I will discuss the growth of big data and the evolution of traditional enterprise models with addition of critical building blocks to handle the intense development of data in the enterprise. According to IDC approximations the size of the digital universe in 2011 will be 1.8 zettabytes. With statistics evolution beyond Moore’s Law the average enterprise will need to manage 50 times more information by the year 2020 while cumulative IT team by only 1.5 percent. With this challenge in mind, the combination of big data models into existing enterprise infrastructures is a critical element when seeing the addition of new big data building blocks while bearing in mind the efficiency.
We have entered an era of Big Data. Huge information is for the most part accumulation of information sets so extensive and complex that it is exceptionally hard to handle them utilizing close by database administration devices. The principle challenges with Big databases incorporate creation, curation, stockpiling, sharing, inquiry, examination and perception. So to deal with these databases we require, "exceedingly parallel software's". As a matter of first importance, information is procured from diverse sources, for example, online networking, customary undertaking information or sensor information and so forth. Flume can be utilized to secure information from online networking, for example, twitter. At that point, this information can be composed utilizing conveyed document frameworks, for example, Hadoop File System. These record frameworks are extremely proficient when number of peruses are high when contrasted with composes.
We have entered an era of Big Data. Huge information is for the most part accumulation of information sets so extensive and complex that it is exceptionally hard to handle them utilizing close by database administration devices. The principle challenges with Big databases incorporate creation, curation, stockpiling, sharing, inquiry, examination and perception. So to deal with these databases we require, "exceedingly parallel software's". As a matter of first importance, information is procured from diverse sources, for example, online networking, customary undertaking information or sensor information and so forth. Flume can be utilized to secure information from online networking, for example, twitter. At that point, this information can be composed utilizing conveyed document frameworks, for example, Hadoop File System. These record frameworks are extremely proficient when number of peruses are high when contrasted with composes.
Key aspects of big data storage and its architectureRahul Chaturvedi
This paper helps understand the tools and technologies related to a classic BigData setting. Someone who reads this paper, especially Enterprise Architects, will find it helpful in choosing several BigData database technologies in a Hadoop architecture.
View the Big Data Technology Stack in a nutshell. This Big Data Technology Stack deck covers the different layers of the Big Data world and summarizes the major technologies in vogue today.
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is
implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
Detailed slides of data resource management. The relationships among the many individual data elements stored in databases are based on one of several logical data structures, or models.
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.
Key aspects of big data storage and its architectureRahul Chaturvedi
This paper helps understand the tools and technologies related to a classic BigData setting. Someone who reads this paper, especially Enterprise Architects, will find it helpful in choosing several BigData database technologies in a Hadoop architecture.
View the Big Data Technology Stack in a nutshell. This Big Data Technology Stack deck covers the different layers of the Big Data world and summarizes the major technologies in vogue today.
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is
implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
Detailed slides of data resource management. The relationships among the many individual data elements stored in databases are based on one of several logical data structures, or models.
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.
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.
What is big data?
Big data is a mix of structured, semi-structured, and unstructured data gathered by organizations that can be dug for data and used in machine learning projects, predictive modeling, and other advanced analytics applications.
Systems that process and store big data have turned into a typical part of data the board architectures in organizations, joined with tools that support big data analytics uses. Big data is regularly portrayed by the three V's:
the enormous volume of data in numerous environments; • the wide variety of data types regularly stored in big data systems, and
the velocity at which a significant part of the data is created, gathered and processed.
These characteristics were first recognized in 2001 by Doug Laney, then, at that point, an analyst at consulting firm Meta Group Inc.; Gartner further promoted them after it gained Meta Group in 2005. All the more as of late, several other V's have been added to various descriptions of big data, including veracity, value and variability.
Albeit big data doesn't liken to a specific volume of data, big data deployments frequently involve terabytes, petabytes, and even exabytes of data made and gathered over time.
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 is a mix of structured, semistructured, and unstructured data gathered by organizations that can be dug for data and used in machine learning projects,
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.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. WHAT IS BIG DATA
Big data is defined as any kind of data source that has at least three
shared characteristics:
▪ Extremely large Volumes of data
▪ Extremely high Velocity of data
▪ Extremely wide Variety of data
3. DEFINING BIG DATA
Big data usually refers to the following kinds of data:
▪ Traditional enterprise data: Includes customer information from CRM
systems, transactional ERP data, web store transactions, and general
ledger data.
▪ Machine-generated /sensor data: Includes Call Detail Records (“CDR”),
weblogs, smart meters, manufacturing sensors, equipment logs (often
referred to as digital exhaust), trading systems data.
▪ Social data: Includes customer feedback streams, micro-blogging sites
like Twitter, social media platforms like Facebook
4. BIG CHARACTERISTICS
Big data are four key characteristics:
▪ Volume: Machine-generated data is produced in much higher quantities than non-
traditional data.
▪ Velocity: Social media data streams – while not as massive as machine-generated data
– produce a huge influx of opinions and relationships valuable to customer
relationship management.
▪ Variety: Traditional data formats tend to be relatively well defined by a data schema
and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of
change. As new services are added, new sensors deployed, or new marketing
campaigns executed, new data types are needed to capture the resultant information.
▪ Value: The economic value of different data varies significantly. Typically, there is good
information hidden amongst a larger body of non-traditional data; the challenge is
identifying what is valuable and then transforming and extracting that data for
analysis.
5. EMERGENCE OF BIG DATA
The huge amount of data is produced as a result of democratization and
ecosystem factors such as the following:
▪ Mobility trends: Mobile devices, mobile events and sharing, and
sensory integration
▪ Data access and consumption: Internet, interconnected systems, social
networking, and convergent interfaces and access models (Internet,
search and social networking, and messaging)
▪ Ecosystem capabilities: Main changes in the information processing
model and the availability of an open source framework; the general-
purpose computing and unified network integration
7. BIG DATA MOVES INTO THE ENTERPRISE
The desires of traditional enterprise data models for application, database, and storage resources
have developed over the years, and the cost and complexity of these models has improved along
the way to meet the needs of big data. This speedy change has encouraged changes in the
fundamental models that define the way that big data is stored, analyzed, and accessed. The new
models are based on a scaled-out, shared-nothing architecture, bringing new challenges to
enterprises to decide what technologies to use, where to use them, and how. One size no longer
fits all, and the traditional model is now being expanded to incorporate new building blocks that
address the tasks of big data with new information processing frameworks purpose-built to meet
big data’s requirements. However, these purpose-built systems also must meet the inherent
requirement for integration into current business models, data plans, and network infrastructures.
9. BIG DATA MOVES INTO THE ENTERPRISE
In traditional data warehousing terms, organizing data is called data addition. Because there is
such a high volume of big data, there is a tendency to organize data at its early destination
location, thus saving both time and money by not moving around huge volumes of data. The
infrastructure required for organizing big data must be able to process and operate data in the
original storage location; support very high throughput (often in batch) to deal with large data
processing steps; and handle a large variety of data formats, from unstructured to structured.
10. BIG DATA COMPONENTS
Two main building blocks are being added to the enterprise stack to accommodate big data:
Hadoop
NoSQL
11. HADOOP
Hadoop is a new technology that permits huge data volumes to be prepared and processed while
keeping the data on the original data storage cluster. Hadoop Distributed File System (HDFS) is the
long-term storage system for web logs for example. These web logs are turned into browsing
behavior (sessions) by running Map Reduce programs on the cluster and generating aggregated
results on the same cluster. These combined results are then loaded into a Relational DBMS
system.
12. NOSQL
NoSQL systems are designed to capture all data without categorizing and parsing it upon entry into
the system, and therefore the data is highly varied. SQL systems, on the other hand, typically place
data in well-defined structures and impose metadata on the data captured to ensure consistency
and validate data types.