The Wall Street Journal stated in 2018 that all companies are tech companies, implying that they will hire tech co-founders for their future business growth. It is impossible to avoid the topic of data when discussing tech. Clive Humby is a renowned mathematician, entrepreneur and expert in data science. He highlighted the importance of the data when he said, "Data Is the New Oil." International Data Corporation estimates that we will accumulate 180 zettabytes in 2025.
How companies will use and leverage this data is an important question. If you think that hiring more data scientists is the solution, we would like to break your bubble and say that hiring data engineers is the best answer. This is primarily due to the need for more frontline workers that can retrieve data from different data sources. According to Michelle Goetz, Forrester Research, "there may be 12 times more unfilled Data Engineering jobs than Data Science jobs."
In the next few sections, we will explore what makes this role unique. We will discuss what a Data Engineer is, what their responsibilities are, what skills they need, what job requirements there are, and how to become one.
What does a Data Engineer do?
Imagine that you want to open a small convenience shop. You'll likely start by deciding which products you want to sell and where you plan to source them. Companies with large reserves of data and who plan to use them need to figure out how they'll retrieve the data.
Data engineers are technical jobs that fall under the big data umbrella. Data engineers' job is to process raw data from various sources for enterprise applications. Let's look at some of the roles and responsibilities a data engineer has in greater detail. But first, let's understand the demand in various industries for these jobs.
What is a Data Engineer?
Data engineers are the first employees to interact with a company's most valuable resource: data. They are responsible for ensuring that the different teams within a company can use and analyze data efficiently. Data engineers source data through ETL pipelines and then make it more readable by the entire organization. Data engineers also perform a variety of other tasks.
To learn the full list of duties of data engineers, please read the section below.
Discover Data Engineer Projects and Learn the Plumbing of Data Sciences
Responsibilities and Role of a Data Engineer
- Prepare, handle and supervise efficient data-pipeline architectures.
- Create and deploy ETL/ELT pipelines to begin with data ingestion and finish various data-related tasks.
- Handle and source different data according to business needs.
- Create algorithms in teams for data storage, collection, accessibility, quality checks and data analytics team.
- Create the infrastructure necessary to identify, design and deploy internal improvements.
- Build efficient ETL data pipelines by using tools such as SQL and Big Data technologies.
- Snowflake experience is a plus.
- Create solutions that highlight data quality, operational efficiency and other features describing data.
- Create scripts or solutions that allow you to move data between different locations.
Data Engineer Skills
This is a list of the technical skills needed to become a Big Data Engineer. This list includes a sample project to help you learn these skills the best way possible and excel at your next data engineering interviews.
Passion/Enthusiasm For Data-Driven Decision Making
Your data will return the love. It's really that simple. You need to have the right mindset in order to start learning data engineering. By the right mindset, we mean simply the desire to learn new things and be challenged. It is not a new art to curate valuable inferences from data. But it has recently reached a thrilling peak. You will likely encounter challenges that require extra effort. However, if you are a strong person, you can master this field.
Structured Query language or SQL is a MUST!! Learn how to interact with the DBMS systems
Data warehouses are often located far away from stations that can access data. Data engineers are responsible for using tools to interact with database management systems. SQL is one of the more popular tools. It is even more popular than Python, and R. Ensure that you have a good understanding of the syntax and commands for SQL, as well as how to deduce.
Knowledge of a Programming/Scripting Language
It is not necessary to spend extra time on programming, but it is important that you learn at least one language. Most data engineers use Python or Java in their daily activities. A big data service engineer's role involves the analysis of data using simple graphs and statistics. Data engineers use Python and other programming languages to perform this task.
Cloud Computing: What are the fundamentals?
In the end, all companies will be forced to move their data-related operations into the cloud. Data engineers will likely be the ones to drive the entire process. Amazon Web Services, Google Cloud Platform and Microsoft Azure are three of the most competitive cloud computing platforms. If you want to be a cloud data engineer, you should spend some time learning the basics of cloud computing. You can also work on projects which will give you an idea of how you could use at least one platform for real-world problems.
Knowledge of ETL and Data Warehousing Tools
This article's previous section highlighted the need for data engineers to create efficient ETL/ELT pipes. These data pipelines will be fundamental for any organization that wants to source data efficiently and organized. To achieve this, tools such as Snowflake and Star are available for cloud data warehouses. Data warehousing is essential for any data engineer, whether they are database administrators or aspiring data engineers.
Big Data Skills
Petabytes of data are the norm in today's world. For handling large datasets, the Hadoop ecosystem and related tools such as Spark, PySpark or Hive are widely used in the industry. As a data engineer, you will need to be familiar with Big Data tools if you are required to work with large datasets.
New-Age Data Engineering Tools
We have already discussed the common skills of data engineering, but in recent years, new tools like Snowflake, dbt, Airflow, and ELT have been introduced. Always look out for these tools and try to do some projects using them.
Data Engineer Certification
Certifications are a great way to help data engineers advance their careers and gain an edge over their competition. These certifications measure a candidate's skills and knowledge against benchmarks in the industry to demonstrate to hiring managers their competence and ability to participate in the creation and implementation of corporate data business strategies/insights.
Here are some valuable certifications for data engineers to upgrade your skills.
Google Professional Data Engineer
Data engineers gather, transform, and distribute data. Earning the Google Professional Data Engineer Certification, which verifies data engineering skills, is a great way to improve your abilities. As part of the certification, you will create ML-powered models for data processing, develop data processing complex systems and supervise solution QAs. You will learn how to orchestrate Google's data platform tools in order to improve end-to-end governance, compliance and security protocols.
IBM Data Engineering Professional Certificate
This professional certificate is for anyone who wants to develop the skills, tools and portfolio of an entry-level data scientist. You'll perform like a data engineer during the self-paced courses. You will also learn the fundamentals of working with relational databases and various tools to develop, deploy and manage structured and unstructured information.
After completing the Professional Certificate, you will be able to identify and perform all of the main responsibilities of a data engineer role. Python programming and Linux/UNIX Shell scripts will be used to extract, convert, and load data (ETL). SQL statements will be used to query data from Relational Database Management Systems. You will learn to work with NoSQL databases and unstructured data. You will work with Spark and Hadoop and learn about big data. You will learn how to build data warehouses and use business intelligence tools to analyze data.
Data Engineering nano degree program (Udacity)
This five-month program will teach you how to build data models, data lakes, and data warehouses and work with large datasets. You will also learn how to automate your data pipelines. You will learn how to create relational and NoSQL models that meet the needs of different data consumers. You will use ETL to create PostgreSQL databases and Apache Cassandra. This program will improve your data infrastructure and data warehouse skills.
You can also build a cloud data warehouse using Amazon Web Services. Spark will be used to run queries on the big data that you keep in a lake. Apache Airflow can be used to automate and monitor data pipelines. You will also be dealing with production data pipelines and performing data quality checks.
How do I become a data engineer?
You may be curious to learn more about data engineering now that you know the skills and responsibilities required for the role. Here are some basic steps to follow in order to begin your career as a data engineer.
- First, you need to earn a degree related to Big Data. Examples include computer science, software engineering and so on.
- Concentrate on developing skills in specific computer science areas, such as data analysis, data modeling, machine learning, and so on.
- Completing a few certifications relevant to big data and cloud computing is a good idea.
- Work on real problems to learn more about these tools.
- Apply for some data engineering jobs in order to get a better understanding of the demands of the industry and plan your career accordingly.
The section below will guide you through the steps to becoming a data engineer.
Common Data Engineering Challenges and Their Solutions
The Strata Conference was held in San Francisco last year before COVID-19 ended. Speakers from around the world, representing a wide range of companies and industries, attended. Many of the sessions were centered around a set of common problems that we also faced when building our data platform. The universality of these problems was striking, and fortunately, they are becoming easier to solve.
In the past, storing and processing large amounts of data was a major challenge. Cloud services providers have commoditized both, allowing teams to focus on more complex problems, such as how best to handle metadata management, integrate multiple data systems and implement DevOps. Below, I will cover each one.
Metadata management
The volume of data increases, and organizing it becomes more difficult. There is a requirement for an extra layer of metadata about the data. A big data platform is required to provide several pieces of important information not available within the data.
The first is a description of the datasets. This includes the columns, the table purpose, etc. These metadata must be searchable to make it easier for users to find and identify relevant data within the system. Data diction is a solution of this type.
The next step is to trace the data. What is the source of data, and where did it come from? It has implications for compliance: If end users consent to share telemetry data with the goal of improving a product, then that data shouldn't be used for any other purpose. For compliance, it is also necessary to tag certain datasets or columns as containing sensitive information, such as personally identifiable information, so that the system can lock them down automatically or delete this sensitive information when required (for instance, if a GDPR "right to be forgotten" request is received).
Information architecture is also required to organize big data. Controlled taxonomies are one way to achieve this: clear definitions for the meaning of various business goals, terms, data elements and canonical queries. It ensures that everyone in the industry has the same understanding. My team, for example, tracks Azure Consumed revenue, or ACR. ACR is defined by a standard query. We need to make sure that everyone on the team uses this same definition whenever we discuss ACR. Data dictionaries are a solution for tracking definitions of this type.
The data layer is made up of queries and tables (and any other datasets). The metadata layer is made up of a schema that documents the tables and a glossary linking business terms to canonical queries.
We are working on a solution that combines data dictionary and glossary functionality. Soon, we will be able to provide more information.
Read More: Big Data Has Become a Big Game Changer in Most of the Modern Industries
Integrating multiple data systems
Data fabric is not a one-size fits all solution. There are some workflows that each storage solution is optimized for and others it may struggle with. There's a very low chance that an entire platform would run on a single storage solution. This could be SQL, Azure Data Explorer (ADX), Databricks or anything else. Some workflows require massive scale (processing Terabytes), others need fast reads, and still, others need interactive query support. Because a data platform must often integrate data from different sources and deliver it to various destinations, we are often unable to make a business decision. Teams upstream want data in formats that suit their needs, while downstream teams prefer data in formats that meet their needs and data visualization.
It is not always possible to standardize a storage system solution. The next best option is to standardize the powerful tools that move data and make sure they are easy to use. Data movement is essential, so it needs to be as reliable and efficient as possible.
Azure Data Factory is used by our team to orchestrate data movement. Azure Data Factory is a great tool for orchestrating ETL pipelines. I discussed this in a previous blog post, "How to build self-serve tools for data environments with Azure". It is used for data ingress and outguess. We run hundreds of pipelines.
Azure DevOps' integration with ADF allows us to track all of our pipelines by source control and deploy them automatically. We also monitor Azure Monitor.
DevOps
A major area of work is to bring engineering rigor into workflows that are supported by other disciplines, such as data science or machine learning engineering. It is relatively simple to create ad-hoc reports and perform ad-hoc machine learning with a small group, but this method does not scale. These workflows need to be reliable once production systems rely on their output.
It is not a problem for software engineers, who have source control, code review, continuous integration consulting and other tools. This type of workflow is not familiar to non-engineering disciplines, so it's important to create, educate and support similar DevOps processes. The analytics and ML are ultimately code (whether SQL, Python, R and so on). They should therefore be treated the same as production code.
Azure Machine Learning is able to run ADF pipelines that are deployed via Git. ADLS is used to distribute data. Azure Monitor is used to monitor the system.
Azure DevOps is used by our team to support these types of workflows. Pipelines can be deployed from Git into production, deploying ML and Analytics platforms. Azure Pipelines deploys everything from source control, and Azure Monitor notifies us when something goes wrong. Bringing DevOps into data science is an important topic. In previous articles, I have covered some aspects, including self-serve analytics software and MLOps.
Data quality
The quality of data is the basis for all machine learning and analytics outputs.
Data quality has many aspects. The article "Data Done Right: Six Dimensions of Data Quality" provides a set of definitions.
- Completeness; the dataset does not lack any required data
- Consistency; Data is consistent between datasets.
- Conformity; All the data are in the correct format, with the appropriate value ranges and so forth.
- The data accurately represents the domain being modeled.
- Integrity; Data is valid for all datasets and relationships.
- Timeliness; Data is available as expected, and datasets do not delay.
A reliable data platform will run different types of data-quality tests on the datasets managed, both during scheduled times and when they are ingressed. Data quality issues must be reported, and the dashboard should show the current state of data quality so that stakeholders can see which datasets have problems.
It is not possible to perform this service in Azure. However, we worked with another team of data engineers within Microsoft to develop a cloud native solution for data testing.
Test definitions and test results are stored in a Cosmos DB database. An orchestrator schedules test runs. A web job is responsible for handling test execution, data fabric-specific details, and other data fabric details, depending on the data fabric. A second web job is responsible for handling test results. It writes them to Cosmos DB and opens incidents for test failures. Power BI provides a report on the test status.
The solution relies on a Cosmos DB to store the test definitions and an Orchestrator Web Job to schedule tests. Service bus queues are used to communicate between components. Web jobs are created for each supported data fabric, and these web jobs handle the testing of that data fabric. Azure Data Lake Storage can be Azure Data Lake Explorer, Azure SQL or Azure Data Lake Storage. This architecture enables us to have an abstract, standard test definition with plug-in support for data fabrics.
The system writes test results back to CosmosDB, and in the event of failure, it opens tickets in IcM (the Microsoft-wide incident tracking system). Data engineers are notified in real time of data quality problems so that they can take immediate action to mitigate the issues and keep everything running smoothly.
A Power BI report is also built on the Cosmos DB to show stakeholders the overall health and quality of data within our platform.
In the last few months, we have written hundreds of data tests, which constantly check the health and business performance of our platform.
Conclusion
This article will look at the challenges that exist in data engineering and how our team has dealt with them on Azure.
- Our custom-built metadata solution is evolving and includes a data dictionary and glossary.
- ADF is used to integrate multiple data sources at scale. DevOps practices, such as Git integration and active Monitoring, are incorporated into the ADF.
- DevOps provides the foundation for a robust system of data. Azure DevOps, Azure Pipelines and Azure Monitor are used to deploy Git and for analytical and machine learning technologies workloads.
- Assuring the quality of data is another important component of a platform. We worked with another Microsoft team to deploy a cloud native data testing solution, which allows us to run thousands of tests on our platform.
In the next few years, I anticipate that we will both have better tools for solving some of these issues and more clearly defined industry standards. At the beginning of this post, I said that storing and processing large amounts of data used to be a difficult problem. Today, organizing and managing data is the biggest challenge. I expect to see a lot of these issues solved in the near future, but a whole new set will be create.
