#QuickSight — Bluesky Posts

8 months ago

Choosing the right approach for generative AI-powered structured data retrieval In this post, we explore five different patterns for implementing LLM-powered structured data query capabilities in A...

#Amazon #Bedrock #Amazon #Bedrock #Knowledge #Bases […]

[Original post on aws.amazon.com]

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8 months ago

Choosing the right approach for generative AI-powered structured data retrieval In this post, we explore five different patterns for implementing LLM-powered structured data query capabilities in A...

#Amazon #Bedrock #Amazon #Bedrock #Knowledge #Bases […]

[Original post on aws.amazon.com]

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8 months ago

Maximize Analytics with Azure ADX and AWS QuickSight Business intelligence (BI) platforms are critical in transforming data into actionable insights. As industries increasingly rely on large datasets to inform decisions, selecting robust analytics platforms becomes essential. Today, two major players stand out in this landscape—Azure Data Explorer (ADX) and AWS QuickSight. Here we delve into their capabilities, evaluate industry-specific scenarios, and highlight cost optimization and complexity reduction strategies. Finally, we will explore emerging analytics trends shaping future BI landscapes.

Choosing the right approach for generative AI-powered structured data retrieval In this post, we explore five different patterns for implementing LLM-powered structured data query capabilities in A...

#Amazon #Bedrock #Amazon #Bedrock #Knowledge #Bases […]

[Original post on aws.amazon.com]

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Greg Leonardo

@gleonardo.bsky.social

9 months ago

Make smarter moves faster! Uncover insights instantly with Azure Data Explorer and AWS QuickSight. #BusinessIntelligence #DataExplorer #QuickSight #CloudAnalytics #CloudDailywire

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AWS Builders Center on 🦋

@communityaws.bsky.social

9 months ago

Accelerating Data Insights with Amazon Q in QuickSight: Self-Service Analytics for Everyone Business intelligence is only as valuable as it is accessible. Traditionally, data teams have built dashboards and reports while business users relied on them for insights. Amazon Q in QuickSight changes this dynamic by bringing natural language querying directly into dashboards, empowering users to ask questions and get instant visual responses — all without writing SQL or waiting on analysts.In this guide, we’ll explore what Amazon Q in QuickSight is, how it works, how to enable it, and how it improves

"Accelerating Data Insights with Amazon Q in QuickSight: Self-Service Analytics for Everyone" by Ashish Kasaudhan

#amazonq #quicksight #devops #platform-enginnering #aws

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10 months ago

Build a financial research assistant using Amazon Q Business and Amazon QuickSight for generative […]

[Original post on aws.amazon.com]

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1 year ago

More “weird” behaviour in #quicksight. Unfortunately I haven’t figured out a way around this yet. I have a date which I convert to another timezone with a calculated field in the dataset, this works perfectly, but as soon as I publish and analyse it, the dates are the same again

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1 year ago

And if they are at it, localisation out of the box would be nice as well AND things like setting labels for values (i.e. 1 = Sunday, 2 = Monday ). Of course things can be done with parameters and calculated fields, but that is a hassle #quicksight

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AntStack TV | Episode 7 | Rahul Kumar Breaks Down AWS re:Invent’s Biggest Data Engineering Reveals Welcome to another insightful episode of AntStack TV! Join Rahul Kumar as he breaks down the biggest AWS re:Invent 2024 announcements, with a special focus on data engineering, cloud modernization, and serverless innovations. Key Highlights from AWS re:Invent 2024: 🔵 Amazon S3 Enhancements – Improved data integrity, rich metadata, and S3 tables optimized for big data. 🔵 AWS SageMaker Lake House – A unified approach to data lakes and data warehouses with zero-ETL integrations. 🔵 DynamoDB Updates – Strongly consistent global tables and zero-ETL integration with SageMaker Lake House. 🔵 AWS Glue 5.0 – 30% faster data processing and cost reductions for big data workloads. 🔵 Redshift Upgrades – Kafka ingestion, expanded query limits, and cross-cloud integrations. 🔵 Aurora DSQL – AWS’s distributed SQL solution for multi-region applications. 🔵 GenAI in AWS Services – QuickSight AI-driven BI, GAI-powered schema conversion in AWS DMS, and more. Why Watch? Whether you're a cloud architect, data engineer, or a tech enthusiast, this episode covers everything you need to stay ahead in the AWS ecosystem. 🔔 Subscribe to AntStack TV for expert insights, AWS deep dives, and discussions on serverless, cloud computing, and AI-driven architectures. 💬 Drop your thoughts in the comments! What AWS re:Invent update excites you the most? Contact us for starting your serverless journey: https://www.antstack.com/build-with-us/?utm_source=youtube&utm_medium=description&utm_campaign=ytConverstion 0:00 - Introduction to AntStack TV 0:10 - Overview of AWS re:Invent 2024 announcements 0:30 - Amazon S3 Tables: Optimized buckets for open table formats 1:00 - Performance benefits: 10x read and write speeds in S3 tables 1:35 - Managing Apache Iceberg tables seamlessly with S3 2:20 - SageMaker Lake House: Unified data lakes and warehouses 3:00 - Zero-ETL integrations: DynamoDB to SageMaker Lake House 3:40 - Zero-ETL for CloudWatch and OpenSearch 4:15 - CloudWatch Insights: Seamless OpenSearch integration 5:00 - Pre-built dashboards for VPC Flow Logs, WAF, and CloudTrail 5:35 - AWS DMS upgrade: GenAI-powered schema conversion 6:10 - AWS Glue 5.0: 30% faster data processing and lower costs 6:40 - DynamoDB Global Tables: Now strongly consistent 7:10 - Redshift upgrades: Increased query limits and Kafka ingestion 7:45 - AWS Clean Rooms: Expanded cross-cloud data sharing 8:15 - QuickSight AI: GenAI-powered business intelligence 8:50 - Aurora DSQL: AWS’s take on distributed SQL for multi-region apps 9:30 - Multi-region active-active replication for databases 10:00 - PostgreSQL compatibility and future roadmap for Aurora DSQL 10:30 - Recap of AWS re:Invent 2024 highlights 11:00 - Closing thoughts and community engagement Website: https://www.antstack.com/ LinkedIn: https://linkedin.com/company/antstackio Behance: https://behance.net/antstack Twitter/X: https://twitter.com/antstack Instagram: https://instagram.com/lifeatantstack #AWSreInvent #AWS #Serverless #DataEngineering #CloudComputing #BigData #DynamoDB #AmazonS3 #AuroraDSQL #ZeroETL #Redshift #AI #Modernization #TechTalk [AWS re:Invent 2024, Amazon S3 updates, AWS serverless, data engineering in AWS, AWS cloud computing, AWS modernization, DynamoDB global tables, S3 tables, S3 rich metadata, AWS Glue 5.0, Redshift Kafka ingestion, Aurora DSQL, distributed SQL on AWS, zero-ETL integrations, AWS SageMaker Lake House, serverless data processing, cloud-native databases, AI in AWS, QuickSight AI, AWS schema conversion, AWS data lakes, AWS BI tools, AWS innovations, scalable cloud solutions, enterprise cloud architecture]

1 year ago

Another day, another #quicksight feature request :) It would be amazing if labels could be changed, I have quite a few technical / calculated fields but those have names that are not readeable for the consumer of the final dashboard

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AntStack

@antstack.com

1 year ago

𝗚𝗲𝗻𝗔𝗜 𝗺𝗲𝗲𝘁𝘀 𝗤𝘂𝗶𝗰𝗸𝗦𝗶𝗴𝗵𝘁!

Upload reports, connect your data & ask, smarter insights, zero hassle. Rahul Kumar explains it all.

𝗪𝗮𝘁𝗰𝗵 𝗻𝗼𝘄: bit.ly/4jPLFn9

#AWS #QuickSight #GenAI #DataAnalytics #AIinBI #Serverless #AntStack

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1 year ago

I do have some feature requests. It would be amazing if you could copy things like calculated fields OR move a calculated field from an analysis to a dataset #quicksight

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