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Data Analytics News
Cloudblog
5h
243
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Understand why your metrics moved with contribution analysis in BigQuery ML, now GA
- BigQuery ML contribution analysis, now generally available, allows for automating insight generation and identifying key change drivers from multidimensional data for quicker decision-making.
- The GA version of contribution analysis introduces new features such as automated support tuning with top-k insights by apriori support and improved insight readability with redundant insight pruning.
- With the new pruning_method option, users can choose to prune redundant insights to see only unique insights, enhancing the clarity of analysis results.
- Further, expanded metric support includes the summable by category metric, enabling analysis of metrics normalized by unique values of a categorical variable.
- This metric is useful for adjusting outliers in data and comparing different numbers of rows in test and control datasets.
- A retail sales example is provided to demonstrate how to utilize contribution analysis in BigQuery ML to identify key contributors to changes in product sales.
- By creating a summable by category metric contribution analysis model, users can efficiently extract insights by setting various options such as top_k_insights_by_apriori_support.
- The model output provides ordered insights by contribution value, aiding in understanding the impact of different variables on the metric of interest.
- Utilizing contribution analysis can help businesses quickly pinpoint areas of improvement based on data-backed insights, ultimately enhancing decision-making processes.
- To explore contribution analysis further, users are encouraged to refer to the tutorial and documentation for a hands-on experience with their own datasets.
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Medium
2d
50
Image Credit: Medium
Google Data Analytics: Completed
- The individual has completed the Google Data Analytics course and is now focusing on bolstering their skills and knowledge before starting to build projects and create a portfolio.
- They have discovered a Data Analytics job at Gamesight, a company related to gaming and streaming, which aligns well with their interests and expertise in the gaming industry.
- Having experience in various types of games like League of Legends, Valorant, CS:GO, and TFT, the individual watches Youtubers and Twitch Streamers to improve their skills, especially focusing on pro players.
- While acknowledging that they are not yet ready to apply for the job, the individual aims to become job-ready in the near future by gaining more knowledge and experience in the complex data world.
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3 Likes
Medium
3d
339
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Coinbase to Acquire Deribit: Becoming the Most Comprehensive Global Crypto Derivatives Platform
- Coinbase has agreed to acquire Deribit, the leading crypto options exchange, in a significant move to enhance its derivatives business.
- The acquisition will position Coinbase as the premier global platform for crypto derivatives, offering a comprehensive range of trading options including spot, futures, perpetual futures, and options.
- Deribit's strong market presence and track record of generating positive Adjusted EBITDA are expected to boost Coinbase's profitability and provide diversified revenue streams.
- This strategic move by Coinbase aims to lead the significant growth expected in the crypto options market and accelerate its global derivatives strategy, catering to institutional and advanced traders worldwide.
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Cloudblog
4d
181
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New column-granularity indexing in BigQuery offers a leap in query performance
- BigQuery has introduced indexing with column granularity to enhance query performance and reduce costs by indexing column data.
- This new feature allows BigQuery to pinpoint relevant data within columns for faster search queries.
- Tables in BigQuery are stored in physical files with each column having its dedicated file block in a columnar format.
- The default search index in BigQuery operates at the file level, meaning it reduces the search space by scanning relevant files.
- File-level indexing can face challenges when search tokens are selective within specific columns but common across others.
- Column-granularity indexing enables BigQuery to leverage indexes to locate data within columns even when tokens are prevalent across files.
- By adding column information in the indexes, BigQuery can significantly improve query performance by scanning only relevant files.
- Benchmark tests show that column-granularity indexing results in faster query execution and improved cost efficiency.
- Benefits of column-granularity indexing include enhanced query performance and cost efficiency in scenarios with selective search tokens within columns.
- Best practices for utilizing column granularity indexing include identifying high-impact columns, monitoring performance, and considering indexing and storage costs.
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Pymnts
4d
324
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From Nice-to-Have to Nonnegotiable: How GenAI Is Redefining the Office of the CFO
- Artificial intelligence (AI) has become essential in the office of the CFO, shifting from a nice-to-have to a must-have component of financial operations.
- AI adoption is rapidly increasing in finance departments, with qualitative and quantitative applications enhancing functions such as treasury, payments, and risk mitigation.
- Generative AI tools like Treasury GPT from FIS are transforming cash forecasting by synthesizing real-time data for more accurate predictions, setting a new standard for the industry.
- AI is recalibrating ROI metrics for CFOs, improving elements like DSO, liquidity optimization, payment security, and efficiency, leading to centralized reporting and decision-making powered by AI in the future.
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19 Likes
Medium
4d
320
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AI Agent Communication Protocols: The Foundation of Collaborative Intelligence
- AI Agent Communication Protocols are reshaping the landscape by enabling interconnected AI systems to collaborate on complex tasks and deliver value.
- The Agent-to-Agent (A2A) protocol, introduced by Google, allows autonomous agents to communicate over standard HTTP connections, enabling effective collaboration.
- Microsoft's integration of A2A with Semantic Kernel Python demonstrates practical applications in travel planning, showcasing specialized agents collaborating effectively.
- Anthropic's Model Context Protocol (MCP) serves as a 'USB port for AI applications,' enabling AI assistants to connect to various data sources without custom integration.
- MCP implements a permission-based model for accessing tools and data during conversations, emphasizing privacy and security requirements.
- The Agent Communication Protocol (ACP) facilitates multimodal exchanges in AI systems, handling diverse data types and communication patterns seamlessly.
- The Agent Network Protocol (ANP) addresses trust and discovery in permissionless AI ecosystems, using decentralized identifiers for verifiable interactions.
- The Agent-to-Agent Communication Protocol (AACP) introduces formal semantics for multi-agent collaborations through structured message formats.
- These protocols complement each other in creating powerful AI ecosystems that span organizational boundaries and enhance collaboration.
- The evolution of AI communication protocols indicates trends towards improved patient outcomes in healthcare, enhanced security in finance, and overall AI ecosystem transformation.
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Semiengineering
4d
52
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AI For Test: The New Frontier
- Dr. Ming Zhang discussed the new frontier of AI for semiconductor testing at the TestConX 2025 conference, emphasizing the importance of investing in AI for enhancing processes and staying competitive.
- Challenges related to data complexity, model adaptability, and security persist in integrating AI into semiconductor testing, but advancements in AI modeling and adaptive testing strategies offer promising solutions.
- The deployment of AI in semiconductor testing requires addressing challenges like heterogeneous data, model maintenance, different deployment constraints, and security sensitivity.
- Opportunities for AI in semiconductor testing include adaptive testing, predictive binning, burn-in reduction, connected data systems, and real-time monitoring, enhancing efficiency and quality across various testing applications.
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Siliconangle
4d
313
Image Credit: Siliconangle
Amplitude shares tick up after earnings results slightly beat estimates
- Amplitude's shares rose slightly in after-hours trading following its fiscal 2025 first quarter earnings report, which showed the company breaking even and generating $80 million in revenue.
- The company reported annual recurring revenue of $320 million, a 12% increase year-over-year, and a cash flow from operations loss of $8 million, down $7.9 million from the previous year.
- Amplitude introduced new features such as Amplitude Guides and Surveys to enhance user engagement, and platform enhancements in response to customer demand, including self-serve data deletion capabilities and Session Replay Everywhere.
- The company's co-founder and CEO, Spenser Skates, highlighted that Amplitude is seeing more enterprise customers embracing their platform, stronger multiproduct attach rates, and rapid innovation, with expectations of continued revenue growth in the upcoming quarters.
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Pymnts
5d
167
Image Credit: Pymnts
Etsy’s ‘Algotorial Curation’ Blends Human Touch With AI Smarts
- Etsy is implementing 'algotorial curation,' a strategy that combines human expertise with advanced AI algorithms to recommend products to shoppers.
- Human experts identify trends and select listings, which are then expanded using machine learning to create collections of about 1,000 items.
- Etsy uses Google's Gemini multimodal model for these experiences, leveraging AI to enhance human insight at scale without eliminating human involvement.
- The implementation of AI has led to improved search functionalities, increased visibility, and sales on Etsy, providing a more personalized shopping experience for consumers.
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10 Likes
TestingXperts
9h
298
Engineering Smarter Data Pipelines with Autonomous AI
- Autonomous data engineering, powered by AI and ML methodologies, aims to automate the entire data engineering lifecycle from discovery to activation, reshaping data workflows.
- Autonomous AI systems operate independently, learn from data, adapt to changes, and encompass perception, planning, action, and database components.
- AI supports autonomous data engineering by automating data profiling, quality analysis, smart data integration, streamlined data pipelines, predictive maintenance, and data augmentation.
- Enhancing data analytics, AI automates tasks, identifies patterns, accelerates processing, and improves accuracy, allowing for strategic focus and better decision-making.
- Benefits for data teams include automation of tasks, anomaly detection, improved data analytics, system reliability, reduced downtime, and faster insights production.
- Tx offers AI-driven testing solutions to enhance data quality, agility, competitiveness, and efficiency in the data-driven landscape.
- Proactive testing with AI-driven test automation and intelligent data profiling helps identify data issues early in the pipeline, ensuring high data quality even in complex environments.
- Leveraging autonomous AI in data engineering streamlines processes, improves integration and pipeline efficiency, and enables real-time analytics for faster insights.
- Tx's robust testing services, powered by AI, detect and resolve data issues early, reduce downtime, support compliance, and empower data teams to focus on strategy and innovation.
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17 Likes
Medium
2d
346
Image Credit: Medium
A Beginner’s Guide to Cross-Validation: Why It Matters and How to Use It
- Cross-validation is important in machine learning to avoid overfitting and ensure models can handle new data.
- It acts like a series of practice tests for machine learning models, testing them on different parts of the dataset.
- K-Fold Cross-Validation is a popular method where the data is split into 'K' folds to test the model's performance.
- Using cross-validation helps in picking the best model settings and ensures more reliable performance evaluation in machine learning projects.
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20 Likes
Medium
2d
328
Image Credit: Medium
Exploratory Data Analysis: Radiation Monitoring with Python and Geiger Counter
- Background radiation is always present, originating from various sources like uranium, thorium, radon, nuclear accidents, and cosmic rays.
- Exploratory data analysis can help uncover patterns in radiation levels and fluctuations using tools like anomaly detection.
- The article demonstrates collecting radiation data with a Geiger counter and a Raspberry Pi, processing it using Python and Pandas.
- For those interested in using the same data, a link to a Kaggle dataset is provided at the end of the article.
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19 Likes
Cloudblog
4d
92
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Expanding BigQuery geospatial capabilities with Earth Engine raster analytics
- Google Cloud introduced Earth Engine in BigQuery, allowing advanced geospatial analytics using SQL.
- Earth Engine excels at raster data, while BigQuery is efficient with vector data, making them a powerful combination.
- Key features of Earth Engine in BigQuery include the ST_RegionStats() function and access to Earth Engine datasets.
- The ST_RegionStats() function allows efficient extraction of statistics from raster data within specified geographic boundaries.
- Five steps involved in performing raster analytics include identifying vector and raster datasets and using ST_RegionStats().
- Earth Engine in BigQuery enables data-driven decision-making in climate, disaster response, agriculture, methane emissions monitoring, and custom use cases.
- Examples of use cases include wildfire risk assessment, sustainable sourcing, methane emissions analysis, and custom analyses using various datasets.
- A detailed example demonstrates how to combine wildfire risk data with weather forecasts using ST_RegionStats() and SQL queries.
- The combination of datasets allows for insights on relative wildfire exposure and risk assessments, aiding in decision-making and visualization.
- Earth Engine in BigQuery opens up new possibilities for geospatial analytics, and more enhancements are expected in the future.
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Siliconangle
5d
358
Image Credit: Siliconangle
Neo4j goes serverless, bringing graph analytics to any data source
- Neo4j Inc. has launched a new serverless offering to simplify the deployment of its graph database for use with AI applications.
- This serverless offering enables graph analytics to work with any data source without the need for complex ETL operations.
- Graph databases like Neo4j differ from traditional SQL platforms by using a graph structure of nodes, edges, and properties for data storage.
- They enable 'vector search' for unstructured data, making them ideal for AI applications to derive richer insights and patterns.
- Graph analytics can improve AI decision-making by uncovering hidden patterns and relationships in complex data.
- The new serverless offering, Neo4J Aura Graph Analytics, aims to make graph analytics accessible to all companies by removing adoption barriers.
- This service comes with over 65 graph algorithms, pay-as-you-go pricing, and optimized performance for AI applications.
- By using graph analytics, AI models can derive insights faster and adapt in real time to changing data, reducing coding tasks significantly.
- Neo4j's serverless platform promises to boost the accessibility of graph analytics for enterprises across different data sources and cloud platforms.
- The new offering eliminates the need for complex queries, ETL, and costly infrastructure setup, allowing organizations to tap into graph analytics' power.
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21 Likes
Cloudblog
5d
69
Image Credit: Cloudblog
How Looker’s semantic layer enables trusted AI for business intelligence
- In the AI era, accurate and consistent data insights are crucial, leading to the importance of a semantic layer for trusted definitions in organizations.
- Looker's semantic layer serves as a single source of truth for business metrics and dimensions, ensuring consistency in data interpretation for AI initiatives.
- A semantic layer aids in reducing errors and inaccuracies in AI applications, particularly gen AI, providing accurate business logic interpretation.
- Looker's semantic layer significantly reduces data errors in gen AI natural language queries, offering a reliable foundation for analytics and BI.
- Trusted gen AI relies on a robust semantic layer for accurate responses grounded in governed data, backed by deep business context and governance.
- Looker's LookML enables the creation of a semantic model that simplifies data structure and logic, ensuring consistent and accurate insights for users.
- The semantic layer ensures organizational alignment by standardizing definitions and terms, leading to consistent data interpretation and insights across the organization.
- Looker's LookML centralizes definitions, offers deterministic calculations, software engineering best practices, time-based analysis, and deeper data drills for comprehensive data understanding.
- By bridging the gap between data sources and business language, LookML allows for more intuitive and accurate data analysis, benefiting decision-makers.
- Looker's semantic layer enhances AI integration in BI by providing a structured data library, enabling AI agents to find relevant information for accurate responses.
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