Deploying Kafka With the ELK Stack

Deploying Kafka With the ELK Stack

Deploying Kafka With the ELK Stack - In this article, I'll show how to deploy all the components required to set up a resilient data pipeline with the ELK Stack and Kafka.

Deploying Kafka With the ELK Stack - In this article, I'll show how to deploy all the components required to set up a resilient data pipeline with the ELK Stack and Kafka.

Following a production incident, and precisely when you need them the most, logs can suddenly surge and overwhelm your logging infrastructure. To protect Logstash and Elasticsearch against such data bursts, users deploy buffering mechanisms to act as message brokers.

Apache Kafka is the most common broker solution deployed together the ELK Stack. Usually, Kafka is deployed between the shipper and the indexer, acting as an entrypoint for the data being collected:

In this article, I'll show how to deploy all the components required to set up a resilient data pipeline with the ELK Stack and Kafka:

  • Filebeat - collects logs and forwards them to a Kafka topic.
  • Kafka - brokers the data flow and queues it.
  • Logstash - aggregates the data from the Kafka topic, processes it and ships to Elasticsearch.
  • Elasticsearch - indexes the data.
  • Kibana - for analyzing the data.
My Environment

To perform the steps below, I set up a single Ubuntu 16.04 machine on AWS EC2 using local storage. In real-life scenarios you will probably have all these components running on separate machines.

I started the instance in the public subnet of a VPC and then set up a security group to enable access from anywhere using SSH and TCP 5601 (for Kibana). Finally, I added a new elastic IP address and associated it with the running instance.

The example logs used for the tutorial are Apache access logs.

Step 1: Installing Elasticsearch

We will start with installing the main component in the stack — Elasticsearch. Since version 7.x, Elasticsearch has been bundled with Java so we can jump right ahead with adding Elastic's signing key:

wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo apt-key add -

For installing Elasticsearch on Debian, we also need to install the apt-transport-https package:

sudo apt-get update
sudo apt-get install apt-transport-https

Our next step is to add the repository definition to our system:

echo "deb https://artifacts.elastic.co/packages/7.x/apt stable main" | sudo 
tee -a /etc/apt/sources.list.d/elastic-7.x.list

All that's left to do is to update your repositories and install Elasticsearch:

sudo apt-get update && sudo apt-get install elasticsearch

Before we bootstrap Elasticsearch, we need to apply some basic configurations using the Elasticsearch configuration file at: */etc/elasticsearch/elasticsearch.yml *:

sudo su
vim /etc/elasticsearch/elasticsearch.yml

Since we are installing Elasticsearch on AWS, we will bind Elasticsearch to localhost. Also, we need to define the private IP of our EC2 instance as a master-eligible node:

network.host: "localhost"
http.port:9200
cluster.initial_master_nodes: ["<InstancePrivateIP"]

Save the file and run Elasticsearch with:

sudo service elasticsearch start

To confirm that everything is working as expected, point curl to: http://localhost:9200* *, and you should see something like the following output (give Elasticsearch a minute or two before you start to worry about not seeing any response):

{
  "name" : "ip-172-31-49-60",
  "cluster_name" : "elasticsearch",
  "cluster_uuid" : "yP0uMKA6QmCsXQon-rxawQ",
  "version" : {
    "number" : "7.0.0",
    "build_flavor" : "default",
    "build_type" : "deb",
    "build_hash" : "b7e28a7",
    "build_date" : "2019-04-05T22:55:32.697037Z",
    "build_snapshot" : false,
    "lucene_version" : "8.0.0",
    "minimum_wire_compatibility_version" : "6.7.0",
    "minimum_index_compatibility_version" : "6.0.0-beta1"
  },
  "tagline" : "You Know, for Search"
}

Step 2: Installing Logstash

Next up, the "L" in ELK — Logstash. Logstash will require us to install Java 8 which is fine because this is also a requirement for running Kafka:

sudo apt-get install default-jre

Verify java is installed:

java -version
openjdk version "1.8.0_191"
OpenJDK Runtime Environment (build 1.8.0_191-8u191-b12-2ubuntu0.16.04.1-b12)
OpenJDK 64-Bit Server VM (build 25.191-b12, mixed mode)

Since we already defined the repository in the system, all we have to do to install Logstash is run:

sudo apt-get install logstash

Next, we will configure a Logstash pipeline that pulls our logs from a Kafka topic, process these logs and ships them on to Elasticsearch for indexing.

Let's create a new config file:

sudo vim /etc/logstash/conf.d/apache.conf

Paste the following configurations:

input { 
  kafka { 
    bootstrap_servers => "localhost:9092" 
    topics => "apache" 
    } 
}
filter {
    grok {
      match => { "message" => "%{COMBINEDAPACHELOG}" }
    }
    date {
    match => [ "timestamp" , "dd/MMM/yyyy:HH:mm:ss Z" ]
    }
  geoip {
      source => "clientip"
    }
}
output {
  elasticsearch { 
    hosts => ["localhost:9200"] 
  }
}

As you can see, we're using the Logstash Kafka input plugin to define the Kafka host and the topic we want Logstash to pull from. We're applying some filtering to the logs and we're shipping the data to our local Elasticsearch instance.

Save the file.

Step 3: Installing Kibana

Let's move on to the next component in the ELK Stack - Kibana. As before, we will use a simple apt command to install Kibana:

sudo apt-get install kibana

We will then open up the Kibana configuration file at: /etc/kibana/kibana.yml, and make sure we have the correct configurations defined:

server.port: 5601
elasticsearch.url: "http://localhost:9200"

These specific configurations tell Kibana which Elasticsearch to connect to and which port to use.

Now, we can start Kibana with:

sudo service kibana start

Open up Kibana in your browser with: http://localhost:5601. You will be presented with the Kibana home page.

Step 4: Installing Filebeat

As mentioned above, we will be using Filebeat to collect the log files and forward them to Kafka.

To install Filebeat, we will use:

sudo apt-get install filebeat

Let's open the Filebeat configuration file at:

/etc/filebeat/filebeat.yml
sudo vim /etc/filebeat/filebeat.yml

Enter the following configurations:

filebeat.inputs:
- type: log
  enabled: true
  paths:
    - /var/log/apache2/access.log
output.kafka:
  codec.format:
    string: '%{[@timestamp]} %{[message]}'
  hosts: ["localhost:9092"]
  topic: apache
  partition.round_robin:
    reachable_only: false
  required_acks: 1
  compression: gzip
  max_message_bytes: 1000000

In the input section, we are telling Filebeat what logs to collect — Apache access logs. In the output section, we are telling Filebeat to forward the data to our local Kafka server and the relevant topic (to be installed in the next step).

Note the use of the *codec.format *directive — this is to make sure the message and timestamp fields are extracted correctly. Otherwise, the lines are sent in JSON to Kafka.

Save the file.

Step 4: Installing Kafka

Our last and final installation involves setting up Apache Kafka — our message broker.

Kafka uses ZooKeeper for maintaining configuration information and synchronization so we'll need to install ZooKeeper before setting up Kafka:

sudo apt-get install zookeeperd

Next, let's download and extract Kafka:

wget http://apache.mivzakim.net/kafka/2.2.0/kafka_2.12-2.2.0.tgz
tar -xvzf kafka_2.12-2.2.0.tgz
sudo cp -r kafka_2.12-2.2.0 /opt/kafka

We are now ready to run Kafka, which we will do with this script:

sudo /opt/kafka/bin/kafka-server-start.sh 
/opt/kafka/config/server.properties

You should begin to see some INFO messages in your console:

[2019-04-22 11:48:16,489] INFO Registered 
kafka:type=kafka.Log4jController MBean 
(kafka.utils.Log4jControllerRegistration$)
[2019-04-22 11:48:18,589] INFO starting (kafka.server.KafkaServer)

Next, we're going to create a topic for our Apache logs:

bin/kafka-topics.sh --create --zookeeper localhost:2181 
--replication-factor 1 --partitions 1 --topic apache
Created topic apache.

We are all set to start the pipeline.

Step 5: Starting the Data Pipeline

Now that we have all the pieces in place, it's time to start all the components in charge of running the data pipeline.

First, we'll start Filebeat:

sudo service filebeat start

Then, Logstash:

sudo service logstash start

It will take a few minutes for the pipeline to start streaming logs. To see Kafka in action, enter the following command in a separate tab:

/opt/kafka/bin/kafka-console-consumer.sh --bootstrap-server 
localhost:9092 --topic apache --from-beginning

If your Apache web server is indeed serving requests, you should begin to see the messages being forwarded to the Kafka topic by Filebeat in your console:

2019-04-23T13:50:01.559Z 89.138.90.236 - - [23/Apr/2019:13:50:00 +0000] 
"GET /mysite.html HTTP/1.1" 200 426 "-" "Mozilla/5.0 
(Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) 
Chrome/73.0.3683.86 Safari/537.36"
2019-04-23T13:51:36.581Z 89.138.90.236 - - 
[23/Apr/2019:13:51:34 +0000] "GET /mysite.html HTTP/1.1" 200 427 "-" 
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 
(KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36"

To make sure Logstash is aggregating the data and shipping it into Elasticsearch, use:

curl -X GET "localhost:9200/_cat/indices?v"

If all is working as expected, you should see a logstash-* index listed:

health status index                      uuid                   pri rep docs.count docs.deleted store.size pri.store.size
green  open   .kibana_task_manager       zmMH6yy8Q6yg2jJHxq3MFA   1   0          2            0     45.4kb         45.4kb
yellow open   logstash-2019.04.23-000001 rBx5r_gIS3W2dTxHzGJVvQ   1   1          9            0     69.4kb         69.4kb
green  open   .kibana_1                  rv5f8uHnQTCGe8YrcKAwlQ   1   0          5            0

If you don't see this index, it's time to do some debugging I'm afraid. Check out this blog post for some tips on debugging Logstash.

All we have to do now is define the index pattern in Kibana to begin analysis. This is done under Management → Kibana Index Patterns.

Kibana will identify the index, so simply define it in the relevant field and continue on to the next step of selecting the timestamp field:

Once you create the index pattern, you'll see a list of all the parsed and mapped fields:

Open the Discover page to begin analyzing your data!

Summing it Up

Resilient data pipelines are a must in any production-grade ELK deployments. Logs are crucial for piecing together events and they are needed most in emergencies. We cannot afford to have our logging infrastructure fail at the exact point in time when it is most required. Kafka, and similar brokers, play a huge part in buffering the data flow so Logstash and Elasticsearch don't cave under the pressure of a sudden burst.

The example above is a basic setup of course. Production deployments will include multiple Kafka instances, a much larger amount of data and much more complicated pipelines. This will involve a considerable amount of engineering time and resources, something to take into consideration. The instructions here will help you understand how to get started though. I also recommend taking a look at our article explaining how to log Kafka itself.

Learn Data Science | How to Learn Data Science for Free

Learn Data Science | How to Learn Data Science for Free

Learn Data Science | How to Learn Data Science for Free. In this post, I have described a learning path and free online courses and tutorials that will enable you to learn data science for free.

The average cost of obtaining a masters degree at traditional bricks and mortar institutions will set you back anywhere between $30,000 and $120,000. Even online data science degree programs don’t come cheap costing a minimum of $9,000. So what do you do if you want to learn data science but can’t afford to pay this?

I trained into a career as a data scientist without taking any formal education in the subject. In this article, I am going to share with you my own personal curriculum for learning data science if you can’t or don’t want to pay thousands of dollars for more formal study.

The curriculum will consist of 3 main parts, technical skills, theory and practical experience. I will include links to free resources for every element of the learning path and will also be including some links to additional ‘low cost’ options. So if you want to spend a little money to accelerate your learning you can add these resources to the curriculum. I will include the estimated costs for each of these.

Technical skills

The first part of the curriculum will focus on technical skills. I recommend learning these first so that you can take a practical first approach rather than say learning the mathematical theory first. Python is by far the most widely used programming language used for data science. In the Kaggle Machine Learning and Data Science survey carried out in 2018 83% of respondents said that they used Python on a daily basis. I would, therefore, recommend focusing on this language but also spending a little time on other languages such as R.

Python Fundamentals

Before you can start to use Python for data science you need a basic grasp of the fundamentals behind the language. So you will want to take a Python introductory course. There are lots of free ones out there but I like the Codeacademy ones best as they include hands-on in-browser coding throughout.

I would suggest taking the introductory course to learn Python. This covers basic syntax, functions, control flow, loops, modules and classes.

Data analysis with python

Next, you will want to get a good understanding of using Python for data analysis. There are a number of good resources for this.

To start with I suggest taking at least the free parts of the data analyst learning path on dataquest.io. Dataquest offers complete learning paths for data analyst, data scientist and data engineer. Quite a lot of the content, particularly on the data analyst path is available for free. If you do have some money to put towards learning then I strongly suggest putting it towards paying for a few months of the premium subscription. I took this course and it provided a fantastic grounding in the fundamentals of data science. It took me 6 months to complete the data scientist path. The price varies from $24.50 to $49 per month depending on whether you pay annually or not. It is better value to purchase the annual subscription if you can afford it.

The Dataquest platform

Python for machine learning

If you have chosen to pay for the full data science course on Dataquest then you will have a good grasp of the fundamentals of machine learning with Python. If not then there are plenty of other free resources. I would focus to start with on scikit-learn which is by far the most commonly used Python library for machine learning.

When I was learning I was lucky enough to attend a two-day workshop run by Andreas Mueller one of the core developers of scikit-learn. He has however published all the material from this course, and others, on this Github repo. These consist of slides, course notes and notebooks that you can work through. I would definitely recommend working through this material.

Then I would suggest taking some of the tutorials in the scikit-learn documentation. After that, I would suggest building some practical machine learning applications and learning the theory behind how the models work — which I will cover a bit later on.

SQL

SQL is a vital skill to learn if you want to become a data scientist as one of the fundamental processes in data modelling is extracting data in the first place. This will more often than not involve running SQL queries against a database. Again if you haven’t opted to take the full Dataquest course then here are a few free resources to learn this skill.

Codeacamdemy has a free introduction to SQL course. Again this is very practical with in-browser coding all the way through. If you also want to learn about cloud-based database querying then Google Cloud BigQuery is very accessible. There is a free tier so you can try queries for free, an extensive range of public datasets to try and very good documentation.

Codeacademy SQL course

R

To be a well-rounded data scientist it is a good idea to diversify a little from just Python. I would, therefore, suggest also taking an introductory course in R. Codeacademy have an introductory course on their free plan. It is probably worth noting here that similar to Dataquest Codeacademy also offers a complete data science learning plan as part of their pro account (this costs from $31.99 to $15.99 per month depending on how many months you pay for up front). I personally found the Dataquest course to be much more comprehensive but this may work out a little cheaper if you are looking to follow a learning path on a single platform.

Software engineering

It is a good idea to get a grasp of software engineering skills and best practices. This will help your code to be more readable and extensible both for yourself and others. Additionally, when you start to put models into production you will need to be able to write good quality well-tested code and work with tools like version control.

There are two great free resources for this. Python like you mean it covers things like the PEP8 style guide, documentation and also covers object-oriented programming really well.

The scikit-learn contribution guidelines, although written to facilitate contributions to the library, actually cover the best practices really well. This covers topics such as Github, unit testing and debugging and is all written in the context of a data science application.

Deep learning

For a comprehensive introduction to deep learning, I don’t think that you can get any better than the totally free and totally ad-free fast.ai. This course includes an introduction to machine learning, practical deep learning, computational linear algebra and a code-first introduction to natural language processing. All their courses have a practical first approach and I highly recommend them.

Fast.ai platform

Theory

Whilst you are learning the technical elements of the curriculum you will encounter some of the theory behind the code you are implementing. I recommend that you learn the theoretical elements alongside the practical. The way that I do this is that I learn the code to be able to implement a technique, let’s take KMeans as an example, once I have something working I will then look deeper into concepts such as inertia. Again the scikit-learn documentation contains all the mathematical concepts behind the algorithms.

In this section, I will introduce the key foundational elements of theory that you should learn alongside the more practical elements.

The khan academy covers almost all the concepts I have listed below for free. You can tailor the subjects you would like to study when you sign up and you then have a nice tailored curriculum for this part of the learning path. Checking all of the boxes below will give you an overview of most elements I have listed below.

Maths

Calculus

Calculus is defined by Wikipedia as “the mathematical study of continuous change.” In other words calculus can find patterns between functions, for example, in the case of derivatives, it can help you to understand how a function changes over time.

Many machine learning algorithms utilise calculus to optimise the performance of models. If you have studied even a little machine learning you will probably have heard of Gradient descent. This functions by iteratively adjusting the parameter values of a model to find the optimum values to minimise the cost function. Gradient descent is a good example of how calculus is used in machine learning.

What you need to know:

Derivatives

  • Geometric definition
  • Calculating the derivative of a function
  • Nonlinear functions

Chain rule

  • Composite functions
  • Composite function derivatives
  • Multiple functions

Gradients

  • Partial derivatives
  • Directional derivatives
  • Integrals

Linear Algebra

Many popular machine learning methods, including XGBOOST, use matrices to store inputs and process data. Matrices alongside vector spaces and linear equations form the mathematical branch known as Linear Algebra. In order to understand how many machine learning methods work it is essential to get a good understanding of this field.

What you need to learn:

Vectors and spaces

  • Vectors
  • Linear combinations
  • Linear dependence and independence
  • Vector dot and cross products

Matrix transformations

  • Functions and linear transformations
  • Matrix multiplication
  • Inverse functions
  • Transpose of a matrix

Statistics

Here is a list of the key concepts you need to know:

Descriptive/Summary statistics

  • How to summarise a sample of data
  • Different types of distributions
  • Skewness, kurtosis, central tendency (e.g. mean, median, mode)
  • Measures of dependence, and relationships between variables such as correlation and covariance

Experiment design

  • Hypothesis testing
  • Sampling
  • Significance tests
  • Randomness
  • Probability
  • Confidence intervals and two-sample inference

Machine learning

  • Inference about slope
  • Linear and non-linear regression
  • Classification

Practical experience

The third section of the curriculum is all about practice. In order to truly master the concepts above you will need to use the skills in some projects that ideally closely resemble a real-world application. By doing this you will encounter problems to work through such as missing and erroneous data and develop a deep level of expertise in the subject. In this last section, I will list some good places you can get this practical experience from for free.

“With deliberate practice, however, the goal is not just to reach your potential but to build it, to make things possible that were not possible before. This requires challenging homeostasis — getting out of your comfort zone — and forcing your brain or your body to adapt.”, Anders Ericsson, Peak: Secrets from the New Science of Expertise

Kaggle, et al

Machine learning competitions are a good place to get practice with building machine learning models. They give access to a wide range of data sets, each with a specific problem to solve and have a leaderboard. The leaderboard is a good way to benchmark how good your knowledge at developing a good model actually is and where you may need to improve further.

In addition to Kaggle, there are other platforms for machine learning competitions including Analytics Vidhya and DrivenData.

Driven data competitions page

UCI Machine Learning Repository

The UCI machine learning repository is a large source of publically available data sets. You can use these data sets to put together your own data projects this could include data analysis and machine learning models, you could even try building a deployed model with a web front end. It is a good idea to store your projects somewhere publically such as Github as this can create a portfolio showcasing your skills to use for future job applications.


UCI repository

Contributions to open source

One other option to consider is contributing to open source projects. There are many Python libraries that rely on the community to maintain them and there are often hackathons held at meetups and conferences where even beginners can join in. Attending one of these events would certainly give you some practical experience and an environment where you can learn from others whilst giving something back at the same time. Numfocus is a good example of a project like this.

In this post, I have described a learning path and free online courses and tutorials that will enable you to learn data science for free. Showcasing what you are able to do in the form of a portfolio is a great tool for future job applications in lieu of formal qualifications and certificates. I really believe that education should be accessible to everyone and, certainly, for data science at least, the internet provides that opportunity. In addition to the resources listed here, I have previously published a recommended reading list for learning data science available here. These are also all freely available online and are a great way to complement the more practical resources covered above.

Thanks for reading!

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 Science Training | Data Science for Beginners | Intellipaat - YouTube

Data Science Training  | Data Science for Beginners | Intellipaat - YouTube

In this data science for beginners video you will learn complete data science starting from what is data science to various data science concepts, data science projects and data science interview questions & answers. This is an end to end data science full course.

Why Data Science is important?

Data Science is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most important aspects of Data Science that are being deployed everywhere from search engines to online movie recommendations. Taking the Intellipaat Data Science training & Data Science Course can help professionals to build a solid career in a rising technology domain and get the best jobs in top organizations.

Why should you opt for a Data Science career?

If you want to fast-track your career then you should strongly consider Data Science. The reason for this is that it is one of the fastest growing technology. There is a huge demand for Data Scientist. The salaries for Data Scientist is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat Data Science project tutorial is your stepping stone to a successful career!