Data Integration
Deliver Agility to Data Warehouse Design, Deployment, and Management with Data Warehouse Automation (DWA)
The most significant inefficiency of traditional data warehousing is the requirement to design enterprise data warehouse models that match future queries before a team creates a warehouse. As a result, data warehouse teams cannot keep up, especially in today's business environment of real-time decision-making and rapidly changing requirements.
Automation, AI and Data Mesh are all playing a role in modernizing Data Management. According to Gartner, through 2024, adopting data fabric design patterns that support augmented data integration will reduce manual data integration tasks by up to 50%[1]. Gartner also predicts that by 2024, AI-enabled augmented data management and integration will reduce the need for IT specialists by up to 30%[1].
Key Capabilities:
First, you need capabilities that automate the design and development process and generate the code for the data warehouse
Model Creation: Automate data model design and source mapping. Data models can be created or imported, then modified and enhanced iteratively.
Automated Mapping: Data pipelines must ingest updates in real time after an initial batch load. A capability such as change data capture provides a real-time backbone to accelerate data movement to your warehouse from the widest variety of heterogeneous databases, data lakes, and enterprise sources like mainframe and SAP.
Code Deployment: Users should streamline data warehouse and ETL generation. They should be able to auto-generate ETL code to populate and load data warehouses. They should be able to deploy data marts without manual coding. They should be able to select from a broad array of datamart options, including transactional, aggregated, or state-oriented.
Second, the automation platform should effectively source and manage metadata and possess lineage and impact assessment capabilities.
Lineage and Impact Analysis: Automatically create metadata during design phases or implementation. Re-generate data lineage when changes are implemented.
Data Profiling: Validate data before it is loaded by identifying and repairing format issues and discrepancies.
Data Quality: Configure and enforce pre-loading rules to automatically discover and remediate issues with values, formats, data ranges, and duplication while implementing exception policies.
Finally, it would help if you drove agility to respond to new requirements and changes with workflow orchestration, templates, and documentation.
Load and sync data from sources easily: Source feeds are loaded in real-time with change data capture (CDC).
Workflow Designer and Scheduler: Run all data warehouse and data mart ETL tasks as a single, end-to-end process. Schedule the execution of workflows to align with business and IT processes.
Monitoring and Notification: Monitor the status of all automatically generated tasks and workflows. Send proactive status alerts.
Templates and Documentation: You should implement best practices and templates for more effective BI projects. It will reduce dependence on highly technical development resources as you can automatically generate end-to-end workflows from data ingest to report generation.
Gartner's Assessment of Qlik's Solution:
Automation plays a significant role in reducing manual tasks, and Gartner specifically called out Qlik's maturity in data warehouse automation
Gartner mentions that reference customers of Qlik praise its real-time data replication and complex data transformation in support of data warehouse automation and data lake enablement scenarios. Qlik's clients highly rated native connections to legacy databases, file systems, object stores, and Kafka (for stream data integration scenarios). The maturity of Qlik Compose for rapid development of data models (through automated modeling, DDL, and ETL generations) was also cited as a critical factor by Qlik's clients for evaluating and selecting Qlik.
Gartner's research also found that Qlik's tooling supports data integration specialists and less-technical personnel to make data available through real-time data streaming. The role-based interface of Qlik Compose allows citizen integrators to develop data marts in their data warehouses. On the other hand, enterprise architects and data engineers use the design and administrative tools within Qlik Compose when more-robust data models are required.
Benefits:
Qlik Data Integration for data warehouse automation allows users to design, build, deploy, manage, and catalog purpose-built data warehouses (especially cloud-based) faster than traditional solutions. Using Qlik, data engineers can meet or exceed the demands for analytics-ready data marts that enable data-driven insights at the speed of change.
More broadly, according to Gartner, users adopting Data Warehouse Automation gain several benefits[2]:
Accelerate development and deployment with a template-driven approach.
The automation platform brings standardization to development with a metadata-driven approach. Users can deliver consistent metadata and version control by closely tracking changes. The metadata enables better integration and quality of deliverables with automated testing and data validation.
The automation platform drives agility to respond to new requirements by embracing a collaborative approach. Users can monitor and track the behavior and usage of data pipeline capabilities. Users can work together to improve transparent and constant communication with changes, thereby accelerating delivery.
Next Steps:
Click here to read how Gartner believes Data Warehouse Automation enables the agile development of analytical systems and data pipelines.
For a hands-on experience, check out a guided demo to see how Qlik Data Integration delivers on the promise of agile data warehousing.
Sources:
[1] Gartner, Magic Quadrant for Data Integration Tools, 17 Aug 2022
[2] Gartner, Assessing the Capabilities of Data Warehouse Automation, 8 Feb 2021