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Today, data can originate from hundreds or thousands of sources across an organization. Little wonder then that many businesses are overwhelmed and struggle to establish successful analytics programs. Evidently, a lack of data is not the problem. Instead, the real challenge is getting access to timely, trusted data on a reliable basis so that data analysts can actually do their job.
The need to handle and process increasing bits of information has led organizations to focus their investments on data engineering solutions. In India, a report by Analysis India Magazine (AIM) finds that the data engineering market is expected to reach USD25.4 billion this year and will further grow to USD108.7 billion by 2028.
While technological investments are crucial to boost competitiveness, economic headwinds and market challenges will require organizations to be more cost-efficient to maintain their standing. One such measure to achieve this is by altering their extract, load, transform (ELT) processes. Not only does this reduce operating costs, but employees will be able to get the most out of their data quickly. There are four key factors that organizations need to focus on when building a cost-effective ELT operation.
- Building data pipelines: Is DIY right for your organization?
For those with the expertise, creating data pipelines manually can help organizations save costs while giving them full control over where their data should go. However, this method comes with several drawbacks. Namely, the time to build, modify, and maintain pipelines can vary depending on API complexity and how many data connectors are involved. Longer maintenance periods mean that data teams become less involved in strategic projects, which can otherwise boost their competitiveness.
As customers and services continuously generate new data points, engineers will find themselves struggling to integrate additional connectors to account for the vast amount of information. Simultaneously, engineers may also have to spend time learning new functionalities, which will further delay data operations.
Open-source connector development kits (CDK) may provide an alternative solution for organizations with a less technical workforce. Despite this, users may still encounter a host of issues, including a lack
of automation and support as well as hidden costs. Ultimately, which methods engineers choose will depend on the nature of the business and customers’ expectations.
- Maintaining data pipelines: Ensuring long-term reliability
When pipelines break down due to schema or API changes, performance suffers, and it can take engineers hours or days to recover. Not only that, but data teams may be forced to work with incomplete or inaccurate data, which can lead to businesses wasting time and resources on fruitless initiatives. Therefore, organizations must ensure that their ELT solution is capable of detecting and addressing changes in real-time so that teams can continue to trust the data they have in their possession.
- Moving data: Maximizing data transfer efficiency
Speed is key to delivering an exceptional service, and customers are more likely to seek out other competitors if companies are unable to deliver on that particular front. To achieve this, companies need to be able to move and manage new and existing data seamlessly without any interruptions.
Specifically, data normalization can cut down on repeated schema creations to account for new or updated models. For example, the tables can list certain business objects, while the columns describe their attributes and characteristics. Some data service providers may host virtual private clouds (VPCs) with data normalization features, allowing organizations to reduce data warehousing costs.
Idempotent recovery processes are also crucial in retaining unique data while avoiding issues like duplication whenever a sync failure occurs. By pairing it with incremental sync features, employees can work with current information without suffering performance or network issues.
Organizations that handle large volumes of data need to be able to filter them so that teams and employees can harness only relevant insights for their operations. This is where selection and deselection features come into play, as users can simply choose which tables or columns they want to view and which to hide. This, in turn, shortens analysis time while allowing employees to make more effective decisions.
Last but not least, ELT platforms should be equipped with pipeline management functions, including bulk actions, application communication, and automated processes. Not only does this help to reduce team members’ workloads, but it also makes integrating new sources a seamless process.
- Transforming data: Maximizing benefits through enhanced data analytics
Getting the best outcomes requires users to transform their data into easily readable insights that can guide teams on the best course of action. Achieving this requires data engineers to integrate key features into their ELT infrastructure.
The first is version control, which archives all transformation model changes and makes it easy for engineers to revert to previous iterations for troubleshooting or comparisons. Secondly, using prebuilt
and modular transformation models that are easily replicable allows teams to start analysis immediately without the need for prior coding. Thirdly, lineage graphs provide a visual representation of the data that is easily interpretable for employees and executives. Finally, transformation scheduling and management give users the ability to determine the best time for data to be updated and which tables should reflect these changes.
Getting more out of data for less
A cost-effective ELT solution can simplify data teams’ workloads while reducing the financial pressures inflicted on organizations. Before building their infrastructure, it is imperative that data engineers review what capabilities each vendor can provide. Choosing the right platform allows teams to harness quick and accurate insights to meet critical business needs, spanning customer retention right up to improved supply chain management.
Disclaimer
Views expressed above are the author’s own.
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