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Amazon
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432
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Design patterns for implementing Hive Metastore for Amazon EMR on EKS
- Hive Metastore (HMS) serves as a central metadata store for data lake table formats, providing clients access to metadata via the Metastore Service API.
- HMS architecture patterns include implementing HMS as a sidecar container, cluster dedicated HMS, and external HMS.
- The sidecar pattern co-locates HMS with the data processing framework, suitable for small-scale deployments with simplicity.
- Cluster dedicated HMS pattern involves running HMS in pods managed by Kubernetes, offering moderate isolation and resource efficiency.
- External HMS pattern deploys HMS in a separate EKS cluster, ideal for scenarios needing a centralized metastore service.
- Implementing these patterns with Spark demonstrates various approaches for managing metadata efficiently.
- Each pattern has distinct advantages based on needs, like simplicity, resource isolation, and scalability.
- The article provides detailed steps for configuring and testing the HMS patterns in AWS EMR on EKS using Spark Operator.
- Cleanup steps after testing are included to avoid incurring charges for resources created during setup.
- Experimenting with these design patterns can optimize Hive Metastore deployments for performance and security in EMR on EKS environments.
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Precisely
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222
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What is Data Integrity?
- Data integrity is essential for accurate, consistent, and contextualized data to empower businesses to make fast and informed decisions.
- Trust in data is vital for impactful decision-making, but many organizations still face challenges in fully trusting their data.
- Data integrity encompasses accuracy, consistency, and real-world context of data, enabling better customer management, cost reduction, and risk management.
- It requires data sources to be integrated to provide a comprehensive view for business users and mitigate data quality issues.
- A sound data integrity strategy includes data quality solutions, data observability, and data governance to ensure security, privacy, and regulatory compliance.
- For AI success, trusted data with integrity is essential to avoid biased outputs, untrustworthy results, and loss of trust in AI systems.
- Data integrity goes beyond data quality by focusing on completeness, accuracy, consistency, and contextual relevance of data.
- Initiatives to improve data integrity start with specific projects to address data quality issues, improve governance, and enrich internal data with external datasets.
- Organizations can enhance data integrity by leveraging tools like the Precisely Data Integrity Suite, which integrates core capabilities for accurate and contextualized data.
- The suite offers data integration, observability, governance, quality, geo addressing, spatial analytics, and enrichment to streamline the data integrity journey.
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Amazon
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214
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Governing streaming data in Amazon DataZone with the Data Solutions Framework on AWS
- Data governance is essential for organizations to maximize the value of their data assets through processes, policies, and practices.
- Amazon DataZone, a service on AWS, allows centralized discovery, control, and evolution of data schemas for data at rest.
- Managing real-time data streams requires adaptations to conventional data governance frameworks.
- Extending Amazon DataZone to support streaming data like Amazon MSK involves using custom asset types and authorizers.
- The Data Solutions Framework on AWS accelerates the implementation of streaming data governance in Amazon DataZone.
- Key components for governing streaming data in Amazon DataZone include representing Kafka topics, managing authorization flows, and updating metadata.
- Data sources for Amazon MSK clusters can be created in Amazon DataZone through AWS Glue Schema registry.
- Custom authorization processes are needed for managing access to unmanaged assets like Amazon MSK topics.
- The subscription grant process involves metadata collection, authorization updates, and internal metadata updates in Amazon DataZone.
- The solution utilizes AWS CDK and DSF to implement streaming governance, allowing seamless data asset registration and access control.
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Amazon
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Amazon Prime Video advances search for sports using Amazon OpenSearch Service
- Prime Video Sports aims to offer an intuitive search experience for sports fans by leveraging Amazon OpenSearch Service.
- Enhancing the search architecture helps in catering to engaged sports audiences, leading to increased viewership and engagement.
- Challenges arose when transitioning the search function to focus on live sports rather than movies and TV shows.
- Prime Video upgraded its sports-specific search capabilities in 2024 to provide a more intelligent search system.
- The solution involved employing semantic search and binary search relevance classification for sports search functionality.
- Amazon OpenSearch Service was used to create a scalable and efficient vector search solution for Prime Video Sports.
- ML connectors facilitated integration of machine learning models with OpenSearch Service for improved search capabilities.
- The implementation resulted in enhanced customer experience, increased viewer engagement, and improved search precision.
- By using AI/ML capabilities, Prime Video developed a best-in-class search experience for sports content consumers.
- This collaboration has led to valuable contributions back to the OpenSearch open source community, benefiting developers.
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TechBullion
4w
415
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The AI Revolution and Its Impact on the Data Center Industry
- Artificial intelligence (AI), especially generative AI, is revolutionizing technology and impacting industries like the data center sector.
- The AI revolution has created both opportunities and challenges within the data center industry, with a need for greater computational power.
- AI applications, particularly high-performance computing (HPC), significantly increase energy demands and power consumption in data centers.
- Efficient cooling systems are crucial due to the heat generated by IT equipment, with innovations like liquid and immersion cooling being developed.
- Utility providers are struggling to meet the rising power demands of AI data centers, causing delays in their setup and operational readiness.
- Further advancements in computer processors are needed to bridge the gap between current hardware capabilities and the requirements of AI applications.
- The environmental impact of AI-driven power consumption raises concerns, with the industry exploring sustainable alternatives like modular nuclear reactors.
- The AI revolution necessitates significant investments in infrastructure and technology to ensure the long-term success of the data center industry.
- Industry leaders are actively seeking a balance between performance, energy efficiency, and environmental sustainability in AI-driven data centers.
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Siliconangle
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330
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Snowflake’s stock surges on earnings crush and revenue beat
- Snowflake Inc. reported impressive fourth-quarter financial results, beating earnings and revenue projections.
- The company's earnings per share were 30 cents, exceeding the forecasted 18 cents.
- Snowflake's product revenue reached $943.3 million, higher than the expected $914 million.
- Despite a widening net loss, Snowflake's stock surged by 9% in after-hours trading.
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Atlan
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116
Image Credit: Atlan
Breaking Down Data Silos: A Practical Framework from the Field
- Data silos hinder effective decision-making by requiring excessive time for data preparation rather than analysis, as per IDC reports.
- Silos arise from isolation within people and technology, leading to issues such as time inefficiencies, data quality concerns, scale problems, and access challenges.
- Breaking down data barriers necessitates a dual strategy involving human cultural change and technological advancements.
- Establishing a data culture involves educating teams on data literacy, fostering collaboration, and gaining leadership support.
- Effective data management demands a comprehensive approach ensuring data quality, consistency, context preservation, and accountability.
- Implementing a 6-part framework, including domain empowerment, clear governance, trust-building standards, unified discovery, automated governance, and connected tools.
- Successful companies like Autodesk, Contentsquare, Porto, Nasdaq, Kiwi.com showcase diverse yet effective governance structures for managing and sharing data.
- Standardized practices, unified discovery layers, automated governance, and connected tools can enhance data efficiency and decision-making processes.
- The framework highlights the importance of clear ownership, governance, trust-building, automation, connectivity, and alignment for breaking down data silos.
- Adopting a systematic approach to dismantling data silos can lead to accelerated organization progress towards becoming data-driven and fostering innovation.
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Amazon
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93
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Top analytics announcements of AWS re:Invent 2024
- AWS re:Invent 2024 featured major analytics advancements aimed at empowering businesses to leverage their data effectively and accelerate insights.
- Key announcements included updates to Amazon SageMaker, introducing enhanced capabilities such as Unified Studio and Lakehouse for unified data access and governance.
- Amazon DynamoDB integration with SageMaker Lakehouse offered zero-ETL automation for data replication, streamlining the process for deriving insights.
- Introduction of Amazon S3 Tables optimized for analytics workloads, supporting Apache Iceberg tables and providing faster query throughput and transaction rates.
- Amazon S3 Metadata simplifies metadata management and queryability for S3 data, improving data organization and accessibility.
- AWS Glue 5.0 enhancements include updated engines, support for SageMaker Lakehouse, and Spark native access control for improved data integration and insights.
- AWS expanded data connectivity for SageMaker Lakehouse and AWS Glue, offering unified data connectivity capabilities and generative AI troubleshooting for Apache Spark.
- Amazon Redshift introduced features like zero-ETL integrations with various applications, incremental refresh for materialized views, and Serverless with AI-driven scaling and optimization.
- Amazon QuickSight unveiled scenarios analysis capabilities with Amazon Q, prompted reports and reader scheduling, and unification of insights from structured and unstructured data.
- Amazon DataZone enhancements included data lineage visualization, enforced metadata rules for governance, and expanded data access with tools like Tableau and Power BI.
- AWS Clean Rooms now supports collaboration with datasets from multiple clouds and data sources, facilitating secure data sharing and collaboration.
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Amazon
4w
93
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Amazon Web Services named a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools
- Amazon Web Services (AWS) named a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools.
- This recognition reflects AWS's commitment to innovation and excellence in data integration.
- AWS Glue, a fully managed, serverless data integration service, simplifies data preparation and transformation across diverse data sources.
- AWS offers a robust data integration system through multiple services, including Amazon EMR, Amazon Athena, and others.
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Precisely
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111
Image Credit: Precisely
AI-Driven Data Integrity Innovations to Solve Your Top Data Management Challenges
- The Precisely Data Integrity Suite introduces AI-driven innovations to enhance data accessibility, governance, and automation, aiding in confident, data-driven decision-making.
- Organizations prioritize data-driven decision-making, but face challenges like limited data access, governance issues, and manual processes.
- The upgrades in the Suite target these challenges with enhanced data governance, AI advancements, expanded data integration capabilities, and improved Data Catalog functionality.
- Key enhancements include microservices-based architecture for governance, AI Manager for trusted data handling, expanded data integration for Snowflake, and enhanced Data Catalog views.
- By leveraging these innovations, organizations can enhance efficiency, automate processes, and improve data readiness for analytics and AI insights.
- The Suite addresses top data management challenges like manual efforts, data accessibility across platforms, disruptions in data delivery, incomplete data lineage understanding, governance inefficiencies, and maximizing ROI.
- To overcome these challenges, organizations can utilize AI Manager, improved data integration capabilities, latency metrics, persona-based lineage visualization, and streamlined governance processes.
- The advancements in the Data Integrity Suite offer opportunities to automate tasks, ensure seamless data integration, resolve disruptions efficiently, enhance data governance, and accelerate AI and analytics projects.
- Businesses need to prioritize data integrity to navigate industry changes effectively, leverage AI-driven solutions, and optimize data governance for confident decision-making.
- By adopting AI-powered tools and modern data governance approaches, organizations can gain stronger insights, increase efficiency, and remain competitive in evolving landscapes.
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Siliconangle
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1.3k
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IBM buys DataStax to boost its watsonx data platform for AI applications
- IBM is acquiring database company DataStax to strengthen its watsonx portfolio of AI development tools.
- DataStax's technology will be integrated into watsonx's generative AI products, providing efficient access to large amounts of data.
- The financial terms of the deal have not been disclosed, and the acquisition is expected to close in the second quarter.
- DataStax will continue working on open-source initiatives, including the Apache Cassandra project and Langflow.
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Atlan
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290
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Convergence, Consumerization, and AI: Unpacking the Top Trends from Gartner’s Data Governance MQ
- Gartner released its first-ever Magic Quadrant for Data and Analytics Governance Platforms, emphasizing the importance of governance in modern organizations.
- Governance has been fragmented, but with AI and decentralized data ecosystems, it is evolving into a business imperative.
- The market for governance platforms is growing rapidly, showcasing the need for comprehensive solutions to address governance challenges.
- Key factors driving the growth of governance platforms include operational and analytical data governance integration and increasing policy management complexity.
- AI has become a major driver for governance initiatives, necessitating new frameworks and tools for managing AI assets effectively.
- The Magic Quadrant reveals a market actively defining its future, emphasizing the importance of vision and innovation in shaping the market.
- Three major trends shaping the future of data governance include platform convergence, consumerization of governance, and AI governance.
- The future of governance involves integrated platforms, making governance accessible to all personas, and addressing the unique challenges of AI governance.
- Successful governance platforms need to be comprehensive, collaborative, intelligent, and adaptable to meet evolving data and analytics needs.
- Organizations should assess their governance maturity, define a visionary governance approach, develop a roadmap, and evaluate platforms aligning with key trends.
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Siliconangle
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44
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Couchbase shares rise despite mixed quarterly results and lower outlook
- Couchbase reported mixed results in its fiscal 2025 fourth quarter, but shares rose in late trading.
- The company's adjusted net loss per share was 30 cents, while revenue reached $54.9 million.
- Couchbase's full-year revenue was $209.5 million, with total annual recurring revenue of $237.9 million.
- For fiscal 2026, the company expects first quarter revenue of $55.1 million to $55.9 million.
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Amazon
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Building and operating data pipelines at scale using CI/CD, Amazon MWAA and Apache Spark on Amazon EMR by Wipro
- Wipro addresses challenges faced by businesses in managing data pipelines by developing a programmatic data processing framework.
- The framework integrates Amazon EMR runtime for Apache Spark and AWS Managed services for scalability and automation.
- It streamlines ETL processes by orchestrating job processing, data validation, transformation, and loading into specified targets.
- Components include Amazon MWAA, Amazon EMR on Amazon EC2, Amazon CloudWatch, Amazon S3, Amazon EC2 for Jenkins build server.
- CI/CD pipelines automate deployment, triggering when code is pushed to Git, building artifacts for Amazon EMR usage.
- Amazon MWAA handles data pipeline orchestration, scheduling, and execution using Airflow.
- Fault tolerance is enhanced with the ability to recover data post-Amazon EMR termination, ensuring job continuity.
- The solution offers scalability, flexibility for customization, support for various file formats, concurrent execution, and proactive error notification.
- Average DAG completion time is 15–20 minutes, handling 18 ETL processes concurrently with large record volumes.
- The framework by Wipro leverages AWS services to provide cost-effective, scalable, and automated data processing solutions.
- Users are encouraged to utilize Amazon MWAA for ETL jobs on Amazon EMR Runtime for Apache Spark.
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Dzone
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The Future of Data Lakehouses: Apache Iceberg Explained
- The future of data lakehouses is being shaped by technologies like Apache Iceberg.
- Data warehouses are structured, governed, and efficient, but expensive and rigid.
- Data lakes are more flexible and allow for storage of vast amounts of data, regardless of structure.
- Lakehouse architecture combines the benefits of data lakes and data warehouses, and Apache Iceberg is a leading open-source table format that improves data value and addresses challenges of data lakes.
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