Introduction to Big Data
Big Data is a massive collection of data that is growing exponentially over time.
It is a data set that is so large and complex that traditional data management tools cannot store or process it efficiently.
Big data is a type of data that is extremely large in size.
Content:
Introduction
What is Big Data?
Big Data facts
Three Characteristics of Big Data
Storing Big Data
THE STRUCTURE OF BIG DATA
WHY BIG DATA
HOW IS BIG DATA DIFFERENT?
BIG DATA SOURCES
BIG DATA ANALYTICS
TYPES OF TOOLS USED IN BIG-DATA
Application Of Big Data analytics
HOW BIG DATA IMPACTS ON IT
RISKS OF BIG DATA
BENEFITS OF BIG DATA
Future of big data
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
I often hear from clients: “We don’t know much about Big Data – can you tell us what it is and how it can help our business?” Yes! The first step is this vendor-free presentation, where I start with a business level discussion, not a technical one. Big Data is an opportunity to re-imagine our world, to track new signals that were once impossible, to change the way we experience our communities, our places of work and our personal lives. I will help you to identify the business value opportunity from Big Data and how to operationalize it. Yes, we will cover the buzz words: modern data warehouse, Hadoop, cloud, MPP, Internet of Things, and Data Lake, but I will show use cases to better understand them. In the end, I will give you the ammo to go to your manager and say “We need Big Data an here is why!” Because if you are not utilizing Big Data to help you make better business decisions, you can bet your competitors are.
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
Content:
Introduction
What is Big Data?
Big Data facts
Three Characteristics of Big Data
Storing Big Data
THE STRUCTURE OF BIG DATA
WHY BIG DATA
HOW IS BIG DATA DIFFERENT?
BIG DATA SOURCES
BIG DATA ANALYTICS
TYPES OF TOOLS USED IN BIG-DATA
Application Of Big Data analytics
HOW BIG DATA IMPACTS ON IT
RISKS OF BIG DATA
BENEFITS OF BIG DATA
Future of big data
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
I often hear from clients: “We don’t know much about Big Data – can you tell us what it is and how it can help our business?” Yes! The first step is this vendor-free presentation, where I start with a business level discussion, not a technical one. Big Data is an opportunity to re-imagine our world, to track new signals that were once impossible, to change the way we experience our communities, our places of work and our personal lives. I will help you to identify the business value opportunity from Big Data and how to operationalize it. Yes, we will cover the buzz words: modern data warehouse, Hadoop, cloud, MPP, Internet of Things, and Data Lake, but I will show use cases to better understand them. In the end, I will give you the ammo to go to your manager and say “We need Big Data an here is why!” Because if you are not utilizing Big Data to help you make better business decisions, you can bet your competitors are.
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
Lake Database Database Template Map Data in Azure Synapse AnalyticsErwin de Kreuk
Database templates in Synapse Analytics are blueprints which can be used by organizations to plan, architect and design solutions.
How can we use these Database Templates in a day-to-day business, in order to speed up to automate this process?
Map data tool can help us with that
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Disclaimer :
The images, company, product and service names that are used in this presentation, are for illustration purposes only. All trademarks and registered trademarks are the property of their respective owners.
Data/Image collected from various sources from Internet.
Intention was to present the big picture of Big Data & Hadoop
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
Power BI Governance - Access Management, Recommendations and Best PracticesLearning SharePoint
This document outlines permissions management for Power BI Workspace and features of new admin, Member and Contributor Roles. Recommendations and best practices for sharing report are also included. Free to Download.
Big data is a huge volume of heterogenous data often generated at high speed.Big data cannot be handles with traditional data analytic tools. Hadoop is one of the mostly used big data analytic tool.Map Reduce, hive, hbase are also the tools for analysis in big data.
Lake Database Database Template Map Data in Azure Synapse AnalyticsErwin de Kreuk
Database templates in Synapse Analytics are blueprints which can be used by organizations to plan, architect and design solutions.
How can we use these Database Templates in a day-to-day business, in order to speed up to automate this process?
Map data tool can help us with that
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Disclaimer :
The images, company, product and service names that are used in this presentation, are for illustration purposes only. All trademarks and registered trademarks are the property of their respective owners.
Data/Image collected from various sources from Internet.
Intention was to present the big picture of Big Data & Hadoop
Very basic Introduction to Big Data. Touches on what it is, characteristics, some examples of Big Data frameworks. Hadoop 2.0 example - Yarn, HDFS and Map-Reduce with Zookeeper.
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
Power BI Governance - Access Management, Recommendations and Best PracticesLearning SharePoint
This document outlines permissions management for Power BI Workspace and features of new admin, Member and Contributor Roles. Recommendations and best practices for sharing report are also included. Free to Download.
Big data is a huge volume of heterogenous data often generated at high speed.Big data cannot be handles with traditional data analytic tools. Hadoop is one of the mostly used big data analytic tool.Map Reduce, hive, hbase are also the tools for analysis in big data.
Big Data, NoSQL, NewSQL & The Future of Data ManagementTony Bain
It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward.
These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to drive deep insights will become a key competitive advantage for many organisations in the future.
Join Tony Bain as he takes us through both the high level drivers for the changes in technology, how these are relevant to the enterprise and an overview of the possibilities a Big Data strategy can start to unlock.
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
The presentation includes the introduction to the topic, the various dimensions of big data, its evolution from big data 1.0 to bid data 3.0 and its impact on various industries, uses as well as the challenges it faces. The concluding slide gives a brief on the future of big data.
Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications.
to effectively analyze this kind of information is now seen as a key competitive advantage to better inform decisions. In order to do so, organizations employ Sentiment Analysis (SA) techniques on these data. However, the usage of social media around the world is ever-increasing, which considerably accelerates massive data generation and makes traditional SA systems unable to deliver useful insights. Such volume of data can be efficiently analyzed using the combination of SA techniques and Big Data technologies. In fact, big data is not a luxury but an essential necessary to make valuable predictions. However, there are some challenges associated with big data such as quality that could highly affect the SA systems’ accuracy that use huge volume of data. Thus, the quality aspect should be addressed in order to build reliable and credible systems. For this, the goal of our research work is to consider Big Data Quality Metrics (BDQM) in SA that rely of big data. In this paper, we first highlight the most eloquent BDQM that should be considered throughout the Big Data Value Chain (BDVC) in any big data project. Then, we measure the impact of BDQM on a novel SA method accuracy in a real case study by giving simulation results.
Big Data is one of the emerging areas in today's technological world. In this socially active world, data is growing at a tremendous pace of 2.5 quintillion bytes a day roughly that is only set to increase over the coming years.
Here is a guide for all beginners who express interest in this new field - Big Data.
BIG DATA ANALYTICS
USING R
Analytics is the combination of mathematical, statistical, and heuristic techniques to glean useful insights from data and to implement actions derived from those insights.
Big Data Analytics servicesWe offer our service of Big Data Analytics for you to be able to see further progress and business prospects. To gain an insight into marketing trends and always be one step ahead of your business rivals, we resort to the following tools:
Data mining.We make your data meaningful to predict future outcomes.
StatisticsWe use statistics to measure the quality of data, define uncertainties and extract only accurate data.
Data modelingWe structure data in order so that it can feet the needs of application
Machine learningWe use machine learning to gather, integrate and process huge volumes of data.
Database managementOur services also include database management, which allows to collect, track and store stream of data, build data warehouses and make a data processing efficient. More than that, you can also receive support and maintenance of your database software, if there is such a need.
Big data visualizationBig data visualization promotes better understanding of the whole data, by breaking it into pieces with the help of colors, graphs, symbols etc.
Business IntelligenceUse Business Intelligence services to receive the assessment and summary of current situations from the point of view of market trends, financial reporting, budget planning, customer analysis and many more.
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
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Introduction to Big Data
1. Introduction to Big Data
Big Data & IoT
Lecture #2
Umair Shafique (03246441789)
Scholar MS Information Technology - University of Gujrat
2. CONTENT
• What is Big Data?
• What is an example of Big Data?
• Why Is Big Data Important?
• Big Data Analytics
• Benefits of Big Data Analytics
• Types of Big Data
• Characteristics of Big Data
• Primary Source of Big Data
• Big Data Tools and Software
• Big Data Mining
• Top Trends in Big Data
3. WHAT IS BIG DATA?
• Big Data is a massive collection of data that is growing exponentially
over time.
• It is a data set that is so large and complex that traditional data
management tools cannot store or process it efficiently.
• Big data is a type of data that is extremely large in size.
4. WHAT IS AN EXAMPLE OF BIG DATA?
• The following are some Big Data examples:
• The New York Stock Exchange, for example, generates approximately one
terabyte of new trade data per day.
• The statistic shows that 500+terabytes of new data get ingested into the
databases of social media site Facebook, every day. This data is mainly
generated in terms of photo and video uploads, message exchanges, putting
comments etc.
• A single Jet engine can generate 10+terabytes of data in 30 minutes of flight
time. With many thousand flights per day, generation of data reaches up to
many Petabytes.
5. WHY IS BIG DATA IMPORTANT?
• Companies use big data in their systems to improve operations,
provide better customer service, create personalized marketing
campaigns and take other actions that, ultimately, can increase
revenue and profits.
• Big data is also used by medical researchers to identify disease signs
and risk factors and by doctors to help diagnose illnesses and medical
conditions in patients.
• In addition, a combination of data from electronic health records,
social media sites, the web and other sources gives healthcare
organizations and government agencies up-to-date information on
infectious disease threats or outbreaks.
6. BIG DATA ANALYTICS
• Big data analytics examines large amounts of data to uncover hidden
patterns, correlations and other insights.
• Big data analytics helps organizations harness their data and use it to
identify new opportunities.
• That, in turn, leads to smarter business moves, more efficient
operations, higher profits and happier customers.
7. BENEFITS OF BIG DATA ANALYTICS
• Real-time forecasting and monitoring of business as well as the
market.
• Identify crucial points hidden within large datasets to influence
business decisions.
• Identify issues in systems and business processes in real-time.
• Dig in customer data to create tailor-made products, services, offers,
discounts, etc.
• Facilitate speedy delivery of products/services that meet and exceed
client expectations.
8. TYPES OF BIG DATA
• Following are the types of Big Data:
Structured
Unstructured
Semi-structured
9. STRUCTURED
• Structured Data is used to refer to the data which is already stored in
databases, in an ordered manner.
• There are two sources of structured data;
• Human-Generated
• Machine-Generated
• All the data received from sensors, web logs and financial systems are
classified under machine-generated data.
• Human-generated structured data mainly includes all the data a
human input a computer, such as his name.
10. UN-STRUCTURED
• Unstructured data is defined as any data with an unknown form or
structure.
• Aside from its massive size, unstructured data presents a number of
challenges in terms of processing and extracting value from it.
• A heterogeneous data source containing a mix of simple text files,
images, videos, and so on is an example of unstructured data.
11. SEMI-STRUCTURED
• Semi-structured data can contain both types of information.
• Semi-structured data appears to be structured, but it is not defined in
the same way that a table definition in a relational DBMS is.
• A data representation in an XML file is an example of semi-structured
data.
13. VOLUME
• The name Big Data itself is related to a size which is enormous.
• Size of data plays a very crucial role in determining value out of data.
Also, whether a particular data can actually be considered as a Big
Data or not, is dependent upon the volume of data.
• Hence, Volume is one characteristic which needs to be considered
while dealing with Big Data solutions.
• For example;
• Organizational data
• Social media data
14. VELOCITY
• The term ‘velocity’ refers to the speed of generation of data.
• How fast the data is generated and processed to meet the demands,
determines real potential in the data.
• Big Data Velocity deals with the speed at which data flows in from
sources like business processes, application logs, networks, and social
media sites, sensors, Mobile devices, etc.
• The flow of data is massive and continuous.
15. VERACITY
• When we are dealing with a high volume, velocity and variety of data,
it is not possible that all of the data is going to be 100% correct, there
will be dirty data.
• The quality of the data being captured can vary greatly.
• The data accuracy of analysis depends on the veracity of the source
data.
16. VALUE
• Value is the most important aspect in the big data.
• Though, the potential value of the big data is huge.
• It is all well and good having access to big data but unless we can turn
it into value it is become useless.
• It becomes very costly to implement IT infrastructure systems to store
big data, and businesses are going to require a return on investment.
17. VARIETY
• Big data is not always structured data and it is not always easy to put
big data into a relational database.
• This means that the category to which Big Data belongs to is also a
very essential fact that needs to be known by the data analysis.
• Dealing with a variety of structured and unstructured data greatly
increases the complexity of both storing and analyzing Big Data.
• 90% of data generated is data is in unstructured form.
19. MEDIA AS A BIG DATA SOURCE
• Media is the most popular source of big data, as it provides valuable
insights on consumer preferences and changing trends.
• Since it is self-broadcasted and crosses all physical and
demographical barriers, it is the fastest way for businesses to get an
in-depth overview of their target audience, draw patterns and
conclusions, and enhance their decision-making.
• Media includes social media and interactive platforms, like Google,
Facebook, Twitter, YouTube, Instagram, as well as generic media like
images, videos, audios, and podcasts that provide quantitative and
qualitative insights on every aspect of user interaction.
20. CLOUD AS A BIG DATA SOURCE
• Today, companies have moved ahead of traditional data sources by
shifting their data on the cloud.
• Cloud storage accommodates structured and unstructured data and
provides business with real-time information and on-demand
insights.
• The main attribute of cloud computing is its flexibility and scalability.
• As big data can be stored and sourced on public or private clouds, via
networks and servers, cloud makes for an efficient and economical
data source.
21. WEB AS A BIG DATA SOURCE
• The public web constitutes big data that is widespread and easily
accessible.
• Data on the Web or ‘Internet’ is commonly available to individuals
and companies alike.
• Moreover, web services such as Wikipedia provide free and quick
informational insights to everyone.
• The enormity of the Web ensures for its diverse usability and is
especially beneficial to start-ups and SME’s, as they don’t have to wait
to develop their own big data infrastructure and repositories before
they can leverage big data.
22. IOT AS A BIG DATA SOURCE
• Machine-generated content or data created from IoT constitute a valuable
source of big data.
• This data is usually generated from the sensors that are connected to
electronic devices.
• The sourcing capacity depends on the ability of the sensors to provide real-
time accurate information.
• IOT is now gaining momentum and includes big data generated, not only
from computers and smartphones, but also possibly from every device that
can emit data.
• With IoT, data can now be sourced from medical devices, vehicular
processes, video games, meters, cameras, household appliances, and the
like.
23. DATABASES AS A BIG DATA SOURCE
• Businesses today prefer to use an incorporation of traditional and modern
databases to acquire relevant big data.
• This integration paves the way for a hybrid data model and requires low
investment and IT infrastructural costs.
• Furthermore, these databases are deployed for several business intelligence
purposes as well.
• These databases can then provide for the extraction of insights that are used to
drive business profits.
• Popular databases include a variety of data sources, such as MS Access, DB2,
Oracle, SQL, and Amazon Simple, among others.
24. BIG DATA TOOLS AND SOFTWARE
• Hadoop
• Atlas.it
• HPCC
• Storm
• Cassandra
• Kaggle
• CouchDB
• Pentaho
25. BIG DATA MINING
• Big data mining is referred to the collective data mining or extraction
techniques that are performed on large sets /volume of data or the
big data.
• Big data mining is primarily done to extract and retrieve desired
information or pattern from humongous quantity of data.
• Big data mining works on data searching, refinement , extraction and
comparison algorithms.
26. TOP TRENDS IN BIG DATA
• Four major trends in big data are helping organizations meet those
challenges.
• More data, increased data diversity drive advances in processing and
the rise of edge computing.
• Big data storage needs spur innovations in cloud and hybrid cloud
platforms, growth of data lakes.
• DataOps and data governance are becoming more prominent.
• Adoption of advanced analytics, machine learning and other AI
technologies increases dramatically.
Edge Computing:
Edge Computing, which shifts the processing load to the devices themselves before the data is sent to the servers. Edge computing optimizes performance and storage by reducing the need for data to flow through networks, reducing computing and processing costs, especially cloud storage, bandwidth and processing expenses. Edge computing helps to speed up data analysis and provides faster responses to the user.
Cloud & Hybrid Cloud Computing:
To deal with the inexorable increase in data generation, organizations are spending more of their resources storing this data in a range of cloud-based and hybrid cloud systems optimized for all the V's of big data. In previous decades, organizations handled their own storage infrastructure, resulting in massive data centers that enterprises had to manage, secure and operate. The move to cloud computing changed that dynamic. By shifting the responsibility to cloud infrastructure providers -- such as AWS, Google, Microsoft and IBM -- organizations can deal with almost limitless amounts of new data and pay for storage and compute capability on demand without having to maintain their own large and complex data centers.
Data Lakes:
One area of innovation is the emergence of DataOps, a methodology and practice that focuses on agile, iterative approaches for dealing with the full lifecycle of data as it flows through the organization. Rather than thinking about data in piecemeal fashion with separate people dealing with data generation, storage, transportation, processing and management, DataOps processes and frameworks address organizational needs across the data lifecycle from generation to archiving.
Machine Learning and AI Technologies:
No technology has been as revolutionary to big data analytics as machine learning and AI systems. AI is used by organizations of all sizes to optimize and improve their business processes. Machine learning enables them to more easily identify patterns and detect anomalies in large data sets to provide predictive analytics and other advanced data analysis capabilities. This includes recognition systems for image, video and text data; automated classification of information; natural language processing capabilities for chatbots and voice and text analysis; autonomous business process automation; high degrees of personalization and recommendation; and systems that can find optimal solutions among the sea of data.