Big data offers opportunities but also security and privacy issues due to its large volume, velocity, and variety. Some key security issues include insecure computation, lack of input validation and filtering, and privacy concerns in data mining and analytics. Recommendations to enhance big data security include securing computation code, implementing comprehensive input validation and filtering, granular access controls, and securing data storage and computation. Case studies on security issues include vulnerability to fake data generation, challenges with Amazon's data lakes, possibility of sensitive information mining, and the rapid evolution of NoSQL databases lacking security focus.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt.
Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com.
Chris Riccomini works as a Software Engineer at WePay.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt.
Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com.
Chris Riccomini works as a Software Engineer at WePay.
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Arne Rossmann outlines why the Business Data Lake works and which Services the Business Data Lake should provide. Organizations can use the Business Data Lake concept best when they standardize, industrialize and innovate.
Presented by Arne Rossman, Capgemini Germany, at the OOP Conference, 31 January 2017
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
An introduction into how IoT backend works. Explained on a example of Gigaset elements smart home system.
Presented at SAP Next-Gen Lab inauguration at WSB Wrocław.
Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
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
RWDG Slides: Data Governance Roles and ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From data stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role, and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar, where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an operating model of roles and responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
• Executive, strategic, tactical, operational, and support-level roles
• How to customize an operating model to fit your organization
• Detailed responsibilities for each level
• Defining who participates at each level
• Using working teams to implement tactical solutions
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
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 | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an 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
An overview of some methods and principles for big data visualization. The presentation quickly hits on the topic of dashboards and some cyber security uses. The topic of a big data lake is also briefly discussed in the context of a cyber security big data setup.
The Internet Services, Web and Mobile Applications, Pervasive Communication widely available today that are meeting many of our needs have stimulated production of tremendous amounts of data (call metadata, texts, emails, social media updates, photos, videos, location, etc.). The computing power available today in conjunction with trending technologies like Data Mining and Analytics, Machine Learning and Computational Linguistics provide an opportunity business and government organizations to manage, search, analyze, and visualize vast amount of data as information.
Companies named data brokers collect consumer data including behavioral and private and then sell to companies those use this data for personalized marketing and selling. There is no doubt that this is good for businesses, but is this same good for consumers? Is this just positively affects buying experience of customers? How much does reliable this kind data event for companies? How to keep a balance between new opportunities derived by Big Data to companies and privacy concern it brings to consumers?
In proposed speech we will try to find out some of the answers to these and other questions.
Data Governance and Data Science to Improve Data QualityDATAVERSITY
Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
Gartner: Master Data Management FunctionalityGartner
Gartner will further examine key trends shaping the future MDM market during the Gartner MDM Summit 2011, 2-3 February in London. More information at www.europe.gartner.com/mdm
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it can complement your data quality strategy
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Arne Rossmann outlines why the Business Data Lake works and which Services the Business Data Lake should provide. Organizations can use the Business Data Lake concept best when they standardize, industrialize and innovate.
Presented by Arne Rossman, Capgemini Germany, at the OOP Conference, 31 January 2017
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
An introduction into how IoT backend works. Explained on a example of Gigaset elements smart home system.
Presented at SAP Next-Gen Lab inauguration at WSB Wrocław.
Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
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
RWDG Slides: Data Governance Roles and ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From data stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role, and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar, where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an operating model of roles and responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
• Executive, strategic, tactical, operational, and support-level roles
• How to customize an operating model to fit your organization
• Detailed responsibilities for each level
• Defining who participates at each level
• Using working teams to implement tactical solutions
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
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 | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an 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
An overview of some methods and principles for big data visualization. The presentation quickly hits on the topic of dashboards and some cyber security uses. The topic of a big data lake is also briefly discussed in the context of a cyber security big data setup.
The Internet Services, Web and Mobile Applications, Pervasive Communication widely available today that are meeting many of our needs have stimulated production of tremendous amounts of data (call metadata, texts, emails, social media updates, photos, videos, location, etc.). The computing power available today in conjunction with trending technologies like Data Mining and Analytics, Machine Learning and Computational Linguistics provide an opportunity business and government organizations to manage, search, analyze, and visualize vast amount of data as information.
Companies named data brokers collect consumer data including behavioral and private and then sell to companies those use this data for personalized marketing and selling. There is no doubt that this is good for businesses, but is this same good for consumers? Is this just positively affects buying experience of customers? How much does reliable this kind data event for companies? How to keep a balance between new opportunities derived by Big Data to companies and privacy concern it brings to consumers?
In proposed speech we will try to find out some of the answers to these and other questions.
Data Governance and Data Science to Improve Data QualityDATAVERSITY
Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
Gartner: Master Data Management FunctionalityGartner
Gartner will further examine key trends shaping the future MDM market during the Gartner MDM Summit 2011, 2-3 February in London. More information at www.europe.gartner.com/mdm
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it can complement your data quality strategy
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
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,
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.
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
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.
The Team Member and Guest Experience - Lead and Take Care of your restaurant team. They are the people closest to and delivering Hospitality to your paying Guests!
Make the call, and we can assist you.
408-784-7371
Foodservice Consulting + Design
Artificial intelligence (AI) offers new opportunities to radically reinvent the way we do business. This study explores how CEOs and top decision makers around the world are responding to the transformative potential of AI.
Senior Project and Engineering Leader Jim Smith.pdfJim Smith
I am a Project and Engineering Leader with extensive experience as a Business Operations Leader, Technical Project Manager, Engineering Manager and Operations Experience for Domestic and International companies such as Electrolux, Carrier, and Deutz. I have developed new products using Stage Gate development/MS Project/JIRA, for the pro-duction of Medical Equipment, Large Commercial Refrigeration Systems, Appliances, HVAC, and Diesel engines.
My experience includes:
Managed customized engineered refrigeration system projects with high voltage power panels from quote to ship, coordinating actions between electrical engineering, mechanical design and application engineering, purchasing, production, test, quality assurance and field installation. Managed projects $25k to $1M per project; 4-8 per month. (Hussmann refrigeration)
Successfully developed the $15-20M yearly corporate capital strategy for manufacturing, with the Executive Team and key stakeholders. Created project scope and specifications, business case, ROI, managed project plans with key personnel for nine consumer product manufacturing and distribution sites; to support the company’s strategic sales plan.
Over 15 years of experience managing and developing cost improvement projects with key Stakeholders, site Manufacturing Engineers, Mechanical Engineers, Maintenance, and facility support personnel to optimize pro-duction operations, safety, EHS, and new product development. (BioLab, Deutz, Caire)
Experience working as a Technical Manager developing new products with chemical engineers and packaging engineers to enhance and reduce the cost of retail products. I have led the activities of multiple engineering groups with diverse backgrounds.
Great experience managing the product development of products which utilize complex electrical controls, high voltage power panels, product testing, and commissioning.
Created project scope, business case, ROI for multiple capital projects to support electrotechnical assembly and CPG goods. Identified project cost, risk, success criteria, and performed equipment qualifications. (Carrier, Electrolux, Biolab, Price, Hussmann)
Created detailed projects plans using MS Project, Gant charts in excel, and updated new product development in Jira for stakeholders and project team members including critical path.
Great knowledge of ISO9001, NFPA, OSHA regulations.
User level knowledge of MRP/SAP, MS Project, Powerpoint, Visio, Mastercontrol, JIRA, Power BI and Tableau.
I appreciate your consideration, and look forward to discussing this role with you, and how I can lead your company’s growth and profitability. I can be contacted via LinkedIn via phone or E Mail.
Jim Smith
678-993-7195
jimsmith30024@gmail.com
The case study discusses the potential of drone delivery and the challenges that need to be addressed before it becomes widespread.
Key takeaways:
Drone delivery is in its early stages: Amazon's trial in the UK demonstrates the potential for faster deliveries, but it's still limited by regulations and technology.
Regulations are a major hurdle: Safety concerns around drone collisions with airplanes and people have led to restrictions on flight height and location.
Other challenges exist: Who will use drone delivery the most? Is it cost-effective compared to traditional delivery trucks?
Discussion questions:
Managerial challenges: Integrating drones requires planning for new infrastructure, training staff, and navigating regulations. There are also marketing and recruitment considerations specific to this technology.
External forces vary by country: Regulations, consumer acceptance, and infrastructure all differ between countries.
Demographics matter: Younger generations might be more receptive to drone delivery, while older populations might have concerns.
Stakeholders for Amazon: Customers, regulators, aviation authorities, and competitors are all stakeholders. Regulators likely hold the greatest influence as they determine the feasibility of drone delivery.
Oprah Winfrey: A Leader in Media, Philanthropy, and Empowerment | CIO Women M...CIOWomenMagazine
This person is none other than Oprah Winfrey, a highly influential figure whose impact extends beyond television. This article will delve into the remarkable life and lasting legacy of Oprah. Her story serves as a reminder of the importance of perseverance, compassion, and firm determination.
2. WHAT IS BIG DATA?
Big data refers to data that is so large and complex that it exceeds the processing
capability of conventional data management systems and software techniques.
Data becomes big data when individual data stops mattering and only a large
collection of it or analysis derived from it are of value
Offers many opportunities - advancement of science, improvement of health care,
promotion of economic growth, enhancement of education system and more ways
of social interaction and entertainment.
But Big data has its issues of security and privacy too due to its huge volume,
high velocity, large variety in data sources and formats etc.
3. DIMENSIONS OF BIG DATA
Big Data possesses characteristics that can be defined by several V’s
Volume
Refers to quantity of data. Big data is defined as massive data sets with measures such as
petabytes and zeta bytes. Vast amounts of data are generated every second. Today big
data is generated by machines, networks and human interaction on systems like social
media. Volume of data to be analysed is massive.
Velocity
Deals with the accelerating speed at which data flows in from sources like business
processes, machines, networks like social media sites, mobile devices, etc. The flow of
data is continuous. Reacting quickly enough to deal with data velocity is a challenge for
most organizations.
Variety
Refers to various formats of data . Structured, numeric data in traditional databases.
Unstructured text documents, email, video, audio, stock ticker data and financial
4. Veracity
Refers to the quality of big data like biases, noise, abnormality of data, immeasurable
uncertainties and truthfulness and trustworthiness of data. Data that are erroneous,
duplicate and incomplete or outdated, as a whole are referred to as dirty data.
Valence
Refers to the connectedness of big data in the form of graphs just like atoms. Data items
are often directly connected to one another like a city is connected to its country. Two
Facebook users are connected as they are friends. A high valence data is denser.
Value
Refers to the fact how big data is going to benefit us and our organization. It helps in
measuring the usefulness of data in decision making. Queries can be run on the stored
data so as to deduce important results and gain insights
5. TOOLS FOR BIG DATA
Big Data storage and management tools
Hadoop- Provides a software framework for distributed storage and processing of big
data using the Map Reduce programming model
Cassandra- used for fast processing during very heavy writes and reads the environment
and stored data which is very large to fit on the server, but still want a friendly familiar
interface
MongoDB- used for dynamic queries, defining indexes for good performance on a big
database which makes applications faster and more efficiently at scale.
Apache Hive- Analysis of large datasets stored in HDFS. Also, used for data
summarization, query and ad-hoc analysis to process structured and semi-structured data
in Hadoop
Hbase- Used for real-time big data applications which contain billions of rows and
millions of columns in tables built for low latency operations
Cloudera- 100% open source and is the only Hadoop solution to offer batch processing,
interactive SQL and interactive search as well as enterprise-grade continuous availability.
6. TYPICAL BIG DATA ARCHITECTURE
Big data architecture varies based on a company's infrastructure and needs, but it usually
contains the following components:
1. Data sources: This can include data from databases, data from real-time sources, and
static files generated from applications, such as Windows logs.
2. Data store: Need storage for the data that will be processed via big data architecture.
Often, data will be stored in a data lake, which is a large unstructured database that
scales easily.
3. A combination of batch processing and real-time processing: Large volume of data
processed can be handled efficiently using batch processing, while real-time data
needs to be processed immediately to bring value.
4. Analytical data store: Helps keep all the data is in one place so analysis can be
comprehensive, and it is optimized for analysis rather than transactions. This might
take the form of a cloud-based data warehouse or a relational database
5. Automation: Ingesting and transforming the data, moving it in batches and stream
processes, loading it to an analytical data store, and finally deriving insights must be
in a repeatable workflow so that you can continually gain insights from your big data
7. GENERAL BIG DATA SECURITY
ISSUESInsecure Computation
Malicious programs are used by attackers to extract sensitive information from data
sources. This can also corrupt the data, leading to incorrect results in prediction or
analysis. It can also result into Denial of Services (DoS)
Input Validation and Filtering
Big Data collects inputs from multiple sources hence input validation is required. This
involves validating trusted data sources and filtering malicious data from the good one.
In big data gigabytes and terabytes of continuous data flow makes it really very difficult
to perform input validation or data filtering on the incoming batch of data.
Privacy Concerns in Data Mining and Analytics
Monetization of Big Data involves sharing of analytical results which involves multiple
challenges like invasion of privacy, invasive marketing and unintentional disclosure of
information. Quite a few examples of these include - AOL Inc. released search logs where
users could be identified easily, which was really concerning.
8. Granular Access Controls
Big data was traditionally designed with almost no security in mind. As a way out, the
parts of needed data sets, that users have right to see, are copied to a separate big data
warehouse and provided to particular user groups. For a medical research, only the
medical info (without the names, addresses) gets copied. Volumes of big data grow even
faster this way. Complex solutions adversely affect the system’s performance and
maintenance.
Insecure data storage
Authentication, authorization and encryption of data at thousands of nodes becomes a
challenging work. Auto–tiering moves cold data, which might be of use, to lesser secure
medium. Also encryption of real time data may have performance impacts. Secure
communication amongst various nodes, middlewares, and end users is disabled by
default, hence it needs to be enabled explicitly.
9. SECURITY ISSUES IN BIG DATA – SOME
RELEVANT USE CASES
Vulnerability to fake data generation
For instance, if a manufacturing company uses sensor data to detect malfunctioning
production processes, cybercriminals can penetrate the system and make the sensors
show fake results. The company can fail to notice alarming trends and miss the
opportunity to solve problems before serious damage is caused. Such challenges can be
solved through applying fraud detection approach.
Amazon’s Galaxy Data Lakes
Challenges faced by Amazon: data silos, difficulty analyzing diverse datasets, managing
data access and security.
1. A data silo is a situation wherein only one group in an organization can access a set of
data. Data is stored in different places and in different ways for international
expansion which keeps important data hidden. A data lake solves this problem by
uniting all the data into one central location.
10. 2. Amazon Prime has data for fulfilment centres and packaged goods, while Amazon
Fresh has data for grocery stores and food. Even shipping programs differ
internationally. For example, different countries sometimes have different box sizes
and shapes. Different systems may also have the same type of information, but it’s
labeled differently. For example, in Europe, the term used is “cost per unit,” but in
North America, the term used is “cost per package.”
Data lakes allow you to import any amount of data in any format because there is no
predefined schema
3. Amazon’s operations finance data are spread across more than 25 databases, with
regional teams creating their own local version of datasets. Audits and controls must
be in place for each database to ensure that nobody has improper access.With a data
lake, it’s easier to get the right data to the right people at the right time
11. Possibility of sensitive information mining
Lack of control within big data solutions may let corrupt IT specialists or evil
business rivals mine unprotected data and sell it for their own benefit.
Companies, can incur huge losses, if such information is connected with new
product/service launch, or users’ personal information. An employee of a
company in charge of the big data store can misuse his power and violate
privacy policies. For example: stalk people by monitoring through chats. To
avoid this, proper security tools should be in place and access controls should
be applied strictly at different levels in the organizations.
12. High speed of NoSQL databases’ evolution and lack of security focus
NoSQL databases, handle many challenges of big data analytics without concerning much
over security issues which is embedded only in the middleware and no explicit security
enforcement is provided. NoSQL databases have weak authentication techniques and
weak password storage mechanisms. They are subjected to attacks like JSON injection,
REST injection, man-in-the-middle attack and schema injection and others. NoSQL
databases are subjected to inside attacks as well due to lenient security mechanisms. To
avoid this the following should be done:
1. Encrypting sensitive database fields
2. Keeping unencrypted values in a sandboxed environment
3. Using sufficient input validation
4. Applying strong user authentication policies
13. RECOMMENDATIONS TO ENHANCE BIG
DATA SECURITY
Secure Your Computation Code
To prevent malicious data entry, implement access control, code signing and dynamic
analysis of the computational code. Proper strategies need to be made to control the
impact of untrusted code if it has been able to get into the big data solution.
There are generally two ways of preventing attacks: securing the data when insecure
mapper is present, and securing the mapper.
Implement Comprehensive Input Validation and Filtering.
For better security practices, implementation of input validation and filtering on internal
and external sources is recommended. Proper evaluation of key input validation and
filtering features is required
14. Implement Granular Access Control.
Defining and enforcing the roles to different the kinds of users like admin,
knowledge workers, end users, developers etc. is the core part for the
implementation of granular access control.
Use policy to define which SUDO sessions are keystroke logged based on risk
and user. Implement granular assignments for who can switch sessions ("SU”)
and Audit privileged activity
Secure data storage and computation.
Important as much part of sensitive data leakage portions are encountered in
this phase. For this, the sensitive data should be segregated. Enabling Data
Encryption for sensitive data and audit administrative access on Data Nodes
marks to be a major step.
Finally the verification of proper configuration of API security of all
components is the final step for secure data storage and computation.
15. CONCLUSION
Big data is trending. No new application can be imagined without it producing
new forms of data, operating on data driven algorithms, and consuming
specified amount of data.
With data storing and computing environments becoming more cheaper–
encryption and compliance have introduced challenges that practically need to
be handled in a very systematic manner.
There is a big ecosystem exists for specific big data problems. Major
recommendations for dealing with the security issues are implementation of
data lakes, access controls, validation, filtration and securing data storage and
computation.