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Migrating to Amazon Fast doesn’t 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’re gazing a whole bunch of dashboards and a migration estimate measured in months. Now you may considerably speed up your migration to Amazon Fast, doubtlessly decreasing timelines from months to days.

On this submit, we stroll by means of the total journey, from establishing your migration workspace in AWS Remodel to subscribing to associate brokers by means of AWS Market to unlocking Amazon Fast capabilities that change how your group consumes information.

The actual value of staying on legacy BI

In case you’re operating a legacy BI instrument, you face compounding pressures that transcend licensing charges:

  • You’re spending time on servers as an alternative 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’s no capability planning and no upkeep home windows.
  • Conventional BI instruments require customized engineering for AI-powered solutions. Amazon Fast consists of native AI capabilities that your groups can use to ask enterprise questions in pure language and automate workflows immediately from dashboards.
  • Your analysts wait too lengthy for solutions. Provisioning capability, managing extracts, and troubleshooting efficiency creates bottlenecks. The Fast Sight SPICE in-memory engine delivers sub-second question efficiency at scale, and you may publish dashboards immediately into your personal purposes utilizing its embedded analytics APIs.

The case for modernization is obvious. The query is find out how to do it with out breaking what already works. To be taught extra about what Amazon Fast provides, see Getting Began with Amazon Fast.

AWS Remodel, an AI-powered service constructed to speed up enterprise modernization, now solutions that how for BI migration. Organizations already use AWS Remodel to modernize mainframe purposes, rework 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 Companion, integrates the EZConvertBI brokers immediately into AWS Remodel, bringing deep Tableau and Energy BI migration experience for accelerating your cloud journey.

The way it works: A two-step, chat-based migration

In AWS Remodel, you create a workspace and launch migration jobs by means of a conversational interface. For BI migration, Wavicle gives 4 specialised brokers obtainable for buy by means of AWS Market: one Analyzer agent and one Converter agent for every BI migration supply (Energy BI and Tableau).

Collectively, these brokers ship a guided, chat-based, AWS-native migration expertise. All the pieces runs inside your personal AWS account: no information ever leaves your setting, no separate instruments to acquire, and no exterior information transfers to approve. This removes the safety and procurement friction that sometimes slows migration tasks.

No matter your supply BI instrument, the migration follows the identical two-step course of:Within the Analyze step, the analyzer agent connects to your present BI setting, 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 establish 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 instrument.

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) gives the execution setting, dealing with credential administration by means of workload identities and AWS Id and Entry Administration (IAM)-based entry management. The area experience comes from Wavicle’s deep BI migration expertise encoded into the agent logic.

Structure overview

The answer is constructed on the next AWS-native providers:

  • AWS Remodel is a collaborative enterprise IT transformation workbench powered by skilled brokers, agentic AI techniques, and steady studying that accelerates cloud migration, legacy app modernization, and tech debt discount. It gives the orchestration layer with a conversational interface powered by Amazon Bedrock, so you may create and handle migration jobs by means of chat, monitor progress throughout workspaces, and coordinate throughout groups.
  • Amazon Bedrock AgentCore serves because the safe runtime setting, managing agent execution, credential storage by means of workload identities, and IAM-based entry management.
  • Amazon Fast Sight acts because the goal BI service, providing serverless scalability, SPICE in-memory engine efficiency, and native integration with AWS information providers.
  • Amazon Easy Storage Service (Amazon S3) shops validation reviews and migration artifacts for audit and evaluate functions.

Your migration journey

Right here’s what the total expertise seems like, from first choice to migrated dashboards in Amazon Fast Sight:

Step 1: Full the stipulations in your supply BI

Earlier than operating your first migration, you should put together your supply BI instrument so the agent can learn your dashboard metadata:

  • For Energy BI: Configure workspace entry and repair principal authentication so the agent can learn your Energy BI tenant metadata. For directions, see Power BI Prerequisites.
  • For Tableau: Allow the Metadata API in your Tableau Server and generate a Private Entry Token (PAT) for authenticated API entry. For directions, see Tableau Prerequisites.

Step 2: Arrange AWS Remodel and Subscribe by means of AWS Market

Observe the steps in this interactive demo.

AWS Remodel gives the orchestration layer on your whole 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 Remodel executes duties in parallel, you may convert a whole bunch of dashboards concurrently with out sacrificing high quality or management.

Step 3: Analyze your BI dashboards

Observe the steps on this Power BI Analyzer agent interactive demo or Tableau Analyzer agent interactive demo.

The excellent evaluation report captures complexity throughout numerous dimensions resembling 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.

Step 4: Convert your BI dashboards

Observe the steps on this Power BI Convertor agent interactive demo or Tableau Convertor agent interactive demo.

The Converter agent rebuilds your chosen dashboards in Amazon Fast: datasets with mapped information sources and information sorts, calculated fields at each the dataset and evaluation stage, visualizations with preserved chart sorts and formatting, and filter controls with parameter inputs. All through the conversion, you may monitor progress immediately within the AWS Remodel chat interface.

After the conversion completes, you obtain your Fast Sight property and might start the ultimate validation and go-live course of.

After migration: From transformed to production-ready

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 staff. This deliberate handoff helps preserve high quality and clear accountability.Notice: The evaluation report flags elements which may want guide refinement after migration, resembling parameters, customized SQL, tool-specific calculations, and third-party visuals. There aren’t any surprises at this stage.

For Fast admin: Assign possession and configure governance

As Fast Sight administrator (the function configured within the Fast Sight connector), you assign possession of every migrated dashboard to the suitable BI authors.Consumer authentication and listing buildings in your supply BI instrument not often map one-to-one to Amazon Fast Sight. For instance, Tableau environments usually 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. It’s essential to manually configure consumer permissions, row-level safety (RLS), and sharing settings in Fast Sight to match your group’s necessities. For enterprises with complicated listing hierarchies, plan for this as a definite workstream.

This step establishes clear accountability: who owns every dashboard’s accuracy, who maintains it, and who controls entry. Nothing goes dwell till permissions are correctly configured.

For Fast authors: Validate and settle for

You obtain the assigned dashboards and personal UAT. This implies verifying that visualizations, calculated fields, filters, and interactivity match the supply by means of side-by-side metric comparability, testing drill-downs and dashboard actions, and confirming structure consistency. As a result of the migration agent doesn’t carry over permissions or row-level safety, contemplate verifying that the appropriate customers can entry the appropriate information in Fast Sight. BI authors know their dashboards higher than automated instruments do. The agent will get the construction throughout. Your staff confirms the substance is true.

Publish and go dwell

After validation, Fast authors publish their dashboards: configuring sharing permissions, establishing e mail subscriptions, and establishing embedding if wanted. For bigger migrations, you may be taught extra about Amazon Fast Sight asset deployment APIs to automate permission assignments and dashboard distribution at scale. At that time, the unique supply dashboards will be archived.

Together with your dashboards dwell in Amazon Fast, your groups unlock capabilities that weren’t potential together with your legacy BI instrument: pure language queries, automated evaluation throughout enterprise information sources, and data-driven actions immediately from dashboards.

Get began

You’ve seen the total journey, from Market subscription to production-ready dashboards. Right here’s find out how to take step one:

Whether or not you’re migrating 10 dashboards or 10,000, AWS Remodel offers you a ruled, repeatable path to Amazon Fast. Mixed with Amazon Bedrock AI capabilities and Wavicle’s migration experience, your staff can cease managing BI infrastructure and begin getting insights sooner. And since AWS Remodel is the one place to go for all of your modernization wants, you need to use the identical workbench on your subsequent modernization problem.You will have invested years in your dashboards. Now convey them to Amazon Fast in days and begin asking questions your legacy BI instrument may by no means reply.


In regards to the authors

Anantha Choppalli is a Chief Architect at Wavicle Information Options, an AWS Superior Consulting Companion, centered on growing AI-powered migration options.

Ahil Gunasekaran is a Sr. Options Architect at Wavicle Information Options, an AWS Superior Consulting Companion, centered on growing AI-powered migration options.

Taher Paratha is a Sr. Software program Engineer at Wavicle Information Options, an AWS Superior Consulting Companion, centered on growing AI-powered migration options.

Rajesh Rathod leads product administration and go-to-market technique for AWS Remodel at Amazon Net Companies.

Srikanth Baheti is a Senior Supervisor for Amazon Fast Sight. He began his profession as a marketing consultant and labored for a number of non-public and authorities organizations. Later he labored for PerkinElmer Well being and Sciences & eResearch Expertise Inc, the place he was liable for designing and growing excessive site visitors net purposes and extremely scalable and maintainable information pipelines for reporting platforms utilizing AWS providers and serverless computing.

Vasha Bhatari 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 complicated information challenges. Outdoors of labor, Vasha is at all times planning her subsequent journey, making an attempt new meals, and exploring the perfect mountaineering and kayaking spots throughout the Pacific Northwest.

Venky Hosur is a Senior Companion Options Architect at AWS. With over 20 years of expertise architecting enterprise cloud and information options, he works intently with AWS companions to design and ship revolutionary 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.

Ying Wang is a Senior Specialist Options Architect within the Generative AI group at AWS, specializing in Amazon Fast and Amazon Q to assist giant enterprise and ISV clients. She brings 16 years of expertise in information analytics and information science, with a robust background as a knowledge architect and software program growth engineering supervisor. As a knowledge architect, Ying helped clients design and scale enterprise information structure options within the cloud. In her function as an engineering supervisor, she enabled clients to unlock the ability of their information by means of Fast Sight by delivering new options and driving product innovation from each engineering and product views.

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