The document provides a timeline of key moments in the history of big data and data science from 1991 to 2020. Some of the major events included the birth of the internet in 1991, the launch of Google search engine in 1997, the release of the Hadoop open-source platform in 2005 which revolutionized data processing, and the prediction that the big data market will reach $203 billion by 2020. The timeline shows how digital storage became more cost effective than paper in the 1990s, how data volumes increased exponentially in the 2000s, and how mobile devices surpassed desktops in data access by 2014.
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 Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
Many believe Big Data is a brand new phenomenon. It isn't, it is part of an evolution that reaches far back history. Here are some of the key milestones in this development.
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 Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
Many believe Big Data is a brand new phenomenon. It isn't, it is part of an evolution that reaches far back history. Here are some of the key milestones in this development.
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.
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've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
Big Data may well be the Next Big Thing in the IT world. The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
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.
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've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
Big Data may well be the Next Big Thing in the IT world. The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
From AI to Z: How AI is changing the relationship between people and dataiGenius
On the occasion of SMAU Milano 2018, Gabriel Cismondi, COO at iGenius, talks about Artificial Intelligence and how it's changing the relationship between people and data.
Big data is used to describe a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques. In most enterprise scenarios the data is too big or it moves too fast or it exceeds current processing capacity. The term big data is believed to have originated with Web search companies who had to query very large distributed aggregations of loosely-structured data.
The mountain of Big Data is growing, presenting immense opportunities for businesses ready to summit its peak, but the journey requires careful preparation. Integra helps businesses equip their network infrastructure to handle big requirements for Big Data—with fully-symmetrical Ethernet solutions designed to deliver low-latency, high-bandwidth connectivity between organizational peers, the cloud, and the servers where your data is stored. Our infographic, "Summiting the Mountain of Big Data" will help you understand how big "Big Data" really is; who's producing, consuming, managing and storing all that data; the business advantages you can capture by tapping into its power; and how you can prepare your organization to meet its demands—resulting in Big Gains from Big Data.
[Webinar] The Internet of Things and the Coming Data DelugeInsightInnovation
"The Internet of Things" was one of the hottest topics in 2014, and is set to grow even faster this year, as we move to a world where the internet isn't just about connecting computers, but is now connecting all of the everyday devices that we use.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
1. A Big Data
Timeline
A timeline of some of the key moments in the history of Big
Data and Data Science 1991 - 2020
2. 1991
Courtesy of Sir Tim Berners-Lee, data
and information can now be posted
online for the first time.
THE BIRTH OF THE INTERNET
3. 1996
Digital storage is more cost-effective for
storing data than paper according to R.J.T.
Morris and B.J. Truskowski, in “The Evolution
of Storage Systems.”
DIGITAL TAKES OVER
4. 1997
The Google search engine is launched.
Today, over 40,000 Google searches are
completed every second, or over 3.5
billion in a single day.
GOOGLE LAUNCHES
5. 2003
We create as much data every two
days as we did from the beginning of
time until 2003.
VOLUMES INCREASE
6. 2005
Big Data open-source platform Hadoop is
released. It revolutionised data processing,
allowing near limitless data storage and
processing over multiple clusters.
HADOOP HERO
7. 2007
The number of information bits in the
digital universe eclipses the number of
stars in the physical universe for the
first time.
A GALAXY OF DATA
8. 2008
There is over 9.57 Zettabytes
that’s 9.57 Trillion Gigabytes of data
processed in 2008 by the world's CPUs.
ZETTABYTES
9. 2011
Over 12 million RFID (radio frequency
identification) tags had been sold to
monitor and track goods worldwide by
2011.
RADIO FREQUENCY IDENTIFICATION
10. 2013
The global Big Data market is said to be
worth $10bn in 2013, and at that time,
was predicted to rise to $54bn by
2017.
GLOBAL MARKET
11. 2014
The number of people accessing the
internet using mobile devices such as
phones and tablets surpasses desktop users
for the first time.
MOBILE DEVICES COME OUT ON TOP
12. 2016
Big Data has grown at such a rate that
90% of the worlds data is said to have
been created in the past two years
alone.
BIGGEST EVER DATA
13. 2016
A whopping 2.5 Quintillion bytes of
data are now created each day.
DAILY DATA
14. 2020
The IDC forecasts that the big data and
analytics market will reach $203 billion
by 2020.
IT’S ONLY GETTING BIGGER…
15. With the market on the rise, it’s looking
good for Big Data professionals around
the world.
Make your next move with Big Cloud!
BIG DATA CAREERS