How to Simplify Data Warehouse Integration
It has been 25 years since a data warehouse first made it to the business scene. Nowadays, a data warehouse is one of the most efficient tools for running a business. Data warehouses and their integration have been essential and helpful in querying, analyzing, and reporting transactional business data.
It all begins with capturing the data, cleaning it, transforming it, integrating it into a data warehouse, and later storing it in an ARDBMS (Analytical Relational Database Management System).
Data warehouses use portions of data that might have been sourced and stored in different yet associated data marts. It is mostly used to aggregate, monitor, and assess sales transactions while including country-precise data marts for further analysis, reports, and query support.
Modernizing Data Warehouse Integration
Earlier, many companies relied on outdated data integration systems when enforcing changes to their data warehouses. However, there is a growing need to simplify the data warehouse integration process at its core.
Given the rising issues in data warehouse integration and management, a suitable solution to accelerate the process is required. By changing the way you integrate data warehouses, you will also modernize your business, reduce recurring changes, cut down the data copying process, enhance agility and ultimately, reduce your overall spending.
How Does Data Warehousing Work?
As the highest data management level, a data warehouse typically revolves around manipulating, mapping, and creating integration models. These models include logical, physical, and conceptual data of a business and its end-user needs.
To properly integrate a data warehouse, some of these models — which define the data warehouse and are usually created from zero up — need to go through a reengineering process.
The data warehouse integration process begins with collecting transactional data from available sources, which enable the warehouse population. The integration of this information is one of the key processes of a Data Warehouse. It also requires the data engineers to map data from the source, choose models, and gather the transformation details in a metadata repository. Various data warehouse design tools support this mapping, documentation, and modeling process.
Once the data is sourced to the Warehouse, the next step is to monitor the process for possible inconsistencies. This way, a data warehouse will provide an integrated and precise view of all organization data.
Best Tools to Simplify Data Warehouse Integration
This section mentions some of the popular tools to help you achieve a more efficient and practical data warehouse integration.
Amazon Redshift is a cloud-based data warehouse for companies. As a fully-managed platform, Redshift processes petabytes of data in a matter of seconds. Among other perks, Redshift is also apt for automatic concurrency scaling. Through this automation, you can manipulate the data querying process and make it suitable for large-volume demands. Additionally, Redshift also helps companies scale data clusters and alternate between node types, thus optimizing the data warehouse performance and reducing the overall process costs.
If you care about full-speed data integration and analysis, then Redshift is a perfect data integration tool for you.
Microsoft Azure SQL Data Warehouse
Also, a cloud-based tool, Azure SQL Data Warehouse, is a handy Microsoft product. The tool helps companies maximize their efficiency in integrating and scaling petabyte volumes of data, both in loading and processing it. It also enables real-time reporting and uses a node system while enforcing MPP (Massively Parallel Processing).
The tool is perfect for optimizing queries intended for simultaneous processing, thus accelerating the extraction and visualizing of all business insights.
BigQuery is yet another efficient data warehousing tool. It can also be easily integrated with other Google cloud services such as CloudML and TensorFlow to build powerful AI and machine learning models.
BigQuery also manages petabyte-scale data queries in seconds and provides you with real-time analysis. As a cloud-based tool, BigQuery can be used to perform geospatial analytics to assess location-based data. The tool also separates data storage and computing, thus scaling the data processing and using memory sources based on your business needs.
Snowflake is a really practical data warehouse integration tool, as it allows you to set up an enterprise-level cloud data warehouse. With Snowflake, you can analyze data stemming from several sources, both structured and unstructured.
Snowflake’s architecture is cluster-oriented and separates the processing power of a data warehouse from data storage. Based on your users’ activity, you can easily scale CPU resources and optimize your query performance. The result? You can obtain fast, accurate insights from your data at a reasonable cost.
Teradata is a data warehouse integration platform that does more than one job. It allows you to collect massive volumes of enterprise data in the cloud. Also, it supports speedy parallel querying and analyses of this data.
Teradata also comes with a smart in-memory processing option, which maximizes the database’s performance at no additional cost.
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This blog was originally published at https://rudderstack.com/blog/how-to-simplify-data-warehouse-integration.