Best Practices For Data Engineering Beginners

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Best Practices For Data Engineering Beginners | Data is helping to power a huge number of trends in the business world. Many companies are using data collection and analysis to help them make better decisions. Others are using it to help train artificial intelligence. Some are built entirely on offering data to advertisers. In short, if you are interested in working in a future-proof profession, consider getting involved with data engineering. The following best practices will help you get started.

Build a Pipeline That Can Handle Concurrent Analyses

For most organizations leveraging data science, it is important to be able to analyze multiple streams of data at once. Ideally, you should be building out a methodology that allows you to easily handle concurrent workloads. While this may not be a requirement from day one as a beginner, it is also beneficial to plan ahead for scalability and increased bandwidth when working on data engineering.

Try to Leverage Your Current Skills

There are a lot of popular data tools and languages such as Python, Apache Spark and Apache Kafka. While it is good to learn new skills regularly, you can also leverage your current skills to get the job done. Many data tasks can be completed with a wide variety of tools. If you have current skills that may be useful, try to take advantage of them. For example, in some setups, you can use direct SQL statements instead of using Kafka. You don’t need to learn all the tools to get started working with data.

Streamline Development of Pipelines

Creating new data pipelines can be very important in any organization. Whether your business is built around data or just uses it for other strategic goals, you should always be ready to expand your analysis capacity. Having the capability to develop new pipelines quickly and efficiently can be very valuable. For example, using a cloud-native approach to building your data infrastructure can make it easier to spin out new computing instances.

Make Solutions Extensible

In a similar vein, it is a best practice to build your solutions to be extensible. This means that they can be readily expanded and grown in the future. The simplest way to do this is by following good object-oriented practices and building microservices that can be reapplied in different ways. Another valuable approach is to create APIs that will allow other solutions to plug into your data pipelines.

Get the Right Data Wrangling Solution

Data wrangling is the process of cleaning and organizing your data so that it is useful for analysis. In many cases, data can be collected in inconsistent or error-prone ways. For example, different sources may report ABC Company as “ABC Co,” “A B C Company” or “ABC Inc.” Data wrangling helps to automatically clean up these issues so that you can more easily take advantage of your data. This is an area that deserves major attention. If you can get your wrangling right, the rest of your work will be easier.

Enable Sharing

Ultimately, data engineering exists to support the work of the organization as a whole. Therefore, it is important to be able to give the right people access to the results. Making data sharing easy will improve the outcomes of your work. Plus, with the right data architecture, you can empower others to run their own data analyses using your systems.

Leverage Integrations

In many cases, your data solution will not exist in a vacuum. Instead, it will likely have other tools and systems that serve data or need to receive results. Integrations with other systems can help to make your data solution more useful and efficient. Fortunately, there are many data standards that can help your various systems to integrate and talk to each other.

Learn More

Discover more about data engineering today. With the above best practices, you will be able to start your data career on a strong footing. In general, it pays to lay a foundation that you can build on in the future.

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