Agile Data Warehouse Automation

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Early data warehouse tools addressed gaps in the development, deployment, and maintenance of data storage systems.

For a long time, the focus of most of these tools was the data warehouse itself. The warehouse was the center of the business analytics universe, simultaneously a central repository for all business-critical information and the go-to platform for processing and producing analytics workloads of all kinds.

This is no longer the case. The warehouse is now one of several potential destinations for business-critical data, one of several business-critical analytics platforms, and, crucially, one of several sources of business-critical data.

Today ’s data warehouse automation tools are iran phone number lead designed to connect to both on- and off-premise data sources. They no longer assume conventional on-premise relational sources and can populate relational or non-relational targets (e.g., platforms like Hadoop and Spark), regardless of where they are deployed.

What does a Data Warehouse Automation Tool do today?
A current Data Warehouse Automation (DWA) tool does mainly 3 things:

Provides a site
It provides a context, a place to consolidate and organize the various tasks in the process of designing, developing, and deploying a data warehouse. Early data warehouse development was an ad hoc, disconnected process that made use of many different tools and was critically dependent on human oversight, especially manually orchestrating interoperability between all the constituent tools.

Reduces complexity
It also allows for a degree of abstraction regarding the design, development, and maintenance aspects of warehouses, masking much of their complexity. In this sense, you can connect, explore, and extract data from various sources; build or change a logical data model; generate (or regenerate) the transformations used to populate data structures in the warehouse; build, change, or maintain different types of denormalized data structures (star schemas, OLAP cubes) that are commonly exposed to front-end BI reporting and analytics tools; move from a warehouse target running in one context (e.g., on-premises Oracle) to a target running in a very different context (e.g., Azure SQL).

All modern DWA tools expose ease-of-use features, wizards, and user-customizable automation capabilities that are intended to speed up these and other tasks.

Data Lifecycle Management
Data Warehouse Automation has evolved into a data warehouse lifecycle management solution .

In addition to the core phases of data warehouse design and implementation, modern Data Warehouse Automation tools address a variety of tasks related to the data warehouse lifecycle, including data warehouse maintenance, data warehouse upgrade or migration, or data warehouse decommissioning.

Agile Data Warehouse Automation
Qlik automates the data warehouse lifecycle to accelerate data availability and analytics readiness. Data warehouse automation ensures success at every step of the process, from data modeling and real-time ingestion to data subsetting and governance .