Introduction

Over the last decade, organisations attempted to hire tons of Data and Machine Learning Scientists in order to extract valuable information from their data that would eventually help them make well-informed decisions and build data-driven products. Unfortunately, this strategy has failed for most of these companies since in many cases the predictive models were suffering due to low data quality. Data is scattered and sometimes not even optimised for analyses.

Additionally, even when models were performing well, their implementation and deployment to production would be a nightmare due to the lack of a scalable architecture, automation and application of best practices when it comes to working with data pipelines. And this is where Data Engineering comes into play.

The field of Data Engineering encompasses a set of techniques and principles that allow engineers to gather data from various different sources by configuring the required processes. Additionally, it is the Data Engineers' responsibility to ensure that any corrupted data is eliminated so that business users using it will have access to cleaned and accurate information.

At the same time, Data Engineers are among the most valued people in modern data-driven organisations that aim to build or enhance products by utilising their most valuable asset, that is .. data!

And to make this happen in an efficient, scalable and cost-effective way, Data Engineers must ensure they are using the right tools that will eventually help them build the tools that the organisation needs.

Databases

A Data Engineer is expected to be able to handle and perform certain operations on Database Systems including both SQL and NoSQL databases. A database is the place where a large amount of data resides where different users can query and extract information or even other applications that can utilise the database as a storage means or even to lookup certain records.

For large-scale systems, a Data Engineer will typically have to setup and maintain Data Warehouses and Data Lakes. I'm not planning to go into in-depth discussion about these concepts and how the differ with traditional databases, but if you are interested in learning more about this topic make sure to read one of my recent articles share below.

(Big) Data Processing Tools

As mentioned already, a Data Engineer should set up all the processes required in order to clean and aggregate data from multiple sources. Therefore, it is crucial to take advantage of tools that enable such process in a scalable, efficient and fault-tolerant fashion.

An example of such technology that is commonly used in pretty much every industry is Apache Spark. It is among the most widely-used engines for scalable computing that can execute data engineering tasks such as batch processing, ML model training, and data analysis at scale. Additionally, it supports multiple languages including Python, Scala, Java and even R.

(Near) Real-Time Data Streaming

Apart from traditional batch processing over historical data, many organisations also need to process data in real time and at scale. For example, consider use-cases where a specific action should be performed when a certain event occurs. Data Engineers should be capable of building event-streaming architectures that will enable such features to be implemented.

In my opinion, the king of real-time data streaming is Apache Kafka. This technology was first implemented as a message queue in LinkedIn and quickly evolved into an open source real-time data streaming platform. In a nutshell, Kafka can be used to produce and consume data to/from streams of events and as a — temporary — message store. Additionally, it can be used to process events streams at real time, or even retrospectively.

In the previous section, we talked about Apache Spark and how it can handle batch processing at scale. Apart form batch, Spark itself can also data streaming with the use of Spark Streaming processing system that natively supports both batch and streaming workloads.

This Spark API extension allows data engineers to perform real-time data processing flowing in from multiple sources including Apache Kafka and Amazon Kinesis.

Scheduling Tools

Other important tools that are quite useful for Data Engineers are scheduling mechanisms that allow certain pipelines or actions to be executed at specific time intervals.

Additionally, such tools can also make your life easier when it comes to executing multiple operations having dependencies with each other. For instance, you cannot run an analysis operation prior to loading the specific data you need. Therefore, the scheduling tool may help you ensure that an action B is executed once action A is also executed and completed successfully.

One example of a scheduling tool is Apache Airflow which is among the most commonly used platforms when it comes to setting up, scheduling, executing and monitoring data workflows.

Monitoring tools and alerting

Finally, another important aspect of the Data Engineer toolbox is monitoring. In today's article we talked about numerous concepts and tools that need to be up and running in order to enable certain processes to be executed in a scalable and timely fashion.

Therefore, it is important to have mechanisms in place that would enable us to perform health checks on various systems and additionally, inform a specific target group (e.g. a specific DevOps, or Dev team) when something unusual is identified — say a Spark node, or even the whole cluster is down.

Most modern data engineering tools could be configured to be highly available — but this does not mean that clusters will be 100% healthy at any given time. High availability simply means that the system is expected to work uninterrupted, even when something unusual happens. But we should be able to have a 360⁰ of our systems' health in order to engage the teams that need to look into problems as they arise.

Final Thoughts

Data Engineering sits at the heart of organisations that aim to utilise their data in order to release new products, update existing ones and improve their overall decision making based on conclusions drawn over the data collected over time and even at real-time.

Therefore, it is quite important to recruit the right people that will form the teams and help the organisations take their products and decision making to the next level.

In today's article we discussed about some of the most important tools that Data Engineers should utilise in order to perform their day-to-day tasks in an efficient and elegant way that will allow them to implement scalable and cost efficient solutions.

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