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Precisely
6d
74
Smart Banking in 2025: The Intelligent Technologies Defining CX and Operations
- Banks focusing on agility and data-driven customer-centricity are thriving in the evolving financial landscape.
- The webinar 'Smart Banking in 2025' discussed the shifts in banking towards digital transformation, compliance, and customer experience.
- Challenges include fragmented systems, siloed data, and lack of system integration hindering omnichannel customer engagement.
- The key solutions highlighted were integration of communication systems, data consolidation for personalized experiences, and centralized customer data for future-ready operations.
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Precisely
6d
387
Smart Banking: The Intelligent Technologies Defining CX and Operations
- In a recent American Banker webinar, the focus was on smart banking and intelligent technologies defining customer experience and operations in the financial sector.
- Banks need to prioritize agility, data-driven customer-centricity, and digital transformation to thrive in an evolving market.
- Integrating communication systems, consolidating data, and focusing on data-driven personalization are key strategies for enhancing customer service and loyalty.
- The session concluded with a call to focus on clean, consolidated data, building empowering engagement systems, and keeping the customer at the center to drive agility and success in the banking industry.
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Dzone
6d
321
Image Credit: Dzone
Data Lake vs. Warehouse vs. Lakehouse vs. Mart: Choosing the Right Architecture for Your Business
- Choosing the right data architecture is essential in today's data-driven world, and this article compares data warehouse, data lake, data lakehouse, and data mart through real-world business scenarios.
- Data lakes store vast amounts of raw data in their original format, offering flexibility in data processing and analysis, making them ideal for organizations collecting diverse data types.
- A real-life example illustrates a tech company utilizing data lakes for storing large-scale logs and unstructured user interaction data for product analytics.
- Data warehouses collect processed data from various sources, best suited for reporting, data analysis, and historical data storage within organizations.
- Data warehouse usage in large retail chains involves analyzing customer purchases and sales data from sources like POS systems, online transactions, and CRM data.
- Data lakehouse combines the strengths of data warehouse and data lake, offering scalability and supporting semi-structured, structured, and unstructured data for real-time fraud detection in financial services.
- Data mart, a subset of data warehouses, provides specialized data access without the complexity, enabling self-service analytics for specific departments like a sales team in a pharmaceutical company.
- Real-world examples and tools for each architecture type are provided, highlighting the data flow processes from sources to respective data repositories.
- End users vary from data scientists using data lakes for exploratory analysis to analysts creating reports in data warehouses and sales teams accessing specialized data marts.
- By understanding the unique functions and applications of data lake, warehouse, lakehouse, and mart, businesses can make informed decisions aligning with their organizational goals and data requirements.
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Amazon
1w
25
Image Credit: Amazon
OpenSearch UI: Six months in review
- OpenSearch UI, launched in November 2024, has gained popularity across various customer use cases, with significant feature enhancements focused on observability and security analytics over the past 6 months.
- The serverless OpenSearch UI dashboard provides a managed interface for data analytics and visualization, uniting data sources like Amazon OpenSearch Service domains, Serverless collections, and AWS services like CloudWatch and Security Lake.
- Integration with Amazon Q Developer enables AI-powered analytics using natural language queries, simplifying complex data analysis and providing actionable insights.
- Generative AI capabilities in the Discover page support anomaly detection, visualization, and alert summaries for quicker issue identification and resolution.
- Enhancements in security include SAML workflows via IAM federation for seamless access control, AWS PrivateLink support for secure private access within VPCs, and workspace-level privacy settings.
- Expanded data access features now include support for cross-cluster search across multiple connected OpenSearch Service domains and regional expansions to Asia Pacific (Hong Kong) and Europe (Stockholm).
- The improvements in OpenSearch UI aim to enhance user-friendliness, availability, and security, reflecting a commitment to simplifying data analytics experiences.
- Authors of the article include Muthu Pitchaimani, a Search Specialist interested in networking and security, and Hang (Arthur) Zuo, a Senior Product Manager passionate about generative AI and cloud technologies.
- For more details on using OpenSearch UI in Amazon OpenSearch Service and updates, refer to the provided resources for a comprehensive understanding of the platform's capabilities.
- The progress made in OpenSearch UI's evolution over the past 6 months showcases a focus on improving user experience, security, and accessibility, aligning with customer needs and preferences.
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Amazon
1w
80
Image Credit: Amazon
Scalable analytics and centralized governance for Apache Iceberg tables using Amazon S3 Tables and Amazon Redshift
- Amazon Redshift supports querying data stored in Apache Iceberg tables managed by Amazon S3 Tables, with a focus on production environments and centralized governance for data access and permissions.
- The post demonstrates how to set up an Apache Iceberg data lake catalog using Amazon S3 Tables, enabling fine-grained access controls and unified analytics with Amazon Redshift.
- It covers steps like creating an S3 Table bucket, loading data using Amazon EMR, granting permissions with Lake Formation, and running SQL analytics on the data.
- Prerequisites include adding a Redshift service-linked role, creating an Amazon EC2 key pair, and utilizing various AWS services like Redshift Serverless, S3 Tables, Glue Data Catalog, Lake Formation, and Spark with EMR.
- Users are guided to create resources using a CloudFormation template, load sample datasets into S3 buckets, and connect Amazon Redshift to query Apache Iceberg data stored in Amazon S3 Tables.
- Detailed steps are provided for creating S3 Tables, loading data, granting permissions to IAM users, and querying the data in both Redshift and S3 Tables.
- The post concludes by showcasing how data can be combined from S3 Tables and local Amazon Redshift tables in a single query for a seamless analytics experience.
- It emphasizes cleanup steps to delete deployed resources using AWS CloudFormation and invites feedback on the features presented.
- Authors of the post include Satesh Sonti, a Sr. Analytics Specialist Solutions Architect with expertise in data platforms, and Jonathan Katz, a Principal Product Manager on the Amazon Redshift team and Core Team member of PostgreSQL.
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Precisely
1w
67
IBM’s New MFA Offering is a Step in the Right Direction – But Only Part of the Picture
- IBM's introduction of native support for multi-factor authentication (MFA) on IBM i signifies the growing importance of MFA in cybersecurity.
- Although IBM's MFA offering is a positive step, Precisely Assure MFA provides more comprehensive and flexible protection for IBM i environments.
- Modern cyber threats and the prevalence of credential theft emphasize the necessity of robust security measures like MFA.
- MFA enhances security by requiring verification through multiple factors such as passwords, devices, and biometrics, significantly reducing unauthorized access risks.
- Precisely Assure MFA excels in user-friendly authentication, centralized management, integration with IAM platforms, incremental MFA definitions, ransomware protection, flexible control, and support for older OS versions.
- Every organization should prioritize implementing MFA on IBM i systems for enhanced security.
- Consider crucial factors when choosing an MFA solution, including hardware and software compatibility, policy adaptability, and integration with IAM platforms.
- Evaluate your MFA strategy thoughtfully to ensure it aligns with your organization's evolving needs and requirements.
- Enhance your MFA deployment with Precisely Assure MFA to establish a scalable and comprehensive security solution tailored to your business.
- Security is an ongoing journey, and MFA plays a crucial role in strengthening access control defenses for IBM i customers.
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Siliconangle
1w
154
Image Credit: Siliconangle
Big-data visualization company Domo smashes Wall Street’s targets and its stock soars
- Domo Inc., a business intelligence and data visualization software firm, surpassed Wall Street's expectations with a strong earnings and revenue beat in late trading.
- The company reported a first-quarter loss of nine cents per share, exceeding the consensus estimate of a 19-cents-per-share loss, with revenue meeting expectations at $80.1 million.
- Domo has transitioned to a consumption-based pricing model, impacting its sales productivity positively by over 60% and posting adjusted operating income of $1.03 million, beating analyst estimates.
- The company provided a robust profit forecast for the current quarter, expecting earnings between three and seven cents per share, and slightly raised its full-year revenue guidance for fiscal 2026 while lowering its earnings target.
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Siliconangle
1w
267
Image Credit: Siliconangle
Snowflake gains momentum with solid earnings and revenue beats
- Snowflake Inc. beats earnings and revenue expectations for the first quarter of the new fiscal year, with earnings per share at 24 cents and revenue surpassing $1 billion.
- Product revenue for Snowflake reached $996.8 million, up 26% from a year ago, while the company posted a net loss of $429.9 million.
- Snowflake sets a product revenue target of $1.035 billion to $1.04 billion for the current quarter, with a full-year revenue forecast of $4.325 billion at the midpoint.
- Snowflake expands product offerings with support for Apache Iceberg table format and achieves Department of Defense Impact Level 5 Provisional Authorization, leading to a stock price increase of more than 7% in late trading.
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Siliconangle
1w
354
Image Credit: Siliconangle
DataHub gets $35M in funding to provide the context needed for AI reliability and safety
- DataHub, an open-source metadata startup, has secured $35 million in Series B funding led by Bessemer Venture Partners.
- The funding will help DataHub accelerate the development of its context management platform for AI models and AI agents.
- DataHub's platform aims to address challenges in data accessibility, reliability, and context awareness faced by enterprises in managing their AI initiatives.
- The startup plans to invest in its open-source community, research, development, and scaling its go-to-market and customer success teams to enhance its AI governance capabilities.
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Dzone
1w
8
Image Credit: Dzone
IoT and Cybersecurity: Addressing Data Privacy and Security Challenges
- The Internet of Things (IoT) has revolutionized connectivity but has raised concerns about security and privacy due to the increasing number of IoT devices, making cybersecurity crucial.
- To address security challenges, regular updates, encryption, multi-factor authentication, and network segmentation are essential for securing IoT devices and networks.
- Data privacy concerns in IoT include data collection transparency, security vulnerabilities, and the lack of user control over personal information.
- Major IoT security challenges include weak authentication, lack of encryption, insecure communication protocols, and difficulty in patching and updates.
- Best practices for securing IoT devices include updating software, changing default passwords, securing routers, reviewing privacy settings, and enabling multi-factor authentication.
- Government regulations like GDPR in the EU and industry standards play a crucial role in ensuring data protection and privacy rights with IoT systems.
- Organizations are investing in cybersecurity solutions to enhance encryption, network security, and combat cyber threats in the IoT ecosystem.
- Future IoT security and privacy require manufacturers to implement strong encryption solutions, users to understand privacy risks, and collaboration among stakeholders to ensure harmonized security practices.
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Siliconangle
1w
366
Image Credit: Siliconangle
DataOps.live debuts new Dynamic Suite data toolkit for Snowflake
- DataOps.live Inc. has launched a new software toolkit called Dynamic Suite for Snowflake Inc. customers to manage data more efficiently.
- The Dynamic Suite includes Dynamic Delivery and Dynamic Transformation components to assist in managing Snowflake objects and data preparation workflows.
- Dynamic Delivery helps implement CI/CD pipelines for automation, while Dynamic Transformation uses the open-source dbt tool to organize business data for analysis.
- DataOps.live aims to enhance data management in Snowflake's ecosystem with additional tools planned under the new multiyear product partnership.
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Dzone
1w
225
Image Credit: Dzone
Securing the Future: Best Practices for Privacy and Data Governance in LLMOps
- LLMs have seen rapid development over the years, becoming integral to various applications from chatbots to enterprise solutions.
- The focus is shifting towards security, compliance, and data protection as LLMs are widely adopted across industries.
- 2025 marks a crucial period for organizations to safeguard their AI programs amidst regulatory shifts.
- Key areas of LLMOps operations include deployment, management, and optimization of large-language models.
- Data governance, privacy, security, and compliance play vital roles in ensuring secure AI deployment.
- Challenges include data privacy, leakage, compliance complexity, adversarial threats, and model security.
- Best practices in LLMOps involve comprehensive data governance, security controls, privacy techniques, and regulatory alignment.
- Measures include governance frameworks, regular audits, access controls, encryption, privacy-preserving techniques, and ongoing security training.
- Emerging trends focus on zero-trust AI security models and automated privacy guardrails for enhanced protection.
- Priority must be given to privacy and data governance to ensure ethical AI practices and trust while scaling AI capabilities.
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Dzone
1w
17
Image Credit: Dzone
Building an AI/ML Data Lake With Apache Iceberg
- Apache Iceberg offers a strong open-source table format for building efficient data lakes for AI and ML workloads.
- It provides features like ACID transactions, optimized metadata handling, schema and partition evolution, time travel, and hidden partitioning.
- The architecture for AI/ML data lakes includes layers for data sources, ingestion, storage, processing, and ML/AI applications.
- Iceberg's metadata design makes it well-suited for Machine Learning workloads, avoiding performance issues with millions of files.
- Implementing a feature store with Iceberg involves setting up the Spark environment, creating tables, and registering features and metadata.
- Creating point-in-time correct training datasets, comparing table snapshots for ML analysis, and executing the main pipeline are essential tasks in working with Iceberg feature stores.
- Benefits of using Apache Iceberg for AI/ML workloads include data quality, schema flexibility, efficient queries, and scalability for large ML applications.
- Iceberg's capabilities around data consistency, schema evolution, metadata management, and query performance contribute to faster model development and better AI/ML outcomes.
- In conclusion, Apache Iceberg is transforming how data lakes are built for AI/ML, offering essential features for modern data architecture.
- Implementing a Machine Learning feature store with Iceberg ensures data consistency, reproducibility, and improved query performance for enhanced AI/ML results.
- As ML workloads expand in complexity, frameworks like Apache Iceberg play a critical role in supporting AI/ML data needs for both new and existing platforms.
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Amazon
1w
294
Image Credit: Amazon
Empower financial analytics by creating structured knowledge bases using Amazon Bedrock and Amazon Redshift
- Amazon Bedrock Knowledge Bases with Amazon Redshift enable easy querying of complex financial data through natural language prompts, benefiting users with varied technical skills.
- Structured data retrieval via Amazon Bedrock allows for natural language processing on data sources like Redshift, simplifying data analysis for all users.
- Developers can implement advanced data querying features by connecting to APIs, enabling convenient exploration of financial data in plain English.
- Using Redshift data, generative AI applications for tasks like text generation and sentiment analysis can be efficiently built.
- Financial professionals can now use natural language queries such as customer account details, with Bedrock translating them into optimized SQL for quick insights.
- The solution outlined involves creating a conversational AI assistant for financial inquiries, utilizing sample datasets and Amazon Redshift as the knowledge base.
- Steps to implement the solution include loading financial datasets to Redshift, enabling Amazon Bedrock's large language model access, and creating knowledge bases with structured data.
- Security and compliance measures are crucial when integrating Bedrock with Redshift, and cost considerations apply for the natural language to SQL conversion.
- Benefits of using generative AI applications in structured data analysis include enhanced customer experience, improved operational efficiency, and streamlined data warehouse usage.
- By facilitating natural language interactions, this approach accelerates decision-making, making complex data analysis accessible to non-technical users in the finance industry.
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Siliconangle
1w
367
Image Credit: Siliconangle
Alation acquires Numbers Station to expand AI agent capabilities for enterprise data workflows
- Alation Inc. acquires Numbers Station Inc., a startup specializing in AI agents for data workflows.
- Numbers Station's AI agents automate complex data workflows, enabling natural language interaction with data.
- The acquisition aims to combine Numbers Station's AI capabilities with Alation's metadata foundation for intelligent applications that facilitate real-time decision-making.
- Numbers Station team joins Alation post-acquisition, ensuring continuity for customers and further development of AI-native applications for enterprise data intelligence.
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