AWS Prescriptive GuidanceMigrating SSIS ETL jobs to AWS

AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWS

AWS Prescriptive Guidance: Migrating SSIS ETL jobs to AWSCopyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWS

Table of ContentsIntroduction .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Targeted business outcomes .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Migration phases .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Discovery phase .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Analysis phase .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Determining the migration approach .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Implementation phase .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

AWS SCT .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5AWS Glue .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Amazon EMR .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Testing .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Best practices .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9FAQ ..... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Should I enhance SSIS jobs during migration? .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11How can I monitor jobs on AWS? .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11When can I decommission my on-premises SSIS jobs? .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Next steps .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Related resources .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13AWS Prescriptive Guidance glossary .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Document history .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWSTargeted business outcomes

Migrating SSIS ETL jobs to AWSDurga Prasad and Harpreet Singh, Migration Consultants, AWS Professional Services

December 2021

Amazon Web Services (AWS) provides services for developing and running extract, transform, and load(ETL) or extract, load, and transform (ELT) jobs and big data workloads. Microsoft SQL Server IntegrationServices (SSIS) is an on-premises ETL tool that has a graphical interface for building ETL jobs. Dependingon the target state architecture, you can use the AWS Glue graphical interface or the Apache Sparkframework with Python libraries to build ETL jobs in the AWS Cloud.

This guide describes the steps for migrating SSIS ETL/ELT jobs from on premises to AWS. It discussesmigration phases, best practices, and recommendations to reduce the migration effort and improve theexperience. The information is applicable to any target AWS architecture.

The guide is for program or project managers, product owners, solution architects, and developers whoare:

• Migrating from SSIS to AWS for various reasons, such as modernization or cost savings.• Primarily using AWS services for ETL and big data workloads.• Looking for a cloud-based solution to take advantage of a serverless model and flexible pricing.

Targeted business outcomesMigrating your SSIS jobs to the AWS Cloud helps you achieve these primary outcomes:

• Modernize existing on-premises ETL processes in a cost-effective way• Respond to the information needs of the organization faster• Merge existing processes and retire unused processes to reduce maintenance and operational overhead• Build a foundation to meet your organization’s future data requirements

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWSDiscovery phase

Migration phasesMigrating SSIS ETL processes to the AWS Cloud consists of four major phases: discover, analyze,determine approach, and implement.

These phases are discussed in the following sections:

• Discovery phase (p. 2)

• Analysis phase (p. 3)

• Determining the migration approach (p. 4)

• Implementation phase (p. 5)

Discovery phaseIn the discovery phase, you create a list of the SSIS packages that you want to migrate to AWS. Differentdevelopment teams follow different styles, standards, and patterns for developing ETL jobs. Werecommend that you review your organization’s existing documents to understand these patterns.However, the documentation is often incomplete. You can automate the extraction of importantinformation from the ETL scripts. This saves manual effort and time, reduces human errors, andstandardizes the migration approach. Here are some of the important details you’ll want to extract:

• Total number of control flow tasks

• Details of control flow tasks

• Total number of data flow tasks

• Data flow transformations used

• Event handlers

• Connection managers

Use this information to understand the ETL patterns used at your organization, to evaluate theircomplexity, and to identify the appropriate AWS service to migrate this information to.

Migrating these ETL details from SSIS forms the bulk of the migration effort. However, additionalproperties can provide insights into design and architectural decisions. Some of these SSIS propertiesare:

• Checkpoints, which are used in SSIS to restart jobs from points of failure

• Propagate variables, which help an SSIS package succeed in specific use cases, even when there is anerror

• Transaction isolation levels, which control the quality of data being read from databases

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWSAnalysis phase

• Logging, to understand the types of logs being captured by the current design and their storagelocations

The outcome of the discovery phase can be an inventory, as the following table shows.

This inventory might include the following information:

• Package: Name of the SSIS package to migrate• Flow: Control flow or data flow• Task: Name of the control flow task or data flow component• Count: Number of times a task was used in the SSIS package

Analysis phaseAnalyzing the information from the discovery phase helps you understand the patterns and design thetarget architecture better. Follow these pointers to improve the outcome of analysis:

• The logging option at the package level logs events and captures runtime information for eachcomponent. Use custom logging so that you can configure log files to include additional information,such as execution IDs and other runtime values.

• Redirect bad records to a different storage location for analysis, correction, and reprocessing.• Understand the data archival and purging strategies implemented in your on-premises SSIS

environment.• Send alerts and notifications by using tasks that are included with SSIS (such as the Send Mail task) or

custom scripts (such as the Script task).• Understand the behavior of transformations in scope to avoid corrupted data. For example, the

Lookup transformation is an equi-join between two datasets, and its comparison is case-sensitive.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWSDetermining the migration approach

You can map each entry in the inventory to an estimated complexity, either in terms of duration (minutesor hours) or level (simple, medium, or complex). This expanded inventory, shown in the following table, isan outcome of the analysis phase.

Determining the migration approachTo decide on a migration approach, you use the analysis you performed on existing patterns inthe previous phase. Your organization’s future data and analytics needs are equally importantconsiderations. Traditional on-premises ETL tools deal with relational data models and structured data. Ifyou have semi-structured and unstructured data to process, you can use AWS services such as AWS Glueor Amazon EMR for the migration. Other factors that can influence the migration approach include:

• Whether you want to use a graphical interface (such as AWS Glue Studio) or a custom framework (suchas Spark/Python libraries)

• Whether you have secure access to on-premises sources and AWS targets• Skills and training required for the team• Audit and compliance requirements

You can select from three migration approaches: big bang, phased, and lift and shift. The following tablecompares these three approaches.

Approach Description Use case Advantages anddisadvantages

Big bang Migrate all SSISpackages within aspecific time period.

• Complexity,scope, and targetarchitecture are clear.

• Team has therequired skills, orthe learning curve isshallow.

• High risk.• Takes less time than

the phased approach.• You can use AWS

Glue, AmazonEMR, or customframeworks.

Phased Identify one SSISpackage for each

• Time is not aconstraint.

• Less risky than thebig bang approach

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWSImplementation phase

Approach Description Use case Advantages anddisadvantages

distinct pattern andcomplexity. Migratethe package to AWS,test, and compareresults with existingarchitecture.

• You want differentdesigns for differentETL patterns.

but takes more timeand effort.

• You can use AWSGlue, AmazonEMR, or customframeworks.

Lift and shift Migrate the currentarchitecture as is toAWS.

• Your on-premiseshardware is no longersupported.

• You don’t havethe resources toplan a migrationimmediately.

• Least amount ofmigration effort andtime required.

• The problems withthe existing solutionremain on AWS.

• SSIS packages are runas is. No other ETLtools or frameworksare needed.

A comparison of data on the source and target systems is fundamental for a successful migration.Because the existing production system gets regular updates from source systems, this comparisonmight become confusing. For this reason, when you’re determining your migration approach, werecommend that you also decide on your data validation strategy.

• Take backups of all applicable databases and files from the production environment on the sourcesystem at a specific date and time.

• Take backups of all databases from the production environment on the target system after all jobshave successfully loaded data from backed up source data.

• Restore the source data in a testing environment, and run the new jobs.

• Agree on a percentage of valid differences between the source and target (old and new) databases. Forexample, you might decide that a difference of less than 1% is acceptable.

• List all the validation rules to be covered.

• Automate the comparison as much as possible, and cover all the rules.

Implementation phaseMigration that follows the big bang or phased approach requires new development and testing. TheAWS Schema Conversion Tool (AWS SCT) can automatically generate AWS Glue jobs from SSIS packages.This reduces the migration time and effort significantly. Or, you can use AWS Glue Studio for graphicalinterface-based development, or build Spark libraries that you can run on either AWS Glue or AmazonEMR.

The following sections provide useful pointers for using AWS SCT, AWS Glue, and Amazon EMR.

AWS SCTThe following screen illustration shows an AWS Glue job script that was converted by AWS SCT.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWSAWS Glue

AWS SCT can convert SSIS packages to AWS Glue jobs in bulk. You can edit the script to update existinglogic or to add new logic, based on your new design. We recommend that you follow the namingconventions in the AWS SCT converted scripts to customize the scripts.

For more information, see Converting SSIS to AWS Glue using AWS SCT in the AWS SCT documentation.

AWS GlueAWS Glue Studio provides a graphical interface and a development experience that’s similar to SSIS, asillustrated in the following screen.

If you prefer not to use a graphical interface, you can also run your custom scripts with the requiredPython libraries from the AWS Glue console. For more information, see Providing your own customscripts in the AWS Glue documentation.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWSAmazon EMR

AWS Glue provides a set of built-in transforms for processing your data. These are similar to SSIS dataflow transformations. Follow these best practices when you migrate your SSIS ETL jobs by using AWSGlue:

• Prepare a mapping from AWS Glue transforms to the equivalent SSIS transformations.• If your transformations cannot be mapped to AWS Glue transforms, build them by using a Python or

Scala custom script.• For custom logging (such as rows read, rows written, or bad records), use custom scripts in addition to

Amazon CloudWatch.• Add a development endpoint to develop and debug custom scripts locally.

Amazon EMRYou can run custom scripts (written in Python or Scala) or compiled Python libraries in EMR clusters, aswith AWS Glue. Follow these best practices:

• Start with memory optimized instance types while creating EMR clusters with the Spark framework.(SSIS uses memory buffers.)

• Build generic Python methods that are equivalent to each SSIS task or transformation. For example,in the following illustration, a method that takes two dataframes as input produces a thirddataframe that has matching records from the two dataframes as output. This works as a merge jointransformation.

TestingA testing framework is required to validate the completeness and correctness of data. This frameworkshould cover all the existing scenarios and any improvements you made while migrating your jobs toAWS.

• Completeness validation:• All jobs are migrated to their target state.• All functionality is migrated in each job.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWSTesting

• All types of logs are available, including job execution details, error messages, bad records, and rowcounts.

• Correctness validation:• The quality of data is consistent in the existing and new environments.• All columns of all tables match, or tables are improved on AWS.• All audit and logging information match.

You should also verify that the performance of your migrated jobs matches the performance of yourexisting jobs.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWS

Best practices

Follow these general best practices when migrating your SSIS jobs to AWS:

• If you’re migrating your databases to an AWS managed service such as Amazon Relational DatabaseService (Amazon RDS), descope maintenance tasks (such as backup, restore, update statistics, or checkdatabase integrity) that are not applicable or managed by AWS.

• Build reusable scripts and methods to standardize development and reduce effort.

• Always test migrated jobs on infrastructure and data volumes that match production resources.

• SSIS packages are in XML format. Build XML parser utilities to automate migration activities wherepossible. The following example shows an SSIS package in XML format.

The following illustration shows a sample XML parser.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWS

• Use checksums and hash values to test correctness and completeness.• If you’re building custom libraries, implement threading to run tasks in parallel. This improves job

performance.• Decommission the existing environment only after all jobs are stable on AWS for a period of time.• Use Amazon CloudWatch for logging, to reduce logging customization efforts.• Use Amazon Simple Notification Service (Amazon SNS) to replace custom tasks for sending

notifications. Use Amazon Simple Email Service (Amazon SES) to replace custom email tasks.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWSShould I enhance SSIS jobs during migration?

FAQThis section provides answers to commonly raised questions about migrating your SSIS ETL jobs to AWS.

Should I enhance SSIS jobs during migration?Yes. Implement any important enhancements or bug fixes in both existing and new jobs at the sametime. Low-priority changes can be implemented after migration.

How can I monitor jobs on AWS?You can use Amazon CloudWatch to monitor jobs and to send rule-based alerts.

When can I decommission my on-premises SSISjobs?

We recommend that you maintain old and new jobs in parallel for a period of time until theperformance, accuracy, and completeness of data are the same in both environments. You can alsoreplicate reporting systems to connect and compare reports from both environments. You candecommission your SSIS jobs when the reports are identical.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWS

Next stepsAs the next step, we recommend that you build a proof of concept to determine your deploymentapproach for the target architecture. For your proof of concept:

• Create AWS Glue jobs by using the AWS Glue Studio graphical interface.• Use the AWS Schema Conversion Tool (AWS SCT) to generate AWS Glue scripts from SSIS packages.• Build a custom migration framework by using Spark libraries.• Build a testing framework.• Identify tools and processes for continuous integration and continuous deployment (CI/CD).

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWS

Related resources• Converting SSIS to AWS Glue using AWS SCT• Amazon RDS backup and restore• AWS Glue documentation• AWS Glue FAQs• Continuous logging for AWS Glue jobs• Amazon EMR documentation• Amazon EMR FAQs• Monitor metrics with CloudWatch• Accelerate large-scale data migration validation using PyDeequ• AWS Data and Analytics Competency Partners

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWS

AWS Prescriptive Guidance glossary

AI and ML terms (p. 14)    |    Migration terms (p. 15)    |    Modernization terms (p. 18)

AI and ML terms The following are commonly used terms in artificial intelligence (AI) and machine learning (ML)-related strategies,guides, and patterns provided by AWS Prescriptive Guidance. To suggest entries, please use the Provide feedbacklink at the end of the glossary.

binary classification A process that predicts a binary outcome (one of two possible classes). Forexample, your ML model might need to predict problems such as “Is this emailspam or not spam?" or "Is this product a book or a car?"

classification A categorization process that helps generate predictions. ML models forclassification problems predict a discrete value. Discrete values are always distinctfrom one another. For example, a model might need to evaluate whether or notthere is a car in an image.

data preprocessing To transform raw data into a format that is easily parsed by your ML model.Preprocessing data can mean removing certain columns or rows and addressingmissing, inconsistent, or duplicate values.

deep ensemble To combine multiple deep learning models for prediction. You can use deepensembles to obtain a more accurate prediction or for estimating uncertainty inpredictions.

deep learning An ML subfield that uses multiple layers of artificial neural networks to identifymapping between input data and target variables of interest.

exploratory data analysis(EDA)

The process of analyzing a dataset to understand its main characteristics. Youcollect or aggregate data and then perform initial investigations to find patterns,detect anomalies, and check assumptions. EDA is performed by calculatingsummary statistics and creating data visualizations.

features The input data that you use to make a prediction. For example, in amanufacturing context, features could be images that are periodically capturedfrom the manufacturing line.

feature transformation To optimize data for the ML process, including enriching data with additionalsources, scaling values, or extracting multiple sets of information from a singledata field. This enables the ML model to benefit from the data. For example, ifyou break down the “2021-05-27 00:15:37” date into “2021”, “May”, “Thu”, and“15”, you can help the learning algorithm learn nuanced patterns associated withdifferent data components.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWS

multiclass classification A process that helps generate predictions for multiple classes (predicting one ofmore than two outcomes). For example, an ML model might ask "Is this producta book, car, or phone?" or "Which product category is most interesting to thiscustomer?"

regression An ML technique that predicts a numeric value. For example, to solve the problemof "What price will this house sell for?" an ML model could use a linear regressionmodel to predict a house's sale price based on known facts about the house (forexample, the square footage).

training To provide data for your ML model to learn from. The training data must containthe correct answer. The learning algorithm finds patterns in the training data thatmap the input data attributes to the target (the answer that you want to predict).It outputs an ML model that captures these patterns. You can then use the MLmodel to make predictions on new data for which you don’t know the target.

target variable The value that you are trying to predict in supervised ML. This is also referredto as an outcome variable. For example, in a manufacturing setting the targetvariable could be a product defect.

tuning To change aspects of your training process to improve the ML model's accuracy.For example, you can train the ML model by generating a labeling set, addinglabels, and then repeating these steps several times under different settings tooptimize the model.

uncertainty A concept that refers to imprecise, incomplete, or unknown information thatcan undermine the reliability of predictive ML models. There are two types ofuncertainty: Epistemic uncertainty is caused by limited, incomplete data, whereasaleatoric uncertainty is caused by the noise and randomness inherent in the data.For more information, see the Quantifying uncertainty in deep learning systemsguide.

Migration terms The following are commonly used terms in migration-related strategies, guides, and patterns provided by AWSPrescriptive Guidance. To suggest entries, please use the Provide feedback link at the end of the glossary.

7 Rs Seven common migration strategies for moving applications to the cloud. Thesestrategies build upon the 5 Rs that Gartner identified in 2011 and consist of thefollowing:

• Refactor/re-architect – Move an application and modify its architecture bytaking full advantage of cloud-native features to improve agility, performance,and scalability. This typically involves porting the operating system anddatabase. Example: Migrate your on-premises Oracle database to the AmazonAurora PostgreSQL-Compatible Edition.

• Replatform (lift and reshape) – Move an application to the cloud, and introducesome level of optimization to take advantage of cloud capabilities. Example:Migrate your on-premises Oracle database to Amazon Relational DatabaseService (Amazon RDS) for Oracle in the AWS Cloud.

• Repurchase (drop and shop) – Switch to a different product, typically by movingfrom a traditional license to a SaaS model. Example: Migrate your customerrelationship management (CRM) system to Salesforce.com.

• Rehost (lift and shift) – Move an application to the cloud without making anychanges to take advantage of cloud capabilities. Example: Migrate your on-premises Oracle database to Oracle on an EC2 instance in the AWS Cloud.

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWS

• Relocate (hypervisor-level lift and shift) – Move infrastructure to the cloudwithout purchasing new hardware, rewriting applications, or modifying yourexisting operations. This migration scenario is specific to VMware Cloudon AWS, which supports virtual machine (VM) compatibility and workloadportability between your on-premises environment and AWS. You can use theVMware Cloud Foundation technologies from your on-premises data centerswhen you migrate your infrastructure to VMware Cloud on AWS. Example:Relocate the hypervisor hosting your Oracle database to VMware Cloud onAWS.

• Retain (revisit) – Keep applications in your source environment. These mightinclude applications that require major refactoring, and you want to postponethat work until a later time, and legacy applications that you want to retain,because there’s no business justification for migrating them.

• Retire – Decommission or remove applications that are no longer needed inyour source environment.

application portfolio A collection of detailed information about each application used by anorganization, including the cost to build and maintain the application, and itsbusiness value. This information is key to the portfolio discovery and analysisprocess and helps identify and prioritize the applications to be migrated,modernized, and optimized.

artificial intelligenceoperations (AIOps)

The process of using machine learning techniques to solve operational problems,reduce operational incidents and human intervention, and increase servicequality. For more information about how AIOps is used in the AWS migrationstrategy, see the operations integration guide.

AWS Cloud AdoptionFramework (AWS CAF)

A framework of guidelines and best practices from AWS to help organizationsdevelop an efficient and effective plan to move successfully to the cloud. AWSCAF organizes guidance into six focus areas called perspectives: business,people, governance, platform, security, and operations. The business, people,and governance perspectives focus on business skills and processes; theplatform, security, and operations perspectives focus on technical skills andprocesses. For example, the people perspective targets stakeholders who handlehuman resources (HR), staffing functions, and people management. For thisperspective, AWS CAF provides guidance for people development, training, andcommunications to help ready the organization for successful cloud adoption. Formore information, see the AWS CAF website and the AWS CAF whitepaper.

AWS landing zone A landing zone is a well-architected, multi-account AWS environment that isscalable and secure. This is a starting point from which your organizations canquickly launch and deploy workloads and applications with confidence in theirsecurity and infrastructure environment. For more information about landingzones, see Setting up a secure and scalable multi-account AWS environment.

AWS Workload QualificationFramework (AWS WQF)

A tool that evaluates database migration workloads, recommends migrationstrategies, and provides work estimates. AWS WQF is included with AWS SchemaConversion Tool (AWS SCT). It analyzes database schemas and code objects,application code, dependencies, and performance characteristics, and providesassessment reports.

business continuity planning(BCP)

A plan that addresses the potential impact of a disruptive event, such as a large-scale migration, on operations and enables a business to resume operationsquickly.

Cloud Center of Excellence(CCoE)

A multi-disciplinary team that drives cloud adoption efforts across anorganization, including developing cloud best practices, mobilizing resources,establishing migration timelines, and leading the organization through large-

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AWS Prescriptive Guidance Migrating SSIS ETL jobs to AWS

scale transformations. For more information, see the CCoE posts on the AWSCloud Enterprise Strategy Blog.

cloud stages of adoption The four phases that organizations typically go through when they migrate to theAWS Cloud:

• Project – Running a few cloud-related projects for proof of concept andlearning purposes

• Foundation – Making foundational investments to scale your cloud adoption(e.g., creating a landing zone, defining a CCoE, establishing an operationsmodel)

• Migration – Migrating individual applications• Re-invention – Optimizing products and services, and innovating in the cloud

These stages were defined by Stephen Orban in the blog post The JourneyToward Cloud-First & the Stages of Adoption on the AWS Cloud EnterpriseStrategy blog. For information about how they relate to the AWS migrationstrategy, see the migration readiness guide.

configuration managementdatabase (CMDB)

A database that contains information about a company’s hardware and softwareproducts, configurations, and inter-dependencies. You typically use data from aCMDB in the portfolio discovery and analysis stage of migration.

epic In agile methodologies, functional categories that help organize and prioritizeyour work. Epics provide a high-level description of requirements andimplementation tasks. For example, AWS CAF security epics include identity andaccess management, detective controls, infrastructure security, data protection,and incident response. For more information about epics in the AWS migrationstrategy, see the program implementation guide.

heterogeneous databasemigration

Migrating your source database to a target database that uses a differentdatabase engine (for example, Oracle to Amazon Aurora). Heterogeneousmigration is typically part of a re-architecting effort, and converting theschema can be a complex task. AWS provides AWS SCT that helps with schemaconversions.

homogeneous databasemigration

Migrating your source database to a target database that shares the samedatabase engine (for example, Microsoft SQL Server to Amazon RDS for SQLServer). Homogeneous migration is typically part of a rehosting or replatformingeffort. You can use native database utilities to migrate the schema.

IT information library (ITIL) A set of best practices for delivering IT services and aligning these services withbusiness requirements. ITIL provides the foundation for ITSM.

IT service management (ITSM) Activities associated with designing, implementing, managing, and supporting ITservices for an organization. For information about integrating cloud operationswith ITSM tools, see the operations integration guide.

Migration AccelerationProgram (MAP)

An AWS program that provides consulting support, training, and services tohelp organizations build a strong operational foundation for moving to thecloud, and to help offset the initial cost of migrations. MAP includes a migrationmethodology for executing legacy migrations in a methodical way and a set oftools to automate and accelerate common migration scenarios.

Migration PortfolioAssessment (MPA)

An online tool that provides information for validating the business case formigrating to the AWS Cloud. MPA provides detailed portfolio assessment(server right-sizing, pricing, TCO comparisons, migration cost analysis) as wellas migration planning (application data analysis and data collection, applicationgrouping, migration prioritization, and wave planning). The MPA tool (requires

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login) is available free of charge to all AWS consultants and APN Partnerconsultants.

Migration ReadinessAssessment (MRA)

The process of gaining insights about an organization’s cloud readiness status,identifying strengths and weaknesses, and building an action plan to closeidentified gaps, using the AWS CAF. For more information, see the migrationreadiness guide. MRA is the first phase of the AWS migration strategy.

migration at scale The process of moving the majority of the application portfolio to the cloud inwaves, with more applications moved at a faster rate in each wave. This phaseuses the best practices and lessons learned from the earlier phases to implementa migration factory of teams, tools, and processes to streamline the migration ofworkloads through automation and agile delivery. This is the third phase of theAWS migration strategy.

migration factory Cross-functional teams that streamline the migration of workloads throughautomated, agile approaches. Migration factory teams typically includeoperations, business analysts and owners, migration engineers, developers,and DevOps professionals working in sprints. Between 20 and 50 percent ofan enterprise application portfolio consists of repeated patterns that can beoptimized by a factory approach. For more information, see the discussion ofmigration factories and the CloudEndure Migration Factory guide in this contentset.

operational-level agreement(OLA)

An agreement that clarifies what functional IT groups promise to deliver to eachother, to support a service-level agreement (SLA).

operations integration (OI) The process of modernizing operations in the cloud, which involves readinessplanning, automation, and integration. For more information, see the operationsintegration guide.

organizational changemanagement (OCM)

A framework for managing major, disruptive business transformations from apeople, culture, and leadership perspective. OCM helps organizations prepare for,and transition to, new systems and strategies by accelerating change adoption,addressing transitional issues, and driving cultural and organizational changes. Inthe AWS migration strategy, this framework is called people acceleration, becauseof the speed of change required in cloud adoption projects. For more information,see the OCM guide.

playbook A set of predefined steps that capture the work associated with migrations, suchas delivering core operations functions in the cloud. A playbook can take the formof scripts, automated runbooks, or a summary of processes or steps required tooperate your modernized environment.

responsible, accountable,consulted, informed (RACI)matrix

A matrix that defines and assigns roles and responsibilities in a project. Forexample, you can create a RACI to define security control ownership or to identifyroles and responsibilities for specific tasks in a migration project.

runbook A set of manual or automated procedures required to perform a specific task.These are typically built to streamline repetitive operations or procedures withhigh error rates.

service-level agreement (SLA) An agreement that clarifies what an IT team promises to deliver to theircustomers, such as service uptime and performance.

Modernization termsThe following are commonly used terms in modernization-related strategies, guides, and patterns provided by AWSPrescriptive Guidance. To suggest entries, please use the Provide feedback link at the end of the glossary.

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business capability What a business does to generate value (for example, sales, customer service,or marketing). Microservices architectures and development decisions can bedriven by business capabilities. For more information, see the Organized aroundbusiness capabilities section of the Running containerized microservices on AWSwhitepaper.

microservice A small, independent service that communicates over well-defined APIs and istypically owned by small, self-contained teams. For example, an insurance systemmight include microservices that map to business capabilities, such as sales ormarketing, or subdomains, such as purchasing, claims, or analytics. The benefitsof microservices include agility, flexible scaling, easy deployment, reusable code,and resilience. For more information, see Integrating microservices by using AWSserverless services.

microservices architecture An approach to building an application with independent components that runeach application process as a microservice. These microservices communicatethrough a well-defined interface by using lightweight APIs. Each microservicein this architecture can be updated, deployed, and scaled to meet demand forspecific functions of an application. For more information, see Implementingmicroservices on AWS.

modernization Transforming an outdated (legacy or monolithic) application and its infrastructureinto an agile, elastic, and highly available system in the cloud to reduce costs,gain efficiencies, and take advantage of innovations. For more information, seeStrategy for modernizing applications in the AWS Cloud.

modernization readinessassessment

An evaluation that helps determine the modernization readiness of anorganization’s applications; identifies benefits, risks, and dependencies; anddetermines how well the organization can support the future state of thoseapplications. The outcome of the assessment is a blueprint of the targetarchitecture, a roadmap that details development phases and milestones for themodernization process, and an action plan for addressing identified gaps. Formore information, see Evaluating modernization readiness for applications in theAWS Cloud.

monolithic applications(monoliths)

Applications that run as a single service with tightly coupled processes. Monolithicapplications have several drawbacks. If one application feature experiences aspike in demand, the entire architecture must be scaled. Adding or improving amonolithic application’s features also becomes more complex when the code basegrows. To address these issues, you can use a microservices architecture. For moreinformation, see Decomposing monoliths into microservices.

polyglot persistence Independently choosing a microservice’s data storage technology based on dataaccess patterns and other requirements. If your microservices have the samedata storage technology, they can encounter implementation challenges orexperience poor performance. Microservices are more easily implemented andachieve better performance and scalability if they use the data store best adaptedto their requirements. For more information, see Enabling data persistence inmicroservices.

split-and-seed model A pattern for scaling and accelerating modernization projects. As new featuresand product releases are defined, the core team splits up to create new productteams. This helps scale your organization’s capabilities and services, improvesdeveloper productivity, and supports rapid innovation. For more information, seePhased approach to modernizing applications in the AWS Cloud.

two-pizza team A small DevOps team that you can feed with two pizzas. A two-pizza team sizeensures the best possible opportunity for collaboration in software development.

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For more information, see the Two-pizza team section of the Introduction toDevOps on AWS whitepaper.

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Document historyThe following table describes significant changes to this guide. If you want to be notified about futureupdates, you can subscribe to an RSS feed.

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Initial publication (p. 21) — December 6, 2021

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