Custom code to connect with data APIs is quickly becoming a thing of the past.
If your company is trying to become data-driven, then you’ve probably realized that you will need to get your various data sources into one place so that you can analyze your data and feed it back into operational systems.
Companies of all sizes find this far from easy because their data exists in so many external sources — Facebook and Google for marketing, Salesforce for CRM, Zendesk for help desk, and so many others.
In the past, data integration typically involved custom code connections created by data engineers to connect data APIs. The problem is that custom code connections and data pipelines take a lot of time to develop and maintain. Moreover, to ensure the ongoing health and performance of the data pipeline, data engineering teams also need to create logging and tracking infrastructure to monitor the health of the pipeline. All of this adds up and eventually overwhelms your data engineers. In turn, this bottlenecks your data scientists and analysts.
Is there a way to integrate your data into your analytical and operational layers without heavy, repetitive lifting?
The solution is to use automated data connectors. In this article, we will look into the problems data engineers face in terms of developing and managing traditional ETL (extract-transform-load) data pipelines, and how automated connectors can provide a way out of the current onslaught of data.
Centralizing data into sources of truth and data warehouses has been the core concern of data engineers and BI developers for decades. Traditionally, data engineers patched together scripts and task managers. This approach was eventually superseded by tools like SSIS (SQL server integration services) and Airflow.
Each of these approaches creates different problems.
Patched-together scripts are highly labor-intensive in terms of the maintenance and management of various jobs. This problem grows considerably with scale, especially when your needs grow from a handful to dozens of data sources. Tools like Airflow and SSIS mostly connect to very specific data sources, usually databases. These tools are used in combination with more traditional ETL-based data pipelines.
Traditional methods of getting data from point A to point B with modern applications require data engineers to create custom connectors for APIs. Tools like Salesforce and Asana have APIs that make it (not that) easy to pull data.
As a data engineer, I have had to create connectors for these systems over and over, each time writing a new package just to deal with another REST or SOAP set of end-points. Sure, with clever engineering you can eventually turn a collection of source-specific connectors into a general solution. But custom connectors are just the beginning of your problems.
In addition to developing your connectors, data engineers also need to create systems that manage logging, dependency management, and version control as well as instill some form of CI/CD (continuous integration/continuous development). Once you build these initial pipelines, you will spend significant time maintaining and updating them as teams need new columns and tables built.
Data engineering is hard and takes a lot of work, time, and focus away from bigger-impact projects. This is why data engineers are often the main bottleneck in the data lifecycle.
All of the work associated with development for connectors bogs down data engineers and frequently makes them bottlenecks. Data scientists and data analysts across a multitude of companies say the same thing: Their data engineers just aren’t able to keep up with their demands. Data engineers concur.
Even with powerful, sophisticated software libraries like Airflow and Luigi, there are always more and more data sources to pull and integrate. This constant need to develop and maintain current pipelines and infrastructure means that data engineers are constantly bogged down.
So how do the new waves of data integration tools address this problem?
There is an alternative to coding and developing custom data connectors. The newest generation of automated data pipelines break the data engineering bottleneck by featuring automated data connectors.
Automated data connectors easily connect to a wide range of data sources with minimal configuration, coding, and user input. This means your team doesn’t have to develop code or infrastructure to manage a multitude of complex API connectors. This is often referred to as low-code and saves development and maintenance time.
If an API changes, your team will not need to encode those changes into their connectors because the vendor is responsible for said changes. This obviates the need to repeatedly code the same Asana or Salesforce connectors, which many data engineers currently have to do.
As previously mentioned, ETL is the classic method of moving data from point A to point B. The problem with ETL is that it is slow and tightly coupled with the data engineering process.
ETL requires data engineers to spend a lot of time developing complex business logic before even loading the data into a company's data warehouse or data lake.
In order to continue reducing the data engineering bottleneck, automated connector platforms use ELT (extract-load-transform) instead of ETL.
The main difference between ETL and ELT is the order of operations. Instead of performing complex business logic before loading your data into your data storage solution, you load your data into your data warehouse or data lake and then analysts and data engineers can apply the logic afterwards.
ELTs provide several benefits. One important benefit is that ELTs allow your analysts access to the data they need much faster, because of a simpler workflow and shorter project turnaround times. In addition, many automated connectors easily integrate with data transformation tools like dbt, allowing your team to take advantage of software development best practices like version control.
Besides reducing redundant code, automated data connectors offer the ability for your team to integrate with transformation tools like dbt. You can connect your automated connectors to a particular source, install the dbt packages for it, and, within a day, have analytics-ready tables and aggregations that enable you to better understand the performance of your support team.
With all of these benefits, your team can quickly go from no data and no centralized reporting system to creating dashboards and insights in days and weeks instead of months.
Big data is not useful to companies by itself. To become data-driven, analysts and data scientists need fast and timely access to their data. Traditional methods like ETL are slow, and slow data is in many cases just as bad as incorrect data. Companies strive to make decisions based on what is currently happening, not what happened yesterday or last month.
Moreover, traditional data integration is heavily labor- and time-intensive, diverting scarce engineering resources from higher-value work.
Off-the-shelf data integration tools with automated data connectors for common data sources can radically reduce the time it takes to go from raw data to final data products like dashboards, with a minimum of engineering time.
This moves the pressure of developing complex data infrastructure away from data engineers and onto the providers of your automated connectors. Automated connectors can help your data engineers and software engineers focus more on solving big, high-value problems instead of writing commodity code for data operations. Ultimately, automation allows your data scientists and analysts to get to their data faster and your data engineers to work on higher-value projects.