Big data engineers are responsible for designing and building large-scale data systems, commonly referred to as “big data.” Big data is a relatively new term, with the emergence of companies such as Google and the start of amassing large amounts of data far beyond what previous generations of software could handle. Google engineers had to pioneer new ways to manage such data. That was the beginning of a new profession known as big data engineering.
Becoming a big data engineer is a long process with great rewards. If you're interested in this career, you'll need to learn basic database skills like relational databases and SQL, and how to manage large amounts of data with distributed software systems like Hadoop. Let's look at these skills in particular.
Here are some technologies to get you started. Please read the documentation for these. Download the software if possible. Or use it in a cloud environment such as Amazon Web Services (AWS).
-
Hadoop [link to https://hadoop.apache.org/ ]: This is one of the original big data projects by the Apache Foundation. The technology inside it is very complex (it's a file system distributed across multiple systems). But Hadoop is very easy to use, and if you're a big data engineer, you'll probably end up building systems on top of Hadoop.
-
spark [link to https://spark.apache.org/ ]: This system works closely with Hadoop to perform data analysis on data distributed across multiple servers. Like Hadoop, it is maintained by the Apache Foundation and the software is free.
-
kafka [link to https://kafka.apache.org/ ]: This software is also from Apache and works closely with both Hadoop and Spark for building pipelines of data. Data is streamed from multiple sources, and applications can receive this streaming data. A big data engineer might set up Kafka and a software developer might write an application that uses streaming data. Alternatively, big data engineers might also create such applications.
-
HBase [link to https://hbase.apache.org/ ] and cassandra [link to https://cassandra.apache.org/_/index.html ]: These are both types of “NoSQL” database systems that do away with traditional relational database architectures and allow for more flexible data storage. It also works with Hadoop.
Pro Tip: The above software was created to manage large systems where data is distributed across hundreds or even thousands of computers (more on this later). However, you can install the software on your own computer. But should you? A better alternative is to use some of the “free tier” services of various cloud platforms such as AWS. And even if you go beyond the free tier, you can usually get it by spending less than $50 per month for a minimal setup that's enough for your practice. This is likely the route you choose and you will need to factor in cost as part of your education.
General technical concepts related to big data engineering
In addition to the above, you should know some general technical concepts regarding computing in general and data in particular. Some of them are listed below.
Distributed computing and data warehousing: In the past, a company's database resided on a single computer, and data retrieval was easy. Currently, data is distributed across many computers. Smaller organizations may have two or even three. Large organizations may have dozens of organizations. Places like Google have tens of thousands of such servers. To further complicate matters, such servers typically run in data centers scattered around the globe. These servers work together, communicate with each other, and share workloads. This concept of all of these working together to run software is known as distributed computing. Additionally, your data is stored across these servers. This is a concept known as data warehousing. Big data engineers must be experts in data warehousing and knowledgeable enough to utilize distributed computing systems. Here are some systems he needs to learn.
ETL (extract, transform, load): It's an industry buzzword that permeates all of data analytics, data science, and data engineering today. The idea itself is deceptively simple. Extract data from multiple sources. Transform that data into something useful. Load the transformed data into another database system. This is collectively called a pipeline. But like most things that sound simple, the details are actually much more complex. Big data engineers are typically responsible for the technical aspects of acquiring and storing data so that it can be used by data analysts and data scientists. This means understanding the software that performs ETL tasks. Here are some things you need to know. Start with one (like AWS Glue ETL or Google Cloud Dataflow) and learn as much as you can. Then research the others and get familiar with them.
Data visualization: This is usually related to data analysis skills, but as a data engineer you may be working in a smaller organization that doesn't have the budget to hire multiple data personnel. This means that in addition to your skills, you also do data analysis and visualization. Or maybe you work in a large organization with data analysts, but still need to train them to be data engineers. Therefore, learning data visualization skills is essential.
Data analysis: The same goes for data analysis. You may need to take on these tasks or train analysts. The technologies you need to master include:
- Data Modeling: This is a method in which data professionals take real-world concepts (such as a customer order system) and create database tables that model real-world data. Data modeling is perhaps the most fundamental skill for any data professional.
- Relational database concepts: Data is typically modeled in a “relational” manner. That is, his one data set, such as orders of multiple customers for one customer, will be associated with another data set, such as details of that customer such as company name, contact information, etc. And a phone number. Databases such as MySQL and SQL Server are relational database systems. MySQL is a free, open-source database system that is easy to install and use. You can start with it and then learn about other relational database systems.
- SQL: This is a language used to model data in relational databases. All modern relational databases use this language.
- NoSQL Database Concepts: Around 2010, people started creating new types of databases that were explicitly non-relational. These databases stored data in various formats, usually as “objects” (basically just collections of data items such as names, addresses, phone numbers, etc.). However, they were not strict on such details. If one item needed to be a little different, that was fine. Because of their non-relational aspects, these database systems became known as NoSQL. The popularity of such systems is increasing, and currently the most popular is his MongoDB.You can install and practice using it for free here [link to https://www.mongodb.com/try/download/community ]. (We also offer a free cloud-hosted version called Atlas, with an optional premium plan.)
- Data cleansing: Refers to finding and correcting incorrect data items.
- Statistics: Every data analyst must understand statistical formulas and how to use them to report on data. I want to learn the basics and then move on as far as I can.
Other technical skills
In addition to all the data, you'll also need other technical skills to complete your skillset, including:
- Python programming. Python is the language used by data professionals. You might want to take a course on it and master it. Within Python, you need to learn how to use tools (called packages) that apply to your data, such as numpy and pandas.
- Containerization: This is a method of running a very small virtual operating system (usually Linux) inside another computer server (also usually Linux, but the “big three” all work: Linux, Windows , Mac). Containers use very few resources, making them ideal for running in the cloud.
- Common Cloud Platforms: The “big three” cloud platforms are: Amazon AWS, Google, and Microsoft Azure. You need to have a good understanding of cloud in general. Choose one and it doesn't matter. Start learning as much as you can. Learn concepts such as provisioning servers, managing containers, running serverless code, storing data objects, managing users and permissions, networking and virtual private clouds, load balancing, and autoscaling. Next, start exploring different hosted database options. For example, Amazon Web Services has a service called RDS for managing hosted MySQL, SQL Server, etc.
Soft skills to support your data engineering career goals
A technical career isn't just about technology. All technical jobs involve working with other people and other soft skills. As a big data engineer, you'll need:
Problem solving skills: Big data engineers don't just manage data. they solve problems. Business managers often ask questions that require deep examination of data, such as data about customers, but they may not actually understand the problem itself. They may ask vague questions. Data analysts are likely asked to find answers to these vague questions. But data analysts rely on you, the big data engineer, to help them piece together where all the data can be found and identify patterns and trends in the data itself.
Big data engineers are also involved in the engineering side of the data, so they need to track issues. For example, some data systems can experience bottlenecks when retrieving data. This is a very common problem when dealing with large data systems. Big data engineers need to find the cause of bottlenecks and fix them. Other problems may occur, such as system failures or distributed data sets not being properly integrated.
communication: Once database jargon becomes second nature, you'll realize that your colleagues working in non-tech fields may not think about data and its organization the same way you do. This means you have to accept their requests and translate them into more technical aspects. For example, if two large companies merge, their data will be stored in different systems, possibly using different technologies, and in very different data formats. Performing analysis on the combined data will likely be very difficult. However, business-minded people may not understand why combining data is so difficult. This requires explaining complex technical information in a simple, non-technical way.
collaboration: You'll often be working with both technical and non-technical people, so you'll need to be comfortable with collaboration and teamwork. Software developers need your help to obtain data for use in their software. Businessmen and managers need information about data. This requires being able to work well with different types of people.
Big data engineering is an advanced career that encompasses a variety of technologies and concepts, as you can see here. It can take years to learn and master.
As your training and education progress, you may find that you enjoy certain parts, such as cloud computing. If so, good luck! You don't need to be a big data engineer. Instead, you can become a cloud computing engineer. Or you may find that big data engineering is just the right fit for you.
Just keep studying and keep learning. And plan to continue learning throughout your career. New technologies are emerging all the time, so you need to stay on top of them.