A naukri.com initiative
Data Analytics News
Siliconangle
1M
257
Image Credit: Siliconangle
AWS aims to strengthen AI and data with GraphRAG support and InfluxDB partnership
- Amazon Web Services (AWS) announced at re:Invent conference that Amazon Bedrock Knowledge Bases now supports GraphRAG.
- Customers can benefit from a graph database without having to learn to use a graph database.
- AWS InfluxData partnership aims to deepen collaboration and enable real-time, intelligent systems.
- Higher resolution data is required to build smarter models for AI applications.
Read Full Article
15 Likes
Siliconangle
1M
284
Image Credit: Siliconangle
Transforming storage solutions: How Commvault and AWS are powering cloud-first cyber resilience
- Commvault Systems Inc. and Amazon Web Services (AWS) are collaborating to provide storage solutions for cyber resilience.
- Commvault combines its data protection and management capabilities with AWS's cloud infrastructure to offer scalable and cost-efficient storage.
- The acquisition of Clumio by Commvault strengthens their position in safeguarding cloud-native applications and data.
- Clumio Backtrack feature enables near-instantaneous recovery of data in Amazon S3 storage environments.
Read Full Article
17 Likes
Hrexecutive
1M
343
HR can be ‘the transformer.’ This industry expert shares how
- HR executives are seen as different roles in their companies such as the connector, the motivator, and the innovator.
- Melissa Anderson, executive vice president and chief people and transformation officer for Albemarle Corp., believes that HR is the transformer of the business, helping leaders bring together business objectives and vision.
- Anderson has a history of leading transformations in her career, including creating the first Fortune 50 shared services center at IBM.
- HR's unique position allows them to lead transformation by leveraging transferrable skill sets, intellectual curiosity, and data analytics capabilities.
Read Full Article
20 Likes
Semiengineering
1M
126
Image Credit: Semiengineering
Understanding Test Quality In Semiconductor Devices: An Overview
- The testing process is designed to catch defects in semiconductor devices, which could prevent the device from functioning correctly, leading to failures at time zero or in the field.
- The effectiveness or “thoroughness” of a semiconductor device test is measured by its coverage.
- The quality of the test is often quantified by the likelihood of a “test escape”, which refers to a defect going undetected.
- Defect mechanisms have become more subtle in advanced semiconductor technologies, making them harder to detect.
- As the devices become more complex, new challenges like die matching have emerged, which add a layer of complexity to the testing process.
- There are several different types of tests used to ensure semiconductor devices meet their specifications, including continuity/contact resistance (CRES), DC Parametrics, Leakage Tests, Electrical Chip ID (ECID), Low Voltage and Nominal Voltage Structural Tests, Stress Tests, and Parametric Tests.
- Test data analytics plays a critical role in resolving the balance between test quality and cost.
- AI/ML models are being integrated into test flows to optimize test processes further, offering faster identification of defects with minimal human intervention.
- The Advantest ACS Real-Time Data Infrastructure (ACS RTDI) empowers customers to boost yield, enhance product quality, and accelerate time to market with cutting-edge real-time data solutions and AI/ML-driven analytics.
- By leveraging comprehensive data, using advanced models, and optimizing test flows, manufacturers can maintain high product quality while controlling costs.
Read Full Article
7 Likes
Semiengineering
1M
280
Image Credit: Semiengineering
AI Predictive Modeling Tools Move Into Critical Role To Leverage AI, ML
- The semiconductor industry is investing in AI and ML skills to develop predictive models for manufacturing problems and negative yield effects.
- PDF Solutions' ModelOps for AI predictive modeling offers a structured approach to managing the lifecycle of machine learning models.
- The ModelOps solution enables the deployment of AI models for various semiconductor manufacturing use cases, such as predictive maintenance and yield optimization.
- The adoption of ModelOps is critical for organizations to effectively leverage AI and maintain a competitive edge in the fast-paced semiconductor market.
Read Full Article
16 Likes
Medium
1M
190
Image Credit: Medium
Understanding Combining WHERE and HAVING Clauses: Advanced Filtering Techniques
- Understanding how to combine WHERE and HAVING clauses effectively is crucial for optimizing SQL queries.
- The WHERE clause is used to filter records before any grouping or aggregation takes place.
- The HAVING clause is used to filter data after the aggregation process.
- When writing complex SQL queries, it is important to combine both WHERE and HAVING clauses for optimal results.
Read Full Article
11 Likes
Medium
1M
353
Image Credit: Medium
The Impact of Data Analysts: A Three-Pillar Approach
- Success begins with precise problem definition.
- Actionable recommendations are vital.
- Implementation must be followed by thorough validation.
- Continuous iteration is essential for success.
Read Full Article
21 Likes
Pymnts
1M
366
Image Credit: Pymnts
Data Quality: The Unsung Hero of AI-Powered B2B Procurement
- AI is emerging as a force in B2B procurement, offering faster and smarter decision-making.
- Data quality is crucial for the success of AI in procurement.
- Clean, accurate, and well-organized data is essential for peak AI performance.
- Companies that prioritize data quality can unlock the full potential of AI in procurement.
Read Full Article
22 Likes
Siliconangle
1M
307
Image Credit: Siliconangle
AWS leads the charge in sustainable data centers with AI-ready innovations
- Amazon Web Services Inc. is focused on creating highly efficient and reliable data centers with innovative solutions.
- AWS is embracing liquid cooling techniques and scalable infrastructure to save more energy.
- They have seen a 46% improvement in efficiency during peak cooling times by using computational fluid dynamics.
- AWS aims to decrease costs, carbon output, and improve efficiency in training AI models.
Read Full Article
18 Likes
Siliconangle
1M
162
Image Credit: Siliconangle
Reimagining data: How AWS S3 Tables redefine analytics and scalability
- Data storage is undergoing tremendous changes shifting from static form to dynamic with AWS S3 Tables, allowing working with mutable and structured query language-like datasets, meaning a significant change from the read-only nature of Apache Parquet files.
- AWS S3 Tables (Iceberg) makes the tables mutable and brings them closer to be a more conventional SQL table, managing changes, metadata, journal, and system table, feedbacking new opportunities of discovery and AI-driven insights.
- By embedding metadata directly into the table infrastructure and disassociating storage from compute, S3 Tables unlocks new levels of flexibility and resilience, significantly boosting system scalability and efficiency. AWS S3 Tables integrates with key AWS services, enabling streamlined data ingestion and visualization.
- AWS continues its S3 transformation from being focused on internet storage to a comprehensive data platform capable of supporting advanced analytics, AI workloads, and application development needs, where—from a storage conversation—shifts to the value of data and discovery spearheading its architecture.
- AWS’ advancement addresses a long-standing challenge for customers who had to cobble together individual solutions for data reads and writes.
- Its architectural shift allows AWS to decouple storage from compute, thus offering new opportunities for data discovery and artificial intelligence-driven insights, unlocking new levels of flexibility and resilience.
- The flexibility for developers is better, AWS can change instances as workload on the drives change.
- The transformation which brings the industry’s focus to data value and discovery reflects the broader movement towards making data more accessible and meaningful.
- Customers can create a table bucket, create multiple tables, and policy set them all, with Iceberg’s own endpoint.
- Data storage is shifting towards open table formats prioritizing flexibility and interoperability.
Read Full Article
9 Likes
Medium
1M
154
Growing your Medium Account And Getting More Visibility For Your Articles Requires
- 1. Optimize Medium's algorithm by including relevant and popular keywords in your niche to increase visibility.
- 2. Leverage Medium Publications by submitting your articles to popular Medium publications in your niche for more visibility.
- 3. Promote your articles on social media platforms like Twitter, LinkedIn, and Instagram where your target audience is active.
- 4. Engage with the Medium community by publishing and applauding other writers' posts, participating in discussions, and posting regularly to keep your audience engaged.
Read Full Article
9 Likes
allthingstalent.org
1M
163
Image Credit: allthingstalent.org
Rupsha Majumdar on Key Skills Shaping the Future of Data Analytics
- The demand for skilled data analytics professionals is at an all-time high as organisations rely on data for decision-making.
- Key skills in data analytics include programming languages (Python, R, SQL) and tools (Tableau, Power BI, Google Data Studio).
- Data wrangling, machine learning and AI integration, statistical analysis, and problem-solving abilities are also important in data analytics.
- Soft skills like business acumen, communication, collaboration, and staying updated with cloud platforms and ethical data management are crucial for future success in data analytics.
Read Full Article
9 Likes
Medium
1M
158
Image Credit: Medium
Two Years as a Product Manager: What I’ve Learned, What I Wish I Knew
- Product management is a challenging role that requires juggling various tasks and responsibilities.
- Learning to lead is crucial, balancing emotional intelligence with decision-making.
- Effective communication is essential for working with designers, engineers, and stakeholders.
- Embracing ambiguity, adapting, and celebrating small wins are key lessons in the product management journey.
Read Full Article
9 Likes
Medium
1M
290
Image Credit: Medium
How AI Advances Scientific Research
- AI plays a transformative role in advancing scientific research in areas like data analysis, simulation, and discovery acceleration.
- AI accelerates research by providing powerful tools for data analysis, enabling researchers to analyze vast amounts of data more accurately and faster.
- AI optimizes simulations, helping researchers in fields like genomics, environmental studies, and materials science to gain deeper insights and make groundbreaking discoveries.
- By leveraging AI, researchers work more efficiently and can explore new scientific frontiers, leading to faster discoveries and solutions for global challenges.
Read Full Article
17 Likes
Siliconangle
1M
354
Image Credit: Siliconangle
Simplifying AI: Nutanix brings ‘GPT in a box’ to Amazon’s EKS
- Nutanix is bringing its Enterprise AI solution to Amazon's Elastic Kubernetes Service (EKS).
- Nutanix Enterprise AI simplifies the inference process for enterprise customers.
- The collaboration aims to provide consistency and a common operating model for AI workloads.
- Nutanix has also utilized AI internally to enhance customer interactions and improve software engineering efficiency.
Read Full Article
21 Likes
For uninterrupted reading, download the app