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Big Data News
Aviationfile
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Challenges Associated with Big Data in Aviation and Solutions from a Velocity Perspective
- Big data has become an essential asset across many industries, and aviation is no exception.
- The immense volume, variety, and velocity of flight data present opportunities and challenges.
- Real-time data processing, data integration and interoperability, data accuracy and reliability, scalability, and cybersecurity risks are some key challenges in big data for aviation that need to be overcome.
- The speed at which data is generated and must be processed in real-time or near-real-time is known as velocity, which is one of the most significant aspects in aviation big data.
- Adopting Edge Computing for real-time processing, utilizing AI and Machine Learning for predictive insights, and enhancing scalability with Cloud Solutions are some strategies to manage challenges associated with big data velocity.
- Benefits of efficient Big Data Velocity Management are Improved Safety, Operational Efficiency, Enhanced Passenger Experience, Predictive, and Preventive Maintenance.
- Efficient velocity management is not only a technological necessity but also a strategic advantage that aviation stakeholders must adopt to flourish in a data-driven future.
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Siliconangle
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Data in the generative AI era: We need more knowledge engineers
- The concerns over data privacy and cyber issues, employee decisions based on erroneous information, and employee misuse and ethical risks have highlighted the need for a professional who understands the data's context to point organizations to the right information.
- AI projects advance from the proof of concept to production, so organizations have to pay serious attention to the data that is used for training and inference.
- AI has produced rightsized models based on a variant of the 80/20 rules by having just enough data and model to deliver results that are considered good enough.
- Understanding the source and knowing how and where the metadata flows are essential steps to understanding the pulse of information across an organization.
- Context engineering is emerging as a discipline that provides a systematic solution to capture context and make it explicit while knowledge engineering update the reference librarian with software engineering skills and borrows from semantic disciplines such as ontology development, knowledge representation and related skills for representing it with graphs.
- Demand for knowledge engineers has coincided with the rising prominence of knowledge graphs to get applied to AI so that training and running models do not generate millions of dollar cloud computing bills and that AI models stay appropriately grounded.
- While AI can assist in the legwork of harvesting data, ultimately, it requires the skills of knowledge engineers to make the final call.
- Knowledge engineering has roots in library science, so it may be time to update the old Harvard Business Review article naming the present-day equivalent of the reference librarian as the sexiest job of the 21st century.
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Siliconangle
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Palantir stock climbs as strong earnings and guidance impress investors
- Palantir's stock jumped over 20% in after-hours trading following its strong Q4 earnings.
- In Q4, Palantir reported adjusted earnings per share of 14 cents and revenue of $827.5 million, beating analyst expectations.
- The company closed significant deals, with 129 contracts of at least $1 million, 58 deals of at least $5 million, and 32 deals of at least $10 million.
- For its fiscal 2025 first quarter, Palantir expects revenue of $858 million to $862 million and projects a full-year revenue of $3.741 billion to $3.757 billion.
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Universe Today
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SETI Researchers Double-Checked 1 Million Objects for Signs of Alien Signals
- SETI uses a data system called COSMIC to search for artificial radio emissions and look for signs of extraterrestrial life.
- The radio astronomy observatory records data from over 950,000 objects throughout the galaxy.
- The VLA is engaged in the VLA Sky Survey (VLASS), a long-term effort to detect transient radio signals in the entire visible sky.
- The system helped scientists learn new things, even though no signs of alien life were found.
- Researchers used this test to prove the system is viable and will need to be more automated to manage vast volumes of data in modern astronomy.
- However, this is just the beginning of the search for extraterrestrial intelligence.
- We need new sorting mechanisms to process the ever-increasing amounts of data.
- COSMIC is one such system, but other methods will continue to be developed so that humanity may find the answer to one of the most pressing questions of all time.
- It is becoming one of the largest SETI experiments ever designed.
- The work thus far is an important milestone, and with the continuing database, the search for life continues.
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Aviationfile
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Aviation Big Data Project: Turbulence Prediction and Flight Route Optimization
- The “Turbulence Prediction and Route Optimization using Big Data” project focuses on developing a system that uses weather data, in-flight sensor data, and historical flight patterns to predict turbulence and optimize flight routes.
- Integrating big data analytics and machine learning models will enhance passenger safety, minimize flight disruptions, and reduce fuel consumption.
- Seven teams with distinct roles, collectively, ensure the successful delivery of the project.
- The Chief Data Officer (CDO), Data Engineers, Data Scientists, Data Analysts, Software Developers, Cloud Architects, and Security Specialists form the project team.
- The System Development Life Cycle (SDLC) framework adopted in the project breaks the project into distinct, manageable phases.
- The Planning Phase includes identifying project objectives, defining scope, and conducting a feasibility study.
- The Requirement Gathering and Analysis phase includes collaboration with stakeholders, identification of critical data sources, and ensuring compliance with aviation regulations.
- The System Design phase includes high-level and detailed design, defining secure communication channels, and selecting cloud infrastructure.
- The Development Phase includes the machine learning model development, real-time data integration pipeline building, and application interface development.
- The Testing Phase includes unit, integration, and system testing, simulated real-world conditions, and security testing.
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Precisely
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Faster, Smarter Customer Experiences Begin Here
- Unified customer communication management (CCM) solution eliminates the reliance on IT for communication updates empowering business users to create and deploy content quickly.
- A fast, personalized, and flawless execution of communication can help in building customer trust and drive loyalty.
- Siloed communication between different departments affects the agility needed to deliver seamlessness to customers.
- Unified CCM empowers everyday content creators to easily update, create, organize and deploy their communication directly without any technical know-how required.
- It centralizes content control and visibility into customer interactions across multiple channels.
- It provides reusable content elements within the CCM solution which help in making updates faster.
- It accelerates the review and approval process while ensuring quick, consistent, and error-free communication.
- With centralized content control, one can end their reliance on IT for communication updates, which helps conserve their valuable time and resources.
- Businesses that provide a fast, seamless and personalized experience to customers are the ones that thrive, build loyalty, and stay competitive in a dynamic business environment.
- 85% of companies that prioritize customer feedback see an increase in revenue, there’s a direct financial that stems from listening to customer needs.
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TechBullion
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The Role of Data Analytics in Enhancing Tax Collection Efficiency
- Data analytics is transforming tax collection by enabling governments to gain insights that were previously unattainable paving the way for a culture of compliance among taxpayers.
- Using vast amounts of information, tax authorities can improve fraud detection, monitor compliance and identify trends in taxpayer behaviour leading to more targeted communication strategies.
- Predictive analytics can forecast revenue streams enabling organizations to allocate resources efficiently. This leads to the prioritization of audits based on risk assessments.
- Data analytics provides transparency to taxpayers who feel more confident in robust systems working to ensure fair practices, encouraging compliance and improving the relationship between taxpayers and authorities.
- Countries such as the UK, India, and Brazil have already utilized data analytics in tax collection to increase compliance and improve revenue collections.
- Resistance to change, lack of resources, skills and concerns over data privacy are the main obstacles faced by tax authorities when integrating data analytics into their systems.
- Investing in robust infrastructure and technology, training employees and establishing metrics to measure success are key strategies to ensure a successful implementation of data analytics in tax collection.
- Data analytics will continue to play a significant role in shaping tax policies and strategies in the future, optimizing operations to maximize revenue collection while improving the relationship between taxpayers and authorities.
- Adapting to emerging technologies in tax collection & management systems will position organizations at the forefront of financial management practices, enabling them to leverage data analytics successfully.
- Despite the obstacles faced, organizations must remain agile and embrace change to ensure effective integration of data analytics into tax collection and management systems.
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TechBullion
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How Do You Blend Human Intuition With Big Data in Decision-Making?
- Strategies for blending quantitative analysis with qualitative intuition involves creating a collaborative team effort between data scientists and intuitive professionals. A balanced approach to data-driven decision-making is more accurate and reliable when considered with human experience insight. Tools like dashboards can provide clear visual insights into data. Human insight fills in the gaps that a map for big data cannot provide. Data analysis should be followed by a review session where an experienced team member can challenge or validate the model’s suggestions.
- The benefit of allowing intuition to drive the car is that it helps teams to see the bigger picture and recognize what is important. Analytics keep things anchored, while intuition allows for the catching of subtle signals the numbers overlook. Fostering a culture of collaboration, transparency, and data literacy throughout organizations can enhance decision-making. A dialog between analysts and decision-makers to understand limitations and potential biases within the data is important for humanizing decision-making.
- Creativity and experimentation are key to data-driven decision-making. Successful integration requires acknowledging that data isn’t a magic bullet. Data provides the “what” and the “how,” intuition provides the “why.” For effective decision-making, businesses should embrace a tool for critical self-reflection. Combining unbiased data analysis with experience and intuition creates a truly dynamic and effective decision-making process.
- The key to a successful partnership is to strike a balance between big data and human instincts. As much as data can give you the numbers, it is the human side, intuition, and creative instincts that can truly guide you towards making the right decision at the right time. By adopting a collaborative approach towards decision-making processes, organizations can not only benefit from an objective analysis of data but also from the expertise and experience of its people. A perfect balance, where data enlightens rather than dictates and where intuition fills in the gaps that numbers can't reach is what makes organizations successful.
- The best choices are made through combining facts and instinct. Big data helps to identify patterns and trends, and human insight provides the necessary depth and context. It’s about letting data and intuition meet in the middle. The real strength of decision-making is not just about data but driven by a deeper, more authentic understanding of people. The combination of data and intuition informs decision-making, leading to improved business outcomes.
- Combining human intuition and big data in decision-making requires starting with a gut feeling and letting data back it up. Intuition gets the ball rolling, but decisions need to be grounded in data. A balanced approach to data-driven decision-making achieved through collaboration between human experience and data insights improves accuracy and reliability. The combined expertise of data scientists and intuitive professionals provides a balanced approach to data-driven decision-making.
- Data can provide insight, spot trends, and identify patterns that might otherwise be missed, but it takes experience and instinct to understand the why behind human behavior. Combining the strengths of big data and human intuition, where each side brings something valuable to the table, results in optimal decision-making. A cultural shift towards data literacy empowers individuals at all levels to understand and interpret data relevant to their roles, enriching the decision-making process. Balancing how data guides us without losing sight of the human perspective gives decisions their depth and meaning.
- Blending data analysis with human intuition leads to more informed and accurate decisions, taking external factors and human elements that can impact results into account. By finding the best of both worlds, data informs intuition, and intuition enhances the value of data, creating a dynamic and effective decision-making process. Combining real-world experience with actionable insights from big data results in optimal decision-making outcomes for businesses.
- Balancing data with intuition involves creating a partnership between the two. While intuition tells you where you need to go, data can provide you with the best possible route. Data analytics should be considered in conjunction with intuition to ensure that decisions are accurate and reliable. Integrating intuition and data-driven analysis allows for deeper insights into customer behaviors, preferences, and needs while providing the ability to gain a competitive advantage.
- Big data offers insight and supports decision-making. However, when there are anomalies, instincts must come into play. Human intuition is invaluable and allows decision-makers to connect the dots beyond what is evident in the data. Blending big data analytics with intuition allows for the cultivation of a solid decision-making process that is both fact-based and flexible enough to accommodate situational and environmental changes.
- Human intuition and big data can be complex when navigating decision-making processes; however, it involves creating a balance between the two that brings value to both. A partnership where data and intuition enlighten one another creates a sound and effective decision-making process, leading to better business outcomes. Strategies combining human intuition with Big Data in decision-making involve allowing each side to bring something valuable to the table, resulting in an informed and accurate decision-making approach.
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TechBullion
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How and why backup and recovery are necessary in Oracle: international database expert Olga Badiukova on critically important knowledge.
- In 2024, 92% of IT leaders reported an increase in the frequency of database hacking attempts compared to 2023.
- Human error, cyber threats, technical failures, and natural disasters are some of the reasons for conducting regular database backups.
- Oracle Recovery Manager (RMAN), Oracle Data Guard, Oracle Flashback, and Oracle Cloud Infrastructure Backup are some of the tools for creating and managing backups offered by Oracle Database.
- Database administrators must create a Disaster Recovery Plan to guarantee quick restoration of applications and data after an accident.
- It is advisable to regularly test backups for integrity, store data copies on different media, and build a high-availability DB system.
- Database-Level Recovery, Point-in-Time Recovery for Test Systems, and Minimizing Downtime are some of the methods of data recovery.
- Despite Oracle Database's powerful tools for backup and data recovery, human errors and backup malfunctions can still cause system failures.
- Data backup and recovery is a strategic task that requires a professional approach and regular updates, testing, and optimization.
- Establishing an efficient backup and recovery system ensures business resilience to unexpected events that may lead to catastrophic data loss.
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Siliconangle
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Preparing for the Super Bowl requires defense to be played off the field
- The tech team at NFL and San Francisco 49ers are working on Super Bowl LX to be held in Levi’s Stadium in Santa Clara in 2026.
- For the Super Bowl, the network is critical to every aspect of holding a game, as all critical services run on the network.
- With every Super Bowl, the growing focus makes it a top-level Homeland concern.
- Security standards have to be heightened to ensure safety against potential threats.
- This year’s Super Bowl will be the NFL’s deputy chief information officer’s 18th and is already immersed in work for future Super Bowls.
- Wi-Fi plays a massive role in stadium connectivity, including accommodating backend technology and making services such as point-of-sale systems flexible.
- AI requires a lot of bandwidth and processing power, and this has to go through the Wi-Fi in the stadium.
- AI is viewed as an opportunity to summarise any threats that come up during the game, aiding in threat detection.
- Every event is watched very closely, especially the Super Bowl since it’s a high-value target for adversaries.
- It’s crucial to have a secure, rock-solid network to ensure business operations in all industries, for it’s the network that is the business for most companies.
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TechBullion
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Devart Launches dbForge SQL Complete 7.0: A Game-Changing Toolkit for Simplifying SQL Tasks and Solving Modern Database Issues
- Devart has launched dbForge SQL Complete 7.0, which introduces real-time script optimization, smarter code suggestions, and even deeper integrations.
- dbForge SQL Complete 7.0 works alongside the developer, offering real-time suggestions and optimizations that save time and reduce errors.
- T-SQL Code Analyzer helps users detect flaws in their scripts and provides solutions to them.
- SQL Complete 7.0 is integrated with the latest versions of Microsoft SQL Server Management Studio and Visual Studio 2022.
- SQL Complete 7.0's upgraded code prompting feature helps developers complete tasks more efficiently and with fewer mistakes.
- The new support for T-SQL graph functions has made writing queries smoother, while the new support for implicit procedure execution has simplified validating stored procedures.
- The SQL Query History feature in dbForge SQL Complete 7.0 has been redesigned for efficiency.
- User-defined layout persistence is now live, which saves time and reduces frustration by not resetting column layouts when restarting SSMS.
- SQL Complete 7.0 also offers the Find Invalid Objects feature that quickly scans through databases and generates scripts to fix any broken or invalid items and a SQL Formatter Wizard for formatting scripts.
- Devart's commitment to innovation ensures that SQL Complete will continue to simplify the process of managing data and keep professionals ahead of the curve.
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TechBullion
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Financial Rights Education Platforms: Understanding Consumer Protections
- Financial rights education platforms are online or community-based resources designed to educate consumers about their legal and financial entitlements.
- They simplify complex laws and provide interactive tools to empower individuals and help them navigate the financial system with confidence.
- Consumer protections exist to prevent unfair practices and promote transparency in financial transactions, and these platforms translate legal rights into actionable steps.
- Financial rights education platforms prioritize accuracy, accessibility, and engagement to amplify consumer knowledge and foster trust in the financial system.
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Amazon
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How Open Universities Australia modernized their data platform and significantly reduced their ETL costs with AWS Cloud Development Kit and AWS Step Functions
- Open Universities Australia (OUA) used AWS services such as AWS Glue, Amazon AppFlow, and AWS Step Functions to reduce ETL operational costs and enhance visibility and maintainability of data pipelines.
- OUA analyzed their contract for the third-party tool being used for their ETL pipelines and realized that they could replicate the functionality using AWS services.
- AWS services such as Amazon Redshift were used for storing data and making it available for analytics and BI. AWS Step Functions were used to define, orchestrate and execute the data pipelines.
- AWS Cloud Development Kit (AWS CDK) was used to consolidate the source code into a code repository that could be deployed using AWS CloudFormation.
- OUA aimed to use as few moving parts as possible, prioritize ease of use for developers, minimize idle resources, and roll out updates in stages to minimize disruption to existing business processes.
- AWS services used by OAU include AWS Glue, AWS Lambda, Amazon AppFlow, Amazon S3, AWS Step Functions, Amazon Redshift, and Amazon EventBridge.
- OUA defined four data pipeline patterns which included data transformation, load, and unload; data replication using AWS Glue; data replication using Amazon AppFlow, and reverse ETL.
- The re-architecture and migration process took 5 months to complete, with significant cost reductions when compared to continuing with the third-party ETL tool. Moving to a code-based approach also made the process of maintaining data pipelines quicker and easier.
- Overall, the move to AWS services was seamless for OUA end users and enabled faster turnaround, lower costs, and enabled rapid development and deployment of data solutions.
- Authors of the article include Michael Davies, a data engineer at OAU and Emma Arrigo, a solutions architect at AWS with expertise in education customers, leveraging cloud technology, and machine learning for business solutions.
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Siliconangle
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Oracle and Google expand service availability and add cross-region replication for disaster recovery
- Oracle Corp. and Google LLC’s cloud unit today announced an expanded partnership with broader regional coverage, additional services aimed at disaster recovery and a low-cost entry offering for customers that want to adopt Oracle’s Exadata high-performance database platform.
- Oracle Database@Google Cloud, which is a version of Oracle Cloud Infrastructure that runs natively on Google Cloud, will expand to eight new regions and data center capacity will also be doubled in some regions.
- Benefits of running Oracle software on the Google Cloud Platform include simplification of workload deployment and easy purchasing and contracting via Google Cloud Marketplace using existing Google Cloud commitments with unified support.
- New cross-region disaster recovery support allows customers to set up a replicated standby database in a separate Google Cloud region, while Oracle autonomous database manages itself and also cross-region recovery.
- The new single-node virtual machine cluster offering for Oracle Exadata Database Service on Dedicated Infrastructure provides a cheaper version of Exadata to customers with lower processing demands.
- Oracle Database@Google Cloud customers can purchase Oracle Database services using their existing Google Cloud commitments and leverage existing Oracle license benefits.
- The expansion also gives Oracle access to businesses in some regions where regulations or practical concerns require services to be delivered locally.
- Oracle Autonomous Database Serverless edition has been enhanced with cross-region disaster recovery and database replication and can be used while accessing Google services such as Vertex AI and BigQuery.
- Exadata for less - the move continues Oracle’s efforts to lower entry barriers to customers that want to adopt Exadata in the cloud.
- Oracle Cloud Infrastructure announced yesterday that it is expanding its reach to help global customers unlock new cloud capabilities.
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Amazon
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Hybrid big data analytics with Amazon EMR on AWS Outposts
- Amazon EMR on AWS Outposts is an extension that brings the power of Amazon EMR directly to your on-premises environments.
- This service merges the scalability, performance, and ease of Amazon EMR with the control and proximity of your data center.
- In this post, we explore the transformative features of EMR on Outposts and how it integrates smoothly with your existing IT infrastructure.
- We examine a hybrid setup where sensitive data remains locally in Amazon S3 on Outposts and public data in an AWS Regional Amazon Simple Storage Service bucket.
- This approach makes sure that all data processing and analytics are performed locally within the on-premises environment, allowing enterprises to maintain compliance with data privacy and regulatory requirements.
- Additionally, we use AWS Lake Formation for access controls on the AWS Glue table.
- We also showcase how you can control access to the tables using Lake Formation.
- Furthermore, to achieve high network performance, we use AWS Direct Connect with a virtual private gateway.
- Overall, Amazon EMR on AWS Outposts is a perfect solution for businesses that require powerful and flexible tools to manage and analyze vast amounts of information.
- It unlocks new data processing possibilities and empowers enterprises to meet stringent regulatory and operational requirements.
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