Data warehouse automation (DWA) is the process that automates the deployment of a data warehouse in the cloud, including creating reusable data models, data storage functions, and maintenance.
A data warehouse is a central repository where large amounts of data are stored for further analysis. Often referred to as a data lake, the scale of data maintained by the warehouse is too high to be handled locally due to disk and memory limitations.
Data warehouse automation reduces manual effort and facilitates efficient analysis and reporting.
Without automating data warehouse tasks, managing big data sets from a variety of sources multiplies the time it takes to obtain accurate business insights.
Data warehouse automation software facilitates the creation of reusable bidirectional connectors with data sources.
With the ability to import data from different sources, be it files, databases, cloud apps, or any other data source, automation can help you take full advantage of no-code data mappings and ETL tools.
Data warehouse automation tools allow for the creation of dimensional models with ease, increasing the agility and efficiency of your data warehouse operations.
Using machine learning algorithms can help identify and build these data models with minimal effort and time.
Engineers and analysts can focus on higher-priority requests by automating routine data management tasks.
With DWA software, there's no need to search StackOverflow frantically for ETL scripts and manual data warehouse workflows.
Automation enables the designing of high-quality data model templates, reducing time and room for error.
Audit real-time data to verify consistent data quality before connecting it to business intelligence visualization tools.
Data warehouse automation software can increase the reliability of your data warehouse solution.
With up-to-date data, downstream reports and analysis will offer more actionable than disparate data providers.
Data pipelines can enforce proper data quality standards to meet privacy and security regulations in advance rather than as an afterthought.
DWA providers such as WhereScape, Azure, and Amazon can help maintain compliance and achieve warehousing goals.
With drag-and-drop tools, creating a workflow for data lake processing, big data analysis, and other tasks can be more manageable.
Automation providers like Microsoft and Amazon provide drag-and-drop tools that simplify the process.
Enterprise data can be extracted and loaded into data warehouses via secure APIs.
Aggregate different data sources into a single data warehouse hosted by Amazon, Google, or Microsoft.
The lifecycle of a data warehouse starts with data collection. It involves all the stages involved in developing and maintaining a data warehouse.
The linear stages of the data warehouse lifecycle are listed below.
Data warehouse automation can speed up and condense each lifecycle stage from weeks to minutes. With DWA, data processing undergoes ETL optimization methods to increase reliability.
Automation can streamline the entire data warehouse lifecycle. It is instrumental in the ETL stage, where huge amounts of data from multiple sources must be consolidated. Setting up ETL automation can significantly reduce the developmental complexities within a data warehouse development project.
Data warehouse development consists of various stages/phases:
Data modeling is an important stage of data warehouse development where the data structure and schemas are developed. The database structure and schemas are the ones that tell how the data will be stored in the data warehouse.
Designing them effectively is thus essential to boost the analytical performance and data retrieval functions.
ETL stands for Extract, Transform, and Load. This method is the process by which data is collected from different data sources, transformed to be compatible with the data warehouse, and then loaded into the data warehouse repositories.
Automated ETL operations are often carried out with the help of ETL tools which can help you set up connectors depending on the data source. If done manually, this process can be highly time-consuming and cumbersome.
Data integration is the resultant process achieved via ETL tools. It is the process by which data is collected from multiple sources and consolidated into a compatible consistent format.
You must ensure that the data quality and integrity are not compromised during the integration process.
Data management is a broad process encompassing a range of data operations relating to managing databases and relevant entities. It is thus also a big part of data warehouse operations.
Master data management, sometimes synonymously used with data warehouse management, is a bit different.
MDM is concerned with entities, whereas a data warehouse manages both transactional and non-transactional data.
Real-time data refers to data updates happening in real time. Data warehouses capable of synchronizing every data transaction as soon as it is made are real-time data warehouse solutions.
These data warehouses allow for more accurate analysis as they reflect real-time information as and when it happens.
Self-service for data warehousing is the capability that lets users get access to data reports and analysis facilities with little to no coding requirement.
Dashboards and instant report generations are some self-service features that allow users to gain quick insights from the data warehouse with minimal effort. Self-service can also be related to the ease of access and availability of data at all times.
Impact analysis is a specific type of data analysis that pertains to understanding the nature of database schemas and structures. It helps identify and optimize the various tasks and jobs in changing the data model, database, or data structure. It also analyzes the data flow in your data transformation operations and workflows.
Data marts in data warehouses are specialized data structures that interface a particular business case. It can be called a subset of a data warehouse focusing on a single functionality.
An enterprise data warehouse is a type of data warehouse that is used to house all the data pertaining to a particular organization. It is the same as any regular data warehouse except that it is semantically related to a business and contains organizational data.
The business requirements dictate the use cases for a data warehouse. Requirements identification is the first stage of data warehouse development. They provide the objectives to be reached and the basis for creating the data models for the data warehouse.
Data warehouse development could take on any methodology that fits its requirements. There are multiple methodologies available, each with a particular focus.
They could follow different strategies, each handling the various aspects such as requirement modeling, architecture design philosophy, Normalization/Denormalization Attribute, scalability, change management, and more in their way.
Many Data Warehouse Automation providers don't publish typical pricing online. From our research, it's reasonable to expect to pay $1,000 to $3,500 per user monthly.
These costs are usually all-inclusive, with data integration connectors, automation scripts, visualization software, ETL functionality, data backups, etc.
WhereScape is a popular data warehouse automation software that enables users to build, deploy, and manage data warehouses quickly and efficiently.
Talend is an open-source data integration software providing automation capabilities for building data warehouses, data lakes, and data marts.
Portable includes 350+ no-code connectors out-of-the-box. Portable is entirely focused on building custom API connectors. Portable already supports Snowflake, Google BigQuery, Amazon Redshift, PostgreSQL, and MySQL as destinations.
As a popular Fivetran alternative, Portable is laser-focused on its growing lineup of long-tail API connectors. Data engineering teams save hundreds of hours writing custom scripts by using Portable.
Portable already supports Snowflake, Google BigQuery, Amazon Redshift, PostgreSQL, and MySQL as destinations. Also, its free plan permits unlimited data volumes.
Matillion is a cloud-based data integration and ETL software that provides data warehouse automation capabilities for building data warehouses on popular cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.
Informatica is a popular data integration and ETL software that provides data warehouse automation capabilities for building data warehouses, data marts, and data lakes.
Snowflake is a cloud-based data warehousing platform that provides automation capabilities for building and managing data warehouses.
Azure Synapse Analytics is a cloud-based data warehousing platform that provides data warehouse automation capabilities for building and managing data warehouses on Microsoft Azure.
Oracle Autonomous Data Warehouse is a cloud-based data warehousing platform that provides automation capabilities for building and managing data warehouses on Oracle Cloud.
When automating your data warehouse, it is essential to focus on various important aspects and features like:
Adequate database performance is a much-needed quality for data warehouses. The data operations required for executing complex analysis can be highly resource intensive. The data warehouse design and data models should be developed with performance optimization in mind. A data warehouse should be able to deliver fast results even when working with massive amounts of data.
You should also look for further optimization opportunities, such as data compression techniques for optimizing storage. Be it an on-premise or cloud-based data warehouse, you pay more for every extra storage byte you need. So optimization features are a must-have to save up on costs as well as boost performance. Look into the query designs, data duplication techniques, and similar optimization features provided.
Pay attention to the computing power and resources required to run your preferred data warehouse solutions. Computational requirements impact the overall running costs of maintaining your data warehouse systems. The more efficiently designed your system is, the more cost-effective it will be.
Data visualizations are an important aspect of what makes a data warehouse self-serviceable. The right visualization tool can help you save time on performing complex queries and get you faster insights. They help reduce the need for coding and facilitate easier decision-making and information sharing across the relevant stakeholders.
Like data visualizations, dashboards must be designed intuitively. They should have the necessary functionalities to make your data warehouse system more accessible and easy to use.
BI tools help achieve the business requirements that motivate data warehouse development in the first place. BI tools can work on top of a data warehouse to give you accurate and quantifiable insights to make data-based business decisions. Your data warehouse solutions should be compatible with BI tools or easily integrate with BI systems.
Most modern analytical models are developed using ML technologies. A data warehouse system that can be readily integrated with ML technologies will be a considerable asset in further improving your analytical and learning models.
Ensure your data warehouse system can support the data models and structures applicable to your business cases, like the ones extracted from Salesforce or SAP.
Metadata is data about data. It gives information based on a template, such as the transactional history of any piece of data, its location, author details, origin details, format, structure, etc. Metadata-driven data warehouse development is often found to be more efficient and optimized.
In simpler terms, metadata can be defined as the extra information that gives info on the data. This meta information could be its data structure, type, format, storage requirements, source, destination tables, etc. It is the index page of your actual data.
Taking a metadata-driven approach to data warehouse automation is efficient and makes room for further improvement quickly. It is well suited for a cost-effective and functionally optimized iterative data warehouse development.
It allows you to build data models and rapidly ensure automated tasks' reliability. Building automation based on metadata also enables you to make a more adaptable and scalable system.
Metadata, in general, can be classified into three major types:
Operational: Contains data status, history, retention policies, and operations applied to the data.
Business: Non-technical information such as compliance and data governance info.
Technical: Metadata that gives information on the data structure and formats.
Streamline development process It can help you create features that align well with your data models and thus avoid developmental issues that might arise in later stages.
Shorten development cycles Meta data-driven automation can help you create rapid prototype models and test them reliably. This helps reduce development times and boosts productivity as well.
Enable self-service for business users With faster development and delivery time, you can easily enable self-service features. Automation can help you set up the data warehouse at a much quicker pace and lets you focus on delivering the core functionalities.
Reduce the burden on IT teams With low code support required, your dependency on IT teams will also be relatively low. Your SQL data teams can easily bypass the previous challenges as much of the ETL scripting, data modeling, and data operational tasks can be replaced with reliable automation.