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DATA WAREHOUSE AUTOMATION

Data Warehouse Automation is a way to make the process of creating and maintaining a large database for storing and analyzing data more efficient and streamlined. This involves using computer systems to do tasks such as gathering data from different sources, making sure the data is accurate, and organizing it so it can be easily understood and used.

The Data Vault methodology is a specific approach to creating this kind of database that helps ensure the data is organized and easy to work with. Data Warehouse Automation helps make the process of using the Data Vault methodology faster and more consistent.

DataOPS

In modern Data Warehousing and enterprise installations by combining DataOps practices and Data Warehouse Automation technologies, organizations can improve the speed, quality, and reliability of their data processing and delivery, enabling them to make more informed business decisions and achieve better results.

ELT

ELT (Extract, Load, Transform) is a technology that assists in automating the process of creating and maintaining a data warehouse by automating the data pipeline. ELT operates by firstly extracting data from various sources, in most cases bringing it to a data lake storage (such as AWS S3 and Google Cloud Storage), and loading it into the target data warehouse system (Raw Data Vault for instance), then transforming the data into a format that can be easily used for reporting and analysis, such as Star Schema publishing layer.

By automating the data pipeline, ELT reduces the time and effort required to construct and maintain a data warehouse, freeing up valuable resources that can be used for other tasks. It also helps to ensure that data is processed consistently and accurately, which improves the quality of the data stored in the data warehouse. Additionally, ELT provides a flexible and scalable infrastructure that can be easily adjusted as business requirements change, making it a valuable tool for organisations that need to manage large amounts of data quickly and efficiently.

Data Vault automation

In the context of the Data Vault methodology, data warehouse automation can help streamline the process of building a data vault by automating the steps involved in integrating source data into the data vault, transforming it into a format that can be easily used for reporting and analysis, and populating the data vault with accurate and up-to-date information. The data vault methodology emphasizes the use of a centralized repository for storing data that is designed to be flexible and scalable (by using hubs, links and satellites), making it a good fit for use in data warehouse automation systems.

Here is a high-level overview of the steps involved in automating this process:

• Extract data - the first step is to extract data from various sources, such as databases, file systems, or cloud-based applications. This can be done using a variety of tools, such as data integration tools or custom scripts (for example Microsoft SSIS, Talend, Apache Hop or using custom Python scripts).
• Load data into the data warehouse - once the data has been extracted, it is loaded into the data warehouse. This can be done using bulk loading techniques, such as parallel load or partition load, or through more sophisticated techniques, such as change data capture. This might include loading to a data lake, raw data vault or staging tables.
• Transform data - after the data has been loaded into the data warehouse, it is transformed into a format that can be used for reporting and analysis. This typically involves tasks such as data cleansing, data mapping, and data aggregation. This can be done through a variety of methods, such as batch loading, incremental loading, or real-time streaming.

To automate the process of loading Data Vault objects or Kimball structures, the steps outlined above can be automated using Data Warehouse automation tools.