Migrating to Amazon Fast doesn\u2019t must imply ranging from scratch. Your dashboards encode hard-won area information: calculated fields your analysts perfected, layouts your executives depend on each Monday morning, safety guidelines tuned to your org chart. You need AI-powered insights and serverless scale, however you\u2019re gazing a whole bunch of dashboards and a migration estimate measured in months. Now you may considerably speed up your migration to Amazon Fast, probably decreasing timelines from months to days.<\/p>\n

On this put up, we stroll via the total journey, from organising your migration workspace in AWS Rework to subscribing to associate brokers via AWS Market to unlocking Amazon Fast capabilities that change how your group consumes information.<\/p>\n

The true value of staying on legacy BI<\/h2>\n
For those who\u2019re working a legacy BI device, you face compounding pressures that transcend licensing charges:<\/p>\n
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You\u2019re spending time on servers as a substitute of analytics. Patching, scaling, and monitoring infrastructure takes effort away from the insights work that drives enterprise worth. Amazon Fast is serverless and totally managed, so there\u2019s no capability planning and no upkeep home windows.<\/li>\n
Conventional BI instruments require customized engineering for AI-powered solutions. Amazon Fast contains native AI capabilities that your groups can use to ask enterprise questions in pure language and automate workflows immediately from dashboards.<\/li>\n
Your analysts wait too lengthy for solutions. Provisioning capability, managing extracts, <\/strong>and troubleshooting efficiency creates bottlenecks. The Fast Sight SPICE in-memory engine delivers sub-second question efficiency at scale, and you’ll publish dashboards immediately into your individual purposes utilizing its embedded analytics APIs.<\/li>\n<\/ul>\n
The case for modernization is obvious. The query is do it with out breaking what already works. To study extra about what Amazon Fast presents, see Getting Began with Amazon Fast<\/a>.<\/p>\n
AWS Rework, an AI-powered service constructed to speed up enterprise modernization, now solutions that how for BI migration. Organizations already use AWS Rework to modernize mainframe purposes, remodel Home windows and SQL Server workloads, migrate VMware environments, and modernize customized purposes. Now, the identical agentic AI platform extends to BI migration. Wavicle Information Options, an AWS Superior Consulting Associate, integrates the EZConvertBI brokers immediately into AWS Rework, bringing deep Tableau and Energy BI migration experience for accelerating your cloud journey.<\/p>\n
The way it works: A two-step, chat-based migration<\/h2>\n
In AWS Rework, you create a workspace and launch migration jobs via a conversational interface. For BI migration, Wavicle supplies 4 specialised brokers obtainable for buy via AWS Market: one Analyzer agent and one Converter agent for every BI migration supply (Energy BI<\/a>\u00a0and\u00a0 Tableau<\/a>).<\/p>\n
Collectively, these brokers ship a guided, chat-based, AWS-native migration expertise. Every thing runs inside your individual AWS account: no information ever leaves your surroundings, no separate instruments to obtain, and no exterior information transfers to approve. This removes the safety and procurement friction that usually slows migration initiatives.<\/p>\n
No matter your supply BI device, the migration follows the identical two-step course of:Within the Analyze step, the analyzer agent connects to your present BI surroundings, extracts metadata solely, cataloging dashboards, datasets, calculations, and dependencies throughout your workspaces, and generates a migration readiness evaluation. The evaluation features a compatibility report that exhibits what is going to convert cleanly and what may require consideration. It helps groups perceive migration scope earlier than continuing.Within the Convert step, you determine the dashboards emigrate and begin a conversion job. The Converter agent rebuilds property in Amazon Fast Sight, together with datasets, calculated fields (each on the dataset and evaluation stage), visualizations and charts, filters, and parameters. This preserves the analytical logic that your groups spent years growing in your BI device.<\/p>\n
The brokers use Amazon Bedrock, a totally managed service that gives the underlying AI capabilities wanted for migration automation. Amazon Bedrock AgentCore (a safe runtime for internet hosting and managing AI brokers) supplies the execution surroundings, dealing with credential administration via workload identities and AWS Id and Entry Administration (IAM)-based entry management. The area experience comes from Wavicle\u2019s deep BI migration expertise encoded into the agent logic.<\/p>\n
Structure overview<\/h3>\n
The answer is constructed on the next AWS-native companies:<\/p>\n
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AWS Rework<\/a> is a collaborative enterprise IT transformation workbench powered by knowledgeable brokers, agentic AI methods, and steady studying that accelerates cloud migration, legacy app modernization, and tech debt discount. It supplies the orchestration layer with a conversational interface powered by Amazon Bedrock, so you may create and handle migration jobs via chat, monitor progress throughout workspaces, and coordinate throughout groups.<\/li>\n
Amazon Bedrock AgentCore<\/a> serves because the safe runtime surroundings, managing agent execution, credential storage via workload identities, and IAM-based entry management.<\/li>\n
Amazon Fast Sight<\/a> acts because the goal BI service, providing serverless scalability, SPICE in-memory engine efficiency, and native integration with AWS information companies.<\/li>\n
Amazon Easy Storage Service (Amazon S3)<\/a> shops validation studies and migration artifacts for audit and evaluate functions.<\/li>\n<\/ul>\n
Your migration journey<\/h2>\n
Right here\u2019s what the total expertise seems like, from first choice to migrated dashboards in Amazon Fast Sight:<\/p>\n
Step 1: Full the stipulations in your supply BI<\/h3>\n
Earlier than working your first migration, you should put together your supply BI device so the agent can learn your dashboard metadata:<\/p>\n
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For Energy BI<\/strong>: Configure workspace entry and repair principal authentication so the agent can learn your Energy BI tenant metadata. For directions, see Energy BI Conditions.<\/a><\/li>\n
For Tableau<\/strong>: Allow the Metadata API in your Tableau Server and generate a Private Entry Token (PAT) for authenticated API entry. For directions, see Tableau Conditions.<\/a><\/li>\n<\/ul>\n
Step 2: Arrange AWS Rework and Subscribe via AWS Market<\/h3>\n
Comply with the steps on this interactive demo.<\/a><\/p>\n
AWS Rework supplies the orchestration layer on your complete migration. It deploys specialised AI brokers that automate assessments, dependency mapping, and transformation planning. Everybody works in the identical shared workspace, collaborating in actual time, monitoring progress, and managing the migration from begin to end. As a result of AWS Rework executes duties in parallel, you may convert a whole bunch of dashboards concurrently with out sacrificing high quality or management.<\/p>\n
Step 3: Analyze your BI dashboards<\/h3>\n
Comply with the steps on this Energy BI Analyzer agent interactive demo<\/a> or Tableau Analyzer agent interactive demo<\/a>.<\/p>\n
The excellent evaluation report captures complexity throughout numerous dimensions reminiscent of variety of information sources, analytical calculations, consumption nuances like conditional guidelines, and cross-dashboard dependencies. This enables migration undertaking managers to outline a migration execution plan based mostly on precedence and utility of the dashboards, even earlier than committing to further sources.<\/p>\n
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Step 4: Convert your BI dashboards<\/h3>\n
Comply with the steps on this Energy BI Convertor agent interactive demo<\/a> or Tableau Convertor agent interactive demo.<\/a><\/p>\n
The Converter agent rebuilds your chosen dashboards in Amazon Fast: datasets with mapped information sources and information varieties, calculated fields at each the dataset and evaluation stage, visualizations with preserved chart varieties and formatting, and filter controls with parameter inputs. All through the conversion, you may monitor progress immediately within the AWS Rework chat interface.<\/p>\n
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After the conversion completes, you obtain your Fast Sight property and may start the ultimate validation and go-live course of.<\/p>\n
After migration: From transformed to production-ready<\/h2>\n
The migration agent delivers your transformed property: Fast Sight datasets and analyses, together with calculated fields, visuals, controls, and parameters. These are the constructing blocks. What comes subsequent, governance, validation, and publishing, is owned by your workforce. This deliberate handoff helps keep high quality and clear accountability.Notice: The evaluation report flags elements which may want guide refinement after migration, reminiscent of parameters, customized SQL, tool-specific calculations, and third-party visuals. There are not any surprises at this stage.<\/p>\n
For Fast admin: Assign possession and configure governance<\/h3>\n
As Fast Sight administrator (the position configured within the Fast Sight connector), you assign possession of every migrated dashboard to the suitable BI authors.Consumer authentication and listing constructions in your supply BI device hardly ever map one-to-one to Amazon Fast Sight. For instance, Tableau environments typically depend on Lively Listing teams, whereas Energy BI makes use of workspace-level service principals. The migration agent transfers the analytical property, not the entry controls. You could manually configure consumer permissions, row-level safety (RLS), and sharing settings in Fast Sight to match your group\u2019s necessities. For enterprises with advanced listing hierarchies, plan for this as a definite workstream.<\/p>\n
This step establishes clear accountability: who owns every dashboard\u2019s accuracy, who maintains it, and who controls entry. Nothing goes reside till permissions are correctly configured.<\/p>\n
For Fast authors: Validate and settle for<\/h3>\n
You obtain the assigned dashboards and personal UAT. This implies verifying that visualizations, calculated fields, filters, and interactivity match the supply via side-by-side metric comparability, testing drill-downs and dashboard actions, and confirming structure consistency. As a result of the migration agent doesn\u2019t carry over permissions or row-level safety, take into account verifying that the fitting customers can entry the fitting information in Fast Sight. BI authors know their dashboards higher than automated instruments do. The agent will get the construction throughout. Your workforce confirms the substance is true.<\/p>\n
Publish and go reside<\/h3>\n
After validation, Fast authors publish their dashboards: configuring sharing permissions, organising e mail subscriptions, and organising embedding if wanted. For bigger migrations, you may study extra about Amazon Fast Sight asset deployment APIs<\/a> to automate permission assignments and dashboard distribution at scale. At that time, the unique supply dashboards might be archived.<\/p>\n
Along with your dashboards reside in Amazon Fast, your groups unlock capabilities that weren\u2019t attainable together with your legacy BI device: pure language queries, automated evaluation throughout enterprise information sources, and data-driven actions immediately from dashboards.<\/p>\n
Get began<\/h2>\n
You\u2019ve seen the total journey, from Market subscription to production-ready dashboards. Right here\u2019s take step one:<\/p>\n
Whether or not you\u2019re migrating 10 dashboards or 10,000, AWS Rework provides you a ruled, repeatable path to Amazon Fast. Mixed with Amazon Bedrock AI capabilities and Wavicle\u2019s migration experience, your workforce can cease managing BI infrastructure and begin getting insights quicker. And since AWS Rework is the one place to go for all of your modernization wants, you should utilize the identical workbench on your subsequent modernization problem.You will have invested years in your dashboards. Now carry them to Amazon Fast in days and begin asking questions your legacy BI device might by no means reply.<\/p>\n
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In regards to the authors<\/h2>\n
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Anantha Choppalli<\/strong> is a Chief Architect at Wavicle Information Options, an AWS Superior Consulting Associate, centered on growing AI-powered migration options.<\/p>\n<\/p><\/div>\n
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Ahil Gunasekaran<\/strong> is a Sr. Options Architect at Wavicle Information Options, an AWS Superior Consulting Associate, centered on growing AI-powered migration options.<\/p>\n<\/p><\/div>\n
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Taher Paratha<\/strong> is a Sr. Software program Engineer at Wavicle Information Options, an AWS Superior Consulting Associate, centered on growing AI-powered migration options.<\/p>\n<\/p><\/div>\n
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Rajesh Rathod<\/strong> leads product administration and go-to-market technique for AWS Rework at Amazon Internet Providers.<\/p>\n<\/p><\/div>\n
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Srikanth Baheti<\/strong> is a Senior Supervisor for Amazon Fast Sight. He began his profession as a guide and labored for a number of non-public and authorities organizations. Later he labored for PerkinElmer Well being and Sciences & eResearch Know-how Inc, the place he was answerable for designing and growing excessive site visitors net purposes and extremely scalable and maintainable information pipelines for reporting platforms utilizing AWS companies and serverless computing.<\/p>\n<\/p><\/div>\n
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Vasha Bhatari<\/strong> is a Senior Product Supervisor at Amazon Fast Sight, the place she drives options that simplify BI migrations and assist clients modernize analytics with ease. Since becoming a member of Amazon in 2017, she has led initiatives throughout last-mile routing optimization, database migration, and enterprise intelligence, bringing broad expertise to advanced information challenges. Outdoors of labor, Vasha is at all times planning her subsequent journey, making an attempt new meals, and exploring the very best climbing and kayaking spots throughout the Pacific Northwest.<\/p>\n<\/p><\/div>\n
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Venky Hosur<\/strong> is a Senior Associate Options Architect at AWS. With over 20 years of expertise architecting enterprise cloud and information options, he works carefully with AWS companions to design and ship modern cloud options that drive measurable buyer outcomes. Venky leads a number of partner-facing initiatives centered on training and enablement, serving to companions construct transformative capabilities for his or her clients. His deep experience in cloud, AI, and information makes him a trusted advisor for organizations modernizing their most crucial workloads.<\/p>\n<\/p><\/div>\n
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Ying Wang<\/strong> is a Senior Specialist Options Architect within the Generative AI group at AWS, specializing in Amazon Fast and Amazon Q to help massive enterprise and ISV clients. She brings 16 years of expertise in information analytics and information science, with a powerful background as a knowledge architect and software program improvement engineering supervisor. As a knowledge architect, Ying helped clients design and scale enterprise information structure options within the cloud. In her position as an engineering supervisor, she enabled clients to unlock the ability of their information via Fast Sight by delivering new options and driving product innovation from each engineering and product views.<\/p>\n<\/p><\/div>\n<\/footer>\n
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Migrating to Amazon Fast doesn\u2019t must imply ranging from scratch. Your dashboards encode hard-won area information: calculated fields your analysts perfected, layouts your executives depend on each Monday morning, safety guidelines tuned to your org chart. You need AI-powered insights and serverless scale, however you\u2019re gazing a whole bunch of dashboards and a migration estimate […]<\/p>\n","protected":false},"author":2,"featured_media":14408,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[55],"tags":[387,6241,2412,925,4494,1495,127],"class_list":["post-14406","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-amazon","tag-automates","tag-aws","tag-days","tag-migration","tag-quick","tag-transform"],"_links":{"self":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/14406","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=14406"}],"version-history":[{"count":1,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/14406\/revisions"}],"predecessor-version":[{"id":14407,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/14406\/revisions\/14407"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/media\/14408"}],"wp:attachment":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14406"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14406"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14406"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

Your migration journey<\/h2>\nRight here\u2019s what the total expertise seems like, from first choice to migrated dashboards in Amazon Fast Sight:<\/p>\n

Your migration journey<\/h2>\n
Right here\u2019s what the total expertise seems like, from first choice to migrated dashboards in Amazon Fast Sight:<\/p>\n