What is ETL?

ETL stands for extract, transform, and load. It's the process of combining data from different sources into a centralized place - a data warehouse while transforming the data into the desired format. Certain rules are applied to clean and reorganize the raw data, making it ready for analytical needs in the data warehouse.

Why do companies need ETL?

Companies often have data scattered across various databases and SaaS tools. ETL allows them to gather and consolidate data from these diverse sources into a centralized location, which is usually a data warehouse these days. With data cleaning applied to the raw data, it’s no longer inconsistent and incomplete. The transformation process helps the company gain a better understanding of the data by ensuring data quality and accuracy. After the data is loaded into the storage, the most common use case is BI. With the transformed data, analysts can build dashboards conveniently. The data consumed by the ETL process can be turned into business insights and help the team make better decisions.

What went wrong?

There is no doubt that ETL plays an instrumental role in ensuring data consistency, accuracy, and accessibility, empowering companies to make informed decisions.

However, modern ETL companies are finding it increasingly hard to differentiate from each other due to the nature of ETL being a middleware served best when standardized in a protocolar way. Moreover, ETL companies don't even possess the power to control both the "E" and "L" in their product development since data sources and destinations can change their requirements for connection at any time of the day.

One of the main problems of ETL is that it’s hard to maintain. ETL has become the typical legacy system that people don’t like to use. Once the pipeline is built, it takes a lot of effort for data engineers to fix the issues caused by the data sources or update them for new business needs. If you want to explore other options, unless you don’t mind paying a fortune, tools like Fivetran can do part of the job for you. Hence, ETL is not a flexible option for the business team to use, they are looking to get the data quickly and extract business insights from the data at any time instead of “structured” data from a heavy pipeline.

Moving Beyond ETL: Empowering Customized ELT with APIs

In the quest to move beyond ETL, we need a game-changing solution that offers greater flexibility and agility. APIs come to the forefront as the answer to these requirements, providing a superior alternative to traditional ETL processes, especially in today's fast-paced and data-driven landscape.

Advantages of Customized ELT with APIs over ETL:

• Flexibility: APIs empower the business team with a flexible way to access data directly from the source system. With APIs, they can request specific data points or perform custom queries tailored to their needs. This real-time access to data enables swift retrieval of the latest information, empowering immediate decision-making.
• Real-time Data Access: APIs offer the advantage of real-time data access, ensuring that the business team can fetch up-to-date information whenever required. This is particularly beneficial for time-sensitive decision-making, as they no longer have to rely on stale or batch-processed data.
• Simplified Data Retrieval: APIs provide a straightforward method for data retrieval, utilizing standard HTTP methods or custom API calls. This simplicity allows business users to access data using familiar tools, reducing the dependency on extensive technical knowledge.
• Real-time Analytics: APIs enable real-time analytics, empowering the business team to perform immediate analysis on the most recent data. This capability leads to faster insights and more agile decision-making processes.

Despite these advantages, one may wonder if the business team can use APIs for data analysis. Generally, the answer is no. While tools like Postman have lowered the barriers to viewing API data, business teams often lack the capability to store and analyze the data. However, with the Acho API Connector, this limitation is overcome. Business teams can directly access data from APIs without the need for data specialists, opening up new possibilities for data analysis and empowering them to make data-driven decisions with ease.

After extracting the data, we encounter another major difference between ETL and Customized ELT with APIs, which lies in the transformation process. With ETL, you typically obtain the transformed data directly in the data warehouse, and making further transformations might not be as straightforward. On the contrary, Customized ELT with APIs offers more flexibility in this regard. Once the data is loaded into your system, you gain the freedom to easily apply additional transformations and manipulate the data into the desired format as needed. We believe that the flexibility of data transformation is a valuable asset for the business team, enabling them to access on-demand information quickly and efficiently. If you are interested in learning more about data transformation and building a data pipeline, we recommend reading this informative blog.

In conclusion, Customized ELT with APIs offer agility, real-time access, and simplicity, making them a valuable tool for the business team to obtain precise data swiftly for decision-making. By embracing the power of APIs and leveraging the Acho API Connector, companies can unlock the full potential of their data and empower their business teams to make informed decisions with confidence. The era of swift and data-driven decision-making has arrived, and embracing APIs is the key to staying ahead in the competitive business landscape.

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