Automated Data Connectors: A new way to data integration
Traditional data integration methods are extremely time and labor intensive. They occupy data engineering teams with mindless tasks that eat away from valuable resources which could be better devoted to strategy and business development.
Automated data connectors make these tasks much easier. Here’s how.
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Data is the lifeblood of today’s analytics teams. A wealth of information is being gleaned from their work, and being used to improve businesses’ decision-making processes. The collection of data from various sources into a single location where queries and transformations can be performed easily is what actually allows analytics teams to do their work. And, obviously, this data must always be correct and up to date.
But the problem is that there are so many external data sources that companies big and small all
find the task difficult to accomplish.
Earlier, data integration used to entail data engineers writing specialised code to link data APIs together. Unfortunately, unique code connections and data pipelines are time consuming to create and maintain. Additionally, data engineering teams needed to develop exclusive tracking infrastructure to keep tabs on the pipeline’s health and performance. The tasks were too many to keep up with even for a team of data engineers. More than that, it slowed down the work of data scientists and analysts – which is essential for strategizing.
Breaking the old-fashioned approach to data integration
For decades, data engineers and business intelligence developers have focused on consolidating data into trustworthy repositories. To do that data engineers in the past either patched together their own scripts and task managers, or after the arrival of Microsoft’s SSIS and Apache’s Airflow, turned to data management platforms. However, these methods come with their own particular set of problems:
• Patched-together scripts require a lot of time and effort to maintain.
• Airflow and SSIS are typically used to connect to a limited number of data sources, typically databases.
• ETL-based data pipelines – that extract, transform, and load data into a data warehouse or other target system – are necessarily used in conjunction with these products, and they require data engineering to develop, maintain and update them.
• Data engineering, in addition, is difficult and time-consuming. Plus, it also diverts attention away from the more important initiatives within the company and, hence, becomes the most significant stumbling block in a data integration process.
So how can companies connect their data to their analytical and operational tiers while doing away with grunt work?
The answer lies in automated data connectors
Automated data connectors link to a wide range of data sources. Their main attraction is that they require nominal configuration, coding, and human involvement. There is no need for your team to develop any code or infrastructure to manage a large number of APIs, which saves on operational as well as integration time.
Further, automated data connectors eliminate the need for data engineers to continuously code the same integrations over and over again because they use ELT (extract-load-transform) instead of ETL, and ELTs come with several advantages. Because ELTs have a more streamlined process and lower project turnaround times, analysts are able to retrieve the data they need much more quickly. They also allow automated connectors to interface seamlessly with data transformation technologies, allowing your team to use software development best practises such as version control.
Utilizing Tools for Change
Automated data connectors allow data teams to connect with transformation tools like dbt, besides decreasing the amount of redundant code. After connecting your automated connectors to a certain database and installing dbt packages, it is easy to analyse the performance of your support team within a day with aggregated dashboards and tables. With all of these advantages, your team will be able to go from having no data and no centralised reporting system to developing and insights within days.
Rapid access to Data
Typical data integration is labour and time intensive, diverting engineering resources from more valuable projects. Moreover, analysts and data scientists require quick and rapid access to their data if they are to become data-driven because when it comes to data quality, a sluggish ETL process can be just as harmful as false data. After all, companies need to make decisions based on data as and when it flows in, not after weeks or months.
A significant amount of engineering effort can be saved by using commercially available data integration technologies that have automatic data connectors for managing data. It will leave a lot of resources open for data engineers and software developers to focus on solving high-value challenges and have time for higher value projects.
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