Data is growing, and managing this growth poses challenges for enterprises. Many of these challenges stem from difficulties in handling the dynamic influx of data. The traditional approach of ETL is struggling, leading to 70% of failed initiatives, prompting a reevaluation of data integration practices. But what caused this setback?

In the traditional setup, ETL captures data from multiple sources and schedules it into batches.

These data sets in the batches are then further extracted (E), transformed (T), and finally loaded (L) into the targeted system. The technique involves bulk processing and periodic updates, causing delays in overall processing and time to expected outcome, resulting in a lack of real-time insights for businesses.

Modern techniques such as ELT can capture real-time data by processing only the incremental changes from the last extraction. It enables organizations to utilize their resources optimally and focus on more resource-conscious data integration. This reduction in latency provides on-demand access to updated data, allowing for prompt decision-making.

Due to its outdated technology, ETL cannot efficiently handle vast amounts of real-time data. While it may have been successful in the past, the modern Web3 landscape demands much more.

What are the different ways in which traditional ETL might be hindering your business growth?

With the exponential increase in data volume, traditional ETL pipelines struggle to keep up, resulting in slower processing and a potentially increased cost. This underperformance not only hampers an organization’s ability to utilize data insights but also hinders innovation.

Additionally, the complex schema in traditional systems requires significant upgrading and maintenance efforts. As businesses scale, the increase in sources makes it difficult for the pipeline to stay up to date.

Moreover, traditional ETL’s reliance on centralized processing introduces single points of failure, compromising data integrity and system security.

What to do? Move from outdated tech and embrace modern alternatives to ETL.

To meet the needs of the impatient consumer, organizations, regardless of their scale, must break free and unlock optimal value from data. Using modern ETL tools, businesses can streamline the complex data landscape, something ETLs have been trying to do for a long time.

ELTs follow the Load-first approach wherein the raw data is initially loaded into the target system, and transformation occurs in-database or using distributed processing frameworks.

Change Data Capture (CDC), for example, processes high volumes of data as soon as it enters the framework, thereby actualizing real-time insights at the other end. Likewise, cloud-based solutions provide scalable and cost-effective data processing, ensuring enterprises can adapt and grow without hardware limitations. Wait for the next section, where I explain a detailed case study.

Data lakes and hubs allow enterprises to store and process vast amounts of raw data from multiple sources. This approach fosters democratization and enables cross-functional teams to analyze data.

A quick case study to understand the impact of modern ETL

A major telecom company, dealing with a high volume of real-time data, struggled to manage the influx with traditional ETL systems. The scattered data across multiple sources hindered its network performance improvement efforts, impacting customer responses during the lockdown.

To transition to newer alternatives, the company implemented Skyvia, a cloud ETL platform that integrates data from multiple sources into a unified warehouse. This strategic solution led to significant improvements in understanding the network’s health. It helped the organization achieve remarkable improvements in network performance, reducing outages by 50% and boosting the average network speed by 20%.

Furthermore, this led to significant cost savings and enhanced customer satisfaction. With a reduction in data integration time from a week to a single day, the business could respond in real-time to critical situations, escalations, and other ad-hoc events.

Ultimately, the company recorded a 10% enhancement in customer experience ratings, reclaiming its lost reputation.

Today, the telecom company is future-ready to thrive in the highly competitive market. Moving from outdated ETL practices to contemporary cloud-based solutions has led to significant growth and loyalty. Skyvia’s cloud-based data integration solution is a reminder for businesses that tapping upon scalability and flexibility isn’t a tough game until they sign up for similar transformations. As we know, a SaaS landscape offers pricing models as per your consumption, drastically abbreviating upfront costs.

Make your enterprise future-ready

The traditional ETL approach stands as a barrier to innovation for modern enterprises. The inherent limitations of batch processing and complete data extraction no longer align with the demands of real-time decision-making and dynamic market landscapes.

As discussed, the inherent limitations of batch processing no longer suffice the in-the-moment decision-making expectations. Businesses must take a leap, embrace agile approaches, and extract true value from the data mountain. It’s no longer a choice but a strategic imperative.