Data Engineering Best Practices
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As Analytics, ML, and AI have become mainstream they have driven a tremendous increase in data, data systems, and data users. New tools, technologies, and companies are constantly emerging to meet the increased demand for faster, cheaper, and easier data storage, processing, and analysis. As data engineers navigating, designing, and implementing in this ever-growing field, it is important to follow industry best practices and not reinvent the wheel. This article will discuss the six most helpful data engineering best practices to stay current and ensure operational efficiency.
Best Practice
Benefits
Design Efficient and Scalable Pipelines
Lowers development costs and lays the groundwork for scaling up
Be Mindful of Where the Heavy Lifting Happens
Avoids the repetition of costly tasks and allows the selection of the right ETL solution
Automate Data Pipelines and Monitoring
Shortens debugging time and ensures data freshness and adherence to SLAs
Keep Data Pipelines Reliable
Helps with making decisions based on trustworthy data
Embrace DataOps
Increases development efficiency and provides faster time to insights
Focus on Business Value
Improves the user experience and key business metrics, increasing return on data investment
Top Six Best Practices in Data Engineering
Design Efficient and Scalable Pipelines
Start with a simple pipeline design and keep it simple as long as possible. It can naturally become complex over time, but don’t let it get complicated. As beautifully put in “The Zen of Python”:
> import this
The Zen of Python, by Tim Peters
...
Simple is better than complex.
Complex is better than complicated.
...
Efficiency comes from using the right tools and techniques. Start by picking a batch, streaming, or hybrid solution appropriate for the business goals and systems involved.
Don’t manage or develop connectors on your own for loading and unloading data. Use a managed data engineering platform that supports dozens of connectors for standard file formats, open-source tools, and third-party vendors. Professionally built connectors can be far more reliable in authentication management, capable of parallelized ingestion, and resilient to errors with their retry mechanics – all things that can take months of development and maintenance work. Moreover, the best of these are bi-directional giving complete flexibility in where to read or write data.
Identify bottlenecks when dealing with increasing data volume, velocity, or variety. Building atomic and decoupled tasks helps scale various parts of the pipeline independently. For example, instead of performing multiple transformations on data in the same pipeline, breaking it down into several smaller, simpler tasks enables orchestration tools to run tasks in parallel, reducing overall pipeline runtime and yielding faster time to analytics.
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Pay Attention to Where the Heavy Lifting Happens
“Heavy lifting” refers to pipeline steps involving costly operations using significant computing and storage resources; an example is joining multiple large files or tables to generate aggregate analytics. Follow these best practices to reduce their impact:
• Isolate resource-heavy operations from the rest of the pipeline, improve their resiliency, and persist their output, so when downstream jobs fail, you don’t have to repeat the costly operations.
• Don’t operate on rows one by one, especially when working with large data sets.
• Pick the appropriate pipeline method: ETL (extract, transform, and load) or ELT, which puts the transform last. Use ETL to ensure that the data in the warehouse is in good shape, PII safe, and erroneous or unnecessary data is filtered out. Use ELT if you want to keep the raw data in the warehouse and meet unforeseen transformation needs quicker. Either way, build a single source of truth.
• When generating high-quality data in the data warehouse using significant resources, it is often a good idea to make this valuable data accessible to the broader organization. This can be done in the form of standardized data-products. Another way is to push data back to standard applications through API calls. This is called Reverse ETL and is implemented by referring the data in the warehouse back to operational systems such as CRM, marketing, and finance.
A basic ETL diagram
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Automate Data Pipelines and Monitoring
Automation is sometimes confused with simply triggering a pipeline based on a schedule, but this is only part of the process. Triggering pipelines doesn’t have to be just based on time: There are also event-based triggers, such as HTTP requests, file drops, new table entries, or even a particular data record in an event stream. The best practice is to build event-based triggers whenever appropriate instead of setting up a schedule and hoping that everything the pipeline needs is ready on time.
Parametrize pipelines to enable code reuse for different dates and other arguments. Sometimes temporary network and disk issues can disrupt a running pipeline, so adding automated retries—preferably with a backoff time of a few minutes—can automatically resolve such issues.
Note that pipelines can increase in complexity naturally. To handle this, ensure that all the dependencies are checked and resolved when running a pipeline. Use orchestration tools with dependency-resolution features that help visualize the pipeline and update individual task statuses.
Finally, monitor your pipelines continually. Capture and log all errors and warnings; never pass them silently. If feasible, extend the automation tool of choice with your error and warning messages. In case of failure, automatically create a monitoring ticket and assign it to team members who are responsible or on call.
Keep Data Pipelines Reliable
Maintaining data reliability is hard, and once a data pipeline is live, applications that consume the data quickly create new downstream dependencies. Schema changes, such as adding, removing, and renaming columns occur as business requirements evolve. The best practice is to build pipelines that are resilient to such schema changes, a.k.a. schema drift, but this can be complicated to implement. Look for options in your data pipeline tool that allow for and automatically handle schema drift. Advanced tools built with a data fabric architecture are able to handle schema changes dynamically and notify users of breaking changes avoiding erroneous processes that can be hard to unravel.
Another strategy to build resilient pipelines is incorporating the ability to handle and quarantine