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Amazon QuickSight powers data-driven organizations with unified (BI) at hyperscale. With QuickSight, all users can meet varying analytic needs from the same source of truth through modern interactive dashboards, paginated reports, embedded analytics, and natural language queries.
Then, we show how to use NVIDIA NIM with MLRun to productize gen AI applications at scale and reduce risks , including a demo of a multi-agent banking chatbot. Ensuring data security, lineage and risk controls. When developers and data scientists need a gen Al app/tech playground. What is a Gen AI Factory?
Increased datapipeline observability As discussed above, there are countless threats to your organization’s bottom line. That’s why datapipeline observability is so important. Realize the benefits of automated data lineage today. Schedule a demo with a MANTA engineer to learn more.
MongoDB for end-to-end AI data management MongoDB Atlas , an integrated suite of data services centered around a multi-cloud NoSQL database, enables developers to unify operational, analytical, and AI data services to streamline building AI-enriched applications. However, this is only the first step.
AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. Deploy the CloudFormation template Complete the following steps to deploy the CloudFormation template: Save the CloudFormation template sm-redshift-demo-vpc-cfn-v1.yaml
A 2025 landscape analysis shows ApacheKafka , Flink , and Iceberg moving from niche tools to fundamental parts of modern data architecture, underscoring how ubiquitous realtime expectations have become. Because in 2025, the winners will be the brands whose data arrives in milliseconds not minutes.
Boost productivity – Empowers knowledge workers with the ability to automatically and reliably summarize reports and articles, quickly find answers, and extract valuable insights from unstructured data. The following demo shows Agent Creator in action.
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Every company today is being asked to do more with less, and leaders need access to fresh, trusted KPIs and data-driven insights to manage their businesses, keep ahead of the competition, and provide unparalleled customer experiences. . But good data—and actionable insights—are hard to get. Optimize recruiting pipelines.
Every company today is being asked to do more with less, and leaders need access to fresh, trusted KPIs and data-driven insights to manage their businesses, keep ahead of the competition, and provide unparalleled customer experiences. . But good data—and actionable insights—are hard to get. Optimize recruiting pipelines.
Key Takeaways Data Mesh is a modern data management architectural strategy that decentralizes development of trusted data products to support real-time business decisions and analytics. It’s time to rethink how you manage data to democratize it and make it more accessible. What is Data Mesh?
Do we have end-to-end datapipeline control? What can we learn about our data quality issues? How can we improve and deliver trusted data to the organization? One major obstacle presented to data quality is data silos , as they obstruct transparency and make collaboration tough. Unified Teams. Get Started.
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As a proud member of the Connect with Confluent program , we help organizations going through digital transformation and IT infrastructure modernization break down data silos and power their streaming datapipelines with trusted data. Let’s cover some additional information to know before attending.
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.” This user interface not only brings Apache Flink to anyone that can add business value, but it also allows for experimentation that has the potential to drive innovation speed up your dataanalytics and datapipelines. Request a live demo to see how working with real-time events can benefit your business.
For data science practitioners, productization is key, just like any other AI or ML technology. Successful demos alone just won’t cut it, and they will need to take implementation efforts into consideration from the get-go, and not just as an afterthought. What are their expectations from this hyped technology?
Thirdly, there are improvements to demos and the extension for Spark. Follow our GitHub repo , demo repository , Slack channel , and Twitter for more documentation and examples of the DJL! There is also work to support streaming inference requests in DJL Serving. Zach Kimberg is a Software Developer in the Amazon AI org.
Every company today is being asked to do more with less, and leaders need access to fresh, trusted KPIs and data-driven insights to manage their businesses, keep ahead of the competition, and provide unparalleled customer experiences. But good data—and actionable insights—are hard to get. What is Salesforce Data Cloud for Tableau?
In that sense, data modernization is synonymous with cloud migration. Modern data architectures, like cloud data warehouses and cloud data lakes , empower more people to leverage analytics for insights more efficiently. Access the resources your data applications need — no more, no less. Advanced Tooling.
Confluent data streams help accelerate innovation for data and analytics initiatives – but only when sourcing from data you can trust. Precisely – the global leader in data integrity and a proud Connect with Confluent program member – helps you build trust in your data to derive the insights your business users need.
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For data science practitioners, productization is key, just like any other AI or ML technology. Successful demos alone just won’t cut it, and they will need to take implementation efforts into consideration from the get-go, and not just as an afterthought. What are their expectations from this hyped technology?
Get a DemoDATA + AI SUMMIT Data + AI Summit Happening Now Watch the free livestream of the keynotes! This standard simplifies pipeline development across batch and streaming workloads. Years of real-world experience have shaped this flexible, Spark-native approach for both batch and streaming pipelines.
Request a demo to see how watsonx can put AI to work There’s no AI, without IA AI is only as good as the data that informs it, and the need for the right data foundation has never been greater. Data lakehouses improve the efficiency of deploying AI and the generation of datapipelines.
AI-ready data comes with comprehensive metadata (schema, definitions) to be understandable by humans and AI alike, it maintains a consistent format across historical and real-time streams, and it includes governance/lineage to ensure accuracy and trust. In short, its analytics-grade data prepared for AI.
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Prior to that, I spent a couple years at First Orion - a smaller data company - helping found & build out a data engineering team as one of the first engineers. We were focused on building datapipelines and models to protect our users from malicious phonecalls.
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Demos & tutorials: Harness Builder → https://www.youtube.com/watch?v=JfQVB_iTD1I reply efromvt 7 hours ago | prev | next [–] Continuing to plug away at Trilogy, a better SQL for data consumption and analytics. Happy to chat if you're into VMs, query engines, or DSLs.
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