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Big Data News
Siliconangle
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Big Tech escapes tariffs’ impact for now, but investors are wary — and should be
- Investors are relieved that Trump's tariffs haven't impacted tech company earnings, with Alphabet, SAP, ServiceNow, and most chipmakers managing to avoid the effects.
- Concerns arise as IBM's stock drops due to exposure to tariff cuts, Intel struggles with layoffs and disappointing guidance under new CEO Lip-Bu Tan.
- Artificial intelligence adoption by enterprises is progressing slowly due to challenges in data foundations, governance, and software development tools.
- Startups are offering solutions to facilitate AI adoption as big tech faces regulatory challenges and struggles with metaverse initiatives.
- Upcoming earnings reports from major tech companies like Amazon, Apple, Microsoft, and Alphabet, along with developments in AI technologies and services are anticipated.
- Cybersecurity sector sees significant funding and new services, with firms like Chainguard raising $356 million and AI tools company Check Point introducing new services.
- Various AI-related initiatives and advancements are announced by companies like Datadog, Supabase, Adobe, and Microsoft, signaling ongoing innovation in the sector.
- Tech earnings remain robust, with companies like Alphabet, IBM, and ServiceNow beating forecasts, while challenges like trade wars impact Intel's stock.
- Policy developments include FTC suing Uber, concerns over DOGE impact, and criticism of Palantir's contract with ICE.
- In cybersecurity, developments focus on enhancing threat detection, cloud security, and AI-powered solutions to address cyber risks facing enterprises.
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Amazon
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Amazon SageMaker Lakehouse now supports attribute-based access control
- Amazon SageMaker Lakehouse now supports attribute-based access control (ABAC) with AWS Lake Formation, using IAM principals and session tags for simplified data access and maintenance.
- Attribute-based access control (ABAC) in SageMaker Lakehouse allows organizations to create dynamic access control policies based on business attributes associated with user identities.
- SageMaker Lakehouse provides unified access to various data sources like Amazon S3, Redshift, DynamoDB, and supports querying using services like Redshift, Athena, EMR, and Glue.
- ABAC offers flexibility in managing access rules, reducing administrative overhead by handling a smaller number of roles, and scalability for larger enterprises with numerous users and resources.
- ABAC grants permissions based on user attributes and is context-driven, enabling administrators to restrict data access based on specific user attribute keys and values.
- By using ABAC, organizations can reduce the number of roles required, grant access based on attributes like department or country, and easily create and maintain data access grants.
- The implementation involves defining user attributes using IAM tags, setting access policies in Lake Formation, and granting permissions based on the predefined criteria.
- The solution architecture includes defining attributes, setting up ABAC policies, and granting database and table permissions to users based on their attributes.
- Users like data analysts, BI analysts, and data scientists can leverage ABAC in SageMaker Lakehouse for specific data access based on their roles and attributes.
- The post demonstrates setting up ABAC for Example Retail Corp, granting specific data access permissions to different user personas, and utilizing analytics services like Athena, Redshift, and EMR.
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Precisely
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Is Your Data Understood and Compliant? Here’s How to Fix It
- Without shared data definitions, ownership, and built-in compliance, data becomes a liability.
- Business-friendly governance and stewardship frameworks enable teams to trust, manage, and use data confidently.
- Start with small steps like clear roles, goals, glossaries, and workflows, and scale towards proactive compliance and increased data visibility.
- Embedding governance into daily operations empowers teams, eliminates inefficiencies, enables innovation, and reduces risk.
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Siliconangle
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Datadog snaps up Metaplane to improve data quality for AI applications and systems
- Datadog Inc. has acquired startup Metaplane to expand its data observability offerings.
- Metaplane's tools will help Datadog users identify and remedy data quality issues in critical business applications.
- Metaplane's platform combines AI and column-level lineage to detect, resolve, and prevent data quality problems.
- The acquisition aims to strengthen Datadog's observability capabilities and enable the building of reliable AI systems.
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TechBullion
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The Strategic Imperative of Data Archiving: Beyond Storage
- Organizations are recognizing data archiving as a strategic imperative due to compliance requirements, operating costs, and unstructured data.
- Data archiving involves moving inactive data out of live systems for long-term preservation, accessibility, and compliance.
- Benefits of data archiving include compliance readiness, cost reduction, improved application performance, and risk reduction.
- Archiving challenges include technological obsolescence, data integrity, security vulnerabilities, and retrievability.
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Siliconangle
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Relyance AI’s new data tracking tool paves the way for greater AI accountability and explainability
- Relyance AI Inc. introduces Data Journeys, a platform focused on data tracking across an organization's systems to enhance AI accountability and explainability.
- Data Journeys tracks the data flow from its origin to destination, as well as its use by various applications and services within the organization.
- Relyance AI received $32 million in Series B funding last October and is known for its governance platform providing visibility into enterprise-wide data and controls for data protection.
- By scanning all data sources, Data Journeys ensures compliance with company policies and regulations to maintain data integrity.
- The platform offers a more comprehensive view than traditional data lineage tools by tracking data movements across different systems, applications, and AI models.
- Enterprises face pressure from regulators, with AI regulation highlighted as a risk by over 25% of Fortune 500 firms, necessitating enhanced visibility into data processes.
- Data Journeys is particularly beneficial for regulated industries like healthcare, with potential to revolutionize AI development and governance frameworks.
- The platform addresses major impediments to enterprise AI adoption, including risk management, bias detection, explainability, and regulatory compliance.
- Enhanced explainability provided by Data Journeys is crucial for AI decision-making in sensitive areas like loan approvals and medical diagnoses.
- Relyance AI aims to build a unified AI-native platform for data governance and compliance, with Data Journeys playing a critical role in ensuring trust and accountability in AI systems.
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Amazon
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Accelerate data pipeline creation with the new visual interface in Amazon OpenSearch Ingestion
- Amazon OpenSearch Ingestion is a managed serverless pipeline for ingesting, filtering, transforming, and routing data.
- The new visual interface simplifies pipeline creation and management from the AWS Management Console.
- Key improvements include a guided visual workflow, automatic permission setup, and real-time validation checks.
- The visual interface allows for automatic discovery of sources and sinks, eliminating manual resource configuration.
- Automated IAM role management simplifies security setup for pipelines, reducing the risk of errors.
- Real-time validation capabilities help identify and resolve issues during pipeline creation.
- The visual interface streamlines source and processor configuration, presenting error messages for any issues.
- Sink configuration offers flexibility for choosing OpenSearch destination and mapping options.
- The interface updates YAML/JSON configuration in real time as users make selections.
- Automated IAM role management across pipeline components saves time and reduces permission-related errors.
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Siliconangle
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Sumer Sports brings AI analytics to the NFL
- Sumer Sports is using AI-based video analytics to study every player on every play in the NFL to evaluate decision-making and success.
- The company combines AI with seasoned football professionals to ensure accurate data interpretation.
- Sumer Sports is working with 20 NFL veterans to bridge the gap between data science and football expertise.
- The AI platform focuses on roster construction, player evaluation, and improving decision-making for NFL teams.
- Sumer Sports emphasizes the importance of combining subjective and objective information with frame-level analysis to evaluate player performance.
- The company's AI platform is being utilized by several NFL and NCAA teams, but there is some resistance from traditionalists in the industry.
- Scott Pioli acknowledges that Sumer Sports represents a challenge in the industry and emphasizes the need for a shift in mindset towards AI-based tools in football.
- The potential for Sumer Sports extends beyond football into high school sports, training tools, and other sports, with a current focus on collegiate and professional football.
- Sumer Sports has created a free draft guide for those following the NFL draft, offering additional insights into player rankings.
- Zeus Kerravala, a principal analyst at ZK Research, wrote this article for SiliconANGLE on how Sumer Sports is revolutionizing AI analytics in the NFL.
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Siliconangle
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SumerSports brings AI analytics to the NFL
- Scouting in the NFL is an imperfect science due to various factors like competition level and visibility of players.
- SumerSports utilizes AI-based video analytics to evaluate player decisions and performance at a detailed level.
- The company works with NFL veterans and data scientists to combine expertise and data interpretation.
- SumerSports focuses on optimized roster construction, player evaluation, and decision-making support using AI.
- The platform offers insights to teams for identifying undervalued players, evaluating player skills, and making strategic decisions.
- SumerSports differs by combining subjective and objective information and providing frame-level insights for each player on every play.
- Despite some resistance, several NFL and NCAA teams are already using SumerSports' AI platform.
- There is potential for AI adoption across all sports, with SumerSports considering expansion to high school football and training tools.
- The company offers a free draft guide with player rankings and insights for football enthusiasts.
- SumerSports is positioned to revolutionize player evaluation and decision-making processes in the world of football.
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Towards Data Science
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MapReduce: How It Powers Scalable Data Processing
- MapReduce is a programming model introduced by Google to enable large-scale data processing in a parallel and distributed manner across compute clusters.
- Tasks in MapReduce are divided into map and reduce phases, where map processes individual data records and reduces aggregates values for distinct keys.
- MapReduce computation is distributed across a cluster with a master handling task scheduling and workers executing map and reduce tasks.
- The MapReduce model is suitable for parallelizing data transformations on distinct data partitions followed by aggregation.
- MapReduce was initially used by Google to build indexes for its search engine and is applicable to various data processing tasks.
- MapReduce jobs involve partitioning data, executing map tasks in parallel, sorting key-value pairs, and aggregating results in reduce tasks.
- MapReduce has influenced modern frameworks like Apache Spark and Google Cloud Dataflow with its fundamental distributed programming concepts.
- While MapReduce introduced key distributed programming concepts, modern frameworks like Spark have evolved to offer more flexibility and efficiency.
- The MapReduce model, though not commonly used today, played a significant role in the design of current distributed programming frameworks.
- MapReduce tasks can be expressed using libraries like mrjob, simplifying the writing of mapper and reducer logic for data transformation.
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Siliconangle
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Supabase reels in $200M for its open-source relational database
- Supabase, an open-source relational database developer, has raised $200 million in funding at a $2 billion valuation.
- The round included participation from Accel, Coatue, Y Combinator, Craft Ventures, and Felicis, along with several angel investors.
- Supabase's platform offers an alternative to Google's Firebase database, claiming to be faster and easier to set up.
- The company has seen rapid growth, with a user base of over 2 million developers and more than 3.5 million database environments.
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Siliconangle
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AI, outages and scale: Inside Dynatrace’s take on navigating digital chaos
- Businesses must address growing digital complexities in a systematic way to remain competitive.
- Managing digital complexities as a strategic advantage is crucial for businesses.
- Dynatrace helps organizations find and fix issues in their digital ecosystems before they disrupt service.
- AI capabilities like anomaly detection and root cause analysis have long supported organizations in optimizing operations.
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Siliconangle
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Ocient raises $42.1M more for its speedy data analytics platform
- Ocient, a startup with a speedy data analytics platform, has raised $42.1 million in funding.
- The investment marks the second extension of the company's Series B round, with the total raised now at nearly $90 million.
- Ocient's platform allows analysis of historical and real-time data in one place, removing the need for multiple analytics environments.
- The platform offers high query performance, advanced compression methods, and managed environment options for customers.
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Amazon
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Read and write Apache Iceberg tables using AWS Lake Formation hybrid access mode
- Enterprises are adopting Apache Iceberg table format for its features like CDC, ACID compliance, and schema evolution.
- AWS Lake Formation allows managing fine-grained data access permissions centrally and scaling data access within and outside organizations.
- Lake Formation hybrid access mode enables using IAM policy-based permissions for write workloads and Lake Formation permissions for read access to Iceberg tables in Amazon S3.
- Use cases for Lake Formation hybrid access mode include avoiding data replication, minimal interruption to existing IAM policy-based user access, and supporting transactional table writes.
- Key steps in setting up permissions involve registering data locations, granting permissions to roles, and verifying permissions for Data-Analyst and IAMAllowedPrincipals.
- Creation of IAM roles, Iceberg tables, opt-in to hybrid access mode, and testing table access as Data-Analyst in Athena are critical steps in the setup.
- Using Amazon EMR Studio for upsert operations on Iceberg tables, verifying data updates, and cleaning up resources post-use are essential parts of the process.
- Hybrid access mode in Lake Formation allows gradual adoption of Lake Formation permissions alongside IAM-based permissions for different use cases, ensuring flexible access control.
- The approach demonstrated can be extended to other open table formats and Data Catalog tables, providing organizations with control over schema and data updates.
- Authors include Aarthi Srinivasan, a Senior Big Data Architect, and Parul Saxena, a Senior Big Data Specialist Solutions Architect, both with expertise in AWS Lake Formation and big data solutions.
- The methodology outlined encourages experimentation and adoption of Lake Formation permissions while maintaining control over data operations through IAM policies.
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Precisely
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What Will the CDO of the Future Look Like?
- The article discusses the future evolution of the Chief Data Officer (CDO), transitioning to a hybrid role combining strategy, innovation, and human engagement.
- The CDO role is compared to the historical evolution of the HR Director, indicating a shift from enforcing rules to implementing strategies aligned with company objectives.
- There is a focus on humanizing the CDO role to promote its evolution and importance in successful digital transformation.
- Communication skills, the ability to speak the language of business, and promoting data culture are highlighted as key for the CDO's success.
- The future CDO is envisioned as a hybrid model merging technological expertise with humanistic sensitivity and the ability to integrate into executive teams.
- Skills necessary for the future CDO include expertise in technology, communication, forming alliances with HR Directors and CIOs, and strategic thinking.
- The article contemplates if the CDO will become a permanent strategic pillar within organizations and suggests potential renaming to reflect a broader scope, like Chief Data & AI Officer.
- The ideal future CDO is described as an empathetic tech expert who can connect technology, human values, and collective engagement for the benefit of all employees.
- The discussion concludes that by 2050, CDOs will play a crucial role in digital transformation, shaping a future where technology and humanity work together for corporate strategy.
- Advice for future CDOs includes staying true to data value extraction, developing communication skills, and embracing a hybrid role focused on serving both technology and human needs.
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