topics from Kafka and reading it in Spark stream by subscribing to a particular topic that is to be provided in option. Following is the code to subscribe Kafka topics in Spark stream and read it using readstream.

val dataFrame = spark
  .option("kafka.bootstrap.servers", "localhost:9092")
  .option("subscribe", "mytopic")

Printing the schema of the DataFrame:


The output for the schema includes all the fields related to Kafka metadata.

 |-- key: binary (nullable = true)
 |-- value: binary (nullable = true)
 |-- topic: string (nullable = true)
 |-- partition: integer (nullable = true)
 |-- offset: long (nullable = true)
 |-- timestamp: timestamp (nullable = true)
 |-- timestampType: integer (nullable = true)

Create a dataset from DataFrame by casting the key and value from the topic as a string:

val dataSet: Dataset[(String, String)] =dataFrame.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]

Write the data in the dataset to the console and hold the program from exit using the method awaitTermination:

val query: StreamingQuery = dataSet.writeStream

The complete code is on my GitHub.

Originally published by Jatin Demla at https://dzone.com

Learn More

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Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Downloadable PDF of Best AI Cheat Sheets in Super High Definition

Let’s begin.

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Data Science in HD

Part 1: Neural Networks Cheat Sheets

Neural Networks Cheat Sheets

Neural Networks Basics

Neural Networks Basics Cheat Sheet

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

Basically, there are 3 different layers in a neural network :

  1. Input Layer (All the inputs are fed in the model through this layer)
  2. Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
  3. Output Layer (The data after processing is made available at the output layer)

Neural Networks Graphs

Neural Networks Graphs Cheat Sheet

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

Part 2: Machine Learning Cheat Sheets

Machine Learning Cheat Sheets

>>> If you like these cheat sheets, you can let me know here.<<<

Machine Learning with Emojis

Machine Learning with Emojis Cheat Sheet

Machine Learning: Scikit Learn Cheat Sheet

Scikit Learn Cheat Sheet

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines is a simple and efficient tools for data mining and data analysis. It’s built on NumPy, SciPy, and matplotlib an open source, commercially usable — BSD license

Scikit-learn Algorithm Cheat Sheet

Scikit-learn algorithm

This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.

If you like these cheat sheets, you can let me know here.### Machine Learning: Scikit-Learn Algorythm for Azure Machine Learning Studios

Scikit-Learn Algorithm for Azure Machine Learning Studios Cheat Sheet

Part 3: Data Science with Python

Data Science with Python Cheat Sheets

Data Science: TensorFlow Cheat Sheet

TensorFlow Cheat Sheet

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.

If you like these cheat sheets, you can let me know here.### Data Science: Python Basics Cheat Sheet

Python Basics Cheat Sheet

Python is one of the most popular data science tool due to its low and gradual learning curve and the fact that it is a fully fledged programming language.

Data Science: PySpark RDD Basics Cheat Sheet

PySpark RDD Basics Cheat Sheet

“At a high level, every Spark application consists of a driver program that runs the user’s main function and executes various parallel operations on a cluster. The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to persist an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.” via Spark.Aparche.Org

Data Science: NumPy Basics Cheat Sheet

NumPy Basics Cheat Sheet

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

***If you like these cheat sheets, you can let me know ***here.

Data Science: Bokeh Cheat Sheet

Bokeh Cheat Sheet

“Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.” from Bokeh.Pydata.com

Data Science: Karas Cheat Sheet

Karas Cheat Sheet

Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

Data Science: Padas Basics Cheat Sheet

Padas Basics Cheat Sheet

Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.

If you like these cheat sheets, you can let me know here.### Pandas Cheat Sheet: Data Wrangling in Python

Pandas Cheat Sheet: Data Wrangling in Python

Data Wrangling

The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.

Data Science: Data Wrangling with Pandas Cheat Sheet

Data Wrangling with Pandas Cheat Sheet

“Why Use tidyr & dplyr

  • Although many fundamental data processing functions exist in R, they have been a bit convoluted to date and have lacked consistent coding and the ability to easily flow together → leads to difficult-to-read nested functions and/or choppy code.
  • R Studio is driving a lot of new packages to collate data management tasks and better integrate them with other analysis activities → led by Hadley Wickham & the R Studio teamGarrett Grolemund, Winston Chang, Yihui Xie among others.
  • As a result, a lot of data processing tasks are becoming packaged in more cohesive and consistent ways → leads to:
  • More efficient code
  • Easier to remember syntax
  • Easier to read syntax” via Rstudios

Data Science: Data Wrangling with ddyr and tidyr

Data Wrangling with ddyr and tidyr Cheat Sheet

If you like these cheat sheets, you can let me know here.### Data Science: Scipy Linear Algebra

Scipy Linear Algebra Cheat Sheet

SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.[3]

Data Science: Matplotlib Cheat Sheet

Matplotlib Cheat Sheet

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented APIfor embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of matplotlib.

Pyplot is a matplotlib module which provides a MATLAB-like interface matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.

Data Science: Data Visualization with ggplot2 Cheat Sheet

Data Visualization with ggplot2 Cheat Sheet

>>> If you like these cheat sheets, you can let me know here. <<<

Data Science: Big-O Cheat Sheet

Big-O Cheat Sheet


Special thanks to DataCamp, Asimov Institute, RStudios and the open source community for their content contributions. You can see originals here:

Big-O Algorithm Cheat Sheet: http://bigocheatsheet.com/

Bokeh Cheat Sheet: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Bokeh_Cheat_Sheet.pdf

Data Science Cheat Sheet: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics

Data Wrangling Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

Data Wrangling: https://en.wikipedia.org/wiki/Data_wrangling

Ggplot Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

Keras Cheat Sheet: https://www.datacamp.com/community/blog/keras-cheat-sheet#gs.DRKeNMs

Keras: https://en.wikipedia.org/wiki/Keras

Machine Learning Cheat Sheet: https://ai.icymi.email/new-machinelearning-cheat-sheet-by-emily-barry-abdsc/

Machine Learning Cheat Sheet: https://docs.microsoft.com/en-in/azure/machine-learning/machine-learning-algorithm-cheat-sheet

ML Cheat Sheet:: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html

Matplotlib Cheat Sheet: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY

Matpotlib: https://en.wikipedia.org/wiki/Matplotlib

Neural Networks Cheat Sheet: http://www.asimovinstitute.org/neural-network-zoo/

Neural Networks Graph Cheat Sheet: http://www.asimovinstitute.org/blog/

Neural Networks: https://www.quora.com/Where-can-find-a-cheat-sheet-for-neural-network

Numpy Cheat Sheet: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE

NumPy: https://en.wikipedia.org/wiki/NumPy

Pandas Cheat Sheet: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM

Pandas: https://en.wikipedia.org/wiki/Pandas_(software)

Pandas Cheat Sheet: https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.HPFoRIc

Pyspark Cheat Sheet: https://www.datacamp.com/community/blog/pyspark-cheat-sheet-python#gs.L=J1zxQ

Scikit Cheat Sheet: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet

Scikit-learn: https://en.wikipedia.org/wiki/Scikit-learn

Scikit-learn Cheat Sheet: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html

Scipy Cheat Sheet: https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.JDSg3OI

SciPy: https://en.wikipedia.org/wiki/SciPy

TesorFlow Cheat Sheet: https://www.altoros.com/tensorflow-cheat-sheet.html

Tensor Flow: https://en.wikipedia.org/wiki/TensorFlow

Data Science vs Data Analytics vs Big Data

Data Science vs Data Analytics vs Big Data

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

We live in a data-driven world. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Now that Hadoop and other frameworks have resolved the problem of storage, the main focus on data has shifted to processing this huge amount of data. When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them.

In this article on Data Science vs Data Analytics vs Big Data, I will be covering the following topics in order to make you understand the similarities and differences between them.
Introduction to Data Science, Big Data & Data AnalyticsWhat does Data Scientist, Big Data Professional & Data Analyst do?Skill-set required to become Data Scientist, Big Data Professional & Data AnalystWhat is a Salary Prospect?Real time Use-case## Introduction to Data Science, Big Data, & Data Analytics

Let’s begin by understanding the terms Data Science vs Big Data vs Data Analytics.

What Is Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

[Source: gfycat.com]

It also involves solving a problem in various ways to arrive at the solution and on the other hand, it involves to design and construct new processes for data modeling and production using various prototypes, algorithms, predictive models, and custom analysis.

What is Big Data?

Big Data refers to the large amounts of data which is pouring in from various data sources and has different formats. It is something that can be used to analyze the insights which can lead to better decisions and strategic business moves.

[Source: gfycat.com]

What is Data Analytics?

Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. It is all about discovering useful information from the data to support decision-making. This process involves inspecting, cleansing, transforming & modeling data.

[Source: ibm.com]

What Does Data Scientist, Big Data Professional & Data Analyst Do?

What does a Data Scientist do?

Data Scientists perform an exploratory analysis to discover insights from the data. They also use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. This involves identifying hidden patterns, unknown correlations, market trends and other useful business information.

Roles of Data Scientist

What do Big Data Professionals do?

The responsibilities of big data professional lies around dealing with huge amount of heterogeneous data, which is gathered from various sources coming in at a high velocity.

Roles of Big Data Professiona

Big data professionals describe the structure and behavior of a big data solution and how it can be delivered using big data technologies such as Hadoop, Spark, Kafka etc. based on requirements.

What does a Data Analyst do?

Data analysts translate numbers into plain English. Every business collects data, like sales figures, market research, logistics, or transportation costs. A data analyst’s job is to take that data and use it to help companies to make better business decisions.

Roles of Data Analyst

Skill-Set Required To Become Data Scientist, Big Data Professional, & Data Analyst

What Is The Salary Prospect?

The below figure shows the average salary structure of **Data Scientist, Big Data Specialist, **and Data Analyst.

A Scenario Illustrating The Use Of Data Science vs Big Data vs Data Analytics.

Now, let’s try to understand how can we garner benefits by combining all three of them together.

Let’s take an example of Netflix and see how they join forces in achieving the goal.

First, let’s understand the role of* Big Data Professional* in Netflix example.

Netflix generates a huge amount of unstructured data in forms of text, audio, video files and many more. If we try to process this dark (unstructured) data using the traditional approach, it becomes a complicated task.

Approach in Netflix

Traditional Data Processing

Hence a Big Data Professional designs and creates an environment using Big Data tools to ease the processing of Netflix Data.

Big Data approach to process Netflix data

Now, let’s see how Data Scientist Optimizes the Netflix Streaming experience.

Role of Data Scientist in Optimizing the Netflix streaming experience

1. Understanding the impact of QoE on user behavior

User behavior refers to the way how a user interacts with the Netflix service, and data scientists use the data to both understand and predict behavior. For example, how would a change to the Netflix product affect the number of hours that members watch? To improve the streaming experience, Data Scientists look at QoE metrics that are likely to have an impact on user behavior. One metric of interest is the rebuffer rate, which is a measure of how often playback is temporarily interrupted. Another metric is bitrate, that refers to the quality of the picture that is served/seen — a very low bitrate corresponds to a fuzzy picture.

2. Improving the streaming experience

How do Data Scientists use data to provide the best user experience once a member hits “play” on Netflix?

One approach is to look at the algorithms that run in real-time or near real-time once playback has started, which determine what bitrate should be served, what server to download that content from, etc.

For example, a member with a high-bandwidth connection on a home network could have very different expectations and experience compared to a member with low bandwidth on a mobile device on a cellular network.

By determining all these factors one can improve the streaming experience.

3. Optimize content caching

A set of big data problems also exists on the content delivery side.

The key idea here is to locate the content closer (in terms of network hops) to Netflix members to provide a great experience. By viewing the behavior of the members being served and the experience, one can optimize the decisions around content caching.

4. Improving content quality

Another approach to improving user experience involves looking at the quality of content, i.e. the video, audio, subtitles, closed captions, etc. that are part of the movie or show. Netflix receives content from the studios in the form of digital assets that are then encoded and quality checked before they go live on the content servers.

In addition to the internal quality checks, Data scientists also receive feedback from our members when they discover issues while viewing.

By combining member feedback with intrinsic factors related to viewing behavior, they build the models to predict whether a particular piece of content has a quality issue. Machine learning models along with natural language processing (NLP) and text mining techniques can be used to build powerful models to both improve the quality of content that goes live and also use the information provided by the Netflix users to close the loop on quality and replace content that does not meet the expectations of the users.

So this is how Data Scientist optimizes the Netflix streaming experience.

Now let’s understand how Data Analytics is used to drive the Netflix success.

Role of Data Analyst in Netflix

The above figure shows the different types of users who watch the video/play on Netflix. Each of them has their own choices and preferences.

So what does a Data Analyst do?

Data Analyst creates a user stream based on the preferences of users. For example, if user 1 and user 2 have the same preference or a choice of video, then data analyst creates a user stream for those choices. And also –
Orders the Netflix collection for each member profile in a personalized way.We know that the same genre row for each member has an entirely different selection of videos.Picks out the top personalized recommendations from the entire catalog, focusing on the titles that are top on ranking.By capturing all events and user activities on Netflix, data analyst pops out the trending video.Sorts the recently watched titles and estimates whether the member will continue to watch or rewatch or stop watching etc.
I hope you have *understood *the *differences *& *similarities *between Data Science vs Big Data vs Data Analytics.

Data Lake & Hadoop : How can they power your Analytics?

Data Lake & Hadoop : How can they power your Analytics?

Powering analytics through a data lake and Hadoop is one of the most effective ways to increase ROI. It’s also an effective way to ensure that the analytics team has all the right information moving forward.

Powering analytics through a data lake and Hadoop is one of the most effective ways to increase ROI. It’s also an effective way to ensure that the analytics team has all the right information moving forward.

There are many challenges that research teams have to face regularly, and Hadoop can aid in effective data management.

From storage to analysis, Hadoop can provide the necessary framework to enable research teams to do their work. Hadoop is also not confined to any single model of working or any only language. That's why it's a useful tool when it comes to scaling up. Since companies can perform greater research, there is more data generated. The data can be fed back into the system to create unique results for the final objective.

Data lakes are essential to maintaining as well. Since the core data lake enables your organization to scale, it's necessary to have a single repository of all enterprise data. Over 90% of the world’s data has been generated over the last few years, and data lakes have been a positive force in the space.

Why Hadoop is effective?

From a research stand-point, Hadoop is useful in more ways than one. It runs on a cluster of commodity servers and can scale up support thousands of nodes. This means that the quantity of data being handled is massive, and many data sources can be treated at the same time. This increases the effectiveness of Big Data, especially in the cases of IoT, Artificial intelligence, Machine Learning, and other new technologies.

It also provides rapid data access across the nodes in the cluster. Users can get an authorized access to a large subset of the data or the entire database. This makes the job of the researcher and the admin that much easier. Hadoop can also be scaled up as the requirement increases over time.

If an individual node fails, the entire cluster can take over. That’s the best part about Hadoop and why companies across the world use it for their research activities. Hadoop is being redefined year over year and has been an industry standard for decades now. Its full potential can be discovered best in the research and analytics space with data lakes.

HDFS – The Hadoop Distributed File System (HDFS) is the primary storage system that Hadoop employs, using a NameNode and DataNode architecture. It provides higher performance across the board and acts as a data distribution system for the enterprise.

YARN – YARN is the cluster resource manager that allocates system resources to apps and jobs. This simplifies the process of mapping out the adequate resources necessary. It’s one of the core components within the Hadoop infrastructure and schedules tasks around the nodes.

MapReduce – It’s a highly effective framework that converts data into a more simple set with individual elements broken down into tuples. From here, the data is translated into games and efficiencies are created within the data network. This is an excellent component for making sense of large data sets within the research space.

Hadoop Common - The common is a collection of standard utilities and libraries that support other modules. It’s a core component of the Hadoop framework and ensures that the resources are allocated correctly. They also provide a framework for the processing of data and information.

Hadoop and Big Data Research

Hadoop is highly effective when it comes to Big Data. This is because there are greater advantages associated with using the technology to it's fullest potential. Researchers can access a higher tier of information and leverage insights based on Hadoop resources. Hadoop can also enable better processing of data, across various systems and platforms.

Anytime there are complex calculations to be done and difficult simulations to execute, Hadoop needs to be put in place. Hadoop can help parallel computation across various coding environments to enable Big Data to create novel insights. Otherwise, there may be overlaps in processing, and the architecture could fail to produce ideas.

From a BI perspective, Hadoop is crucial. This is because while researchers can produce raw data over a significant period, it's essential to have streamlined access to it. Additionally, from a business perspective, it's necessary to have strengths in Big Data processing and storage. The availability of data is as important as access to it. This increases the load on the server, and a comprehensive architecture is required to process the information.

That's where Hadoop comes in. Hadoop can enable better processing and handling of the data being produced. It can also integrate different systems into a single data lake foundation. Added to that, Hadoop can enable better configuration across the enterprise architecture. Hadoop can take raw data and convert it into more useful insights. Anytime there is complexities and challenges, Hadoop can provide more clarity.

Hadoop is also a more enhanced version of simple data management tools. Hadoop can take raw data and insight and present it in a more consumable format. From here, researchers can make their conclusions and prepare intelligence reports that signify results. They can also accumulate on-going research data and feed it back into the central system. This makes for greater on-going analysis, while Hadoop becomes the framework to accomplish it on.

Security on Hadoop and Implementing Data Lakes

There are a significant number of attacks on big data warehouses and data lakes on an on-going basis. It’s essential to have an infrastructure that has a steady security feature built-in. This is where Hadoop comes in. Hadoop can provide those necessary security tools and allow for more secure data transitions.

In the healthcare space, data is critical to preserving. If patient data leaks out, it could lead to complications and health scares. Additionally, in the financial services domain, if data on credit card information and customer SSN leaks out, then there is a legal and PR problem on the rise. That’s why companies opt for greater control using the Hadoop infrastructure. Hadoop is also beneficial regarding providing a better framework for cybersecurity and interoperability. Data integrity is preserved throughout the network, and there is increased control via dashboards provided.

From issuing Kerberos to introducing physical authentication, the Hadoop cluster is increasingly useful in its operations. There is an additional layer of security built into the group, giving rise to a more consistent database environment. Individual tickets can be granted on the Kerberos framework and users can get authenticated using the module.

Security can be enhanced by working with third-party developers to improve your overall Hadoop and Data lake security. You can also increase the security parameters around the infrastructure by creating a stricter authentication and user-management portal and policy. From a cyber-compliance perspective, it’s a better mechanism to work through at scale.

Apache Ranger is also a useful tool to monitor the flow of data as well. This is increasingly important when performing research on proprietary data in the company. Healthcare companies know all too well the value of data, which is why the Ranger can monitor the flow of data throughout the organization. Apache YARN has enabled an exact Data lake approach when it comes to information architecture.

That's why the Ranger is effective in maintaining security. The protocol can be set at the admin level, and companies can design the right tool to take their research ahead. The Ranger can also serve as the end-point management system for when different devices connect onto the cluster.

The Apache Ranger is also a handy centralized interface. This gives greater control to researchers, and all stakeholders in the research and analytics space are empowered. Use-cases emerge much cleaner when there is smooth handling of all data. There is also a more systematic approach to analytics, as there is an access terminal of all authorized personnel. Certain tiers of researchers can gain access to certain types of data and others can get a broader data overview. This can help streamline the data management process and make the analytics process that much more effective.

The Ranger serves as a visa processing system that gives access based on the required authorization. This means that junior researchers don't get access to highly classified information. Senior level researchers can gain the right amount of insight into the matter at hand and dig deep into core research data. Additionally, analysts can gain access to the data they've been authorized to use.

This enables researchers to use Hadoop as an authorization management portal as well. Data can be back-tracked to figure out who used the data portal last. The entire cluster can become unavailable to increase security against outsiders. However, when researchers want a second opinion, they can turn towards consultants who can gain tertiary access to the portal.

Recognizing the analytics needs of researchers and data scientists

It’s important for researchers to understand the need for analytics and vice versa. Hadoop provides that critical interface connect disconnected points in the research ecosystem. Additionally, it creates a more collaborative environment within the data research framework.

Healthcare, Fintech, Consumer Goods and Media & Research companies need to have a more analytical approach when conducting research. That’s why Hadoop becomes critical to leverage, as it creates a more robust environment. The analytics needs are fully recognized by Hadoop, providing more tools for greater analytics.

For forming the right data lake, there needs to be a search engine in place. This helps in streamlining the data and adding a layer of analysis to the raw information. Additionally, researchers can retrieve specific information through the portal. They’re able to perform more excellent analysis of core data that is readily available to them.

Data scientists can uncover accurate insights when they’re able to analyze larger data sets. With emerging technologies like Spark and HBase, Hadoop becomes that much more advanced as an analytics tool. There are more significant advantages to operating with Hadoop and data scientists can see more meaningful results. Over time, there is more convergence with unique data management platforms providing a more coherent approach to data.

The analysis is fully recognized over Hadoop, owing to its scale and scope of work. Hadoop can become the first Data lake ecosystem, as it has a broad range within multiple applications. There is also greater emphasis given to the integrity of data, which is what all researchers need. From core principles to new technology additions, there are components within Hadoop that make it that much more reliable.

Democratizing data for Researchers & Scientists

Having free access to data within the framework is essential. Hadoop helps in developing that democratic data structure within the network. Forecasting to trend analysis can be made that much more straightforward, with a more democratic approach to data. Data can be indeed sorted and retrieved based on the access provided.

Data can also be shared with resources to enable a more collaborative environment in the research process. Otherwise, data sets may get muddied as more inputs stream into the data lake. The lake needs to have a robust democratized approach so that researchers can gain access to that when needed. Additionally, it's essential to have more streamlined access to the data, which is another advantage of using Hadoop. Researchers need to deploy the technology at scale to obtain benefits that come along with it.

Data scientists can also acquire cleaner data that is error-free. This is increasingly important when researchers want to present their findings to stakeholders as there is no problem with integrity. The democratization of data ensures that everyone has access to the data sets that they're authorized to understand. Outsiders may not gain access and can be removed from the overall architecture.

Scientists can also study some aspects of the data lake and acquire unique insights that come with it. From a healthcare perspective, a single outbreak or an exceptional case can bring in new ideas that weren’t previously there before. This also adds immense value to distributed instances wherein there is no single source identified. Unique participants can explore the data like and uncover what is required from it.

It’s essential to have a more democratic approach when it comes to data integrity and data lake development. When the data lake is well maintained, it creates more opportunities for analysis within the research space. Researchers can be assured that their data is being presented in the best light possible. They can also uncover hidden trends and new insights based on that initial connection. The information is also sorted and classified better, using Hadoop’s extensive line of solutions and tools built-in.

Researchers have an affinity for using Hadoop, owing to its scale-readiness and great solution base. They can also be used to present information via other platforms, providing it with a more democratic outlook. The data can also be transmitted and shared via compatible platforms across the board. The researchers present within the ecosystem can even compile data that is based on your initial findings. This helps in maintaining a clean record and a leaner model of data exploration.

Benefits & Challenges of Hadoop for enterprise analysis

Hadoop is one of the most excellent solutions in the marketplace for extensive research and enterprise adoption. This is because of its scale and tools available to accomplish complex tasks. Researchers can also leverage the core technology to avail its benefits across a wide range of solution models.

The data can also be shared from one platform to another, creating a community data lake wherein different participants can emerge. However, overall integrity is maintained throughout the ecosystem. This enables better communication within the system, giving rise to an enhanced approach to systems management.

Hadoop benefits the research community in the four main data formats –

Core research information – This is data produced during trials, research tests and any algorithms that may be running on Machine Learning or Artificial Intelligence. This also includes raw information that is shared with another resource. It also provides information that can be presented across the board.

Manufacturing and batches – This data is essential to maintain as it aids in proper verification of any tools or products being implemented. It also helps in the check of process owners and supplies chain leads.

Customer care – For enterprise-level adoption, customer care information must be presented and stored effectively.

Public records – Security is vital when it comes to handling public records. This is why Hadoop is used to provide security measures adequately.

One of the main problems with Hadoop is leveraging massive data sets to one detailed insight. Since Hadoop requires the right talent to uncover insights, it becomes a complicated procedure. Owing to its complexities it also needs better compliance frameworks that can define specific rules based on instances.

Additionally, as Hadoop is scaled up, there are challenges with storage and space management. That’s why cloud computing is emerging as a viable solution to prevent data loss due to storage errors. Hadoop is also facing complexities regarding data synchronization. That's why researchers need to ensure that all systems are compliant with Hadoop and can leverage the scope of the core platform. For best results, it's ideal to have a more holistic approach to Hadoop.


With the vast quantities of data being generated every day, there is a need for greater analytics and insight in the research space. While every industry, from Healthcare to Automobile, relies on data in some form, it’s essential to have a logical research architecture built into the system. Otherwise, there may be data inefficiencies and chances of the data lake getting contaminated. It’s best to opt for a hybrid Hadoop model with proper security and networking capabilities. When it comes to performing accurate research, it’s essential to have all the right tools with you.

How to Installing Kafka Docker on Kubernetes

How to Installing Kafka Docker on Kubernetes

A simple step-by-step tutorial on installing Kafka Docker on Kubernetes

In this ultimate guide I will give you a simple step-by-step tutorial on installing Kafka Docker on Kubernetes. This post includes a complete video walk-through.

There has been a lot of interest lately about deploying Kafka to a Kubernetes cluster. If you are wanting to take the deep dive yourself then you found the right article. Now that we have Kafka Docker, deploying a Kafka cluster to Kubernetes is a snap.

Deploy ZooKeeper to Kubernetes

Kafka relies on ZooKeeper to keep track of its configuration including what topics are available.

Before we deploy Kafka we need to deploy ZooKeeper.

Create a file called zookeeper.yml and add these contents:

kind: Deployment
apiVersion: extensions/v1beta1
name: zookeeper-deployment-1
app: zookeeper-1
- name: zoo1
image: digitalwonderland/zookeeper
- containerPort: 2181
value: "1"
value: zoo1

apiVersion: v1
kind: Service
name: zoo1
app: zookeeper-1

  • name: client
    port: 2181
    protocol: TCP
  • name: follower
    port: 2888
    protocol: TCP
  • name: leader
    port: 3888
    protocol: TCP
    app: zookeeper-1

    This creates a Kubernetes Deployment that will schedule zookeeper pods and a Kubernetes Service to route traffic to the pods. The service has a short name of zoo1 which we will use later when we deploy the Kafka Brokers.

    Create the resource:

    $ kubectl create -f zookeeper.yml

    Now lets start deploying Kafka.

    Deploying a Kafka Docker Service

    The first thing we need to do is deploy a Kubernetes Service that will manage our Kafka Broker deployments.

    Create a new file called kafka-service.yml and add the following contents:

    apiVersion: v1
    kind: Service
    name: kafka-service
    name: kafka
  • port: 9092
    name: kafka-port
    protocol: TCP
    app: kafka
    id: "0"
    type: LoadBalancer

    You might notice that we have set the type to LoadBalancer. If your Kubernetes Cluster is deployed to bare-metal don't freak out. There is a new Kubernetes add-on called MetalLB that allows this. Checkout my article Kubernetes metallb bare metal loadbalancer for instructions on how to enable it. It will make your life much easier.

    Create the service.

    $ kubectl create -f kafka-service

    Now we need to get the external IP for the service, because we will need it in order to spin up a Kafka Broker in the next section.

    $ kubectl describe svc kafka-service
    Name: kafka-service
    Namespace: default
    Labels: name=kafka
    Annotations: kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"v1","kind":"Service","metadata":{"annotations":{},"labels":{"name":"kafka"},"name":"kafka-service","namespace":"default"},"spec":{"ports...
    Selector: app=kafka,id=0
    Type: LoadBalancer
    LoadBalancer Ingress:
    Port: kafka-port 9092/TCP
    TargetPort: 9092/TCP
    NodePort: kafka-port 30718/TCP
    Session Affinity: None
    External Traffic Policy: Cluster
    Events: <none>

    In the example above, I would note that the LoadBalancer Ingress is set to Now we can start our Kafka Broker.

    Deploying the Kafka Broker to Kubernetes

    We have the Kubernetes Service deployed but all it does is load balance our Kafka pods which are not deployed yet.

    Follow these steps to deploy them.

    Create a new file called kafka-broker.yml and add the following contents:

    kind: Deployment
    apiVersion: extensions/v1beta1
    name: kafka-broker0
    app: kafka
    id: "0"
    • name: kafka
      image: wurstmeister/kafka
      • containerPort: 9092
        value: "30718"
        value: zoo1:2181
      • name: KAFKA_BROKER_ID
        value: "0"
        value: admintome-test:1:1

    Notice that the KAFKA_ADVERTISED_HOST_NAME is set to the IP address we noted earlier. Also note we tell the Kafka Broker to automatically create a topic admintome-test with 1 partition and 1 replica. You can create multiple topics using the same vernacular and separating them by commas (i.e. - topic1:1:1,topic2:1:1).

    Save the file and create the resource.

    $ create -f kafka-broker.yml

    You can validate that everything is running.

    $ kubectl get pod kafka-broker0

    To scale your Kafka Brokers, create another file but give it a different name (i.e. kafka-broker1) and update the ID to match.

    Lets test our Kafka Deployment

    Testing With KafkaCat

    We are going to test our Kafka deployment by using an application called KafkaCat.

    To install:

    $ apt-get install kafkacat

    After the application is installed we will run it in consumer mode (which is the default).

    kafkacat -b -t admintome-test

    This should not show anything yet because we haven't sent anything to our topic yet...

    To send stuff we can copy any text file into our current directory and send it to our Kafka Topic. In another window, run the following command.

    $ cat README | kafkacat -b -t admintome-test

    You should see the output in the first window which has KafkaCat still running in consumer mode.

    Congratulations! You have successfully deployed a Kafka Cluster to Kubernetes.

    Thanks for reading. If you liked this post, share it with all of your programming buddies!

    Further reading

    ☞ The Complete Node.js Developer Course (3rd Edition)

    ☞ Angular & NodeJS - The MEAN Stack Guide

    ☞ NodeJS - The Complete Guide (incl. MVC, REST APIs, GraphQL)

    ☞ MongoDB - The Complete Developer’s Guide

    ☞ The Complete Developers Guide to MongoDB

    ☞ Creating RESTful APIs with NodeJS and MongoDB Tutorial

    ☞ MEAN Stack Tutorial MongoDB, ExpressJS, AngularJS and NodeJS

    ☞ How To Build a Node.js Application with Docker

    ☞ Authenticate a Node ES6 API with JSON Web Tokens

    ☞ Creating a RESTful Web API with Node.js and Express.js from scratch

    Originally published on https://www.admintome.com