Migrating to Amazon Fast doesn’t need to 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 tons of 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.
On this put up, we stroll by means of the total journey, from establishing your migration workspace in AWS Rework to subscribing to companion 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
Should 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’ll publish dashboards immediately into your individual functions 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 gives, see Getting Began with Amazon Fast.
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 functions, rework Home windows and SQL Server workloads, migrate VMware environments, and modernize customized functions. Now, the identical agentic AI platform extends to BI migration. Wavicle Knowledge 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.
The way it works: A two-step, chat-based migration
In AWS Rework, you create a workspace and launch migration jobs by means of a conversational interface. For BI migration, Wavicle offers 4 specialised brokers out there 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. Every little thing runs inside your individual 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 initiatives.
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 current 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’s 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 instrument.
The brokers use Amazon Bedrock, a completely 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) offers 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 Rework is a collaborative enterprise IT transformation workbench powered by professional brokers, agentic AI programs, and steady studying that accelerates cloud migration, legacy app modernization, and tech debt discount. It offers 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 stories and migration artifacts for audit and assessment functions.
Your migration journey
Right here’s what the total expertise seems to be like, from first choice to migrated dashboards in Amazon Fast Sight:
Step 1: Full the conditions 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 Energy BI Conditions.
- 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 Conditions.
Step 2: Arrange AWS Rework and Subscribe by means of AWS Market
Observe the steps on this interactive demo.
AWS Rework offers the orchestration layer to 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 Rework executes duties in parallel, you may convert tons of of dashboards concurrently with out sacrificing high quality or management.
Step 3: Analyze your BI dashboards
Observe the steps on this Energy BI Analyzer agent interactive demo or Tableau Analyzer agent interactive demo.
The excellent evaluation report captures complexity throughout varied dimensions equivalent to variety of information sources, analytical calculations, consumption nuances like conditional guidelines, and cross-dashboard dependencies. This enables migration challenge managers to outline a migration execution plan based mostly on precedence and utility of the dashboards, even earlier than committing to extra sources.
Step 4: Convert your BI dashboards
Observe the steps on this Energy 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 Rework chat interface.
After the conversion completes, you obtain your Fast Sight property and may 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 group. This deliberate handoff helps preserve high quality and clear accountability.Be aware: The evaluation report flags parts that may want guide refinement after migration, equivalent to 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 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 instrument 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 have to manually configure person 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 reside 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, take into account verifying that the correct customers can entry the correct information in Fast Sight. BI authors know their dashboards higher than automated instruments do. The agent will get the construction throughout. Your group confirms the substance is true.
Publish and go reside
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 reside 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 Rework offers you a ruled, repeatable path to Amazon Fast. Mixed with Amazon Bedrock AI capabilities and Wavicle’s migration experience, your group can cease managing BI infrastructure and begin getting insights sooner. And since AWS Rework is the one place to go for all of your modernization wants, you need to use the identical workbench to your subsequent modernization problem.You’ve invested years in your dashboards. Now carry 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 Knowledge Options, an AWS Superior Consulting Associate, targeted on growing AI-powered migration options.
Ahil Gunasekaran is a Sr. Options Architect at Wavicle Knowledge Options, an AWS Superior Consulting Associate, targeted on growing AI-powered migration options.
Taher Paratha is a Sr. Software program Engineer at Wavicle Knowledge Options, an AWS Superior Consulting Associate, targeted on growing AI-powered migration options.
Rajesh Rathod leads product administration and go-to-market technique for AWS Rework at Amazon Internet Companies.
Srikanth Baheti is a Senior Supervisor for Amazon Fast Sight. He began his profession as a advisor and labored for a number of personal and authorities organizations. Later he labored for PerkinElmer Well being and Sciences & eResearch Expertise Inc, the place he was answerable for designing and growing excessive visitors net functions 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 all the time planning her subsequent journey, making an attempt new meals, and exploring one of the best climbing and kayaking spots throughout the Pacific Northwest.
Venky Hosur 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 progressive cloud options that drive measurable buyer outcomes. Venky leads a number of partner-facing initiatives targeted 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 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 by means of Fast Sight by delivering new options and driving product innovation from each engineering and product views.
