What emerging data warehouse architecture trends and technologies can you use?

1 Cloud-based data warehouses

One of the most prominent trends in data warehousing is the shift to cloud-based platforms, such as Amazon Redshift, Google BigQuery, or Microsoft Azure Synapse Analytics. These platforms offer several advantages over traditional on-premise data warehouses, such as scalability, flexibility, cost-efficiency, and performance. Cloud-based data warehouses can also leverage the cloud ecosystem of services and tools, such as data lakes, data pipelines, machine learning, and analytics. However, cloud-based data warehouses also pose some challenges, such as data security, governance, and migration.

2 Data warehouse automation

Another trend in data warehousing is the adoption of data warehouse automation (DWA) tools, such as WhereScape, Attunity, or Matillion. DWA tools aim to simplify and accelerate the data warehouse development and maintenance process, by automating tasks such as data modeling, ETL, documentation, testing, and deployment. DWA tools can also help to ensure data quality, consistency, and compliance, as well as to adapt to changing data sources and requirements. However, DWA tools also require some upfront investment, training, and customization, and may not suit every data warehouse scenario.

3 Data warehouse federation

A third trend in data warehousing is the emergence of data warehouse federation (DWF) approaches, such as Snowflake, Dremio, or Starburst. DWF approaches enable the query and analysis of data across multiple data sources and platforms, without requiring the physical movement or replication of data. DWF approaches can provide a unified and consistent view of data, regardless of where it is stored or how it is structured. DWF approaches can also offer high performance, scalability, and flexibility, as well as support for various data types and formats. However, DWF approaches also involve some trade-offs, such as data latency, complexity, and dependency.

4 Data warehouse modernization

A fourth trend in data warehousing is the need for data warehouse modernization (DWM), which refers to the process of updating and transforming existing data warehouses to meet the current and future needs of the organization. DWM can involve various strategies and techniques, such as migrating to the cloud, integrating with data lakes, adopting DWA or DWF solutions, implementing new data models and architectures, or enhancing data governance and security. DWM can help to improve the data warehouse performance, functionality, and value, as well as to align it with the business goals and expectations. However, DWM also requires careful planning, execution, and management, as well as stakeholder involvement and support.

5 Data warehouse as a service

A fifth trend in data warehousing is the rise of data warehouse as a service (DWaaS) offerings, such as Panoply, Firebolt, or Yellowbrick. DWaaS offerings provide a fully managed and hosted data warehouse solution, that can be accessed and used via a web browser or an API. DWaaS offerings can eliminate the need for hardware, software, and maintenance costs, as well as provide scalability, reliability, and security. DWaaS offerings can also integrate with various data sources and tools, and offer advanced features and capabilities, such as machine learning, real-time analytics, or data visualization. However, DWaaS offerings also have some limitations, such as data sovereignty, customization, and control.

6 Data warehouse testing

A sixth trend in data warehousing is the growing importance of data warehouse testing (DWT), which refers to the process of verifying and validating the data warehouse functionality, quality, and accuracy. DWT can involve various types of tests, such as unit, integration, regression, performance, or security tests, as well as various methods, such as manual, automated, or hybrid tests. DWT can help to ensure the data warehouse reliability, usability, and compliance, as well as to identify and resolve issues and errors. However, DWT also requires some resources, skills, and tools, as well as a systematic and comprehensive approach.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?