Unit-I_Big data life cycle.pptx, sources of Big Data

Course – Big Data Analytics (Professional Elective-II)
Course code-IT314B
Unit-II- BIG DATA ANALYTICS LIFE CYCLE
Sanjivani Rural Education Society’s
Sanjivani College of Engineering, Kopargaon-423603
(An Autonomous Institute Affiliated to Savitribai Phule Pune University, Pune)
NAAC ‘A’ Grade Accredited, ISO 9001:2015 Certified
Department of Information Technology
(NBA Accredited)
Mr. Rajendra N Kankrale
Asst. Prof.
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BDA- Unit-I BIG DATA ANALYTICS LIFE CYCLE Department of IT
Unit-I BIG DATA ANALYTICS LIFE CYCLE
• Syllabus
• Introduction to Big Data, sources of Big Data, Data Analytic Lifecycle:
Introduction, Phase 1: Discovery, Phase 2: Data Preparation, Phase 3: Model
Planning, Phase 4: Model Building, Phase 5: Communication results, Phase 6:
Operationalize.
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Unit-I BIG DATA ANALYTICS LIFE CYCLE
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
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Why Big Data analytics?
Take the music streaming platform Spotify for example. The company has nearly 96
million users that generate a tremendous amount of data every day. Through this
information, the cloud-based platform automatically generates suggested songs—
through a smart recommendation engine—based on likes, shares, search history, and
more. What enables this is the techniques, tools, and frameworks that are a result of
Big Data analytics.
If you are a Spotify user, then you must have come across the top recommendation
section, which is based on your likes, past history, and other things. Utilizing a
recommendation engine that leverages data filtering tools that collect data and then
filter it using algorithms works. This is what Spotify does.
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Uses and Examples of Big Data Analytics
There are many different ways that Big Data analytics can be used in order to improve
businesses and organizations. Here are some examples:
• Using analytics to understand customer behavior in order to optimize the customer
experience
• Predicting future trends in order to make better business decisions
• Improving marketing campaigns by understanding what works and what doesn't
• Increasing operational efficiency by understanding where bottlenecks are and how
to fix them
• Detecting fraud and other forms of misuse sooner
These are just a few examples — the possibilities are really endless when it comes to
Big Data analytics. It all depends on how you want to use it in order to improve your
business.
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What is Big Data analytics?
• What is Big Data?
• Big Data is a massive amount of data sets that cannot be stored, processed, or
analyzed using traditional tools.
• Big Data analytics is a process used to extract meaningful insights, such as
hidden patterns, unknown correlations, market trends, and customer
preferences. Big Data analytics provides various advantages—it can be used
for better decision making, preventing fraudulent activities, among other
things.
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Big Data sources
• Today, there are millions of data sources that generate data at a very rapid
rate. These data sources are present across the world. Some of the largest
sources of data are social media platforms and networks. Let’s use
Facebook as an example—it generates more than 500 terabytes of data
every day. This data includes pictures, videos, messages, and more.
• Data also exists in different formats, like structured data, semi-structured
data, and unstructured data. For example, in a regular Excel sheet, data is
classified as structured data—with a definite format. In contrast, emails fall
under semi-structured, and your pictures and videos fall under unstructured
data. All this data combined makes up Big Data.
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Types of Big Data analytics
The following are the four types of big data analytics:
1. Prescriptive Analytics- (What is the solution?)
2. Diagnostic Analytics- (why did happened?)
3. Predictive Analytics- (What will happen?)
4. Descriptive Analytics- (What has happened ?)
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Tools used in Big Data analytics
• Apache Spark: Spark is a framework for real-time data analytics, which
is a part of the Hadoop ecosystem.
• Python: Python is one of the most versatile programming languages that
is rapidly being deployed for various applications including machine
learning.
• SAS: SAS is an advanced analytical tool that is used for working with large
volumes of data and deriving valuable insights from it.
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• Hadoop: Hadoop is the most popular big data framework that is
deployed by a wide range of organizations from around the world for
making sense of big data.
• SQL: SQL is used for working with relational database management
systems.
• Tableau: Tableau is the most popular business intelligence tool that is
deployed for the purpose of data visualization and business analytics.
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• Splunk: Splunk is the tool of choice for parsing machine-generated data
and deriving valuable business insights out of it.
• R: R is the no. 1 programming language that is being used by data
scientists for statistical computing and graphical applications alike.
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Cassandra
APACHE Cassandra is an open-source NoSQL distributed database that is used to
fetch large amounts of data. It’s one of the most popular tools for data analytics and
has been praised by many tech companies due to its high scalability and availability
without compromising speed and performance. It is capable of delivering thousands of
operations every second and can handle petabytes of resources with almost zero
downtime. It was created by Facebook back in 2008 and was published publicly.
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Apache Storm
A storm is a robust, user-friendly tool used for data analytics, especially in
small companies. The best part about the storm is that it has no language
barrier (programming) in it and can support any of them. It was designed to
handle a pool of large data in fault-tolerance and horizontally scalable
methods. When we talk about real-time data processing, Storm leads the chart
because of its distributed real-time big data processing system, due to which
today many tech giants are using APACHE Storm in their system. Some of the
most notable names are Twitter, Zendesk, NaviSite, etc.
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Applications of Big Data Analytics
• Customer Acquisition and Retention: Customer information helps tremendously
in marketing trends, through data-driven actions, to increase customer satisfaction.
For example, personalization engines for Netflix, Amazon, and Spotify help with
improved customer experiences and gaining customer loyalty.
• Targeted Ads: Personalized data about interaction patterns, order history, and
product page viewing history can help immensely to create targeted ad campaigns
for customers on a larger scale and at the individual level.
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Applications of Big Data Analytics
• Product Development: It can generate insights on development decisions, product
viability, performance measurements, etc., and direct improvements that positively
serve the customers.
• Price Optimization: Pricing models can be modeled and used by retailers with the
help of diverse data sources to maximize revenues.
• Supply Chain and Channel Analytics: Predictive analytical models help with
B2B supplier networks, preemptive replenishment, route optimizations, inventory
management, and notification of potential delays in deliveries.
• Risk Management: It helps in the identification of new risks with the help of data
patterns for the purpose of developing effective risk management strategies.
• Improved Decision-making: The insights that are extracted from the data can help
enterprises make sound and quick decisions.
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Examples/Areas Using Big Data Analytics Tools
• Healthcare: Big data analytics technologies and tools are being used in healthcare
to predict patient outcomes, identify at-risk patients, and improve population health.
• Retail: Big data analytics tools are being used by retailers to improve customer
experience, target marketing campaigns, and prevent fraud.
• Manufacturing: Big data analytics tools are being used in manufacturing to
improve quality control, reduce downtime, and optimize production processes.
• Banking: Real time big data analytics tools are being used by banks to detect
fraudulent activities, prevent money laundering, and improve customer service.
• Government: Big data analytics tools are being used by government agencies to
improve public services, combat fraud and corruption, and better understand citizen
needs.
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• Life Cycle of Data Analytics
• The Data analytics lifecycle was designed to address Big Data problems and data
science projects. The process is repeated to show the real projects. To address the
specific demands for conducting analysis on Big Data, the step-by-step
methodology is required to plan the various tasks associated with the acquisition,
processing, analysis, and recycling of data.
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• Phase 1: Discovery –
• The data science team is trained and researches the issue.
• Create context and gain understanding.
• Learn about the data sources that are needed and accessible to the project.
• The team comes up with an initial hypothesis, which can be later confirmed
with evidence.
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• Phase 1: Discovery –
• The first phase of the Data Analytics Lifecycle is the data discovery step. This
stage involves identifying potential data sources, both internal and external,
that are relevant to the business problem at hand. It is essential to define the
scope of the analysis and gather data from various databases, applications, and
online repositories. Data can come in different formats, including structured,
unstructured, and semi-structured data.
• The key to success in this phase is to ensure the data collected is accurate,
relevant, and comprehensive. Missing or flawed data can lead to misleading
insights and decisions down the line. Rigorous data quality checks and
validation procedures are necessary to maintain data integrity.
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• Phase 2: Data Preparation -
• Once the data is collected, it is crucial to clean and preprocess it before
analysis. Data preparation involves identifying and rectifying errors,
duplications, and inconsistencies in the dataset. This process ensures that the
data is of high quality and ready for further analysis.
• Data preprocessing tasks may include data transformation, normalisation, and
handling missing values. Cleaning and preprocessing are time-consuming but
vital steps that significantly impact the accuracy and reliability of the final
results. Proper data preprocessing can also help in dealing with noise and
irrelevant data, leading to better outcomes.
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• Methods to investigate the possibilities of pre-processing, analysing, and
preparing data before analysis and modelling.
• It is required to have an analytic sandbox. The team performs, loads, and
transforms to bring information to the data sandbox.
• Data preparation tasks can be repeated and not in a predetermined sequence.
• Some of the tools used commonly for this process include - Hadoop, Alpine
Miner, Open Refine, etc.-
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• Data preparation and processing involves gathering, sorting, processing and
purifying collected information to make sure it can be utilized by subsequent
steps of analysis.
• Data Collection: Draw information from external sources.
• Data Entry: Within an organization, data entry refers to creating new points of
information using either digital technologies or manual input procedures.
• Signal Reception: Accumulating data from digital devices like the Internet of
Things devices and control systems.
• An analytical sandbox is essential during the data preparation stage of data
analytics Life Cycle. This scalable platform is used by data analysts and
scientists alike for processing their data sets; once executed, loaded, or altered
it resides securely inside this sandbox for later examination and modification.
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• Phase 3: Model Planning -
• The team studies data to discover the connections between variables. Later, it
selects the most significant variables as well as the most effective models.
• In this phase, the data science teams create data sets that can be used for
training for testing, production, and training goals.
• The team builds and implements models based on the work completed in the
modelling planning phase.
• Some of the tools used commonly for this stage are MATLAB and
STASTICA.
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• Phase 3: Model Planning -
• The team studies data to discover the connections between variables. Later, it
selects the most significant variables as well as the most effective models.
• In this phase, the data science teams create data sets that can be used for
training for testing, production, and training goals.
• The team builds and implements models based on the work completed in the
modelling planning phase.
• Some of the tools used commonly for this stage are MATLAB and
STASTICA.
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• Phase 4: Model Building -
• The team creates datasets for training, testing as well as production use.
• The team is also evaluating whether its current tools are sufficient to run the
models or if they require an even more robust environment to run models.
• Tools that are free or open-source or free tools Rand PL/R, Octave, WEKA.
• Commercial tools - MATLAB, STASTICA.
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• Phase 5: Communication Results -
• Following the execution of the model, team members will need to evaluate the
outcomes of the model to establish criteria for the success or failure of the
model.
• The team is considering how best to present findings and outcomes to the
various members of the team and other stakeholders while taking into
consideration cautionary tales and assumptions.
• The team should determine the most important findings, quantify their value to
the business and create a narrative to present findings and summarize them to
all stakeholders.
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• Phase 6: Operationalize -
• The team distributes the benefits of the project to a wider audience. It sets up a
pilot project that will deploy the work in a controlled manner prior to
expanding the project to the entire enterprise of users.
• This technique allows the team to gain insight into the performance and
constraints related to the model within a production setting at a small scale and
then make necessary adjustments before full deployment.
• The team produces the last reports, presentations, and codes.
• Open source or free tools such as WEKA, SQL, MADlib, and Octave.
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Lifecycle of Big Data analytics
The Big Data Analytics Life cycle is divided into nine phases, named as :
1. Business Case/Problem Definition
2. Data Identification
3. Data Acquisition and filtration
4. Data Extraction
5. Data Munging(Validation and Cleaning)
6. Data Aggregation & Representation(Storage)
7. Exploratory Data Analysis
8. Data Visualization(Preparation for Modeling and Assessment)
9. Utilization of analysis results.
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Unit-I_Big data life cycle.pptx, sources of Big Data

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