Handling big data
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Data warehouse modernization: a how-to guide
July 6, 2023
Data warehouse (DWH) modernization involves an architectural rethinking of traditional, typically on-premise data warehouses. It addresses the challenges and requirements of modern data management and analytics, including scalability, information silos, processing workloads, and cost-efficiency.
To streamline your modernization journey, consider relying on Itransition’s holistic expertise and data warehousing services.
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
TDWI
of respondents choose DWH automation as their main modernization approach
BARC
of companies modernizing their solution achieve broader data warehouse usage
BARC
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.
Adapting to new analytics trends
New tools and techniques (data mining, graph querying, NoSQL, data virtualization, etc.) have emerged to handle the diversification of data types and formats, forcing companies to keep pace by modernizing their data warehouses.
Need for real-time insights
Facing ever-evolving business trends and faster innovation cycles, companies require proper operational analytics tools to perform real-time data stream ingestion and processing.
Revamping a legacy architecture
As technology evolves, the design and architecture of a legacy data warehouse may no longer be aligned with the surrounding technology ecosystem and require an overall revamp or re-platforming.
High business risk
An enterprise can adopt a new data warehouse to benefit from more advanced predictive analytics features, which deliver forecasts of upcoming events and insights into future business outcomes or challenges.
Pursuit of data democratization
In order to democratize your corporate data assets, you can implement a new data warehousing architecture providing self-service data access and analytics for an expanding user base.
Issues with data silos
Many corporations suffer from fragmented data assets spread across on-premises and cloud-based repositories and need a modern, unified data hub to integrate and consolidate information more efficiently.
Strict legal requirements
Many organizations should comply with specific industry regulations and standards, such as GDPR, HIPAA, or PCI-DSS and therefore need a more compliant data warehouse infrastructure.
Cyberthreats
The growing range of data sources and integrations with external systems can introduce new vulnerability points, which can be addressed by implementing a modern data warehouse with more extensive cybersecurity features.
Alignment with business goals
According to TDWI, the most relevant catalysts for data warehouse modernization are more business-related than technical, including cost reductions, pressure from competitors, and security issues.
Upgrade your data warehouse with Itransition’s guidance
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
Scheme title: Automated ETL workflow through AWS solutions
Data source: aws.amazon.com — Orchestrate an ETL process using AWS Step Functions for Amazon Redshift
Cloud data warehousing
To better handle evolving operational scenarios and faster innovation cycles, several corporations have chosen to migrate their data warehouses to the cloud due to the flexibility of this model. The storage and processing capacity of cloud-based solutions can be scaled up or down without purchasing new hardware. Furthermore, cloud-based architectures can easily connect with other cloud applications, facilitating data integration.
Cloud data warehousing can also entail more complex deployment models, such as multi-cloud and hybrid cloud.
Multi-cloud
involves the distribution of apps and cloud assets across multiple cloud services offered by different providers to optimize costs, benefit from a wider feature selection, or comply with local data management legislations.
Hybrid cloud
combines on-premises and cloud resources or public and private cloud environments to scale up computing resources on-demand and get the best of both worlds in terms of applications and data warehousing capabilities.
Scheme title: Hybrid cloud data warehouse architecture
Data source: smartbridge.com — The Path Towards Modern Cloud Data Warehousing with Snowflake resources.enterprisetalk.com — The Definitive Guide to Data Warehouse Modernization
Operational data warehouses
Unlike traditional data warehouses optimized for historical analysis, this type of DWH supports real-time analytics and reporting, enabling operational business intelligence.
An operational data warehouse typically has intuitive self-service querying functionalities that allow non-technical employees to access up-to-date information. Equipped with specific features or integrated with monitoring tools, it can ingest and analyze continuous data streams.
- Azure Stream Analytics for Synapse
- Amazon Kinesis for Redshift
- BigQuery’s Datastream
Integration with a data lake
Data warehouses have extensive data querying capabilities, but specific analytic scenarios require additional storage to keep massive volumes of raw data for future use. Data lakes, which are high-capacity repositories designed to ingest data of any format, fit this purpose. To combine the benefits of both solutions, data warehouses and data lakes can be integrated according to a variety of architectural frameworks.
- Data warehousing outside the data lake: Incoming data lands on the data lake and is then transferred to the DWH via ETL.
- Data warehousing inside the data lake: The DWH is a subset of the data lake from which it draws raw and partially refined data.
- Data warehousing in front of the data lake: The DWH ingests data first and then sources the data lake to keep an additional copy.
- Data warehouse and data lake inside/outside hybrid: Analytics-focused DWHs are merged in the data lake, while those used for reporting remain outside.
A step further has been taken with the so-called data lakehouse. This recent architectural concept blends the features of both platforms to enable diverse workloads, expand the range of storage formats, and support real-time streaming.
Scheme title: Reference architecture involving data warehouse and data lake integration
Data source: informatica.com — 5 Steps to a Modern Data Warehouse with Cloud-Native Data Management
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
Pricing
On-demand
Flexible costs with no upfront fees
Pricing calculator available on AWS website
Microsoft’s highly integrated ecosystem combines advanced big data analytics and data warehousing into a single service while offering near-limitless scaling of storage and computing resources.
Key features
Native integrations with Power BI and Azure ML
Flexible SQL querying
Support for real-time data stream analytics
Solid security (encryption, granular access control, etc.)
Pricing
From $1.20/hour to $360/hour based on the service level
Up to 65% discount for 3 years reserved
BigQuery’s core strengths include cost-effective big data storage, sub-second query response times, built-in machine learning capabilities, and integration with other Google Cloud services.
Key features
Support for real-time dashboards via BigQuery BI Engine
Over 100 pre-built data source connectors
Streaming data ingestion and analysis
Automated database administration
Pricing
From $0.02 per GB or $0.01 per GB for long-term storage, both on-demand and flat-rate pricing for analysis
Snowflake is a cloud-agnostic data warehouse running on AWS, GCP, and Azure. Its unique architecture separates computing and storage, enabling seamless scaling without disruption or downtime.
Key features
Support for continuous and bulk data ingestion
Multiple service connections
Multi-cluster warehouse support
Native support of JSON, Avro, ORC, Parquet, and XML
Pricing
On-demand
Four different pricing plans
Compute usage billed on a per-second basis
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:
1
Discovery
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.
2
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.
3
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).
4
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.
5
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:
Integration
Integration
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
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
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
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.
Leverage our solutions to seize value from your data
FAQ
What are the most common data warehouse modernization strategies?
- Migrating a corporate data warehouse to the cloud using platforms like Amazon Redshift, Azure Synapse Analytics, or Google BigQuery.
- Augmented data management and analytics via DWH automation tools that can perform ETL and other processes faster and more accurately.
- Implementation of operational business intelligence to acquire and process data streams in real-time while offering self-service data querying features.
What are the main models of cloud data warehouse implementation?
- Bring Your Own License (BYOL)
Organizations redeploy their on-premises data warehouse platforms to a cloud-based infrastructure, which allows them to keep their favorite functionalities while leveraging the benefits of the cloud. However, not all vendors offer this option, so consider it when selecting a suitable solution. - Data Warehouse as a Service (DWaaS)
This involves the subscription to a cloud-native DWH platform offered by a service provider, which ensures ease of use and faster implementation. Still, this may come with hidden costs to access additional features and services (such as data backup, recovery, and security), and vendor lock-in issues due to various adjacent services that are non-compatible with other cloud or on-premises environments.
How much does it cost to modernize a DWH via cloud migration?
The total investment in data warehouse migration to the cloud encompasses the following:
- Costs to perform the migration process, which depend on the number of servers, the volume of data assets, required integrations, and the professionals involved (cloud engineers, database administrators, data modelers, ETL developers, etc.). According to the ODSC, this typically ranges between $1,000 and $3,000 per server, with more complex migrations reaching $15,000 per server.
- Costs to maintain the solution, based on the implementation model (BYOL vs DWaaS) and the selected cloud platform. A cloud solution can cost between $18 and $84 per terabyte per month.
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