Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.
Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.
Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.
"Big Data" is big business, but what does it really mean? How will big data impact industries and consumers? This slide deck goes through some of the high level details of the market and how it is revolutionizing the world.
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.
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...BigMine
Talk by Usama Fayyad at BigMine12 at KDD12.
Virtually all organizations are having to deal with Big Data in many contexts: marketing, operations, monitoring, performance, and even financial management. Big Data is characterized not just by its size, but by its Velocity and its Variety for which keeping up with the data flux, let alone its analysis, is challenging at best and impossible in many cases. In this talk I will cover some of the basics in terms of infrastructure and design considerations for effective an efficient BigData. In many organizations, the lack of consideration of effective infrastructure and data management leads to unnecessarily expensive systems for which the benefits are insufficient to justify the costs. We will refer to example frameworks and clarify the kinds of operations where Map-Reduce (Hadoop and and its derivatives) are appropriate and the situations where other infrastructure is needed to perform segmentation, prediction, analysis, and reporting appropriately – these being the fundamental operations in predictive analytics. We will thenpay specific attention to on-line data and the unique challenges and opportunities represented there. We cover examples of Predictive Analytics over Big Data with case studies in eCommerce Marketing, on-line publishing and recommendation systems, and advertising targeting: Special focus will be placed on the analysis of on-line data with applications in Search, Search Marketing, and targeting of advertising. We conclude with some technical challenges as well as the solutions that can be used to these challenges in social network data.
Tools and Methods for Big Data Analytics by Dahl WintersMelinda Thielbar
Research Triangle Analysts October presentation on Big Data by Dahl Winters (formerly of Research Triangle Institute). Dahl takes her viewers on a whirlwind tour of big data tools such as Hadoop and big data algorithms such as MapReduce, clustering, and deep learning. These slides document the many resources available on the internet, as well as guidelines of when and where to use each.
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Big Data Analysis Patterns - TriHUG 6/27/2013boorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
"Big Data" is big business, but what does it really mean? How will big data impact industries and consumers? This slide deck goes through some of the high level details of the market and how it is revolutionizing the world.
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.
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...BigMine
Talk by Usama Fayyad at BigMine12 at KDD12.
Virtually all organizations are having to deal with Big Data in many contexts: marketing, operations, monitoring, performance, and even financial management. Big Data is characterized not just by its size, but by its Velocity and its Variety for which keeping up with the data flux, let alone its analysis, is challenging at best and impossible in many cases. In this talk I will cover some of the basics in terms of infrastructure and design considerations for effective an efficient BigData. In many organizations, the lack of consideration of effective infrastructure and data management leads to unnecessarily expensive systems for which the benefits are insufficient to justify the costs. We will refer to example frameworks and clarify the kinds of operations where Map-Reduce (Hadoop and and its derivatives) are appropriate and the situations where other infrastructure is needed to perform segmentation, prediction, analysis, and reporting appropriately – these being the fundamental operations in predictive analytics. We will thenpay specific attention to on-line data and the unique challenges and opportunities represented there. We cover examples of Predictive Analytics over Big Data with case studies in eCommerce Marketing, on-line publishing and recommendation systems, and advertising targeting: Special focus will be placed on the analysis of on-line data with applications in Search, Search Marketing, and targeting of advertising. We conclude with some technical challenges as well as the solutions that can be used to these challenges in social network data.
Tools and Methods for Big Data Analytics by Dahl WintersMelinda Thielbar
Research Triangle Analysts October presentation on Big Data by Dahl Winters (formerly of Research Triangle Institute). Dahl takes her viewers on a whirlwind tour of big data tools such as Hadoop and big data algorithms such as MapReduce, clustering, and deep learning. These slides document the many resources available on the internet, as well as guidelines of when and where to use each.
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Big Data Analysis Patterns - TriHUG 6/27/2013boorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
Kaarina Ringstad presenterar ett av SGUs regeringsuppdrag inom mineralstrategin, att öka kunskapen om geologins betydelse för samhällsbyggnad och tillväxt.
Abstract: Knowledge has played a significant role on human activities since his development. Data mining is the process of
knowledge discovery where knowledge is gained by analyzing the data store in very large repositories, which are analyzed
from various perspectives and the result is summarized it into useful information. Due to the importance of extracting
knowledge/information from the large data repositories, data mining has become a very important and guaranteed branch of
engineering affecting human life in various spheres directly or indirectly. The purpose of this paper is to survey many of the
future trends in the field of data mining, with a focus on those which are thought to have the most promise and applicability
to future data mining applications.
Keywords: Current and Future of Data Mining, Data Mining, Data Mining Trends, Data mining Applications.
A Review Paper on Big Data and Hadoop for Data Scienceijtsrd
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. Hadoop is an open source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Mr. Ketan Bagade | Mrs. Anjali Gharat | Mrs. Helina Tandel "A Review Paper on Big Data and Hadoop for Data Science" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29816.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/29816/a-review-paper-on-big-data-and-hadoop-for-data-science/mr-ketan-bagade
This article useful for anyone who want to introduce with Big Data and how oracle architecture Big Data solution using Oracle Big Data Cloud solutions .
Guest Speaker in the 2nd National level webinar titled "Big Data Driven Solutions to Combat Covid 19" on 4th July 2020, Ethiraj College for Women(Auto), Chennai.
Real World Application of Big Data In Data Mining Toolsijsrd.com
The main aim of this paper is to make a study on the notion Big data and its application in data mining tools like R, Weka, Rapidminer, Knime,Mahout and etc. We are awash in a flood of data today. In a broad range of application areas, data is being collected at unmatched scale. Decisions that previously were based on surmise, or on painstakingly constructed models of reality, can now be made based on the data itself. Such Big Data analysis now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences. The paper mainly focuses different types of data mining tools and its usage in big data in knowledge discovery.
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.
Hadoop was born out of the need to process Big Data.Today data is being generated liked never before and it is becoming difficult to store and process this enormous volume and large variety of data, In order to cope this Big Data technology comes in.Today Hadoop software stack is go-to framework for large scale,data intensive storage and compute solution for Big Data Analytics Applications.The beauty of Hadoop is that it is designed to process large volume of data in clustered commodity computers work in parallel.Distributing the data that is too large across the nodes in clusters solves the problem of having too large data sets to be processed onto the single machine.
1. INF2190 - Data Analytics:
Introduction, Methods and Practical
Approaches
Winter 2016 – Week 1
Dr. Attila Barta
atibarta@cs.toronto.edu
2. Introduction to the Course
Instructor: Attila Barta, Ph.D. Computer Science UofT.
Details of the course can be found in the syllabus (published
on the Blackboard).
Current course is based on the course first taught by Prof.
Periklis Andritsos in Winter 2014 with updates to reflect the
current trends in Big Data Technologies.
All material is under copyright by FI unless specified explicitly.
Time and place: Thursday, 6:30pm-9:30pm.
2
3. Data Analytics – (old) definitions
Analysis of data is a process of inspecting, cleaning,
transforming, and modeling data with the goal of discovering
useful information, suggesting conclusions, and supporting
decision-making.
Data Mining is a particular data analysis technique that
focuses on modeling and knowledge discovery for predictive
rather than purely descriptive purposes.
Business Intelligence covers data analysis that relies
heavily on aggregation, focusing on business information.
(Wikipedia, Jan 2016).
3
4. Where Data Analytics fits in the (new) big picture?
4
Enterprise Data Analytics Architecture – Copyright Attila Barta
The Data Analytics world changed significantly in the last 5 years with the arrival of the Big Data.
5. Evolution of the database technologies
Before data analytics there was data, lots of it:
Hierarchical databases (early ‘70), IBM IMS still extensively in use
Network databases (mid ‘70s), CA IDMS still in use
Relational databases (mid ‘80s), DB2, Sybase, Oracle, MS-SQL Server
Object-oriented databases (early ’90s), Poet, O2
Data Warehouses (early ‘90s)
all started with RedBrick – first time when the database research community
had to catch-up to industry
The Inmod vs Kimball debates starts, as well as normalized vs de-normalized,
star vs snowflake schema…
Data Analytics (early ’90s), the famous beer and diapers story
Graph Databases (mid ‘90s), UofT leader in web databases, semantic databases
Semi-structured database (late ‘90s), ToX (UofT) still one of the best XML native
databases
Data Mining (late ‘90s)
Stream databases (early ‘2000s), network sensors – Berkeley
Big Data (late ‘2000s)
5
6. Big Data – How we got here
In a 2001 research report[1] Gartner analyst Doug Laney defined data growth challenges and
opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity
(speed of data in and out), and variety (range of data types and sources). Gartner, and now
much of the industry, continue to use this "3Vs" model for describing Big Data[2]. (Wikipedia).
What was happening in 2001? Three major trends:
Sloan Digital Sky Survey began collecting astronomical data in 2000 at a rate of 200GB/night – volume
Sensor networks (web of things) and streaming databases (Message Oriented Middleware) – velocity
Semi-structured databases, XML native databases beside object-oriented, relational databases – variety
What happened after 2001?
Rise of search engines and portals - Yahoo and Google:
Problem: how to store and query (cheaply large amounts of (semi-structured) data.
Answer: Hadoop on commodity Linux farms.
Memory got cheaper – in-memory data grids.
Rise of Social Media – petabytes in pictures, unstructured and semi-structured data.
Increased computational power and large memory – visual analytics.
6
7. Big Data – Definitions and Examples
7
•In 2012, Gartner updated its definition as follows: "Big data are high-volume, high-velocity, and/or high-variety
information assets that require new forms of processing to enable enhanced decision making, insight discovery
and process optimization“[3].
• In 2012 IDC defines Big Data technologies as “a new generation of technologies and architectures designed
to extract value economically from very large volumes of a wide variety of data by enabling high-velocity
capture, discovery, and/or analysis”[4].
•In 2012 Forrester characterize Big Data as “increases in data volume, velocity, variety, and variability”[5].
•Big Data Characteristics:
1. Data Volume: data size in order of petabytes.
• Example: Facebook on June 13, 2012 announced that their had reached 100 PB of data. On
November 8, 2012 they announced that their warehouse grows by half a PB per day.
2. Data Velocity: real time processing of streaming data, including real time analytics.
• Example: a jet engine generates 20TB data/hour that has to be processed near real time.
3. Data Variety: structured, semi-structured, text, imagines, video, audio, etc.
• Example: 80% of enterprise data is unstructured. YouTube - 500TB of video uploaded per year
4. Data Variability: data flows can be inconsistent with periodic peaks.
• Example: blogs commenting the new Blackberry 10; stock market data that reacts to market events.
8. 8
Big Data – Reference Architecture
An Architecture for Big Data has to address following the capabilities:
1. Real-time complex event processing (including sense and response, streaming
data).
2. Massive volumes of data (petabytes) relational and non-relational (i.e. social
media, location, RFID).
3. Parallel processing/fast loading, typically based on Hadoop/Sparks.
4. High-performance query systems based on in-memory data architectures.
5. Advanced analytics, e.g. visual analytics, columnar databases.
9. Big Data – Reference Architecture (contd.)
9
Virtual Infrastructure Workload Management
Infrastructure Services
Event Mgmt.
Query
(SQL, non-SQL)
Processing
Advanced
Analytics
Shared nothing hwd,
massively parallel
Commodity;
own or rent
Massive load via
parallel processing
Data Stream
Stream Processing
Non-relational dbms
Data Management
Relational dbms
Distributed File System
In-Memory Data Grid
Big Data Reference Architecture – Copyright by Attila Barta
10. 10
Virtual Infrastructure Workload Management
Infrastructure Services
Event Mgmt.
Query
(SQL, non-SQL)
Processing
Advanced
Analytics
Client Omni-Channel
Interactions
Tableau, SAS
Spotfire, HANA
Tibco
BusinessEvents
Stream Processing
Non-relational dbms
Data Management
Relational dbms
Distributed File System
In-Memory Data Grid
Tibco ActiveSpaces,
HANA, Kafka
R, MapReduce,
Sparks SQL
PaaS, IaaS
Big Data – Sample Technology Placement
HDFS, Sparks,
Casandra
11. 11
Traditional Data Analytics
Enterprise Data
Warehouse
Highly normalized, usually multi-level, relational or start
schema.
Data Marts
A simple form of a data warehouse that is focused on a
single subject (or functional area).
Data Cubes
Multi-dimensional data sets, usually specific for a certain BI
tool (e.g. Cognos, BO, MS).
OLAP
Analyze multidimensional data interactively using
consolidation (roll-up), drill-down, and slicing and dicing.
Works on data cubes (MOLAP) or RDBMS (ROLAP).
Fixed, regularly scheduled (canned) reports usually based
on decision support systems.
Mgmt. Inf.
System
Statistical
Computing (R)
Statistical computing and modeling packages, e.g. SAS, R.
Diagnostic
Operational analytics that address the “why did it happen” based
on data aggregation and/or modeling.
• Complex to deploy (a new data warehouse takes months to build); most run on specialized hardware (e.g. SAS only
runs on AIX).
• Proprietary technologies of significant up front and running cost; difficult to migrate them to a cloud solution.
• Difficult to change both at the data source level (data warehouse) and at the analytical level (canned reports).
Characteristics:
12. 12
Big Data era Data Analytics
Stream processor for sensor data, multi-media, geo-
location, GIS, etc.
Sense and Response capability, in memory data
aggregation.
Object pair, document, semi-structured, XML in-
memory databases.
In-memory columnar databases, support for R
language.
Distributed File System (HDFS, Casandra) based
relational, non-relational, multi-media, sensor or
document data
Analytical
Appliances
Specialized analytical hardware, e.g. Netezza,
Oracle Exadata.
Columnar
Database
NO-SQL
Database
In-Memory Data
Grid
Stream
Processing
Operational
Reporting
Real time in-sights based on streaming data, e.g.
sensor, geo-location, GIS, multi-media.
Data
Visualization
Self-service data visualization tools, e.g. Tableu,
Spotfire.
Big Data Search MapReduce real-time or batch search.
Descriptive
Analysis
What happened?
Predictive
Analysis
What will happened?
Prescriptive
Analysis
What do to about? Decision support automation.
• High volume and data diversity, support for new data
types.
• High horizontal and vertical scalability.
• Easy to setup and change.
• Low ownership most, mostly open source and commodity
hardware, cloud solutions readily available.
Characteristics:
Data Lakes
13. Objective of this course: the illusive Data Scientist…
13
“Data Scientist: The Sexiest Job of the 21st Century” –
Harvard Business Review, Oct 2012
Data scientists today are akin to the Wall Street “quants” of the
1980s and 1990s.
The Hot Job of the Decade.
185 Data Scientist Job vacancies available in Toronto
as Jan 6, 2016 on Indeed Canada, alone.
How this course will qualify you?
Foundation in Data Mining algorithms and techniques.
Foundation on Big Data architecture and challenges.