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With the unstoppable expansion of data, modern businesses must gather and combine content from various sources. The average organization collects data from over 400 different sources, making data integration challenging. This, in turn, necessitates the use of powerful ETL solutions that are tailored to today’s data requirements. In order to handle such complex data needs with traditional ETL tools, businesses need to invest heavily in engineering bandwidth, physical data warehouses or data centers.
To address and overcome all these issues, businesses are increasingly turning to Cloud ETL Tools, which provide comprehensive and automated ETL pipelines that users can implement in minutes. Allowing users to store data in cloud data warehouses eliminates the need for consumers to invest in any hardware.
ETL stands for extract, transform, and load and refers to the process of integrating data from many sources, transforming it into an analysis-ready format, and putting it into the desired destination, which is commonly a data warehouse. It assists in bringing in data and storing it in a centralized location, allowing users to analyze various information.
ETL traditionally used physical warehouses to store the combined data from numerous sources in the past. Using Cloud ETL, the data sources and destination warehouses are completely online. Businesses do not need to maintain a physical data warehouse or any other gear. Powerful Cloud ETL Tools enable Cloud ETL to manage dataflows and allow users to construct and monitor automated ETL data pipelines from a single user interface.
The three steps of Cloud ETL are as follows:
- Extract relates to the integration of structured and unstructured data from various databases, data warehouses, marketing tools, CRMs, or mobile apps. Cloud ETL makes data extraction easier by allowing users to link and transfer data with a few clicks rather than having to write sophisticated code over and over.
- Transform is crucial for the ETL process as it entails enriching and transforming data into an analysis-ready format. Utilized techniques include sorting, cleaning, removing redundancy, verifying, and so on.
- Load is the process of loading ready-to-use data into the chosen destination. The full-loading technique loads the entire data, while the incremental loading technique loads data at predetermined intervals. Aside from loading structured data into data warehouses, Cloud ETL also supports loading unstructured data into data lakes, which can then be examined using BI tools to quickly extract crucial insights.
Scattering portions of the process onsite, remote, and in the cloud can make integration a nightmare. With cloud-based ETL technologies, you can manage the entire process with just one tool, decreasing the number of layers of dependencies.
While hand-coding and maintaining the ETL process might be advantageous in the short term, scaling and managing it becomes increasingly difficult as data sources, volumes, and other complexities grow. ETL tools, particularly cloud-based ones, eliminate this barrier because they scale with your needs.
It’s challenging to build a real-time ETL process manually, especially without affecting business operations. Quick access to real-time data from sources across the enterprise becomes a lot easier with ETL tools handling this for you.
Using ETL tools means that maintenance is handled automatically, as patches and updates flow without your intervention. Data completeness, accuracy, and integrity can also be assured using ETL testing tools.
With Cloud ETL in place, companies can perform ETL operations quickly and efficiently and optimize expenses on hardware purchases or maintenance. Furthermore, rather than charging users for substantial fixed fees, most Cloud ETL providers adopt a pay-as-you-go pricing model, charging users only for the resources they consume.
There are many popular ETL tools that can be used.
Here are some of them:
MuleSoft is a data integration platform that comes with all of the extract/transform/load (ETL) tools you’ll need to connect to data sources, extract and process data, and send it across multiple channels. MuleSoft Anypoint is based on the Mule Enterprise Service Bus (ESB) and Event Driven Architecture (EDA). It builds a network of data, applications, and devices through APIs.
The MuleSoft platform provides similar characteristics to other dedicated ETL solutions, such as connecting to a wide range of databases, such as DB2, MSSql, MySQL, AS400, Oracle, PostgreSQL, any JDBC compliant database, and NoSql databases like Hadoop, Casandra, and MongoDB.
MuleSoft data transformation and mapping capabilities with Dataweave are wide and flexible, enabling capabilities similar to those provided by ETL-specific products.
Mule has the advantage of having all of the other characteristics that an enterprise-level ESB offers, so when you develop your ETL process, all of the other tools like connectivity, scheduling, instrumentation, security, batch, and orchestration become a seamless part of the whole application.
The primarily ETL (extract, transform, load) tools that MuleSoft provides are:
Mule ESB and MuleSoft’s graphical design environment Anypoint Studio have a graphical data mapping functionality that provides extensive mapping and transformation capabilities with an easy-to-use interface.
The MuleSoft Anypoint Connectors library makes it easy to connect to an almost unlimited number of popular apps and services and to extract and load data into common sources and endpoints.
MuleSoft’s Anypoint Connectors provides options to connect to relational databases as well as emerging Big Data platforms like MongoDB and Hadoop for scenarios when direct database interaction is necessary.
You can run a batch job that breaks messages into individual records, affects each record, reports on the results, and has the capacity of pushing the processed output to alternative systems or queues using Mule’s Batch processor within an application. Тhis feature comes in handy when dealing with streaming input or integrating “near real-time” data between SaaS services.
The option to use any of Mule’s other processors, such as DataWeave, in the Input phase of Batch is one of the many capabilities that substantially simplify the design of a high-performance ETL process.
Input, Process Records, and On Complete are the three main sections of the Batch processor. You can create an orchestration by chaining as many steps as necessary in the Process Records section of the processor.
Reporting is among the post-processing operations that can be performed in the On Complete area.
Visual data mapping:
Offering support for flat and structured data formats like XML, JSON, CSV, POJOs, Excel, and more, businesses may choose which data types to utilize.
Overall, MuleSoft is a robust platform that any company may utilize as an all-in-one solution for various integration and API-related use cases.
2. Informatica PowerCenter
Informatica PowerCenter is a metadata-driven platform that optimizes data pipelines and boosts cooperation between business and IT teams.
Fivetran is a cloud-based ETL tool that provides high-end performance and one of the most extensive integration options, with over 90+ SaaS sources, as well as databases and other specific integrations.
4. AWS Glue
Amazon’s AWS Glue is a popular Cloud ETL Tool for big data analytics. It makes ETL workloads easier to manage and offers excellent interoperability with other AWS ecosystem services.
Xplenty is a powerful Cloud ETL Tool that offers an easy-to-use data integration platform for integrating data from various sources. Its simple user interface makes it simple to set up data pipelines.
Skyvia is a popular Cloud ETL tool that offers users reliable data integration, transfer, and backup services.
Finally, many ETL operations include considerably more than merely extracting data from one system, reformatting a few fields, and putting it into another. Other external systems are frequently contacted to search up comparative data, apply rules from a rules engine, or enrich the data with data from other systems while the original data is still being processed.
While there are many Cloud ETL tools on the market available to choose from, it is worthy of mentioning that Mule’s extensive orchestration, batching, and clustering features, together with its connectivity and transformation capabilities, make it a logical choice for most ETL workloads.
Scalefocus delivers integrations using MuleSoft and our experts have a deep understanding of the integration principles and technologies.
Our experience in complex enterprise projects and leveraging MuleSoft guarantees we can help you with any integration challenge, so contact us today and book a chat with our consultants.