A data pipeline is a process of moving data from one location to another, from source to target. A data ETL pipeline (extract/transform/load) is a data pipeline that makes use of a program or code for:
In most cases, the ETL pipeline is a response to a growing need for data analytics. The ability to transform raw data into analytics-ready data that can be analyzed and acted upon is crucial for modern organizations. By building a strong ETL pipeline architecture, companies can obtain raw data from multiple sources, and format that data for injection into any number of possible data analysis engines available on the market today.
For example, a shipping company might receive data from any number of vessels in its fleet from across the world. This could include raw data regarding ship engine status, fuel gauge readings, GPS readings, weather alerts, and more. A data pipeline might receive this data, in a raw format, relayed via satellites in intervals. An ETL solution would gather this data from satellites, transform the data into a useful format which can then be used by a database or data warehouse for data analysis. The results could then be used to update delivery estimates or even improve delivery time by calculating a more efficient route.
There are a number of potential challenges to an ETL data pipeline solution, As we will outline below:
Building a pipeline that ensures data reliability from scratch is slow and difficult. They require complex code and typically have very limited reusability, no matter how similar an organization’s many environments may be.
One of the biggest challenges may simply be latency or connectivity, particularly if sources are remote. In the above-mentioned shipping scenario, for example, satellite connectivity is only available in windows, meaning that data may only be transmitted in batches. Bandwidth and latency can also be issues in such a scenario, and might require some form of network acceleration technology. When considering an ETL pipeline solution, it is imperative that it is planned so that it can survive network failure and latency without loss of data.
Scaling your solution can also be a problem. With the increasing number of data-generating devices in a given environment, (smartphones, IoT devices, personal computers, applications, etc.) the amount of data that needs to be handled is growing constantly. A streaming ETL data pipeline solution should be considered when working with data that is generated continuously.
Extracting the numerous types of data to a standardized format usable in a single data warehouse solution can be incredibly difficult. Separate data warehouses and separate data pipelines are often required just to handle various subsets of the ingested data. As pipelines grow in complexity and scale, so too does the operational cost of managing the solution (or solutions).
Centering your ETL pipeline solution around a data lake can mitigate these issues to an extent, but only with strong planning, communication between departments, and expertise. If you are considering building an ETL Pipeline, be sure to consider these requirements, and follow ETL pipeline best practices.
Some of the above issues can be mitigated by using off-the-shelf solutions. Such solutions can homogenize ETL data pipelines across an organization, limiting the issues of complexity and scale a home-grown solution inevitably entails. However, there are risks involved in this approach as well.
One of the key problems that can occur is that with a vendor solution, you have little control over the development and lifecycle of the product. The vendor might have the right solution for the moment, but they will build according to the needs of their client-base at large, or with a focus on key large-scale clients that do not necessarily have the same priorities as you. As your organizational needs change, and their development path diverges, the solution that fit so well might require integration of additional technologies, or homegrown stop-gaps. Of course, this could result in the same challenges faced in a homegrown solution.
Another issue that can arise is vendor lock-in. Because you have invested heavily in a single solution, you are at the mercy of that organization’s pricing, as well as their development. You may not be able to utilize competitor solutions that might be a better fit for your needs because they do not integrate well with your chosen vendor. You might need to pay costly licensing fees for add-ons to achieve the same functionality as another solution, simply because it would be too great a challenge to disentangle yourself from the vendor environment.
Open-source data integration solutions offer the temptation of free software, but on the other hand they require a lot of in-house development to manage, maintain and implement – which is why many open-source vendors thrive off of selling services or additional features for open-source tools such as security and high availability.
A strong, middle-of-the-road approach may be to use a proprietary tool while ensuring that data is stored in open rather than proprietary file formats, which will allow you to later replace any vendor-based solution with another proprietary, open-source or homegrown tool. For example, building a streaming ETL pipeline for Kafka based on an open lake architecture is far simpler than creating a homegrown solution or relying on arcane open-source frameworks, while ensuring a good level of flexibility in how you choose to further operationalize the data as you design your data architecture.
Despite the many considerations and challenges that must be dealt with in tailoring an ETL pipeline to your organization, the benefits far outweigh the difficulties. In today’s business environment, it is not the organization that has the most data that wins, but the organization that makes the best use of it. An ETL pipeline is one of the key tools that make data usable and relevant. An ETL Pipeline:
ETL pipelines are already an important part of a typical organization’s data topography and are evolving constantly to maintain or increase that importance. Standard data pipelines based on batch processing are making way for streaming ETL designed to ingest real-time data, and drive real-time business analytics. Modern data pipelines leverage data lakes, increasing capacity, leveraging cheaper cloud object storage, and taking advantage of their capacity to store any data format, including raw data. Chances are, your ETL data pipeline will be the backbone of your organization’s data solution for years to come, even if the eventual configuration is unrecognizable when compared to the original implementation.