The most efficient systems may be extremely valuable and essential, but everything goes out the window when there's a vulnerability.
Take, for instance, air travel. It’s the most efficient way to get you from Point A to Point B.
The passengers of Alaska Airlines would’ve agreed too, when suddenly, on January 5th, 2024, a piece of the aircraft snapped off with a bang.
Thankfully, they made it back safely, and the aircrew acted heroically to ensure everyone was safe. But it brings up an important issue.
Like an aircraft, your data pipeline process should be accurate and protected at all times. That’s why ensuring your pipeline is safeguarded from future vulnerabilities, breakages, and interruptions is critical.
First, we’ll define a data pipeline. Then, we’ll go over some types you can expect, steps, and benefits for data pipelines.
Finally, we’ll learn how data contracts can help protect your data pipeline process for better accuracy and efficiency.
A data pipeline is how a company defines and executes how raw data moves between systems, gets verified, stored, and maintained.
The data pipeline process clarifies how we incorporate our data strategy.
When the data is processed, it goes to the data sink for use case applications, a data warehouse for analytics, or the data lake for continued data science and machine learning.
Data pipeline strategies vary based on your industry and needs. But the pipeline can be split into two types.
The batch method is one of the more traditional options companies choose for enterprise data. It’s when a company processes data through fixed, predefined parameters. Your team can process larger quantities of data at a time.
Since such a big data load can cause latency issues, the information is often processed during odd hours with less user activity, such as at 3 a.m.
Batch processing is effective for processing lots of data that aren’t as time-sensitive. It can include historical data or metadata that could be useful in the future.
Batch pipelines, however, are challenging to maintain, with systems often having to process data even if it hasn’t changed (which is a lot of work for humans and computers). Since you load volumes of data, your system will also have a knowledge gap and contain outdated information.
Streaming processing pipelines are a great way to manage your data in the fast-paced world of computers and the internet.
A streaming pipeline is when companies process data as it arrives, giving them low latency and current insights. Data engineers and teams can work with new data and updated datasets through automation.
Unlike batch data, stream processing is constantly changing. It influences your dashboards with metrics, data analytics, and decision-making information.
While this pipeline is very effective, it can be risky if you don’t have the right data strategy in place. You want to ensure you have the right tools and management to verify data quality and prevent hiccups as data is gathered and processed.
Pipelines can also include focused categories, depending on your type of business and where your data flow is. Some examples include:
As you improve your data pipeline process, you may come across the terms ELT and ETL. They sound the same, and for the most part they are.
Both of these models focus on the order of steps for pipelines.
The initials stand for the same but place emphasis on the order:
E = Extract
L = Load
T = Transform
They cover how data is collected, changed, and stored. These days, different pipelines can have multiple stages where ELT and ETL are involved. Often, the methods are talked about interchangeably and already function within your pipeline.
ELT (extract, load, and transform) might be good for a SaaS with strict rules and a critical schema to follow.
On the other hand, ETL (extract, transform, load) might be a good option to store information in your data warehouse or lake.
ETL and ELT fall under the umbrella of your data pipeline and how you process information. While two different things, the ETL and ELT refer to your tactical steps while the pipeline encompasses the process.
Data pipelines create a better way to manage your data and save you time. Below are the top advantages of a healthy process:
When you implement a data pipeline process, you get cleaner results. The pipeline can refine the information, find and correct redundancies, and provide a usable dataset to work with.
Data pipelines, thanks to data contracts, make it possible to automate the process. This saves thousands of hours of data engineering work. Instead, your team can use the saved time to find ways to improve your data approach or analyze the information.
Through automation and the ability to bring your data together, your pipeline can integrate your data sources, transform data, and create a holistic product. It checks values from different sources and finds consistency errors. Additionally, it flags and corrects other common errors.
ETL and ELT are part of the data pipeline infrastructure. The concepts are also commonly summarized into the following:
1. Data ingestion
This is when data is extracted from sources and brought to a central location for company access and analyses.
The data is often found from a source like a CRM (Salesforce, Hubspot, or Zoho). It can also come from other sources that collect data.
2. Data transformation
Data is converted and structured to fit your system as a usable format.
Transformation can take many forms. It may be the way you need data recognized, filtered, or segmented. Company pipelines may include data contracts to automatically sort and manipulate the data.
3. Data storage
Finally, your data is stored so that the company can access it and use it for business operations.
It usually arrives at a data warehouse or data lake. From there, your data team can analyze the information or use it.
Apache Kafka is a popular solution for data pipeline processing. It is an open-source platform that helps manage and transform your real-time data—which is great for streaming pipelines.
There are plenty of benefits to using Apache Kafka.
Data is only as good as its accuracy. The minute it’s compromised, you can’t trust it. Your business decisions, customer behavior analytics, and planning are now done in the dark.
Thankfully, we now have the technology to prevent data pipeline issues before they happen.
You no longer have to worry about a problem after that fact, you can rely on data contracts to execute smooth processes and eliminate avoidable mistakes.
Data contracts define the structure, format, and criteria for data as it is extracted, transformed, and stored. It eliminates human error and can find issues before they become a problem.
Data contracts automate your pipeline for a smoother, more accurate data process and workflow.
When you think of a literal pipeline that moves water, it’s a powerful force. It’s efficient, it moves fast, and it can push the resource to its destination.
Where pipelines might depend on water pressure, gravity, and energy to function properly, your data pipeline needs its power source too.
Data contracts move, transform, and store your information efficiently through automatic processes. Choosing the right solution provides the power source to make it possible.
Gable automatically generates contract templates, manages contract versioning, and enforces contracts using a variety of alerting thresholds.
You can collaborate with data engineers, data scientists, and software engineers to create and modify contracts over time.
With Gable, you can prevent breaking changes programmatically and alert consumers when contract updates will impact them. You can manage contract versions and evolution simply and easily.
It’s perfect for your data pipeline process, especially if you want to take control of your data management and use your information to its full potential—so you can grow your company with secure, reliable, and efficient data.
You can join the product waitlist for Gable today and be one of the first to benefit from powerful data contracts to boost your pipeline.
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