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Looker developers gain speed and accuracy with debut of Continuous Integration

Looker introduces Continuous Integration to help developers streamline code development workflows and enhance user experience.
Continuous Integration in Looker ensures data consistency by unifying changes to data pipelines, models, reports, and dashboards automatically.
The feature proactively tests new code changes before deployment to maintain a strong user experience and data integrity.
Benefits of Continuous Integration in Looker include early error detection, improved data quality, enhanced efficiency, and increased confidence in deployments.
It offers validators to flag SQL changes, identify outdated LookML definitions, and validate LookML for errors and antipatterns.
Developers can manage Continuous Integration directly within Looker, monitor test runs, manage configurations, and trigger runs manually or automatically.
Continuous Integration helps promote developmental best practices, reduce errors in production, and increase organizational confidence in data.
Overall, Continuous Integration in Looker provides a consistently reliable experience for users, enhancing reliability across all use cases.

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Siliconangle

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Cycling with data: How Q36.5 and and Qlik are breaking the limits of performance and precision

Cycling professionals are utilizing data analytics to enhance performance in areas like rider health, training, and strategic decision-making.
QlikTech International AB is collaborating with the Q36.5 Pro Cycling Team to leverage real-time analytics, governed data, and automation in revolutionizing performance and logistics.
Adam Nunn, digital strategist at Q36.5, emphasizes the importance of using data to make smart decisions for riders and bikes, which is facilitated through their partnership with Qlik.
Data analytics is becoming a crucial differentiator in the sports industry, and Q36.5 integrates data from various sources to inform strategic decisions.
Challenges include assisting staff from different disciplines in understanding and trusting analytics, with Qlik's platform aiding in simplifying the shift.
Qlik's platform focuses on handling diverse data, governance, accuracy, and user-friendly visualization, essential for real-time insights during cycling events.
The platform is designed to display data in ways that are easily understandable to cyclists and support staff, who may not be familiar with traditional data representations like pie charts.
Qlik Connect's video interview delves deeper into the collaboration between Q36.5 and Qlik, showcasing how data analytics is reshaping sports performance.
TheCUBE's coverage of Qlik Connect is under a paid media partnership, with the editorial content not controlled by sponsors.
John Furrier, co-founder of SiliconANGLE, emphasizes the importance of support to keep content free and accessible.
A community of over 15,000 experts, including industry luminaries like Amazon.com CEO Andy Jassy and Dell Technologies founder Michael Dell, supports the mission of providing relevant and deep content.
TheCUBE is recognized as a key industry partner, creating appreciated content for events and the industry overall.
Supporting the mission by joining the community on YouTube is encouraged by SiliconANGLE and TheCUBE.
Andy Jassy acknowledges TheCUBE's significance in event coverage and content creation, expressing appreciation for the contribution.
The community's support is acknowledged and appreciated by SiliconANGLE and TheCUBE for enabling the continuous provision of free and relevant content.

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Medium

350

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Still Using Excel? Pandas Is the Upgrade You Didn’t Know You Needed

Discover the benefits of upgrading from Excel to Pandas in Python.
Excel can be powerful and familiar, but Pandas offers more capabilities for scaling data and automation.
Many users struggle with managing multiple versions of Excel files with complex names and formulas.
Pandas provides solutions for tasks like combining files, dynamic filtering, and automating reports.
The transition to Pandas can be a game-changer for users dealing with large datasets.
Pivot tables, conditional formatting, and macros in Excel have their strengths but may have limitations for advanced tasks.
Excel is known for its user-friendly interface and functionalities.
Pandas offers enhanced capabilities for handling complex data operations.
Users who find Excel limiting for tasks like data scaling and automation may benefit from learning Pandas.
Pandas in Python provides a robust toolset for data manipulation and analysis.
Excel's limitations become apparent when dealing with extensive datasets and complex operations.
The transition to Pandas may require learning new skills but can significantly improve data management processes.
Pandas can streamline tasks like combining files and automating repetitive processes.
Consider moving to Pandas for advanced data manipulation and automation tasks.
Excel can be effective for basic tasks, but Pandas excels in handling complex data operations.
Pandas in Python offers a versatile and powerful alternative to Excel for data analysis and management.

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Siliconangle

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Qlik Connect reveals data company’s roadmap for AI-powered analytics

QlikTech International AB aims to bridge customer data and AI-generated insights at Qlik Connect.
Qlik showcased ways organizations leverage data for business benefits and automate workflows.
89% of companies have an AI strategy, with 26% deploying AI at scale.
Qlik launched a fully managed, open lakehouse to boost AI deployment at scale.
Qlik updated Qlik Answers for agentic AI, enabling a feedback loop with users.
Challenge lies in cultural and process changes to accelerate AI adoption.
TheCUBE coverage of Qlik Connect discussed Qlik's path in the enterprise AI world.

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Pymnts

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Unlocking Cardholder Loyalty: 3 Strategies for Crafting Compelling Card-Linked Offers

Credit cards' appeal to consumers is tied to the rewards and benefits they offer, prompting financial institutions to strategize ways to make their cards top-of-wallet choices.
Strategic design and deployment of card-linked offers can enhance usage, engagement, and loyalty among cardholders.
Crafting tailored offers using advanced data analytics helps predict spending patterns and increase card usage, especially beneficial for premium cardholders.
Premium cardholders are more likely to engage with and benefit from card-linked offers compared to those with entry-level cards.
Integrating tailored offers into a robust rewards ecosystem can drive cardholder satisfaction, strategic engagement, and positive recommendations.
Enhancing the value of premium cards through personalized offers and compelling rewards programs can influence cardholder behavior positively.
By focusing on these strategies, financial institutions can boost usage, loyalty, and advocacy among cardholders.

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Hackers-Arise

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Network Forensics: Getting Started With Stratoshark

Stratoshark is introduced as a companion application to Wireshark, focusing on system call analysis and obtaining deeper insights into system activity.
It captures system activity directly from the Linux kernel, using libsinsp and libscap libraries to create .scap files for detailed analysis.
Stratoshark extends cloud security monitoring by collecting audit logs and retrieving AWS CloudTrail logs for potential threat analysis.
System calls are standard functions that applications use to interact with external devices, managed by the operating system for hardware abstraction.
Stratoshark supports multiple capture sources like Falcodump and Sshdig for recording system calls and logs.
Key features of Stratoshark include real-time system activity monitoring, comprehensive filtering options, cloud integration, visualization tools, container visibility, and threat detection.
It uses visual indicators to identify different system calls and potential security issues, similar to Wireshark's color-coding for packet types.
Stratoshark and Wireshark focus on system calls and network packets respectively, complementing each other in system observation.
For Windows and macOS, development packages are available through Wireshark's automated builds, while Linux users need to build Stratoshark from source.
Stratoshark's interface mirrors Wireshark's layout, with a clean workspace designed for system call analysis.
Analyzing SCAP files with Stratoshark involves exploring expandable headers like System Event, Arrival Time, Event Information, Process Information, and File Descriptor Information.

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Medium

120

Quantum Resonance Field Theory (QRFT): A Proposal for Unification of Gravity, Entanglement, and…

Quantum Resonance Field Theory (QRFT) suggests spacetime, gravity, and entanglement stem from a quantum field.
QRFT integrates Singularity Mechanics Theory, Quantum Transmutation, and Multi-Dimensional Gravity.
The theory introduces a field evolution equation involving quantum resonance amplitudes and various parameters.
It proposes a collapse limit for quantum resonance intervals, showing a duality between coherence time and state complexity.
QRFT makes experimental predictions in gravitational wave signatures, quantum transmutation in plasma, and micro-curvature fluctuations.
The theory also invites feedback for open theoretical critique and discussion from peers.

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Medium

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You Don’t Have to Be a Math Wizard to Work in Data Analytics

The author initially thought they were bad at math and disliked word problems but later discovered a passion for calculus and statistics.
Despite not aiming for a math or data career initially, the author found themselves drawn to data projects and problem-solving involving real contexts.
They enjoyed investigating transactions, finding patterns, and using logic to resolve issues in the data field, realizing their aptitude for it.
The author highlights that data analytics roles are not as intimidating as they may seem, emphasizing the importance of practical skills over advanced math.
Advice given includes focusing on problem clarity, understanding the data context, and practicing with tools like SQL, Excel, and BI software.
The key is being willing to learn, analyze data step by step, and not feel pressured to know everything instantly in the data analytics field.
In data analytics, problems are grounded in real situations, providing a clear context for analysis and logic application compared to abstract math problems.
The author encourages people to start where they are, grow into data analytics, and emphasizes that helping others make better decisions is a core aspect of this field.
The journey from disliking math to excelling in data analysis showcases that one doesn't need to be a math wizard to succeed in this field.

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Medium

# Why Everyone Should Understand the Basics of Data in 2025

Data is ubiquitous, with individuals generating significant amounts daily through various activities like reading, watching videos, leaving likes, taking routes, and making purchases, all of which are collected and analyzed for or against them.
Understanding data basics grants individuals the ability to make informed decisions, ask better questions, identify bias, interpret data accurately, and enhance testing and improvement processes.
Three essential data skills to master include Excel or Google Sheets for basic data handling, data visualization tools like Tableau and Power BI for creating visual stories, and analytical thinking to question data reliability and interpretation.
One does not require a tech background to delve into data analysis, as learning through tutorials, courses, and practical applications like Excel can pave the way to becoming adept in utilizing data for personal and professional growth.
In today's data-driven world, those capable of interpreting and utilizing data effectively take the lead, while others lag behind, emphasizing the importance of engaging with data to stay ahead in various aspects of life.

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Medium

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“Lights, Camera, AI”: Video Trailer Generation using Multimodal Data Analysis

Researchers developed a novel approach for video trailer generation focusing on auditory features, crucial for the horror genre.
A dataset of 311 short films and their official trailers was compiled from various sources like YouTube, Vimeo, and others.
Audio and video files were extracted using the Python library youtube-dl for feature extraction.
Audio cues were found to be predominant for horror-thriller genres, leading to an audio-guided design.
Trailer features were clustered using k-means to identify 'trailer-worthy' audio moments during inference.
Evaluation metrics included Hamming Score (HS), Intersection Over Union (IOU), and Task Accuracy (TA) to assess machine learning models' effectiveness.
Average Hamming Score, indicating trailer-worthiness, was found to be 0.6930 over 20% of the dataset.
Average IOU, representing overlap with actual trailer segments, was computed as 0.3455, showcasing room for additional trailer-worthy segments.
Average Task Accuracy, measuring segment prediction accuracy, stood at 0.5625 during testing.
An audio-guided visual framework automated trailer generation for horror films, achieving notable Hamming Score, IOU, and Task Accuracy values.
The framework predicted highly trailer-worthy segments effectively, with potential for further research to enhance its robustness.

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Medium

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7 Python Libraries You Need as a Data Analyst in 2025

In 2025, data analysts need these 7 Python libraries for problem-solving, automation, and clear insights.
The libraries mentioned are versatile and cover various tasks such as data cleaning, feature engineering, reporting, Excel exports, exploratory analysis, and more.
The emphasis is on using Python for creating plots instead of relying on JavaScript or drag-and-drop tools.
These libraries are recommended for tasks like exploratory data analysis, creating slide-ready charts, and sharing visual insights within notebooks.
One of the libraries is suitable for math-heavy work, simulations, and tasks requiring avoidance of slow loops.
Another library is ideal for quick machine learning prototypes, churn prediction, A/B analysis, and segmentation.
One of the libraries is designed for creating client dashboards, executive reports, web visualizations, and monitoring key performance indicators (KPIs).
A recommended library can be used for reporting pipelines, Excel automation, and dynamic file generation.
There is also a library suggested for connecting data pipelines, querying production databases, and loading large tables into Pandas.
The article advises users to choose these libraries based on their specific use cases rather than succumbing to the Fear of Missing Out (FOMO).
The key message is to focus on using tools that work harmoniously together, address real problems, and simplify the user's workflow by sticking to a core set of 7 effective libraries.
The recommendation is to build proficiency using these libraries, generate valuable insights, and iterate the process for continual improvement.

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Semiengineering

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Improving Fab Engineering Efficiency With Autonomous Data Analytics

As a process integration engineer, identifying yield enhancement opportunities involved analyzing relationships between bin failures and process parameters within the fab.
Challenges included integrating various data types like sort maps, electrical test maps, parametric data, in-line metrology, and defect scans.
Efficiency in analyzing quality excursions is crucial due to the impact on tool utilization and fab shipments.
Synopsys' Decision Support System (DSS) efficiently analyzes diverse data sources stored in a data lakehouse, identifying latent relationships among different data types.
DSS provides ranked lists of data behaviors, prioritizing higher-correlating data that could be of interest to users.
The value of DSS lies in correlating map patterns and identifying relationships between different data parameters in the semiconductor industry.
DSS handles large data volumes, facilitating quick analysis and reducing manual work during investigations.
The Subscription feature in DSS notifies users of new data models or behaviors based on their set keywords, aiding in timely insights.
Increasing complexity in semiconductor fabs necessitates advanced data management approaches, making autonomous data analytics crucial for fab engineers.
The Decision Support System by Synopsys enhances fab engineering efficiency by identifying latent behaviors, correlating data, and presenting results effectively.

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Medium

256

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Beyond Automation: Building an Intelligent Data Engineering Assistant

Introducing the AI Data Engineering Assistant, a next-generation accelerator for data engineers.
Shift from reactive to proactive approach with the AI assistant empowering teams with intelligence.
Features include asking natural questions, detecting issues early, providing recommendations, and automating tasks.
Deep awareness of modern data lifecycle using open-source technologies like dbt Core, Apache Airflow, Snowflake, etc.
The Assistant understands and integrates seamlessly into various data ecosystem components.
With organizations scaling data platforms, the need for AI assistants to support engineering teams is crucial.
The AI Data Engineering Assistant offers flexibility, modularity, context-awareness, and transparency in its approach.
It is designed to be a copilot that reasons, communicates, and augments engineering talent effectively.

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Cloudblog

396

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BigQuery under the hood: Enhanced vectorization in the advanced runtime

BigQuery on Google Cloud's data to AI platform separates storage and compute for flexibility, with features like compressed storage and compute autoscaling increasing efficiency.
Technologies like Borg, Colossus, Jupiter, and Dremel contribute to BigQuery's performance and continuous push for query price/performance improvements.
Enhanced vectorization in BigQuery's advanced runtime enhances query processing through data encodings, expression folding, and common subexpression elimination.
Data encodings like dictionary and run-length encodings optimize storage efficiency and query performance by reducing redundant computations.
Expression folding and common subexpression elimination reduce unnecessary data processing, improving query performance in BigQuery.
Enhanced vectorization leverages parallelizable algorithms for joins and aggregations, leading to accelerated query execution in BigQuery.
Tighter integration with Capacitor, enhanced vectorization improves efficiency through optimized data access, pruning, and filter pushdown.
Enhanced vectorization achieved a 21 times speedup in a query execution example, showcasing the performance boost from these techniques.
BigQuery's continuous advancements in enhanced vectorization and storage formats aim to enhance query efficiency and handle a broader range of queries.
Enhanced vectorization in BigQuery will be enabled for all customers by default, with upcoming features like support for Parquet files and Iceberg tables.

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Cloudblog

167

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Google is a Leader in the 2025 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms

Google has been named a Leader in the 2025 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms for the second consecutive year, emphasizing their comprehensive BI platform accessible to entire organizations.
The integration of Google's Gemini models into Looker has enhanced AI capabilities, introducing Conversational Analytics and AI-powered development tools.
New features include advanced code interpretation, automated slide generation, Looker agents for AI insights, and continuous integration for data quality and reliability.
The focus is on infusing trusted data into every workflow, offering natural language querying, robust data modeling, and embedded AI insights throughout the platform.
Looker reports offer enhanced visualization, collaborative workflows, and responsive design for data storytelling and exploration.
Developers benefit from Conversational Analytics API, Model Context Protocol, and Agent Development Kit for building custom BI agents within their applications.
Code Interpreter in Conversational Analytics allows advanced analysis with transparent code display, ensuring accuracy and understanding of data queries.
The combined power of Looker's semantic model and Google's AI capabilities aims to provide more intelligent and impactful business intelligence, emphasizing data consistency and quality.
Gartner's report underlines the importance of trusted data agents and semantic layers in modern organizations for reliable decision-making.
Google focuses on enhancing AI-powered tools and embedding data insights in everyday applications, aiming to deliver more accurate and impactful results.
The 2025 Gartner Magic Quadrant report showcases Google's leadership in BI platforms, emphasizing the transformation brought by AI and trusted data usage.

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