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Big Data News
Precisely
2d
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Image Credit: Precisely
An IBM Z Data Integration Success Story
- A leading automotive manufacturer faced challenges in integrating and accessing data across a complex IBM Z environment.
- With hundreds of applications relying on the IBM Z, the company needed a more automated and streamlined way to access data.
- They implemented the Precisely Data Integrity Suite's Data Integration Service to unlock and synchronize IBM Z data efficiently.
- The solution enabled faster access to data, improved performance efficiency, and high user satisfaction.
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Dzone
4d
355
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Ensuring Data Quality With Great Expectations and Databricks
- Great Expectations is a popular open-source data quality and testing framework that helps data teams to define, document, and monitor data quality expectations for their datasets.
- Integrating Great Expectations with Databricks allows you to automate data quality checks within your Databricks workflows, ensuring that your data is accurate, consistent, and reliable.
- Great Expectations can be used with a wide variety of data platforms, including relational databases, data warehouses, data lakes, file systems, and big data platforms like Apache Spark and Databricks.
- By following the steps outlined in the article, you can create, validate, save, and load expectations for your data and generate data documentation to visualize the validation results, ensuring data quality and reliability in your data pipelines.
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Amazon
5d
53
Image Credit: Amazon
Build multi-Region resilient Apache Kafka applications with identical topic names using Amazon MSK and Amazon MSK Replicator
- Amazon MSK Replicator allows building multi-Region resilient Kafka applications for business continuity and reduced latency.
- It supports identical topic names for seamless replication, simplifying failover processes in active-passive setups.
- To enable cross-Region resilience, multi-VPC connectivity must be set up for the primary cluster and a replicator in the secondary Region.
- During failover, clients should be transitioned from the primary to the secondary cluster, with data deduplication managed on the consumer side.
- Deployment involves provisioning MSK clusters with Express brokers, configuring MSK Replicator, and monitoring replication metrics.
- The failback process includes syncing data from the secondary to the primary cluster using MSK Replicator and monitoring MessageLag for completeness.
- Key metrics like ReplicationLatency and ReplicatorThroughput help in monitoring data replication performance.
- Client failover strategies like pilot light deployment or hot standby are recommended for critical workloads.
- Setting up MSK Replicator, configuring clients for both primary and secondary clusters, and simulating failover and failback are essential steps.
- Ensuring no duplicates during failback, verifying message replication, and cleanup steps are crucial for maintaining data integrity.
- Overall, the solution enhances Kafka resilience through active-passive disaster recovery and seamless cross-Region replication with identical topic names.
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Siliconangle
5d
110
Image Credit: Siliconangle
Komprise brings more simplicity to petabyte-scale data migrations
- Komprise is expanding its Elastic Data Migration platform with new capabilities to automate every step of enterprise data migrations.
- The platform can accelerate data migrations by up to 25 times over existing methods using its proprietary Hypertransfer technology.
- Komprise aims to simplify complex data migrations by automating tasks such as creating and mapping destination share hierarchies.
- The company has partnered with Microsoft and Amazon Web Services, becoming the prescribed migration service for petabyte-scale workloads.
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TechBullion
5d
53
Image Credit: TechBullion
Decoding Credit Reports in the Digital Age
- Understanding credit reports is essential as they play a significant role in financial decision-making.
- Credit reports contain personal information, credit accounts, public records, and inquiries, influencing credit scores.
- Advancements in technology have made credit reporting more convenient, but data privacy and cyber threats are growing concerns.
- The future of credit reporting is evolving with blockchain, AI, and alternative credit scoring models.
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Siliconangle
6d
181
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Once valued at $3.5B, 23andMe files for Chapter 11 bankruptcy
- Genetic testing company 23andMe has filed for Chapter 11 bankruptcy.
- The company plans to operate during the proceedings and secure a buyer.
- Financial difficulties, declining customer base, and low retention were factors leading to the bankruptcy.
- The company's co-founder will step down, and an interim CEO will take over.
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Siliconangle
6d
197
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
6d
78
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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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Aviationfile
3h
50
Image Credit: Aviationfile
Skywise: How Airbus Is Transforming Aviation Through Big Data
- Skywise is a powerful aviation data platform developed by Airbus, in partnership with Palantir Technologies.
- It is an integrated ecosystem that helps airlines, MRO providers, and OEMs unlock actionable insights from complex aircraft data.
- Skywise aggregates and analyzes data to enable predictive maintenance, fleet performance monitoring, maintenance and engineering optimization, and secure data sharing and collaboration.
- Over 140 airline operators and thousands of aircraft are currently connected to Skywise.
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Amazon
2d
281
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Enhance governance with metadata enforcement rules in Amazon SageMaker
- Amazon SageMaker Catalog now supports metadata rules to enforce metadata standards across data publishing and subscription workflows within the Amazon SageMaker Unified Studio portal.
- Metadata rules ensure required information compliance when creating subscription requests or publishing data assets, facilitating alignment with organizational standards.
- By standardizing metadata practices, Amazon SageMaker Catalog enhances compliance, audit readiness, and access workflows efficiency.
- Use cases for metadata rules include enforcing metadata fields like Model Year, Region, and Compliance Status for data publishing or subscription requests.
- Key benefits of metadata enforcement rules include enhanced control for domain owners and support for custom workflows in fulfilling subscriptions.
- Setting up metadata enforcement rules involves creating domain units, metadata forms, and enforcing rules for asset publishing or subscription requests.
- To publish assets with metadata rules, domain admins set up rules and metadata forms that must be adhered to before assets can be published to the catalog.
- For asset subscription, domain admins create rules specifying the required metadata fields that subscribers need to provide when requesting access to data assets.
- The new metadata enforcement rule in Amazon SageMaker strengthens data governance by allowing domain unit owners to set clear metadata requirements for users, ensuring a governed data workflow experience.
- This feature enables organizations to align with metadata standards, implement custom workflows, and enhance data governance practices for access requests.
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Currentanalysis
3d
290
Image Credit: Currentanalysis
Tech Leaders Leverage Unique Strengths to Tackle AI Agents
- Technology providers across the GenAI ecosystem are leveraging strengths across the cloud stack to differentiate and appeal to customers.
- Cisco and LLM partner Mistral are co-creating an AI renewals agent to streamline customer renewals.
- ServiceNow accelerates its presence in the GenAI space through the acquisition of Moveworks for $3 billion.
- Alibaba's latest AI assistant app, Quark, integrates advanced reasoning and partners with Manus AI to compete with DeepResearch.
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Dzone
4d
56
Image Credit: Dzone
Lakehouse: Manus? MCP? Let’s Talk About Lakehouse and AI
- AI has become an unavoidable topic across various industries since the launch of ChatGPT by OpenAI in late 2022, leading many companies to transform into AI companies quickly.
- Databricks, Snowflake, and Elasticsearch have all shifted to AI data platforms or AI-ready data analytics and search products.
- The article explores the relationship between Lakehouse and AI in the data analytics domain, focusing on the Model Context Protocol (MCP) introduced by Anthropic in late 2024.
- MCP serves as a communication protocol between large models and data sources, facilitating easy interaction and collaboration.
- By integrating with MCP, tools like Claude Desktop have enhanced efficiency in working with AI data sources.
- Apache Doris MCP Server allows for direct access and exploration of data stored in Apache Doris, demonstrating the integration of AI models with data services.
- The use of a Data Lake enables seamless collaboration among different compute engines in AI development, ensuring data consistency and real-time access.
- Analytics engines like Apache Doris provide high performance and richer SQL expression capabilities, supporting complex AI scenarios with acceptable user experience.
- In the AI era, open data formats like Iceberg and data APIs play crucial roles in enabling seamless data integration and analysis for AI applications.
- Apache Doris supports both open data formats and APIs, positioning itself as a leading data analytics engine for AI applications.
- Future articles will delve into more features of Lakehouse architecture, real-time data warehouses, and query engines like Doris in supporting AI applications and data analysis.
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Currentanalysis
5d
114
Image Credit: Currentanalysis
Generative AI Watch: At GTC 2025, NVIDIA Envisioned a World Beyond Large Language Models
- NVIDIA envisions a shift from large language models (LLMs) to physical AI, spatial AI, and multimodal AI in enterprises.
- At GTC 2025, NVIDIA showcased the trend towards 'reasoning' models offering more analysis and autonomy.
- The event aimed to focus on industries like industrial, automotive, and manufacturing sectors.
- NVIDIA introduced specialized Blackwell Ultra chips and new roadmap updates at the conference.
- NVIDIA plans to release the Vera Rubin chip in 2026 and debuted the hybrid architecture GB300 chip.
- The company also launched AI data center software, NVIDIA Dynamo, to optimize performance.
- NVIDIA emphasized a shift in the supply chain back from Asia to the US, following market trends.
- NVIDIA announced acquisitions, investments, and consortium participation to boost AI advancements.
- The company opened a quantum lab in Boston to accelerate innovation and collaboration in the field.
- Despite optimism, challenges remain in balancing computing power demands and grid capacity limitations.
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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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