Data warehouse modernization: 
a how-to guide

Data warehouse modernization: a how-to guide

July 6, 2023

The role of data warehouse modernization

Research by BARC and TDWI highlights the most common reasons, approaches, and results of data warehouse modernization.

of organizations aim for greater capacity to scale data, users, and analyses


of respondents choose DWH automation as their main modernization approach


of companies modernizing their solution achieve broader data warehouse usage


Top 10 data warehouse modernization essentials

Before delving into specific use cases and strategies, let’s briefly review the general cornerstones of the DWH modernization process.

Ability to handle any type of data

including structured (i.e., financial transactions), semi-structured (emails), or unstructured (imagery).

Real-time data processing

to get actionable business insights from continuous data streams and enable operational business intelligence.

Compatibility with major data processing engines

available on the market, including Spark, Flink, Hadoop, Samza, and Storm.

Reliance on cloud technologies

to benefit from improved scalability, processing power, faster deployment, and managed infrastructure and security.

Support for various deployment models

including on-premises, multi-cloud, or hybrid cloud, and interoperability among multiple cloud environments.

Self-service querying

to facilitate access to data for users with different roles and needs, including data scientists, data analysts, and report writers.

Data integration

between the data warehouse and other data management platforms to create a unified ecosystem and minimize information silos or inconsistencies.

Automation of numerous tasks

such as data ingestion, transformation, and management to speed up analyses and improve accuracy.

Adoption of AI and machine learning (ML) algorithms

for automated data integration, cleansing, and stream processing.

Focus on compliance

by implementing cybersecurity features and data governance policies that ensure enterprise data assets are properly stored and shared.

10 data warehouse modernization use cases

An organization should consider data warehouse modernization when dealing with the following scenarios:

Scheme title: Top 10 leading drivers for data warehouse modernization
Data source: — The Modernization of the Data Warehouse

Handling big data

As businesses become increasingly data-driven, they need an upgraded solution to collect and process massive data volumes from an expanding range of sources, including corporate systems, social media, and IoT devices.

Upgrade your data warehouse with Itransition’s guidance

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Major data warehouse modernization strategies

Depending on your business priorities and requirements, there are different ways to approach data warehouse modernization.

Data warehouse automation

This strategy aims to automate each phase of the data warehouse lifecycle to minimize manually-driven tasks and better handle ever-growing data volumes and analytical workloads. The range of automated processes includes, for example, ETL pipelines (which integrate data from multiple sources, transform and consolidate it, and finally load it into the DWH) and SQL code generation for faster querying. All major data warehousing platforms currently offer a comprehensive range of automation solutions to help you modernize your system.
  • Azure Data Factory
  • Azure Synapse
  • Microsoft SSIS
  • Microsoft SQL Server
  • AWS Glue
  • AWS Step Functions
  • Informatica’s Intelligent Data Management Cloud
  • Oracle's Autonomous Data Warehouse
1AWS CloudWatch EventAutomation Script (using AWS CLI or AWS SDK)2ETL WorkflowAWS Step Functions workflowSubmit JobMonitor Status3AWS LambdaJob Initiate4AWS BatchJob Execute5Fetch ETL WorkflowScriptAmazon S3Input data sql filesPSQL ContainerGet Container ImageAmazon ECSAmazon RedshiftExecute ETLWorkflow Script

Scheme title: Automated ETL workflow through AWS solutions
Data source: — Orchestrate an ETL process using AWS Step Functions for Amazon Redshift

Top platforms for data warehouse modernization

The range of cloud-based solutions available on the market is rather extensive. Here are some popular options you may consider when modernizing your data warehouse.

Companies relying on Amazon's data warehousing solution can perform SQL-based querying of petabytes of data, whether structured or unstructured, and benefit from a broad range of integrated services.
Key features
Hadoop/Spark-based big data processing Native integrations with AWS’s ecosystem Machine learning model training via Amazon ML End-to-end encryption
On-demand Flexible costs with no upfront fees Pricing calculator available on AWS website

Benefits of data warehouse modernization

Companies that modernize their data warehouse may expect to achieve the following pay-offs.

Superior performance

Modern data warehouses can efficiently handle large volumes of data and complex queries due to faster processing times.

Enhanced scalability

Cloud-based DWHs can scale up or down depending on business needs without requiring additional hardware or infrastructure investments.

Cost optimization

Organizations can mitigate infrastructure and maintenance costs by migrating to a cloud-based data warehouse managed by a service provider.

Real-time analyses

Modern DWHs offer real-time analytics capabilities, enabling faster report and analysis cycles and ongoing operational adjustments.

Wider data pool

A modern data warehouse supports many types of data and formats, allowing organizations to collect information from numerous sources.

Improved security

Modern DWHs use data encryption and multi-factor authentication to protect sensitive data from unauthorized access.

Better collaboration

Modern data warehouses improve cooperation across teams by providing a single source of truth and facilitating data sharing.

Regulatory compliance

DWH automation can foster compliance by automatically performing data management and reporting tasks more accurately than humans.

Data warehouse modernization roadmap

Data warehouse modernization is a complex process requiring careful planning and execution. These are the key steps to update your existing infrastructure and move towards a more flexible and scalable environment:



You assess your current data warehouse environment to identify improvement areas, including architecture, data quality, and integration processes. You can use data profiling, lineage, and mapping to get a comprehensive view of your data assets and identify dependencies.


Defining goals

You identify the critical pain points you want to address, such as data quality issues, slow query performance, or difficulty integrating new data sources. You also determine the outcomes you expect to achieve, such as faster query response times, better data governance, or improved analytics.


Migration planning

You identify and prioritize key use cases, estimate a realistic timeframe, and calculate overall migration costs. You also select a suitable technology stack for ETL, data storage, integration, modeling, and analytics (including cloud tools and platforms if you opted for a cloud-based solution).


Design and development

Depending on the model and strategy selected, you can build a new data warehouse from scratch. This requires designing its architecture and data models, developing ETL/ELT pipelines, setting up integrations, defining data cleansing and security policies, and creating a UI.


Migration execution

The actual process of migrating data from an existing data warehouse to a new environment encompasses several tasks: updating your schema (which defines how data is structured), translating queries to the new data query language, verifying data quality, and migrating data pipelines and applications.

Data warehouse modernization barriers and best practices

Data warehouse modernization often comes with several technical and business challenges. Here are some recommendations for overcoming issues:

DW modernization  requires integrating data from multiple systems and applications (including legacy solutions), which can be an issue due to differences in data formats, schemas, and platforms.
Companies can use cloud data integration tools providing pre-built connectors and APIs, such as Amazon API Gateway or Azure API Apps. They can also rely on middleware architectures or data virtualization techniques.
Vendor lock-in
Modern DWHs require a flexible and scalable tech stack that can evolve with changing business needs. However, legacy solutions are often built on proprietary technologies that limit upgrades.
Organizations can adopt open-source and vendor-neutral technologies. They should also consider multi-cloud and hybrid cloud architectures that provide a vendor-agnostic infrastructure and enable interoperability across different cloud platforms.
Data security
Protecting sensitive data is critical for any data warehouse, and modernization can introduce new security risks, such as data breaches or unauthorized access, due to additional integrations with external sources.
Implementing robust security features, including multi-factor authentication, data encryption, and access control, can help mitigate such risks. Also, cloud providers deliver several solutions to safeguard data, such as AWS CloudTrail or Azure Synapse's security functionalities.
Get started with DW modernization

Get started with DW modernization

The data tide unleashed by mass digitalization is paving the way for new, unexpected business opportunities. However, it's also putting a strain on data management and business intelligence ecosystems, including data warehouses that will require a radical overhaul by means of cloud technologies, automation, and operational BI. Having an experienced partner like Itransition will help you get the most out of your data warehouse modernization investment while overcoming potential challenges.

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