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Siliconangle
1w
200
Image Credit: Siliconangle
Salesforce refines agentic platform with flexible database
- Salesforce boosts its agentic AI platform, Agentforce, with updates that enhance agents' proactivity and autonomy, along with an AI agent builder for developers.
- Teaching agents context is vital for their autonomy, achieved through topics, instructions, and guardrails embedded in the platform.
- Salesforce emphasizes combining structured and unstructured data for optimal AI agent performance, illustrated by Heathrow Airport's success with Agentforce.
- A metadata lens, security, governance layer, and semantic model enhance Salesforce's agentic AI platform, facilitating efficient responses and consistent KPI standards.
- The company's focus is not just on individual agent improvements but on making the entire system smarter by leveraging insights into repeatable rules.
- Salesforce envisions AI harmonizing APIs across platforms and solving complex problems without deterministic coding, leading the way in agentic AI advancements.
- The company's strategy in agentic AI aims to integrate all necessary components into a comprehensive platform, positioning itself as a leader in the evolving AI landscape.
- Salesforce's approach to agentic AI aligns with enabling dynamic composition of capabilities at runtime to effectively address evolving business challenges.
- The platform's focus on multi-agent behavior and ability to have agents converse with each other showcases Salesforce's commitment to AI evolution.
- Salesforce's comprehensive agentic AI platform is designed to not just innovate in individual agents but to drive systemic intelligence transformation across enterprises.
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Amazon
1w
79
Image Credit: Amazon
Using Amazon S3 Tables with Amazon Redshift to query Apache Iceberg tables
- Amazon Redshift now supports querying data stored using Apache Iceberg tables, making it easier to manage tabular data on Amazon S3.
- Amazon S3 Tables is the first cloud object store with built-in Iceberg support, optimizing table performance and streamlining data storage.
- Amazon SageMaker Lakehouse unifies data across S3 Tables and Redshift, enabling powerful analytics and AI/ML applications.
- To use S3 Tables with Redshift, create a table bucket, set permissions, load data with Athena, and query data using Redshift.
- Prerequisites include Amazon Redshift Serverless, Amazon S3 Tables, AWS Lake Formation, and Amazon Athena for the examples in the post.
- Steps involve creating a table bucket in S3 Tables, setting up Lake Formation, loading data with Athena, and utilizing Redshift for queries.
- You can configure Lake Formation to make Iceberg tables available in SageMaker Lakehouse for Redshift querying.
- Query Iceberg data in S3 Tables using Amazon Redshift by setting up permissions and using the Redshift Query Editor v2.
- Cleanup steps include removing resources like Redshift Serverless workgroups and SageMaker Lakehouse data.
- Overall, using Amazon Redshift with Iceberg tables in S3 Tables offers efficient data analysis and storage, with various possibilities for further optimization and control.
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Siliconangle
1w
92
Image Credit: Siliconangle
Nvidia GTC takeaway: AI will follow the data
- At Nvidia's GTC keynote, CEO Jensen Huang discussed AI in the context of cloud, data centers, and robotics, emphasizing the importance of data locality.
- The rise of a new computing era driven by extreme parallel processing and accelerated computing is transforming the trillion-dollar data center business.
- Data center spending grew significantly in 2024, with AI workloads seeing a substantial increase while traditional workloads declined.
- During market transitions like the shift to AI workloads, companies must adapt quickly to align with new conditions and technologies to succeed.
- David Floyer's methodology analyzes technology adoption based on value, volume, and velocity, offering insights into market transitions.
- Upcoming analysis will focus on the AI inference at the edge of networks and the robotic opportunity, aiming to quantify the potential market.
- Quantifying the massive AI market will aid in forecasting the transition to accelerated workloads and drive better understanding of emerging technologies.
- Disclosure: Statements made regarding companies are views held by SiliconANGLE Media and should not be considered as investment advice.
- SiliconANGLE co-founder, John Furrier, emphasizes the importance of community support to provide free, relevant content in the industry.
- Community support is vital for platforms like TheCUBE to continue delivering valuable content and insights from industry experts.
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Dzone
1w
8
Image Credit: Dzone
When Doris Meets Iceberg: A Data Engineer's Redemption
- Apache Doris and Iceberg are transforming the way data lakes operate, offering faster queries, seamless schema evolution, and data consistency guarantees.
- The combination of Apache Doris and Iceberg provides a qualitative leap in data engineering, addressing issues like slow cross-source queries and complex schema changes.
- Apache Doris' lakehouse architecture enhancements in version 2.1 offer improved reading and writing capabilities for mainstream data lake formats and multi-SQL dialect compatibility.
- Doris native support for Iceberg features includes various Iceberg Catalog types, Iceberg table formats, snapshot history querying, time travel, and Iceberg table engine support.
- The integration of Doris and Iceberg simplifies lakehouse architectures, allowing for efficient data analysis, processing, and storage.
- Xiao Zhang's practical experiences with Doris + Iceberg highlight benefits like seamless schema evolution, data version management, and efficient catalog services.
- Doris combines the MPP engine with Iceberg for efficient data organization and storage optimization, enhancing performance.
- Operations and maintenance of Iceberg tables are streamlined through metric monitoring, snapshot management, and fault recovery methods.
- With Doris + Iceberg implementation, Xiao Zhang's data platform saw significant improvements in query performance, storage costs reduction, and operations efficiency.
- The integration of Doris and Iceberg continues to elevate data lakehouse capabilities, promising more benefits and advancements in data engineering.
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Amazon
1w
288
Image Credit: Amazon
Connect, share, and query where your data sits using Amazon SageMaker Unified Studio
- Organizations need to analyze data across multiple sources quickly to maintain a competitive edge.
- Amazon SageMaker Unified Studio helps in connecting, sharing, and querying data sitting in various sources.
- Retail analytics teams can use SageMaker Unified Studio to analyze customer data from Amazon S3, Redshift, and DynamoDB.
- SageMaker Unified Studio provides a unified experience for using data, analytics, and AI capabilities.
- It enables creating insights into customer behaviors without the traditional barriers of data silos.
- Amazon SageMaker Data and AI Governance ensures compliance and regulatory standards.
- SageMaker Lakehouse streamlines connecting to, cataloging, and managing permissions on data from multiple sources.
- It organizes data through federated and managed catalogs, providing secure access with fine-grained controls.
- The infrastructure team enables tools and technologies for business units to build data products in a governed environment.
- Teams can publish, share, and query data assets from various sources using SageMaker Unified Studio.
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TechBullion
1w
381
Image Credit: TechBullion
9 SEO Trends Every Digital Marketer Should Follow
- SEO trends play a crucial role in boosting online visibility and engagement for digital marketers.
- Understanding user behavior shifts and search engine algorithm updates is essential for SEO success.
- Focus on enhancing User Experience (UX) and Core Web Vitals to keep visitors engaged.
- Mobile optimization is non-negotiable, given the significant web traffic from mobile devices.
- Voice search optimization is crucial, with a focus on natural language and local SEO.
- Featured snippets and rich answers enhance visibility and engagement in search results.
- Semantic search and Natural Language Processing (NLP) prioritize user intent over keywords.
- Video and visual content optimization are essential for captivating audiences with shorter attention spans.
- Local SEO, Google My Business listings, and E-A-T (Expertise, Authoritativeness, Trustworthiness) are key for building credibility and visibility.
- Technical SEO and site structure improvements ensure search engines can efficiently understand and rank your content.
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Amazon
1w
131
Image Credit: Amazon
Introducing vector search with UltraWarm in Amazon OpenSearch Service
- Amazon OpenSearch Service has been offering vector database capabilities for efficient vector similarity searches since 2019, supporting various use cases such as semantic search and RAG with large language models.
- OpenSearch Service offers UltraWarm and Cold tiers for cost-effective storage of less-active data, with UltraWarm being suitable for immutability-required scenarios like log analytics.
- Previously, UltraWarm and Cold tiers did not support k-NN indexes, leading to high costs for customers due to memory and storage constraints.
- To address cost concerns, k-NN indexes are now supported in both UltraWarm and Cold tiers, offering savings for less-active data scenarios.
- New capability allows enabling UltraWarm and Cold tiers for k-NN indexes from version 2.17, benefiting use cases like long-term semantic search and large-scale image similarity.
- For cost reduction, a balanced approach can be implemented by leveraging hot storage for frequently accessed data and UltraWarm for less-active data, resulting in significant savings.
- Multi-tiered storage strategy aids in managing growing datasets efficiently, automating data migration between tiers based on access patterns.
- The introduction of k-NN vector search in UltraWarm and Cold tiers offers a scalable solution for balancing performance and cost in vector search workloads.
- Best practices include optimizing data placement across tiers, using Index State Management for data lifecycle management, and monitoring cache hit rates for effective tiering.
- Authors of the article include Kunal Kotwani, Navneet Verma, and Sorabh Hamirwasia, who are software engineers at Amazon Web Services specializing in OpenSearch core and vector search technologies.
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Amazon
1w
229
Image Credit: Amazon
Build a data lakehouse in a hybrid Environment using Amazon EMR Serverless, Apache DolphinScheduler, and TiDB
- Building a serverless data lakehouse on AWS Cloud involves using services like Amazon EMR Serverless, Amazon Athena, Amazon S3, Apache DolphinScheduler, and TiDB.
- The solution uses TiDB as the on-premises enterprise data warehouse, where data is processed by Amazon EMR Serverless Job to implement data lakehouse tiering logic.
- Different tiers like ODS (Operational data store) and ADS (Analytical data store) are stored in separate S3 buckets within the same Amazon S3.
- Apache DolphinScheduler aids in job orchestration, offering benefits like scalability, task-level controls, and multi-tenancy capabilities.
- Configuring DolphinScheduler requires strong DevOps capabilities, as it involves setup and maintenance effort.
- Prerequisites include creating an AWS account, IAM user setup, DolphinScheduler installation, IAM configuration for EMR serverless job, and TiDB Cloud table provisioning.
- Data synchronization between on-premises TiDB and AWS involves using TiDB Dumpling to sync historical and incremental data to Amazon S3.
- EMR Serverless jobs are used to sync data between AWS Glue tables and on-premises databases like TiDB.
- Integration with DolphinScheduler involves switching DolphinScheduler Resource Center storage from HDFS to Amazon S3 for improved job status checking and orchestration.
- Cleaning up resources post-implementation is recommended using AWS APIs to delete EC2 instances, RDS instances, and EMR Serverless applications.
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Precisely
1w
391
Image Credit: Precisely
Connected Data, Better Insights: Data Enrichment Done Right
- Change is a constant in business, with technology disruptions like AI, machine learning, and cloud computing shaping the landscape.
- Data enrichment, combined with AI applications, helps in deriving valuable insights by integrating external information with internal data.
- The accuracy, speed, and scale of data enrichment have evolved to make data-driven decision-making more efficient.
- Data enrichment augments internal data with third-party datasets, providing real-world context that uncovers hidden patterns for more informed decisions.
- Standardizing datasets from multiple providers poses a challenge, requiring resolving duplicates and ensuring consistency with each new data source.
- A new solution, akin to a 'universal data adapter,' simplifies data enrichment with pre-linked datasets, offering faster time-to-value and reduced complexities.
- Data Link Solution brings together data providers to facilitate rapid discovery, connection, and utilization of essential data for analytics and operations.
- Utilizing curated datasets and apps for tasks like geo addressing within platforms like Snowflake AI Data Cloud enhances operational efficiency.
- Connecting data through unique identifiers enables more informed decision-making by aligning data from various sources seamlessly.
- Seamlessly enriching data from multiple sources accelerates time-to-value with meaningful insights, paving the way for further advancements in data enrichment.
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TechBullion
1w
320
Image Credit: TechBullion
How Pritam Mukherjee Ensures IT Application and Data Security and GRC for America’s Largest State Public Power Organization
- Pritam Mukherjee leads IT (SAP) Application and Data Security, Governance, and Compliance at NYPA, ensuring data security and leveraging advanced technologies for digital transformation.
- He has expertise in IT Application Security, GRC, AI, ML, and Fintech and led the design and management of SAP security solutions across multiple business processes.
- Pritam presented a case study at the 2023 Americas’ SAP Users’ Group (ASUG) conference and is focused on sustainable energy development through AI and ML technologies.
- With a Master's in Financial Innovation and Technology, he emphasizes the importance of data protection and security mechanisms in digitization journeys.
- Pritam navigates challenges in application security, highlighting the difficulties in legacy systems, vendor limitations, and balancing security controls with business needs.
- He stresses proactive risk identification and assessment as crucial to avoiding detrimental impacts and ensuring compliance with industry regulations.
- Incorporating GRC measures and aligning with regulatory compliance, Pritam aims to create secure and sustainable products in the clean energy sector.
- He advocates a security-conscious culture within organizations, emphasizing the role of training, awareness, and leadership commitment in fostering a proactive security approach.
- Pritam highlights the importance of staying current on new threats, regulations, and emerging risks in the digital landscape, especially concerning AI-driven systems.
- He discusses the significance of AI risk assessments, governance policies tailored to AI, and creating accountability frameworks for AI decision-making to address compliance challenges.
- By promoting security awareness and fostering a shared responsibility for cybersecurity, organizations can lower risks and enhance resilience against cyber threats.
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TechBullion
1w
34
Image Credit: TechBullion
When to Hire an Analytics Consultant: Key Signs Your Business Needs Data Expertise
- Hiring an analytics consultant can help businesses turn raw data into actionable insights.
- Analytics consultants can assist in interpreting and leveraging data to enhance decision-making procedures.
- They evaluate and recommend scalable solutions to improve the analytics framework of a business.
- Analytics consultants ensure standardization of reporting methods and data governance for regulatory compliance.
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Amazon
2w
223
Image Credit: Amazon
Implement Amazon EMR HBase Graceful Scaling
- Amazon EMR with HBase on Amazon S3 provides random, strictly consistent real-time access for tables with Apache Kylin, ingesting data through spark jobs and querying HTables through Apache Kylin cubes.
- To scale in long-running Amazon EMR HBase clusters, consider issues like EC2 scheduling events, budget concerns, and Spot Instances usage for parallel computation power.
- Unexpected shutdown on HBase region servers can lead to WAL splits, causing extra load and potential inconsistencies in HTables in an Amazon EMR HBase cluster.
- Administrators aim to scale-in Amazon EMR HBase clusters gracefully by stopping all HBase region servers on task nodes.
- The solution includes using built-in scripts to move regions, raising decommission priority, and configuring software settings to prevent region server provisioning on task nodes.
- Method 1 involves decommissioning HBase region servers through resizing and manipulating decommission priority.
- Method 2 focuses on manually decommissioning target core nodes with careful consideration to avoid data loss and ensure HDFS block replication.
- Prevent Amazon EMR from provisioning HBase region servers on task nodes for new clusters by configuring HBase settings and scaling in task groups.
- Administrators can run scripts as Amazon EMR steps to gracefully scale in HBase clusters and handle provisioning of region servers on task nodes effectively.
- The post highlights detailed steps, syntax examples, and scripts for gracefully decommissioning target region servers in Amazon EMR HBase clusters.
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Precisely
2w
210
Image Credit: Precisely
Gartner Data & Analytics Summit Takeaway: “Why is nobody listening?”
- The Gartner Data & Analytics Summit highlighted the importance of being AI-ready in data management and analytics.
- Key recommendations included tying data strategy to measurable business value, prioritizing data quality and building a scalable data governance program.
- The event emphasized the need for organizations to focus on data management fundamentals to accelerate their journey towards AI-readiness.
- However, the challenge lies in execution, with many organizations still working through foundational data challenges.
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Siliconangle
2w
1.2k
Image Credit: Siliconangle
Couchbase realigns the database’s trajectory for the AI era
- Couchbase is realigning its database trajectory to meet the demands of the AI era.
- Couchbase has a big head start in the database space, offering vector search capability from cloud to edge.
- Couchbase's new Columnar analytics feature enhances data processing, reducing latency and costs.
- Couchbase is the only data platform that unifies transactional, mobile, analytics, and AI services.
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Precisely
2w
348
Image Credit: Precisely
How to Unlock SAP® Efficiency with Fiori Apps, REST APIs, and More
- Adopt a cloud-first automation strategy to streamline SAP processes, integrate applications seamlessly, and enhance security and scalability.
- Leverage REST APIs for easy SAP integration, reducing manual data entry, improving process governance, and connecting SAP with third-party applications.
- Automate SAP Fiori and GUI for HTML workflows using no-code/low-code tools to improve efficiency, data accuracy, and business agility while preparing for SAP S/4HANA.
- Manual data entry, disconnected workflows, and integration challenges can slow down SAP processes, impacting efficiency and accuracy.
- Automation can transform data and workflow management, making businesses more agile, secure, and cost-efficient.
- Automation enables the elimination of repetitive tasks, improved data integrity, and efficient end-to-end integration.
- Cloud-first automation solutions streamline SAP processes, improve data quality, and integrate applications while maintaining security and control.
- REST APIs facilitate secure data exchanges, automated updates, and seamless communication between SAP and external systems.
- SAP Data APIs, Digital Access APIs, and Third-Party API Support in Automate Evolve enhance SAP automation and integration capabilities.
- Automate Studio offers automation for SAP Fiori and GUI for HTML apps, empowering users with Excel-based automation and deep SAP integration.
- By embracing workflow automation, RESTful APIs, and cloud-hosted solutions, businesses can enhance efficiency, data quality, and scalability in SAP processes.
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