Three tools that every data engineer needs to master
In the modern, digital world, every business or organisation now relies heavily on data. Data has become a crucial aspect for the success of any business and is continuously growing with each passing day. As a result, the data market is on the rise and the influx of large amounts of unstructured and raw data created a stark increase in demand for data engineers.
Data engineers are key to building data pipelines and are essential for making data more useful to companies. To build such complex data infrastructure, data engineers need a mix of programming languages, data management tools and much more. Here, we discuss the three essential data engineering tools every engineer should know.
Python is one of the most popular programming languages in the industry and is an essential tool for all data engineers.
Python has gained its popularity as it is easy to learn, and its extreme versatility is a powerful attribute that makes it a key tool for data engineers. It’s multi-use function means that data models can be created, data sets can be systematised, and ML-powered algorithms can be developed; as well as having the ability to complete various tasks in a short amount of time. The key benefit of Python is that it helps to reduce development time, which results in fewer expenses and faster results for companies.
When it comes to the cloud, Python is a popular choice for cloud platform providers for implementing and controlling their services. Some of the biggest cloud platforms, namely Amazon Web Services (AWS) and Microsoft Azure, all accommodate Python users.
Regardless of what sector a data engineer forges their career in, they’re almost certain to encounter Python along the way.
MongoDB is a key tool for any data engineer as it’s easy-to-use, highly flexible, and can accommodate a large amount of both structured and unstructured data. NoSQL databases like MongoDB gained traction among data professionals thanks to their advanced functionalities that allow them to seamlessly handle large quantities of data. MondoDB is effective, dynamic and stores data in simple forms that are easy to understand.
As a data engineer, mastering MongoDB is essential to master as a large part of your role is processing huge data volumes, and this is where this tool excels. Its flexible schema makes it as simple to evolve and store data in a way that helps programmers, making it a popular choice among many organisations. Expertise in MongoDB can make a data engineer a crucial cog in any company’s team and increases their chances of embarking on a rewarding career journey.
Databand.ai is a fantastic tool that enables data engineers to track real-time data metrics from all their tools in a user-friendly dashboard. This tool is essential as it enables data professionals to follow all of their data pipeline issues such as delays or task failures and solve them quickly.
Databand is an excellent tool for providing visibility throughout your pipeline and tracking data lakes, allowing you to manage data quality, freshness, and lineage. It is also extremely effective when it comes to tracking SLA violations; monitoring the use of resources; and running checks on your data assets.
This brilliant tool offers seamless integration with a range of popular data engineering tools such as Apache Airflow. It also features a detailed documentation library and the tools needed to help you develop your own custom integrations. Databand can make a world of difference to your workload by effectively streamlining all of your projects and displaying them in one place, making it an essential tool to master.
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