Cloud-based ETL vs. local
Traditional data warehouses consist of on-premises physical servers, which refers to local data management and warehousing. These regional data warehouses cleanse and convert data from diverse sources before storing it in their physical databanks.
Cloud-based ETL services provide the same function as their on-premises counterparts; however, the data warehouse and most data sources are now exclusively online. Cloud ETL technologies enable customers to control their data flow through a single interface that connects to the data’s origin and destination.
Now Let’s dive into some benefits of Cloud ETL solutions.
Benefits of Cloud ETL solutions
Cloud-based ETL tools provide different advantages for companies compared to on-premises data management. Here are some of them:
Scalability: The scalability of cloud computing is significantly greater than on-premises data management. If you approach the storage or processing restrictions of the cloud, you may quickly acquire a new server or purchase more space. For on-premise computing, however, you would need to buy more hardware, which is both costly and time-intensive.
Mobile-Friendliness: Cloud platforms increasingly enable mobile devices, including smartphones, tablets, and laptops, granting consumers access from any location. On-premises ETL, on the other hand, can be adjusted for mobile compatibility but often does not have this feature by default.
Real-time data management: Eliminating data stream delays by collecting and transforming data from several applications and keeping it in a central, easily accessible location. In addition, ETL on the cloud places the required data within microseconds of the user’s fingertips.
Fully managed services: For the convenience of end users, public cloud services offer fully integrated applications that adhere to service and maintenance responsibilities. Having an on-premises ETL solution assures that you will be responsible for handling these difficulties on your own, necessitating the hiring of competent in-house tech personnel.
Loss Prevention: There is a possibility of losing data stored locally and on a few servers. However, with a cloud-based server, all information transmitted to the cloud remains secure and easily accessible from any internet-connected device.
ETL is a crucial method for consolidating all relevant data into a single repository to make it actionable, i.e., to analyze it and enable executives, administrators, and other stakeholders to make critical business decisions based on the data.
ETL use cases
The following are the use cases of ETL:
Data warehousing: A data warehouse can be called a database that combines data and analyzes it from multiple sources for business purposes. Frequently, ETL is used to transfer data to a data warehouse.
Marketing data integration: Marketing data integration is transferring all marketing data, such as customer, social networking, and web analytics data, into a centralized location to be analyzed and used to design future strategies. Utilize ETL to gather and organize marketing data for analytical purposes.
Machine Learning (ML) and artificial intelligence (AI): Machine learning (ML) is a technique for extracting meaning from data without actively constructing analytic models. Instead, the system uses artificial intelligence algorithms to learn from data. One can use ETL to consolidate data for machine learning purposes.
IoT data integration: IoT is a collection of linked devices capable of collecting and transferring data via hardware-integrated sensors. IoT devices may include factory equipment, network servers, smartphones, and many other machines, including wearables and implantable ones. ETL enables data consolidation from numerous IoT sources into a single location for analysis.
Cloud Migration: Companies are transferring their data and apps from on-premises to the cloud to save money, increase the scalability of their programs, and protect their data. ETL usage is common in organizations to manage these transitions.
Changes to data are visible immediately at the destination when using an efficient cloud ETL service. Data analysts can extract relevant insights much faster, providing businesses with the competitive advantage they require.
Companies that use the DoubleCloud platform for their Cloud ETL tools find it easy and efficient to integrate their data and take valuable insights from it almost immediately.
DoubleCloud’s platform helps our clients build sub-second data analytical solutions and pipelines across open-source technologies like ClickHouse® and Apache Kafka® in less than 5 minutes.
- ClickHouse® is a trademark of ClickHouse, Inc. https://clickhouse.com
- Apache® and Apache Kafka® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.